Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil

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Experimental and techno-economic modeling were coupled to gain insight into and optimize the solid-state fermentation conditions for minimizing the laccase selling price. At relatively small scales, processing only 230 Mg of prairie biomass per year, it was determined that a minimum laccase selling price of $ 0.05/kU was needed to achieve a 5-year return on investment assuming a 10% discount rate. Insights into the capital and operating costs were discussed, as well as a sensitivity analysis, which showed the minimum laccase selling price to be most sensitive to parameters affecting laccase output from the plant. Limitations and future outlooks were provided, emphasizing the need for further process optimization to minimize fermentation time and downstream losses. Laccase techno-economic analysis optimization solid-state fermentation bio-oil aqueous phase prairie perennial biomass Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Fast pyrolysis is a popular technology for the rapid depolymerization of biomass into three fractions: gas, char, and bio-oil. The gas consists primarily of carbon dioxide, carbon monoxide, and methane, and is often used for process heating (Zhang et al., 2023 ). The char consists mostly of inert carbon and ash, and has been studied for many applications, including bioremediation, anaerobic digestion, and as a soil amendment (El-Naggar et al., 2019 ; Patel et al., 2022 ; Zhou et al., 2020 ). Lastly, the bio-oil is a tar-like material containing various anhydrosugars, aromatic compounds, and more, and can be upgraded into a renewable petroleum alternative (Lachos-Perez et al., 2023 ). To facilitate the upgrading of bio-oil into renewable products, the aqueous fraction (hereafter referred to as the aqueous phase, AP) can be separated from the valuable heavy ends through stage fractionation processes (Pollard et al., 2012 ). The AP consists predominantly of water and carboxylic acids, but also contains various alcohols and low molecular weight lignin fractions, like phenols (Liang et al., 2013 ). The high organic content of AP has garnered attention as a feedstock for microbial bioprocessing. Specifically, most studies have attempted to utilize AP as a carbon source for heterotrophic fermentation, through anaerobic digestion, microalgae cultivation, or other means (Zhao et al., 2016 ; Zhou et al., 2019 ). However, the toxicity of AP has proven difficult to overcome, even when detoxification measures are employed. Recently, AP was reported to be an effective inducer for laccase production in the white-rot fungus, Pleurotus ostreatus (Rahic, 2024 ). Laccase is a multi-copper oxidase found naturally in plants, fungi, and bacteria. In plants, laccase plays a role in lignin polymerization, whereas in fungi, laccase often assists in lignin depolymerization reactions (Dwivedi et al., 2011 ). Fungal and bacterial laccases may possess differing characteristics. Bacterial laccases are generally more active and stable at higher temperatures and pH, but are often produced in smaller quantities and have a lower redox potential relative to fungal laccases (Janusz et al., 2020 ). Nevertheless, laccase is capable of catalyzing oxidation reactions on a wide range of substrates. As a result, laccase has been studied for its potential role in the food and beverage industry, textile industry, bioremediation, bioenergy industry, and more (Mayolo-Deloisa et al., 2020 ). Laccase can be produced from submerged or solid-state fermentation. Solid-state fermentation may offer several advantages for white-rot fungi, as it mimics the environmental conditions that these fungi naturally grow in. In addition, solid-state fermentation requires significantly less process water, making it a potentially more sustainable culturing method for white-rot fungi. To date, many studies have investigated laccase production from white-rot fungi using solid-state fermentation; however, the literature data on its economic viability is extremely limited. In our previous study, laccase production was performed using prairie biomass as the substrate for solid-state fermentation (Rahic, 2024 ). Perennial biomass offers numerous ecological services, such as improved nutrient, soil, and water retention, as well as improvements in wildlife biodiversity (Schulte et al., 2017 ). Prairie strips offer a means for farmers to integrate diverse perennial species into their existing landscape to provide these ecological services. However, to encourage farmers to implement perennials into their farming operation, economic value must be obtained from the biomass. As a lignocellulosic biomass, it has potential to be utilized in biorefining processes. To date, very few studies evaluating valorization pathways for prairie biomass exist (Olafasakin et al., 2024 ; Rahic et al., 2024 ; Wild et al., 2025 ). The aim of this study is to conduct a techno-economic analysis (TEA) to investigate the viability of a novel laccase production process, in the pursuit of valorizing both AP and perennial biomass. 2. Materials and Methods 2.1 Substrate and inoculum origin The prairie biomass used in this study originated from the Iowa State University Comparison of Biofuel Cropping Systems research plots, seeded with a mixture of ~ 59 different species. Kordbacheh et al. ( 2019 ) provides details on prairie speciation, while biomass characterization is provided in Table 1 . The P. ostreatus grain inoculum used in this study was purchased from Field & Forest Products. The inoculum was stored at 4°C upon delivery and used within one week. The AP used in this study was derived from the fast pyrolysis of corn stover in a fluidized bed reactor. The AP was subjected to overliming treatment prior to use, following the method detailed by Zhao et al. ( 2015 ). Polin et al. ( 2019 ) provides details on AP production, while its characterization can be found in Table 1 . Table 1 Characteristics of the prairie biomass and treated AP used for this study. Characteristic Unit Prairie biomass AP Water % wt 6.1 43.8 Volatile Solids % wt 86 - Lignin % wt 16.7 - Hemicellulose % wt 24.9 - Cellulose % wt 31.3 - pH - 9.93 Total phenolics g/L gallic acid equiv. - 15.3 2.2 Growth nutrient screening for Pleurotus ostreatus Nutrient screening was performed by cultivating P. ostreatus on plates containing: 20 g/L agar, 10 g/L finely milled prairie biomass, 4 g/L KH 2 PO 4 , 1 g/L MgSO 4 , 0.2 g/L CaCl 2 , 0.2 g/L CuSO 4 , and a nitrogen-containing nutrient at a concentration of 5 g/L, unless otherwise specified. Four nutrients were tested: corn steep solids, peptone, yeast-extract, and ammonium sulfate. Each plate was inoculated with a 1cm mycelial plug of P. ostreatus grown on potato-dextrose agar at 28°C for 7 days. The nutrient screening plates were incubated at 28°C for four days, followed by harvesting of the mycelial biomass by scraping and quantifying the growth gravimetrically. Each condition was evaluated in triplicate. 2.3 Two-stage fermentation for laccase production Laccase production from P. ostreatus was conducted following the protocol by Rahic ( 2024 ) with modifications. In short, the fermentation process was divided into two stages: growth stage and induction stage. During the growth stage, P. ostreatus was cultivated in rectangular LDPE containers to mimic a tray bioreactor. Each container held sterilized prairie biomass moistened to 80% moisture content with liquid media containing: 10 g/L corn steep solids, 4 g/L KH 2 PO 4 , 1 g/L MgSO 4 , 0.2 g/L CaCL 2 , and 0.2 g/L CuSO 4 . Commercial grain spawn of P. ostreatus was used as inoculum. The quantity of substrate and inoculum used, as well as the growth stage time, was based on the experimental design, as described further in Section 2.6 . After the growth stage, the fungal culture was transferred to 500 mL Erlenmeyer flasks and submerged with non-sterile water to 94% moisture content. AP and CuSO 4 were added at 2.5% (v/v) and 1.1 g/L, respectively, and the cultures were held on orbital shakers at 25°C with 155 rpm agitation. 2.4 Analyses Laccase activity was measured calorimetrically through the oxidation of 0.2 mM 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) (ε = 36,000 M − 1 cm − 1 ) at 420 nm in 0.1 M citrate-phosphate buffer (pH 3) at 25°C. The laccase activity was calculated following the suggested equation by Baltierra-Trejo et al. ( 2015 ) and reported in units per gram of prairie biomass. One unit of laccase activity was defined as the quantity of laccase required to oxidize 1 µmol of ABTS per min. Total phenolic content was measured using the folin-ciocalteu method. Prairie biomass samples were submitted to Celignis Analytical (Limerick, Ireland) for lignin, cellulose, and hemicellulose for analysis. 2.5 Techno-economic analysis Spreadsheet modeling of the laccase production facility was performed for TEA (Supplementary Information). Experimental data from this study, as well as existing literature, were used to inform mass and energy flows for the model. Laccase production was modeled as a batch process, depicted in Fig. 1 . The process is divided into three stages: growth stage, induction stage, and downstream processing. The growth stage begins with mixing and preparation of the prairie biomass with liquid media to achieve a final moisture content of 80% (Fig. 1 a). The substrate mixture is then sterilized prior to inoculation. Once inoculated, the fungus is cultured statically at 28°C in a tray bioreactor. Meanwhile, AP is treated using the overliming method outlined in Section 2.1 (Fig. 1 b). Once the growth stage is complete, the colonized biomass is loaded into a mixing tank and submerged with water, CuSO 4 , and AP, following the exact parameters outlined in Section 2.3 (Fig. 1 c). The mixture is mixed continuously, during which laccase is produced extracellularly. After the induction phase, the mixture is subjected to filtration by a plate and frame filter press, where it is assumed that 10% of the laccase is lost to the solids fraction. After filtration, the laccase solution is concentrated via ultrafiltration. Zaccaria et al. (2019) reported a volumetric concentration factor of 60 with 183% laccase activity recovery. Meanwhile, Bryjak & Rekuć (2010) reported a concentration factor of ~ 27 with a ~ 90% laccase activity recovery. In this study, the concentration factor was averaged between the two reports (43.5), and the more conservative laccase activity recovery of 90% was used. Afterward, the laccase solution is mixed with sorbitol (33% v/v) as a stabilizing agent and assumed to be packaged in one-gallon jugs. An additional 25% loss in laccase activity is assumed from storage and shipping, for a final laccase recovery of 60.6%. No water reuse or valorization of side streams was considered for this analysis. The laccase production plant was scaled based on the quantity of prairie substrate used, from 2–15 Mg/batch. Plant construction time was set to 24 months with a 6-month start-up time at 50% capacity. The plant was assumed to operate for 7,446 h per year, equivalent to an 85% capacity factor, for a plant lifetime of 20 years. Given the long fermentation time, the plant was assumed to run on a concurrent schedule around the rate-limiting operation. This means that preparations for a new batch are started during the longest process of the current batch. This is done to maximize the number of batches that can be completed per year; however, this study assumes only 5 concurrent batches can be completed before the scheduling must restart. The full spreadsheet model with all associated assumptions can be found in the Supplementary Information. 2.6 Economic analysis 2.6.1 Optimization of laccase production parameters Given the lack of information regarding commercial laccase prices, this laccase production facility was evaluated based on its minimum laccase selling price (MLSP). MLSP was calculated as the price needed to achieve a 5-year break-even period (after construction) assuming a 10% discount rate. Laccase yield and induction stage time for the model were evaluated experimentally based on a central composite design studying three factors: substrate bed depth, substrate:inoculum (S:I) ratio, and growth stage time (Table 2 ). The laccase yield and induction time were not modeled as responses; rather, the experimental results were implemented directly into the process model to model the MLSP at each scale. Table 1 lists the experimental conditions for each run. The three factors were correlated through a second-order polynomial equation: Y = β 0 + β i x i + β j x j + β k x k β ii x i 2 + β jj x j 2 + β kk x k 2 + β ij x i x j + β ik x i x k + β jk x i x k (1) Where Y is the predicted MLSP, β are coefficients estimated by the model, and x i , x j , and x k represent the variables: substrate bed depth, S:I ratio, and growth stage time, respectively. The model was generated using non-linear regression and the significance was evaluated using the F-test. All statistical analyses were performed using JMP16. Table 2 Central composite design with minimum laccase selling price (MLSP) as the response. Run Factors MLSP ( $ /kU) Bed depth (cm) S:I ratio (g/g) Time (days) 2 Mg/batch 4 Mg/batch 8 Mg/batch 10 Mg/batch 15 Mg/batch 1 2 2.75 6 0.119 0.095 0.078 0.075 0.070 2 2.41 2.01 4.81 0.142 0.113 0.091 0.087 0.081 3 2.41 2.01 7.19 0.122 0.097 0.078 0.075 0.070 4 2.41 3.49 4.81 0.154 0.123 0.099 0.095 0.088 5 2.41 3.49 7.19 0.121 0.096 0.078 0.074 0.069 6 3 2 6 0.135 0.105 0.085 0.080 0.075 7 3 3 4 0.214 0.166 0.134 0.127 0.117 8 3 3 6 0.151 0.117 0.095 0.089 0.083 9 3 3 6 0.147 0.114 0.092 0.087 0.081 10 3 3 6 0.158 0.123 0.099 0.094 0.087 11 3 3 6 0.159 0.124 0.100 0.095 0.087 12 3 3 8 0.140 0.109 0.088 0.083 0.077 13 3 4 6 0.134 0.105 0.084 0.080 0.074 14 3.59 2.01 4.81 0.140 0.107 0.086 0.082 0.076 15 3.59 2.01 7.19 0.121 0.093 0.075 0.071 0.066 16 3.59 3.49 4.81 0.147 0.112 0.090 0.086 0.079 17 3.59 3.49 7.19 0.128 0.097 0.078 0.075 0.069 18 4 2.75 6 0.112 0.087 0.070 0.067 0.062 2.6.2 Capital costs The total capital investment is defined as the sum of all equipment purchase costs, multiplied by the Lang Factor. As a solid-liquid processing plant, a Lang Factor of 4.9 was chosen for this process model (Verret et al., 2020 ). Temporal adjustments for purchase costs were made based on the Chemical Engineering Plant Cost Index (CEPCI), assuming a current CEPCI of 798. Eq. 2 shows the formula used to calculate purchase costs. $$\:\begin{array}{c}Purchase\:cost=Reference\:cost\:\times\:{\left(\frac{Designed\:capacity}{Reference\:capacity}\right)}^{n}\times\:\:\left(\frac{Current\:CEPCI}{Reference\:CEPCI}\right)\:\#\left(2\right)\end{array}$$ Here, \(\:n\) represents the scaling factor to include economies of scale. For equipment with unknown scaling factors, a factor of 0.6 was assumed (Williams, 1947 ) . Information on solid-state bioreactors is scarce, particularly for tray bioreactors. As such, the bioreactor purchase price was based off a packed-bed bioreactor (Vasco-Correa & Shah, 2019 ) with the following size and cost adjustments. The trays are assumed to be 15’ x 45’ with two feet of vertical space between each tray, which includes empty space and the height of the substrate on each tray. The maximum volume (including empty space) for one bioreactor is 500 m 3 , with a 70% working volume. To account for automation or additional complexities, an additional two-times multiplier was applied to the bioreactor cost. The blower size was calculated based on the airflow rate used by Manan & Webb ( 2020 ) and adjusted based on bioreactor size. Detailed breakdown on individual equipment costs can be found in the Supplementary Information. 2.6.3 Operating Costs Table 3 lists the assumptions and breakdown of the annual operating costs for the laccase production plant. The operating costs are broken down into four groups: materials cost, utilities cost, labor cost, and facility cost. The inoculum cost was estimated from Field & Forest (2024), which was the source of inoculum used in the experimental portion of this study. The prairie biomass and AP were assumed to cost $ 150/Mg and $ 500/m 3 , respectively. Although the cost of the prairie biomass may seem high relative to other lignocellulosic biomass ( Billion-Ton Report , 2023), prairie is assumed to be grown in replacement of commodity crops, such as corn or wheat, suggesting that the prairie biomass should be valued at a higher cost relative to conventional feedstocks. Given the uncertainty in price for the biomass and AP, their cost was included for sensitivity analysis. Chemical prices were based on various commercial vendor prices. Further information on materials cost can be found in the Supplementary Information. Labor cost is dependent on plant size. We assume a minimum of four full-time operators, plus one for every additional tray bioreactor unit needed. Wages are set at $ 30/hr, with an additional 45% to account for benefits. Table 3 Component assumptions for operational costs. Parameters Cost Source Materials Cost (C m ) Prairie biomass $ 150/Mg Assumed Corn steep $ 0.50/kg Alibaba (2024) K2HPO4 $ 1/kg Molbase (2024) CuSO4 * 5H2O $ 8.4/kg Molbase (2024) CaCl2 $ 0.17/kg Molbase (2024) MgSO4 * 7H2O $ 0.09/kg Alibaba (2024) Water $ .03/Mg Assumed Inoculum Calculated Field and Forest (2024) Raw AP $ 500/m3 Assumed CaOH (lime) $ 120/Mg Alibaba (2024) 70% Sorbitol $ 760/Mg ChemAnalyst (2024) Packaging $ 0.58/bottle Alibaba (2024) Utilities Cost (C u ) Electricity $ 0.07/kWh Rosentrater and Zhang (2021) Steam $ 12/Mg Poliafico (2007) Labor Cost (C l ) Wage $ 30/hr Assumed Benefits factor 0.45 Assumed Facility Costs (C f ) Maintenance 0.02xCPC Peters et al. (2003) Depreciation Straight-line method Internal Revenue Service Property tax & insurance 0.01xTCI Assumed 2.6.4 Discounted cash flow analysis The discounted cash flow analysis was performed by considering the cash flow based on the time value of money. The construction period was assumed to last two years, followed by a six-month start-up period. As such, a negative cash flow is assumed for the first two years. The plant was modeled to run for 20 years total. The discount rate was set at 10% with a 40% income tax (Yang & Rosentrater, 2019 ). All equipment and material costs are presented in 2024 U.S. dollars. 2.6.5 Sensitivity analysis Sensitivity analysis is a useful tool to evaluate the effect of individual parameters on the profitability of a plant. Seven parameters were chosen for sensitivity analysis: prairie biomass cost, AP cost, inoculum cost, number of batches completed per year, operating cost, capital cost, and laccase activity loss. These parameters were chosen based on their level of uncertainty and assumed impact. Table 4 lists the individual parameters for each scenario, as well as their optimistic and pessimistic ranges. Table 4 Parameters to be evaluated for sensitivity analysis. Parameter Pessimistic Base Optimistic Prairie biomass cost ( $ /Mg) 250 150 75 Inoculum cost + 50% - -50% Laccase activity loss (% loss) 60 40 30 # Batches per year + 25% - -25% AP cost ( $ /m3) 750 500 250 Operating cost + 25% - -25% Capital Cost + 25% - -25% 3. Results 3.1 Preliminary evaluation of nutrients to enhance Pleurotus ostreatus growth In an effort to improve the growth rate of P. ostreatus , four nitrogen-containing nutrients were evaluated for their effect on mycelial growth. As shown in Fig. 2 a, corn steep resulted in the greatest growth for P. ostreatus. Meanwhile, the differences between ammonium sulfate, peptone, and yeast extract were insignificant (p > 0.05). Figure 2 b depicts the mycelial growth of P. ostreatus at different corn steep concentrations. Corn steep addition improved the growth of P. ostreatus up to a concentration of 10 g/L, beyond which showed no improvement in growth. As such, for the following work, corn steep solids were included into the media at 10 g/L. 3.2 Economic optimization of growth stage parameters The effect of substrate bed depth, S:I ratio, and growth time on laccase production yields and total fermentation time was evaluated experimentally based on a central composite design (Table 1 ) and incorporated into the techno-economic model. Table 5 shows the parameter estimates for Eq. (1) and corresponding significance levels for the base scenario. Growth time, as well as all quadratic terms showed statistical significance, while none of the interactive variables were deemed insignificant within this boundary. Table 5 Parameter estimates and statistical significance values for Eq. (1) at 8 Mg/batch. Coefficient Variable Estimate F-value p-value β0 Intercept -0.108 > 100 < 0.0001 βi Bed Depth 0.139 1.90 0.206 βj S:I Ratio 0.103 0.362 0.564 βk Time -0.04 36.9 0.0003 βii Bed Depth 2 -0.026 28.9 0.0007 βjj S:I Ratio 2 -0.015 10.1 0.013 βkk Time 2 0.003 5.74 0.0435 βij Bed Depth · S:I Ratio -0.0004 0.004 0.954 βik Bed Depth · Time 0.0019 0.42 0.534 βjk S:I Ratio · Time -0.0016 0.28 0.610 Table 6 lists the predicted optimal conditions to minimize the MLSP at each scale. At each scale, the model R 2 was 0.92 with an insignificant lack-of-fit test (p > 0.05), suggesting adequate model reliability. Maximizing the substrate bed height was optimal for each scale evaluated, as it reduced cost by minimizing reactor volume; however, the optimal S:I ratio and growth time were scale dependent. At 2, 10, and 15 Mg/batch scales, the optimized conditions were nearly identical, showing a lower S:I ratio to be advantageous. At 4 and 8 Mg/batch, a higher S:I ratio was more advantageous, but only slightly. Meanwhile, the optimal growth stage time only differed significantly for the 8 Mg/batch plant. Table 6 Optimal growth stage parameters for minimizing the MLSP. Optimized Conditions MLSP Scale (Mg/batch) Bed Height (cm) S:I Ratio (g/g) Growth Time (days) Predicted ( $ /kU) Validated a ( $ /kU) 2 4 1.5 6.83 0.0793 0.0847 4 4 4 6.77 0.0599 0.0657 8 4 4 7.44 0.0483 0.0508 10 4 1.5 6.86 0.0475 0.0509 15 4 1.5 6.86 0.0439 0.047 a. MLSP after performing validation experiments under the predicted optimal conditions. The fermentation was repeated under the predicted optimal conditions for validation. The validated results were within 5.2–9.7% of the predicted results, with the 8 Mg/batch scale having the closest fit. Figure 3 depicts the relationship between scale and MLSP and unit production cost for the validated results. As scale increases, MLSP and unit production costs generally decrease, but scaling beyond 8 Mg/batch provided only marginal reductions in cost and price. As such, 8 Mg/batch was chosen as the base scenario for this study. 3.3 Capital and operating costs Figure 4 provides a breakdown of the total capital and annual operating costs for the optimized conditions outlined in Table 5 . Under the base scale, the total capital and annual variable operating costs are estimated at 15.2 and 1.06 million USD. Solid media processing equipment and the tray bioreactor are the largest contributors to capital cost at each scale. The costs for solid media processing are largely attributed to the sterilization unit. Labor was the largest operating cost, comprising 42–67% of the variable operating costs, but did not increase proportionally based on scale. At scales of 2–8 Mg/batch, all non-labor operating costs were relatively comparable with each other. Beyond that, the inoculum cost began to greatly increase relative to the other costs, largely due to the reduction in S:I ratio assumed for those scales (Table 5 ). 3.4 Discounted cash flow analysis Figure 5 illustrates the cumulative discounted cash flow (after tax) at the base scale over the plant life. Given that there are no publicly available commercial selling prices for laccase enzymes, the selling price was adjusted to explore the potential cash flow returns under different pricing scenarios. As expected, greater laccase prices resulted in greater cash flows and shorter breakeven times for the facility. The relationship between the selling price and 20-year net present value (NPV) was linear (Fig. 5 b), but its relationship with the breakeven time was not. A minimum of $ 0.025/kU was needed to achieve a net positive return within the plant life. 3.5 Sensitivity analysis Figure 6 depicts the sensitivity of MLSP to changes in individual parameters under the base scenario. The prairie biomass, inoculum, and AP costs were insignificant, resulting in a < 1% change in MLSP. This is likely due to the relatively low quantities of these resources used at this scale. Changes to the operating cost had a slight effect on MLSP, while changes to capital costs had more significant effects on MLSP. The MLSP also showed high sensitivity to changes in laccase output from the production facility, as changes to laccase activity losses and the number of batches produced annually were among the most significant factors. 4. Discussion, limitations, and future outlook Given the novelty of this laccase production method, it is likely that many process improvements can be made to reduce costs, increase product output, or decrease product losses. However, this study provides critical insights into the current, preliminary prospects of this process system. Table 7 presents a summary of the laccase production facility for the base scenario at 8 Mg of biomass per batch. Table 7 Process simulation summary for an 8 Mg/batch plant. Parameter Scenario Total capital investment ( $ ) 15,241,822 Annual operating cost ( $ ) 1,059,097 Batches (#/year) 28.2 Laccase yield (kU/year) 149.9 × 10 6 MLSP ( $ /kU) 0.051 Discounted ROI 109% IRR (after tax) 24.5% NPV ( $ ) 16,574,382 At this scale, approximately 28 batches are generated annually, with the largest scheduling bottlenecks being the growth stage and induction stage of fermentation. While the concurrent scheduling method allowed for more batches to be completed annually, this amount is still quite low. To achieve a five-year return-on-investment (ROI) at a 10% discount rate, a MLSP of $ 0.05 was needed. While it is difficult to assess whether this price would be competitive in the market given the lack of publicly available data on commercial laccase prices, comparisons can be made with laboratory-grade, “off-the-shelf” prices, with the understanding that these may cost significantly more per unit of enzyme relative to bulk prices. Brugnari et al. ( 2021 ) reported a range of literature estimates and off-the-shelf laccase prices between 0.40–155 $ /kU, which is ~ 8–3,100x greater than the MLSP estimated in this study. This indicates that, given the early-stage nature of this process, this novel production method shows promise for cost-competitive laccase production. The fermentation process was modeled based on experimental data gathered from a three-factor central composite design (Table 2 ), showing a significant effect from the growth stage fermentation parameters on the MLSP. The optimal configurations for the fermentation parameters were found to be scale dependent. For all scales evaluated, maximizing the substrate bed height (4 cm) was optimal for decreasing MLSP. Bed height is a particularly important consideration for tray bioreactors, as larger bed sizes allow for higher throughput and smaller bioreactors and facility sizes, thus reducing costs. However, increasing the bed height generates more compaction of the biomass, which can prevent airflow from passing through the substrate (Pitol et al., 2016 ), thereby limiting microbial growth. The optimal S:I ratio was determined to be at the outer boundary of the experimental design, either 1.5 or 4 g/g, depending on scale. It should be noted that the difference in MLSP at the low and high bounds were minor. Lastly, the optimal growth stage time ranged from 6.77–7.44 days depending on scale. This is because the longer growth times typically resulted in increased laccase yields (Supplementary Information) without becoming the rate limiting process for the laccase production plant. Further investigation of the fermentation process should focus on the following: 1) evaluating the relationship between substrate particle size and bed height to minimize bioreactor size without limiting fungal growth, 2) increasing the S:I ratio to minimize the cost of inoculum needed for fermentation, and 3) reducing the total fermentation time to increase the number of batches produced annually. Additional research in these areas could provide substantial economic benefits. The laccase production system is estimated to be a capital-intensive process (Fig. 4 a), similar to other solid-state bioprocesses reported (Hafid et al., 2021 ; Lin et al., 2019 ; Vasco-Correa & Shah, 2019 ). The capital cost breakdown was consistent for each scale, with solid media processing and the tray bioreactor system incurring the largest costs. Together, the cost for the tray bioreactor system and autoclave contribute to 43–52% of the total capital investment, demonstrating the need to innovate and reduce size or costs for this technology. The operating cost breakdown differed slightly depending on scale. For all plant sizes evaluated, labor contributed the most to operating costs. At 10 and 15 Mg/batch scales, the inoculum cost increased significantly, mostly due to the low S:I ratio used at those scales requiring a greater quantity of inoculum. In this study, the inoculum was assumed to be purchased through a commercial vendor; however, producing the inoculum on-site could lower this cost at the expense of higher capital investment. Several limitations for this process model should be taken into consideration. First, this study employs lab-scale data that may differ from production-scale operations. Additionally, data availability for solid-state bioprocessing equipment is scarce, and thus, difficult to model. However, TEA is often rife with uncertainty and variability, particularly for technologies at low technological readiness levels (van der Spek et al., 2017 ). One advantage of TEA for early-stage technologies is that design parameters can be evaluated under a consistent set of assumptions, which was the purpose behind the methodology for the experimental work in this study. Although some optimization work was performed, it is of the authors’ opinion that significant improvements to this laccase production system can still be made. Region-specific limitations may also exist for this fermentation process. While prairie biomass could be substituted for other, more abundant, lignocellulosic biomass, this may result in differences in laccase yield and fermentation conditions. As stated previously, prairie biomass was chosen for the various environmental and ecological benefits that perennials can provide. However, prairie biomass may not be available in all regions, so alternative feedstocks should be evaluated for this process as well. With additional research, significant improvements in plant design and economics could be achieved. First, reductions in fermentation time would greatly increase the number of batches produced annually (Fig. 6 ). Currently, the rate-limiting operation is the induction stage of fermentation. Optimizing the environmental conditions for the induction stage may significantly lower the duration for laccase production. Based on the sensitivity analysis (Fig. 6 ), laccase recovery significantly impacts MLSP, suggesting that further research and optimization of downstream processes should be done while considering the different applications for laccase enzymes. Integration of the process model with other processes may also enhance the economics and help build more circular systems. For example, after solid-liquid separation, the solid substrate can be utilized as a feedstock for composting or anaerobic digestion (Gao et al., 2021 ; Lou et al., 2017 ; Vasilakis et al., 2023 ), which may provide added revenue to the plant. Additionally, process water could be reused to decrease the cost of waste storage and disposal. 5. Conclusions This study evaluated the economic viability of an early-stage, novel laccase production process that provides a valorization strategy for perennial biomass and bio-oil aqueous phase. A process model was generated based on experimental data to model and optimize the effect of three fermentation parameters on the economics of a laccase production plant. At a base scale of 8 Mg perennial biomass per batch, 28 batches can be generated each year, achieving a 24.5% internal rate of return at a laccase selling price of $ 0.05/kU. Sensitivity analysis showed laccase recovery and the annual number of batches to be the most significant towards impacting the MLSP. Future efforts should be dedicated towards fermentation optimization and generating pilot scale data on downstream processing and solid-state fermentation technologies. Abbreviations AP Bio-oil aqueous phase TEA Techno-economic analysis ABTS 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) CEPCI Chemical Engineering Plant Cost Index S I:Substrate:inoculum ratio MLSP Minimum laccase selling price NPV Net present value ROI Return on investment Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Data is available in Supplementary Information. Any additional data will be made available upon request. Competing interests The authors declare that they have no competing interests Funding This work is supported by the Agriculture and Food Research Initiative Sustainable Agricultural Systems program, project award no. 2020-68012-31824, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy. Authors’ contributions ER: methodology, investigation, data curation, conceptualization. NC: data curation. KR: resources, project administration, conceptualization. Acknowledgements We would like to thank Rob Hartmann for the procurement and milling of the prairie biomass and Tannon Daugaard for procurement of the bio-oil AP. Graphical abstract was created in BioRender. Rahic, E. (2025) https://BioRender.com/2gpq593. 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Enhanced anaerobic digestion of prairie biomass through alkaline-based hydroxycinnamic acid extraction. Bioresource Technology Reports , 27 , 101896. https://doi.org/10.1016/j.biteb.2024.101896 Schulte, L. A., Niemi, J., Helmers, M. J., Liebman, M., Arbuckle, J. G., James, D. E., Kolka, R. K., O’Neal, M. E., Tomer, M. D., Tyndall, J. C., Asbjornsen, H., Drobney, P., Neal, J., Van Ryswyk, G., & Witte, C. (2017). Prairie strips improve biodiversity and the delivery of multiple ecosystem services from corn–soybean croplands. Proceedings of the National Academy of Sciences , 114 (42), 11247–11252. https://doi.org/10.1073/pnas.1620229114 U.S. Department of Energy. (2023). 2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources . https://www.energy.gov/sites/default/files/2024-03/beto-2023-billion-ton-report_2.pdf van der Spek, M., Ramirez, A., & Faaij, A. (2017). Challenges and uncertainties of ex ante techno-economic analysis of low TRL CO2 capture technology: Lessons from a case study of an NGCC with exhaust gas recycle and electric swing adsorption. Applied Energy , 208 , 920–934. https://doi.org/10.1016/j.apenergy.2017.09.058 Vasco-Correa, J., & Shah, A. (2019). Techno-Economic Bottlenecks of the Fungal Pretreatment of Lignocellulosic Biomass. Fermentation , 5 (2), Article 2. https://doi.org/10.3390/fermentation5020030 Vasilakis, G., Rigos, E.-M., Giannakis, N., Diamantopoulou, P., & Papanikolaou, S. (2023). Spent Mushroom Substrate Hydrolysis and Utilization as Potential Alternative Feedstock for Anaerobic Co-Digestion. Microorganisms , 11 (2), Article 2. https://doi.org/10.3390/microorganisms11020532 Verret, J., Qiao, R., & Barghout, R. A. (2020). Foundations of Chemical and Biological Engineering I . BCcampus. https://pressbooks.bccampus.ca/chbe220/ Wild, K., Rahic, E., Schulte, L., & Mba Wright, M. (2025). Techno-economic and environmental assessment of converting mixed prairie to renewable natural gas with co-product hydroxycinnamic acid. Biofuels, Bioproducts and Biorefining , 19 (2), 288–304. https://doi.org/10.1002/bbb.2710 Williams Jr., R. (1947). Six-Tenths Factor Aids in Approximating Costs. Chemical Engineering , 54 , 124–125. Yang, M., & Rosentrater, K. A. (2019). Techno-economic analysis of the production process of structural bio-adhesive derived from glycerol. Journal of Cleaner Production , 228 , 388–398. https://doi.org/10.1016/j.jclepro.2019.04.288 Zhang, Y., Liang, Y., Li, S., Yuan, Y., Zhang, D., Wu, Y., Xie, H., Brindhadevi, K., Pugazhendhi, A., & Xia, C. (2023). A review of biomass pyrolysis gas: Forming mechanisms, influencing parameters, and product application upgrades. Fuel , 347 , 128461. https://doi.org/10.1016/j.fuel.2023.128461 Zhao, X., Davis, K., Brown, R., Jarboe, L., & Wen, Z. (2015). Alkaline treatment for detoxification of acetic acid-rich pyrolytic bio-oil for microalgae fermentation: Effects of alkaline species and the detoxification mechanisms. Biomass and Bioenergy , 80 , 203–212. https://doi.org/10.1016/j.biombioe.2015.05.007 Zhao, X., Jarboe, L., & Wen, Z. (2016). Utilization of pyrolytic substrate by microalga Chlamydomonas reinhardtii: Cell membrane property change as a response of the substrate toxicity. Applied Microbiology and Biotechnology , 100 (9), 4241–4251. https://doi.org/10.1007/s00253-016-7439-2 Zhou, H., Brown, R. C., & Wen, Z. (2019). Anaerobic digestion of aqueous phase from pyrolysis of biomass: Reducing toxicity and improving microbial tolerance. Bioresource Technology , 292 , 121976. https://doi.org/10.1016/j.biortech.2019.121976 Zhou, H., Brown, R. C., & Wen, Z. (2020). Biochar as an Additive in Anaerobic Digestion of Municipal Sludge: Biochar Properties and Their Effects on the Digestion Performance. ACS Sustainable Chemistry & Engineering , 8 (16), 6391–6401. https://doi.org/10.1021/acssuschemeng.0c00571 Supplementary Files GraphicalAbstract.png SupplementaryInformation.xlsm Cite Share Download PDF Status: Published Journal Publication published 31 Jul, 2025 Read the published version in Bioresources and Bioprocessing → Version 1 posted Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 28 Apr, 2025 First submitted to journal 24 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6525069","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":449134501,"identity":"2a171bbb-740a-4bcc-9497-5f9a2c5712cc","order_by":0,"name":"Elmin Rahic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACNoYEBhseBP8AcVrSgFqYoaqJ0sLAcJiBeC380oefPXhQc16Gv//8wc8fKhjk+G4k4Nci2ZdmbpBw7DaPxI1kZokDZxiMJQlpMTjDYCaR2HCbh+EGM4PEwTaGxA2EtNifYf8G1HKOR/78YeYfB/8x1BPUYsDDA7LlAI/BgWQ2iYMNDAkGhLRInOEpk0g4lsxjeCPZzOLMMQnDmWce4NfC38O+TfJHjZ293PmDj29U1NjI8x0nYAuGraQpHwWjYBSMglGAHQAA0dVEbwebnlkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0003-0216-5992","institution":"Iowa State University","correspondingAuthor":true,"prefix":"","firstName":"Elmin","middleName":"","lastName":"Rahic","suffix":""},{"id":449134502,"identity":"12387e70-9930-41dd-8dcc-a10ed15449da","order_by":1,"name":"Nicholas Cassady","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Cassady","suffix":""},{"id":449134503,"identity":"4cfea06f-8ed8-49d8-9da6-ffa3413f8760","order_by":2,"name":"Kurt Rosentrater","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Kurt","middleName":"","lastName":"Rosentrater","suffix":""}],"badges":[],"createdAt":"2025-04-25 04:19:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6525069/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6525069/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40643-025-00924-2","type":"published","date":"2025-07-31T16:21:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81691426,"identity":"b472a1d4-d24f-4a1c-ac1c-8f24103e5824","added_by":"auto","created_at":"2025-04-30 11:35:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140768,"visible":true,"origin":"","legend":"\u003cp\u003eProcess flow diagram showing a) upstream and fungal growth-stage processes, b) AP preparation, and c) downstream processing.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/b12bf76336d55736f1f51ad6.png"},{"id":81691424,"identity":"88101a1b-5664-41db-872d-a6ec358bdb42","added_by":"auto","created_at":"2025-04-30 11:35:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45188,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of growth rate of P. ostreatus on agar media by a) comparison of different nitrogen sources, and b) evaluating different concentrations of corn steep. Data is presented as means of three replicates, with error bars representing standard deviations.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/280fb5810c70312d04925a42.png"},{"id":81691425,"identity":"73e9dcb4-9154-431c-9711-89bf81872fc5","added_by":"auto","created_at":"2025-04-30 11:35:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26667,"visible":true,"origin":"","legend":"\u003cp\u003eUnit production cost and MLSP at different scales. Price is provided in USD per kU (1,000 laccase units). Scales are represented as the quantity of prairie biomass used for fermentation per batch. Empty circles represent the unit production cost, while the filled circles represent the minimum selling price.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/164ad0c06ecf06cc43ec2467.png"},{"id":81690222,"identity":"977a3b84-936b-4e6a-91c3-2f9e4f3d95c8","added_by":"auto","created_at":"2025-04-30 11:27:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96261,"visible":true,"origin":"","legend":"\u003cp\u003eComparison and breakdown of a) total capital investment and b) annual operating variable costs at different scales. Scales are represented as quantity of prairie biomass used for fermentation.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/e8b6914b315eb5295ee108f6.png"},{"id":81690220,"identity":"6d29cbeb-3a21-49cb-a9f3-f8fdefbec96d","added_by":"auto","created_at":"2025-04-30 11:27:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96767,"visible":true,"origin":"","legend":"\u003cp\u003ea) Discounted cash flow analysis under different laccase price assumptions under the base scale (8 Mg/batch). b) Relationship between laccase selling price and the 20-year NPV and breakeven time of the facility at the base scale. Hollow circles represent breakeven time, while filled circles represent the NPV. Breakeven time does not account for construction.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/aa3e686620961facbf35582e.png"},{"id":81690223,"identity":"fb9f5162-89ec-4488-9b16-f8b0ec403cc2","added_by":"auto","created_at":"2025-04-30 11:27:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":49228,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of different parameters at a scale of 8 Mg/batch based on pessimistic and optimistic assumptions defined in Table 4.\u003c/p\u003e","description":"","filename":"FIgure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/7794ba5474bfffd669569163.png"},{"id":88268279,"identity":"7cc86b9d-4732-48cb-b88b-5f992dd2c183","added_by":"auto","created_at":"2025-08-04 16:50:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1822920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/b7c7254e-5121-4226-80e2-cbc7149920af.pdf"},{"id":81690225,"identity":"6a294a8c-fb6a-47ed-bb46-80b9597c58e4","added_by":"auto","created_at":"2025-04-30 11:27:52","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":67818,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/2a4258c1374b6e68a87f1988.png"},{"id":81690233,"identity":"d1c4b7cb-bdaf-47aa-aee4-fb72369c84e4","added_by":"auto","created_at":"2025-04-30 11:27:53","extension":"xlsm","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":272069,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.xlsm","url":"https://assets-eu.researchsquare.com/files/rs-6525069/v1/10c97382bf600293aafc0036.xlsm"}],"financialInterests":"","formattedTitle":"Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFast pyrolysis is a popular technology for the rapid depolymerization of biomass into three fractions: gas, char, and bio-oil. The gas consists primarily of carbon dioxide, carbon monoxide, and methane, and is often used for process heating (Zhang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The char consists mostly of inert carbon and ash, and has been studied for many applications, including bioremediation, anaerobic digestion, and as a soil amendment (El-Naggar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lastly, the bio-oil is a tar-like material containing various anhydrosugars, aromatic compounds, and more, and can be upgraded into a renewable petroleum alternative (Lachos-Perez et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo facilitate the upgrading of bio-oil into renewable products, the aqueous fraction (hereafter referred to as the aqueous phase, AP) can be separated from the valuable heavy ends through stage fractionation processes (Pollard et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The AP consists predominantly of water and carboxylic acids, but also contains various alcohols and low molecular weight lignin fractions, like phenols (Liang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The high organic content of AP has garnered attention as a feedstock for microbial bioprocessing. Specifically, most studies have attempted to utilize AP as a carbon source for heterotrophic fermentation, through anaerobic digestion, microalgae cultivation, or other means (Zhao et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the toxicity of AP has proven difficult to overcome, even when detoxification measures are employed. Recently, AP was reported to be an effective inducer for laccase production in the white-rot fungus, \u003cem\u003ePleurotus ostreatus\u003c/em\u003e (Rahic, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Laccase is a multi-copper oxidase found naturally in plants, fungi, and bacteria. In plants, laccase plays a role in lignin polymerization, whereas in fungi, laccase often assists in lignin depolymerization reactions (Dwivedi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Fungal and bacterial laccases may possess differing characteristics. Bacterial laccases are generally more active and stable at higher temperatures and pH, but are often produced in smaller quantities and have a lower redox potential relative to fungal laccases (Janusz et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, laccase is capable of catalyzing oxidation reactions on a wide range of substrates. As a result, laccase has been studied for its potential role in the food and beverage industry, textile industry, bioremediation, bioenergy industry, and more (Mayolo-Deloisa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLaccase can be produced from submerged or solid-state fermentation. Solid-state fermentation may offer several advantages for white-rot fungi, as it mimics the environmental conditions that these fungi naturally grow in. In addition, solid-state fermentation requires significantly less process water, making it a potentially more sustainable culturing method for white-rot fungi. To date, many studies have investigated laccase production from white-rot fungi using solid-state fermentation; however, the literature data on its economic viability is extremely limited.\u003c/p\u003e \u003cp\u003eIn our previous study, laccase production was performed using prairie biomass as the substrate for solid-state fermentation (Rahic, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Perennial biomass offers numerous ecological services, such as improved nutrient, soil, and water retention, as well as improvements in wildlife biodiversity (Schulte et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prairie strips offer a means for farmers to integrate diverse perennial species into their existing landscape to provide these ecological services. However, to encourage farmers to implement perennials into their farming operation, economic value must be obtained from the biomass. As a lignocellulosic biomass, it has potential to be utilized in biorefining processes. To date, very few studies evaluating valorization pathways for prairie biomass exist (Olafasakin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rahic et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wild et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The aim of this study is to conduct a techno-economic analysis (TEA) to investigate the viability of a novel laccase production process, in the pursuit of valorizing both AP and perennial biomass.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Substrate and inoculum origin\u003c/h2\u003e \u003cp\u003eThe prairie biomass used in this study originated from the Iowa State University Comparison of Biofuel Cropping Systems research plots, seeded with a mixture of ~\u0026thinsp;59 different species. Kordbacheh et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provides details on prairie speciation, while biomass characterization is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The \u003cem\u003eP. ostreatus\u003c/em\u003e grain inoculum used in this study was purchased from Field \u0026amp; Forest Products. The inoculum was stored at 4\u0026deg;C upon delivery and used within one week. The AP used in this study was derived from the fast pyrolysis of corn stover in a fluidized bed reactor. The AP was subjected to overliming treatment prior to use, following the method detailed by Zhao et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Polin et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) provides details on AP production, while its characterization can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the prairie biomass and treated AP used for this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrairie biomass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% wt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolatile Solids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% wt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLignin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% wt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemicellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% wt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% wt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phenolics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/L gallic acid equiv.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Growth nutrient screening for Pleurotus ostreatus\u003c/h2\u003e \u003cp\u003eNutrient screening was performed by cultivating \u003cem\u003eP. ostreatus\u003c/em\u003e on plates containing: 20 g/L agar, 10 g/L finely milled prairie biomass, 4 g/L KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 1 g/L MgSO\u003csub\u003e4\u003c/sub\u003e, 0.2 g/L CaCl\u003csub\u003e2\u003c/sub\u003e, 0.2 g/L CuSO\u003csub\u003e4\u003c/sub\u003e, and a nitrogen-containing nutrient at a concentration of 5 g/L, unless otherwise specified. Four nutrients were tested: corn steep solids, peptone, yeast-extract, and ammonium sulfate. Each plate was inoculated with a 1cm mycelial plug of \u003cem\u003eP. ostreatus\u003c/em\u003e grown on potato-dextrose agar at 28\u0026deg;C for 7 days. The nutrient screening plates were incubated at 28\u0026deg;C for four days, followed by harvesting of the mycelial biomass by scraping and quantifying the growth gravimetrically. Each condition was evaluated in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Two-stage fermentation for laccase production\u003c/h2\u003e \u003cp\u003eLaccase production from \u003cem\u003eP. ostreatus\u003c/em\u003e was conducted following the protocol by Rahic (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with modifications. In short, the fermentation process was divided into two stages: growth stage and induction stage. During the growth stage, \u003cem\u003eP. ostreatus\u003c/em\u003e was cultivated in rectangular LDPE containers to mimic a tray bioreactor. Each container held sterilized prairie biomass moistened to 80% moisture content with liquid media containing: 10 g/L corn steep solids, 4 g/L KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 1 g/L MgSO\u003csub\u003e4\u003c/sub\u003e, 0.2 g/L CaCL\u003csub\u003e2\u003c/sub\u003e, and 0.2 g/L CuSO\u003csub\u003e4\u003c/sub\u003e. Commercial grain spawn of \u003cem\u003eP. ostreatus\u003c/em\u003e was used as inoculum. The quantity of substrate and inoculum used, as well as the growth stage time, was based on the experimental design, as described further in \u003cem\u003eSection 2.6\u003c/em\u003e. After the growth stage, the fungal culture was transferred to 500 mL Erlenmeyer flasks and submerged with non-sterile water to 94% moisture content. AP and CuSO\u003csub\u003e4\u003c/sub\u003e were added at 2.5% (v/v) and 1.1 g/L, respectively, and the cultures were held on orbital shakers at 25\u0026deg;C with 155 rpm agitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analyses\u003c/h2\u003e \u003cp\u003eLaccase activity was measured calorimetrically through the oxidation of 0.2 mM 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) (ε\u0026thinsp;=\u0026thinsp;36,000 M\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at 420 nm in 0.1 M citrate-phosphate buffer (pH 3) at 25\u0026deg;C. The laccase activity was calculated following the suggested equation by Baltierra-Trejo et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and reported in units per gram of prairie biomass. One unit of laccase activity was defined as the quantity of laccase required to oxidize 1 \u0026micro;mol of ABTS per min. Total phenolic content was measured using the folin-ciocalteu method. Prairie biomass samples were submitted to Celignis Analytical (Limerick, Ireland) for lignin, cellulose, and hemicellulose for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Techno-economic analysis\u003c/h2\u003e \u003cp\u003eSpreadsheet modeling of the laccase production facility was performed for TEA (Supplementary Information). Experimental data from this study, as well as existing literature, were used to inform mass and energy flows for the model. Laccase production was modeled as a batch process, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe process is divided into three stages: growth stage, induction stage, and downstream processing. The growth stage begins with mixing and preparation of the prairie biomass with liquid media to achieve a final moisture content of 80% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The substrate mixture is then sterilized prior to inoculation. Once inoculated, the fungus is cultured statically at 28\u0026deg;C in a tray bioreactor. Meanwhile, AP is treated using the overliming method outlined in \u003cem\u003eSection 2.1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Once the growth stage is complete, the colonized biomass is loaded into a mixing tank and submerged with water, CuSO\u003csub\u003e4\u003c/sub\u003e, and AP, following the exact parameters outlined in \u003cem\u003eSection 2.3\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The mixture is mixed continuously, during which laccase is produced extracellularly. After the induction phase, the mixture is subjected to filtration by a plate and frame filter press, where it is assumed that 10% of the laccase is lost to the solids fraction. After filtration, the laccase solution is concentrated via ultrafiltration. Zaccaria et al. (2019) reported a volumetric concentration factor of 60 with 183% laccase activity recovery. Meanwhile, Bryjak \u0026amp; Rekuć (2010) reported a concentration factor of ~\u0026thinsp;27 with a\u0026thinsp;~\u0026thinsp;90% laccase activity recovery. In this study, the concentration factor was averaged between the two reports (43.5), and the more conservative laccase activity recovery of 90% was used. Afterward, the laccase solution is mixed with sorbitol (33% v/v) as a stabilizing agent and assumed to be packaged in one-gallon jugs. An additional 25% loss in laccase activity is assumed from storage and shipping, for a final laccase recovery of 60.6%. No water reuse or valorization of side streams was considered for this analysis.\u003c/p\u003e \u003cp\u003eThe laccase production plant was scaled based on the quantity of prairie substrate used, from 2\u0026ndash;15 Mg/batch. Plant construction time was set to 24 months with a 6-month start-up time at 50% capacity. The plant was assumed to operate for 7,446 h per year, equivalent to an 85% capacity factor, for a plant lifetime of 20 years. Given the long fermentation time, the plant was assumed to run on a concurrent schedule around the rate-limiting operation. This means that preparations for a new batch are started during the longest process of the current batch. This is done to maximize the number of batches that can be completed per year; however, this study assumes only 5 concurrent batches can be completed before the scheduling must restart. The full spreadsheet model with all associated assumptions can be found in the Supplementary Information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Economic analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Optimization of laccase production parameters\u003c/h2\u003e \u003cp\u003eGiven the lack of information regarding commercial laccase prices, this laccase production facility was evaluated based on its minimum laccase selling price (MLSP). MLSP was calculated as the price needed to achieve a 5-year break-even period (after construction) assuming a 10% discount rate.\u003c/p\u003e \u003cp\u003eLaccase yield and induction stage time for the model were evaluated experimentally based on a central composite design studying three factors: substrate bed depth, substrate:inoculum (S:I) ratio, and growth stage time (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The laccase yield and induction time were not modeled as responses; rather, the experimental results were implemented directly into the process model to model the MLSP at each scale. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the experimental conditions for each run. The three factors were correlated through a second-order polynomial equation:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ej\u003c/sub\u003ex\u003csub\u003ej\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ek\u003c/sub\u003ex\u003csub\u003ek\u003c/sub\u003e β\u003csub\u003eii\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ejj\u003c/sub\u003ex\u003csub\u003ej\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ekk\u003c/sub\u003ex\u003csub\u003ek\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003eij\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ej\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003eik\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ek\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ejk\u003c/sub\u003ex\u003csub\u003ei\u003c/sub\u003ex\u003csub\u003ek\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003eWhere Y is the predicted MLSP, β are coefficients estimated by the model, and x\u003csub\u003ei\u003c/sub\u003e, x\u003csub\u003ej\u003c/sub\u003e, and x\u003csub\u003ek\u003c/sub\u003e represent the variables: substrate bed depth, S:I ratio, and growth stage time, respectively. The model was generated using non-linear regression and the significance was evaluated using the F-test. All statistical analyses were performed using JMP16.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCentral composite design with minimum laccase selling price (MLSP) as the response.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMLSP (\u003cspan\u003e$\u003c/span\u003e/kU)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed depth (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS:I ratio (g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 Mg/batch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 Mg/batch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8 Mg/batch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10 Mg/batch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15 Mg/batch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Capital costs\u003c/h2\u003e \u003cp\u003eThe total capital investment is defined as the sum of all equipment purchase costs, multiplied by the Lang Factor. As a solid-liquid processing plant, a Lang Factor of 4.9 was chosen for this process model (Verret et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Temporal adjustments for purchase costs were made based on the Chemical Engineering Plant Cost Index (CEPCI), assuming a current CEPCI of 798. Eq.\u0026nbsp;2 shows the formula used to calculate purchase costs.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}Purchase\\:cost=Reference\\:cost\\:\\times\\:{\\left(\\frac{Designed\\:capacity}{Reference\\:capacity}\\right)}^{n}\\times\\:\\:\\left(\\frac{Current\\:CEPCI}{Reference\\:CEPCI}\\right)\\:\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e represents the scaling factor to include economies of scale. For equipment with unknown scaling factors, a factor of 0.6 was assumed (Williams, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1947\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eInformation on solid-state bioreactors is scarce, particularly for tray bioreactors. As such, the bioreactor purchase price was based off a packed-bed bioreactor (Vasco-Correa \u0026amp; Shah, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with the following size and cost adjustments. The trays are assumed to be 15\u0026rsquo; x 45\u0026rsquo; with two feet of vertical space between each tray, which includes empty space and the height of the substrate on each tray. The maximum volume (including empty space) for one bioreactor is 500 m\u003csup\u003e3\u003c/sup\u003e, with a 70% working volume. To account for automation or additional complexities, an additional two-times multiplier was applied to the bioreactor cost. The blower size was calculated based on the airflow rate used by Manan \u0026amp; Webb (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and adjusted based on bioreactor size. Detailed breakdown on individual equipment costs can be found in the Supplementary Information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3 Operating Costs\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the assumptions and breakdown of the annual operating costs for the laccase production plant. The operating costs are broken down into four groups: materials cost, utilities cost, labor cost, and facility cost. The inoculum cost was estimated from Field \u0026amp; Forest (2024), which was the source of inoculum used in the experimental portion of this study. The prairie biomass and AP were assumed to cost \u003cspan\u003e$\u003c/span\u003e150/Mg and \u003cspan\u003e$\u003c/span\u003e500/m\u003csup\u003e3\u003c/sup\u003e, respectively. Although the cost of the prairie biomass may seem high relative to other lignocellulosic biomass (\u003cem\u003eBillion-Ton Report\u003c/em\u003e, 2023), prairie is assumed to be grown in replacement of commodity crops, such as corn or wheat, suggesting that the prairie biomass should be valued at a higher cost relative to conventional feedstocks. Given the uncertainty in price for the biomass and AP, their cost was included for sensitivity analysis. Chemical prices were based on various commercial vendor prices. Further information on materials cost can be found in the Supplementary Information. Labor cost is dependent on plant size. We assume a minimum of four full-time operators, plus one for every additional tray bioreactor unit needed. Wages are set at \u003cspan\u003e$\u003c/span\u003e30/hr, with an additional 45% to account for benefits.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComponent assumptions for operational costs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMaterials Cost (C\u003csub\u003em\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrairie biomass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e150/Mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorn steep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.50/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlibaba (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK2HPO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolbase (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuSO4 * 5H2O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e8.4/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolbase (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaCl2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.17/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMolbase (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMgSO4 * 7H2O\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.09/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlibaba (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e.03/Mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInoculum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCalculated\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eField and Forest (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e500/m3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaOH (lime)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e120/Mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlibaba (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70% Sorbitol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e760/Mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemAnalyst (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePackaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.58/bottle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlibaba (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUtilities Cost (C\u003c/b\u003e\u003csub\u003e\u003cb\u003eu\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.07/kWh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRosentrater and Zhang (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e12/Mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoliafico (2007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLabor Cost (C\u003c/b\u003e\u003csub\u003e\u003cb\u003el\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e30/hr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenefits factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFacility Costs (C\u003c/b\u003e\u003csub\u003e\u003cb\u003ef\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02xCPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeters et al. (2003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepreciation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStraight-line method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternal Revenue Service\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProperty tax \u0026amp; insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01xTCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAssumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.4 Discounted cash flow analysis\u003c/h2\u003e \u003cp\u003eThe discounted cash flow analysis was performed by considering the cash flow based on the time value of money. The construction period was assumed to last two years, followed by a six-month start-up period. As such, a negative cash flow is assumed for the first two years. The plant was modeled to run for 20 years total. The discount rate was set at 10% with a 40% income tax (Yang \u0026amp; Rosentrater, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All equipment and material costs are presented in 2024 U.S. dollars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis is a useful tool to evaluate the effect of individual parameters on the profitability of a plant. Seven parameters were chosen for sensitivity analysis: prairie biomass cost, AP cost, inoculum cost, number of batches completed per year, operating cost, capital cost, and laccase activity loss. These parameters were chosen based on their level of uncertainty and assumed impact. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists the individual parameters for each scenario, as well as their optimistic and pessimistic ranges.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters to be evaluated for sensitivity analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePessimistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOptimistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrairie biomass cost \u003c/p\u003e \u003cp\u003e(\u003cspan\u003e$\u003c/span\u003e/Mg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInoculum cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaccase activity loss \u003c/p\u003e \u003cp\u003e(% loss)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e# Batches per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP cost \u003c/p\u003e \u003cp\u003e(\u003cspan\u003e$\u003c/span\u003e/m3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperating cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Cost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Preliminary evaluation of nutrients to enhance Pleurotus ostreatus growth\u003c/h2\u003e \u003cp\u003eIn an effort to improve the growth rate of \u003cem\u003eP. ostreatus\u003c/em\u003e, four nitrogen-containing nutrients were evaluated for their effect on mycelial growth. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, corn steep resulted in the greatest growth for \u003cem\u003eP. ostreatus.\u003c/em\u003e Meanwhile, the differences between ammonium sulfate, peptone, and yeast extract were insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb depicts the mycelial growth of \u003cem\u003eP. ostreatus\u003c/em\u003e at different corn steep concentrations. Corn steep addition improved the growth of \u003cem\u003eP. ostreatus\u003c/em\u003e up to a concentration of 10 g/L, beyond which showed no improvement in growth. As such, for the following work, corn steep solids were included into the media at 10 g/L.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Economic optimization of growth stage parameters\u003c/h2\u003e \u003cp\u003eThe effect of substrate bed depth, S:I ratio, and growth time on laccase production yields and total fermentation time was evaluated experimentally based on a central composite design (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and incorporated into the techno-economic model. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the parameter estimates for Eq.\u0026nbsp;(1) and corresponding significance levels for the base scenario. Growth time, as well as all quadratic terms showed statistical significance, while none of the interactive variables were deemed insignificant within this boundary.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameter estimates and statistical significance values for Eq.\u0026nbsp;(1) at 8 Mg/batch.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS:I Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed Depth\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβjj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS:I Ratio\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβkk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβij\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed Depth \u0026middot; S:I Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed Depth \u0026middot; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβjk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS:I Ratio \u0026middot; Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e lists the predicted optimal conditions to minimize the MLSP at each scale. At each scale, the model R\u003csup\u003e2\u003c/sup\u003e was 0.92 with an insignificant lack-of-fit test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting adequate model reliability. Maximizing the substrate bed height was optimal for each scale evaluated, as it reduced cost by minimizing reactor volume; however, the optimal S:I ratio and growth time were scale dependent. At 2, 10, and 15 Mg/batch scales, the optimized conditions were nearly identical, showing a lower S:I ratio to be advantageous. At 4 and 8 Mg/batch, a higher S:I ratio was more advantageous, but only slightly. Meanwhile, the optimal growth stage time only differed significantly for the 8 Mg/batch plant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimal growth stage parameters for minimizing the MLSP.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOptimized Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMLSP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale (Mg/batch)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBed Height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS:I Ratio (g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrowth Time (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePredicted (\u003cspan\u003e$\u003c/span\u003e/kU)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eValidated \u003csup\u003ea\u003c/sup\u003e (\u003cspan\u003e$\u003c/span\u003e/kU)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea. MLSP after performing validation experiments under the predicted optimal conditions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe fermentation was repeated under the predicted optimal conditions for validation. The validated results were within 5.2\u0026ndash;9.7% of the predicted results, with the 8 Mg/batch scale having the closest fit. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the relationship between scale and MLSP and unit production cost for the validated results. As scale increases, MLSP and unit production costs generally decrease, but scaling beyond 8 Mg/batch provided only marginal reductions in cost and price. As such, 8 Mg/batch was chosen as the base scenario for this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Capital and operating costs\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a breakdown of the total capital and annual operating costs for the optimized conditions outlined in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Under the base scale, the total capital and annual variable operating costs are estimated at 15.2 and 1.06\u0026nbsp;million USD. Solid media processing equipment and the tray bioreactor are the largest contributors to capital cost at each scale. The costs for solid media processing are largely attributed to the sterilization unit. Labor was the largest operating cost, comprising 42\u0026ndash;67% of the variable operating costs, but did not increase proportionally based on scale. At scales of 2\u0026ndash;8 Mg/batch, all non-labor operating costs were relatively comparable with each other. Beyond that, the inoculum cost began to greatly increase relative to the other costs, largely due to the reduction in S:I ratio assumed for those scales (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Discounted cash flow analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the cumulative discounted cash flow (after tax) at the base scale over the plant life. Given that there are no publicly available commercial selling prices for laccase enzymes, the selling price was adjusted to explore the potential cash flow returns under different pricing scenarios. As expected, greater laccase prices resulted in greater cash flows and shorter breakeven times for the facility. The relationship between the selling price and 20-year net present value (NPV) was linear (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), but its relationship with the breakeven time was not. A minimum of \u003cspan\u003e$\u003c/span\u003e0.025/kU was needed to achieve a net positive return within the plant life.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the sensitivity of MLSP to changes in individual parameters under the base scenario. The prairie biomass, inoculum, and AP costs were insignificant, resulting in a\u0026thinsp;\u0026lt;\u0026thinsp;1% change in MLSP. This is likely due to the relatively low quantities of these resources used at this scale. Changes to the operating cost had a slight effect on MLSP, while changes to capital costs had more significant effects on MLSP. The MLSP also showed high sensitivity to changes in laccase output from the production facility, as changes to laccase activity losses and the number of batches produced annually were among the most significant factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion, limitations, and future outlook","content":"\u003cp\u003eGiven the novelty of this laccase production method, it is likely that many process improvements can be made to reduce costs, increase product output, or decrease product losses. However, this study provides critical insights into the current, preliminary prospects of this process system. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents a summary of the laccase production facility for the base scenario at 8 Mg of biomass per batch.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProcess simulation summary for an 8 Mg/batch plant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal capital investment (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,241,822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual operating cost (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,059,097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBatches (#/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaccase yield (kU/year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.9 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLSP (\u003cspan\u003e$\u003c/span\u003e/kU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscounted ROI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIRR (after tax)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPV (\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,574,382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt this scale, approximately 28 batches are generated annually, with the largest scheduling bottlenecks being the growth stage and induction stage of fermentation. While the concurrent scheduling method allowed for more batches to be completed annually, this amount is still quite low. To achieve a five-year return-on-investment (ROI) at a 10% discount rate, a MLSP of \u003cspan\u003e$\u003c/span\u003e0.05 was needed. While it is difficult to assess whether this price would be competitive in the market given the lack of publicly available data on commercial laccase prices, comparisons can be made with laboratory-grade, \u0026ldquo;off-the-shelf\u0026rdquo; prices, with the understanding that these may cost significantly more per unit of enzyme relative to bulk prices. Brugnari et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported a range of literature estimates and off-the-shelf laccase prices between 0.40\u0026ndash;155 \u003cspan\u003e$\u003c/span\u003e/kU, which is ~\u0026thinsp;8\u0026ndash;3,100x greater than the MLSP estimated in this study. This indicates that, given the early-stage nature of this process, this novel production method shows promise for cost-competitive laccase production.\u003c/p\u003e \u003cp\u003eThe fermentation process was modeled based on experimental data gathered from a three-factor central composite design (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), showing a significant effect from the growth stage fermentation parameters on the MLSP. The optimal configurations for the fermentation parameters were found to be scale dependent. For all scales evaluated, maximizing the substrate bed height (4 cm) was optimal for decreasing MLSP. Bed height is a particularly important consideration for tray bioreactors, as larger bed sizes allow for higher throughput and smaller bioreactors and facility sizes, thus reducing costs. However, increasing the bed height generates more compaction of the biomass, which can prevent airflow from passing through the substrate (Pitol et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), thereby limiting microbial growth. The optimal S:I ratio was determined to be at the outer boundary of the experimental design, either 1.5 or 4 g/g, depending on scale. It should be noted that the difference in MLSP at the low and high bounds were minor. Lastly, the optimal growth stage time ranged from 6.77\u0026ndash;7.44 days depending on scale. This is because the longer growth times typically resulted in increased laccase yields (Supplementary Information) without becoming the rate limiting process for the laccase production plant. Further investigation of the fermentation process should focus on the following: 1) evaluating the relationship between substrate particle size and bed height to minimize bioreactor size without limiting fungal growth, 2) increasing the S:I ratio to minimize the cost of inoculum needed for fermentation, and 3) reducing the total fermentation time to increase the number of batches produced annually. Additional research in these areas could provide substantial economic benefits.\u003c/p\u003e \u003cp\u003eThe laccase production system is estimated to be a capital-intensive process (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), similar to other solid-state bioprocesses reported (Hafid et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vasco-Correa \u0026amp; Shah, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The capital cost breakdown was consistent for each scale, with solid media processing and the tray bioreactor system incurring the largest costs. Together, the cost for the tray bioreactor system and autoclave contribute to 43\u0026ndash;52% of the total capital investment, demonstrating the need to innovate and reduce size or costs for this technology. The operating cost breakdown differed slightly depending on scale. For all plant sizes evaluated, labor contributed the most to operating costs. At 10 and 15 Mg/batch scales, the inoculum cost increased significantly, mostly due to the low S:I ratio used at those scales requiring a greater quantity of inoculum. In this study, the inoculum was assumed to be purchased through a commercial vendor; however, producing the inoculum on-site could lower this cost at the expense of higher capital investment.\u003c/p\u003e \u003cp\u003eSeveral limitations for this process model should be taken into consideration. First, this study employs lab-scale data that may differ from production-scale operations. Additionally, data availability for solid-state bioprocessing equipment is scarce, and thus, difficult to model. However, TEA is often rife with uncertainty and variability, particularly for technologies at low technological readiness levels (van der Spek et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One advantage of TEA for early-stage technologies is that design parameters can be evaluated under a consistent set of assumptions, which was the purpose behind the methodology for the experimental work in this study. Although some optimization work was performed, it is of the authors\u0026rsquo; opinion that significant improvements to this laccase production system can still be made.\u003c/p\u003e \u003cp\u003eRegion-specific limitations may also exist for this fermentation process. While prairie biomass could be substituted for other, more abundant, lignocellulosic biomass, this may result in differences in laccase yield and fermentation conditions. As stated previously, prairie biomass was chosen for the various environmental and ecological benefits that perennials can provide. However, prairie biomass may not be available in all regions, so alternative feedstocks should be evaluated for this process as well.\u003c/p\u003e \u003cp\u003eWith additional research, significant improvements in plant design and economics could be achieved. First, reductions in fermentation time would greatly increase the number of batches produced annually (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Currently, the rate-limiting operation is the induction stage of fermentation. Optimizing the environmental conditions for the induction stage may significantly lower the duration for laccase production. Based on the sensitivity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), laccase recovery significantly impacts MLSP, suggesting that further research and optimization of downstream processes should be done while considering the different applications for laccase enzymes. Integration of the process model with other processes may also enhance the economics and help build more circular systems. For example, after solid-liquid separation, the solid substrate can be utilized as a feedstock for composting or anaerobic digestion (Gao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vasilakis et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which may provide added revenue to the plant. Additionally, process water could be reused to decrease the cost of waste storage and disposal.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study evaluated the economic viability of an early-stage, novel laccase production process that provides a valorization strategy for perennial biomass and bio-oil aqueous phase. A process model was generated based on experimental data to model and optimize the effect of three fermentation parameters on the economics of a laccase production plant. At a base scale of 8 Mg perennial biomass per batch, 28 batches can be generated each year, achieving a 24.5% internal rate of return at a laccase selling price of \u003cspan\u003e$\u003c/span\u003e0.05/kU. Sensitivity analysis showed laccase recovery and the annual number of batches to be the most significant towards impacting the MLSP. Future efforts should be dedicated towards fermentation optimization and generating pilot scale data on downstream processing and solid-state fermentation technologies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBio-oil aqueous phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTechno-economic analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCEPCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChemical Engineering Plant Cost Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eI:Substrate:inoculum ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinimum laccase selling price\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNet present value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReturn on investment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eData is available in Supplementary Information. Any additional data will be made available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by the Agriculture and Food Research Initiative Sustainable Agricultural Systems program, project award no. 2020-68012-31824, from the U.S. Department of Agriculture\u0026rsquo;s National Institute of Food and Agriculture.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAny opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026rsquo; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eER: methodology, investigation, data curation, conceptualization. NC: data curation. KR: resources, project administration, conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Rob Hartmann for the procurement and milling of the prairie biomass and Tannon Daugaard for procurement of the bio-oil AP. Graphical abstract was created in BioRender. Rahic, E. (2025) https://BioRender.com/2gpq593.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaltierra-Trejo, E., M\u0026aacute;rquez-Benavides, L., \u0026amp; S\u0026aacute;nchez-Y\u0026aacute;\u0026ntilde;ez, J. M. (2015). Inconsistencies and ambiguities in calculating enzyme activity: The case of laccase. \u003cem\u003eJournal of Microbiological Methods\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e, 126\u0026ndash;131. https://doi.org/10.1016/j.mimet.2015.10.007\u003c/li\u003e\n\u003cli\u003eBrugnari, T., Braga, D. M., dos Santos, C. S. A., Torres, B. H. C., Modkovski, T. 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Biochar as an Additive in Anaerobic Digestion of Municipal Sludge: Biochar Properties and Their Effects on the Digestion Performance. \u003cem\u003eACS Sustainable Chemistry \u0026amp; Engineering\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(16), 6391\u0026ndash;6401. https://doi.org/10.1021/acssuschemeng.0c00571\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bioresources-and-bioprocessing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biob","sideBox":"Learn more about [Bioresources and Bioprocessing](http://bioresourcesbioprocessing.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/biob/default.aspx","title":"Bioresources and Bioprocessing","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Laccase, techno-economic analysis, optimization, solid-state fermentation, bio-oil, aqueous phase, prairie, perennial, biomass","lastPublishedDoi":"10.21203/rs.3.rs-6525069/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6525069/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explored a novel laccase production process utilizing perennial biomass and the aqueous phase from bio-oil. Experimental and techno-economic modeling were coupled to gain insight into and optimize the solid-state fermentation conditions for minimizing the laccase selling price. At relatively small scales, processing only 230 Mg of prairie biomass per year, it was determined that a minimum laccase selling price of \u003cspan\u003e$\u003c/span\u003e0.05/kU was needed to achieve a 5-year return on investment assuming a 10% discount rate. Insights into the capital and operating costs were discussed, as well as a sensitivity analysis, which showed the minimum laccase selling price to be most sensitive to parameters affecting laccase output from the plant. Limitations and future outlooks were provided, emphasizing the need for further process optimization to minimize fermentation time and downstream losses.\u003c/p\u003e","manuscriptTitle":"Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 11:27:48","doi":"10.21203/rs.3.rs-6525069/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-08T08:23:38+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-28T13:27:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-28T09:31:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bioresources and Bioprocessing","date":"2025-04-25T00:19:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioresources-and-bioprocessing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biob","sideBox":"Learn more about [Bioresources and Bioprocessing](http://bioresourcesbioprocessing.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/biob/default.aspx","title":"Bioresources and Bioprocessing","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3070d49d-67f1-4813-8088-309c51f20d41","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:43:55+00:00","versionOfRecord":{"articleIdentity":"rs-6525069","link":"https://doi.org/10.1186/s40643-025-00924-2","journal":{"identity":"bioresources-and-bioprocessing","isVorOnly":false,"title":"Bioresources and Bioprocessing"},"publishedOn":"2025-07-31 16:21:26","publishedOnDateReadable":"July 31st, 2025"},"versionCreatedAt":"2025-04-30 11:27:48","video":"","vorDoi":"10.1186/s40643-025-00924-2","vorDoiUrl":"https://doi.org/10.1186/s40643-025-00924-2","workflowStages":[]},"version":"v1","identity":"rs-6525069","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6525069","identity":"rs-6525069","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}