Prediction of phosphoric acid plus hydrogen peroxide (PHP) pretreatment efficiency using artificial neural network modeling

Prediction of phosphoric acid plus hydrogen peroxide (PHP) pretreatment efficiency using artificial neural network modeling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of phosphoric acid plus hydrogen peroxide (PHP) pretreatment efficiency using artificial neural network modeling Qing Wang, Jinguang Hu, Li Zhao, Mei Huang, Dong Tian, Yongmei Zeng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2083176/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cellulose from lignocellulosic biomass is the most promising renewable feedstock which may become a substitute for petrochemical products. However, it is challenging to extract cellulose from biomass because of the structural resistance of lignocellulose. Phosphoric acid plus hydrogen peroxide (PHP) pretreatment is an efficient approach that might be applied to get the cellulose-enriched fraction (CEF) from biomass. This study employed the artificial neural network (ANN) to predict the PHP pretreatment efficiency. The critical conditions, including pretreatment time ( t ), temperature ( T ), H 3 PO 4 concentration ( C p ), and H 2 O 2 concentration ( C h ), were employed as input variables for the ANN model to predict the output variables: cellulose content (C- C ), cellulose recovery (C- R y ), hemicellulose removal (H- R l ), and lignin removal (L- R l ). The key parameters of ANN models are selected depending on the root mean square errors (RMSE). ANN models' final optimal topological structure contains one hidden layer with 9, 10, 10, and 12 neurons for C- C , C- R y , H- R l , and L- R l , respectively. The actual testing data fit the predicted data with an R 2 of 0.8070–0.9989. Additionally, we computed the relative importance ( RI ) of input variables on output variables using the Garson equation with net weight matrixes. And the results revealed that C p and C h ( RI 12.0–62.6%) impacted the effectiveness of PHP pretreatment primarily. T ( RI 78.6%) dominates the removal efficacy of hemicellulose, and t ( RI 9.5–24.6%) has less influence compared to the other conditions. The study provides insights into the optimization of biomass pretreatment. Lignocellulosic biomass ANN model Pretreatment efficiency Prediction Relative importance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The fast growth of human civilization relies on the consumption of fossil resources such as petroleum, coal, and natural gas, which generate major issues including environmental pollution and climate change (Rashid et al., 2021 ). Such a challenging scenario leads to an increasing interest in alternative and renewable energy sources (Hosseini Koupaie et al., 2019 ). Lignocellulosic biomass is the world's most abundant renewable organic carbon-based resource, and it is regarded as the most potential alternative to fossil resources (Luterbacher et al., 2014 ). Lignocellulosic biomass consists mostly of cellulose, hemicellulose, and lignin. Three components do not exist individually but are strongly related by physical forces and chemical interactions to produce a 3D cross-linked structure. Hemicellulose and lignin fill in the cellulose chains to strengthen the mechanical strength of the plant's cell wall via hydrogen bonding and ester linkages (Tocco et al., 2021 ). Hence, it is tough to extract each component from lignocellulosic biomass adequately. Our earlier work developed an effective biomass pretreatment method entitled phosphoric acid plus hydrogen peroxide (PHP) pretreatment (Wang et al., 2014 ). The systematic investigation revealed that PHP pretreatment could handle diverse softwoods, hardwoods, straws, and herbs with gentle processing conditions and minimal grinding needs to obtain satisfactory lignocellulose deconstruction. Under normal pretreatment conditions, the final recovered cellulose enriched-fraction (CEF) held a cellulose recovery of 92%, while 83.7% lignin and 100% hemicellulose were removed. It can harvest 29.1–32.6 g glucose from 100 g wheat straw by enzyme hydrolysis (Wang et al., 2016 ). Furthermore, with the simultaneous saccharification and fermentation (SSF) process at 15.3% solid loading for 120 h, it could harvest 15.5 g of ethanol from 100 g of wheat straw (Qiu et al., 2018 ). PHP pretreatment might also separate the hemicellulose and lignin from biomass to manufacture high-value products, such as oligosaccharides or supercapacitors (Liu et al., 2021 ; Wan et al., 2019 ). Besides, the oxidative tail-gas from PHP pretreatment may be applied to produce 68.0–98.3% methyl blue degradation (Lei et al., 2022 ). Recycle experiment indicated that 86.0% phosphoric acid could be recovered after ≥ 11 cycles of pretreatment (Yao et al., 2019 ). The previous research has explained the transformation mechanism of major components and the formation of multiple oxidation systems during PHP pretreatment, which is of great significance for the in-depth development of the PHP method (Tian et al., 2021 ; Wang et al., 2018 ). However, component separation is crucial in most application situations, which highlights the importance of conducting optimization experiments to boost the component separation efficiency, notably the separation of cellulose. Response surface methodology (RSM) is a regularly used strategy for response surface mapping to the region of interest, response optimization, and operation condition selection (Pereira et al., 2021 ). Efforts have been conducted to choose PHP pretreatment conditions by RSM with Box-Behnken design, and notable improvement has been realized (Qiu et al., 2017 ). However, both lignocellulose deconstruction and lignin/hemicellulose degradation processes are complicated and non-linear. They are tough to predict using RSM, particularly with limited experimental groups. Hence, from our perspective, PHP pretreatment conditions may optimize even more for the difference, dependent on the purpose of their applications. Nowadays, a powerful prediction tool named Artificial Neural Network (ANN) has been applied in numerous study domains because of its modeling capabilities, even if limited experimental data is available (Rashid et al., 2021 ). ANN technology is inspired by the operating mode of the human brain and nervous system, which comprises many neurons in multiple layers. A neuron may link to all or a subset of the neurons in the subsequent layer, with these connections simulating the brain's synaptic connections (Walczak & Cerpa, 2001 ). For this reason, ANN may learn from complicated, linear, and non-linear systems that approximate non-linear without any previous fitting function specified (Rashid et al., 2021 ). A recent study collected a total of 482 samples, and it evaluated the primary pyrolysis products of lignocellulosic biomass via ANN modeling, which successfully achieved the best possible results over different reactor systems, conditions, and biomass for the solid, liquid, and gaseous pyrolysis product yields (Tsekos et al., 2021 ). A prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of biomass was proposed, which implies that the ANN model may predict the pretreatment efficiency with constrained circumstances and groups of pretreatment (Luo et al., 2021 ). Moreover, the applications of the ANN model in biomass component estimation (Kartal & Özveren, 2021 ), kinetic parameters prediction of biomass oxidation (Sunphorka et al., 2017 ), and pretreatment for lignocellulose degradation (Bhange et al., 2017 ) were studied. And these applications completely highlight the potential of the ANN model in biomass valorization. The current study seeks to create an ANN model for assessing PHP pretreatment efficiency. Herein, four significant factors (time, temperature, concentration of H 3 PO 4 , and H 2 O 2 ) that impact PHP pretreatment were utilized as input variables. The content and recovery of cellulose were regarded as the output variables. Furthermore, the research also evaluates the relative importance of those pretreatment conditions for achieving the pretreatment target (recovering cellulose and removing hemicellulose or lignin) by evaluating the neural net weights in the created ANN model. This study would provide a novel approach for evaluating and improving lignocellulosic biomass pretreatment. 2. Materials And Methods 2.1 Materials and reagents Wheat straw (WS) is typical lignocellulosic biomass with a large annual output, which is employed as the raw material in this study. WS was collected on the farm of Sichuan Agricultural University in Chengdu, China. It was air-dried and milled through a 40-mesh sieve (≤ 0.45 mm) before PHP pretreatment. All reagents (analytically pure) were provided by Sigma-Aldrich unless mentioned elsewhere. 2.2 PHP pretreatment To carry out the PHP pretreatment, use 30% H 2 O 2 reagent to dilute 85% H 3 PO 4 reagent to prepare the PHP solution. Then, add WS to the PHP solution with a solid/liquid ratio of 1:10 (w/w) in a 250 mL screw-cap bottle and mix them completely. The mixture was shaken at the specified pretreatment temperature and reaction time at a 160 r/min rotation speed. Add 1.0 L of deionized water to halt the pretreatment process when it reaches the predetermined time. Finally, the treated WS is filtered and washed to a neutral pH to obtain the cellulose-enriched fraction (CEF), then stored at − 20°C for further use. The design of PHP pretreatment conditions is given in Table 1 . Table 1 PHP pretreatment conditions Purpose Time t (h) Temperature T (°C) H 3 PO 4 C p (%) H 2 O 2 C h (%) ANN training and validation 2–4 30–50 65–85 0–7.06 ANN testing 2 45 65–85 0–7.06 Note: The comprehensive test was applied in the experimental design, and the collected raw data for ANN training, validation, and testing are presented in Table S1 and Table S2 , respectively. 2.3 Analytical methods The main components, including cellulose, hemicellulose, and lignin of WS material and CEF, were analyzed according to the NREL (National Renewable Energy Lab of the US) method (Sluiter et al., 2010). Wherein the hydrolysate sugars were separated by a Shodex SH1011 column at 60°C using the 0.05 mol/L H 2 SO 4 mobile phase at a flow rate of 0.8 mL/min. The separated sugars were quantified using an Agilent 1260 Infinity Ⅱ HPLC system with a G7162A differential refractive index detector. The solid recovery (SR) of WS after pretreatment was estimated according to the equation SR (%) = m 1 / m 0 × 100% (1) where m 0 is the dry weight of WS (herein, m 0 = 5.00 g), and m 1 is the dry weight of the recovered CEF after PHP pretreatment. The cellulose recovery ( R y ) and component (hemicellulose or lignin) removal ( R l ) after PHP pretreatment was calculated following the equation: R y (%) = SR × ( C 1 / C 0 ) × 100% (2) R l (%) = 100% − R y (3) where C 0 and C 1 represent the related component content in WS material and CEF, respectively. 2.4 ANN modeling Figure 1 a depicts the flow diagram of PHP pretreatment. The pretreatment time ( t ), pretreatment temperature ( T ), concentration of phosphoric acid ( C p ), and concentration of hydrogen peroxide ( C h ) are the primary parameters that impact the efficiency of PHP pretreatment. After pretreatment, the main components of the produced CEF, including the solid recovery, cellulose/hemicellulose/lignin content, and the associated components’ recovery/removal, would be simultaneously analyzed (the results see Table S1 ). The ANN model was developed using the Neural Network Fitting program (version 1.33, based on the neuralnet library in R software) in OriginPro 2022b (OriginLab Corp., USA). As shown in Fig. 1 b, the ANN model proposed in the current study is a multiple-layer neural network with interconnected neurons arranged into three layers of input, hidden, and output layers, consisting of 4 input variables, 1 hidden layer with n neurons, and 1 output variable. The number of neurons ( n ) in the hidden layer was determined according to an empirical equation Eq. 4 (Yang et al., 2020 ). $$n\le \sqrt{i+k}+\alpha$$ 4 where n is the number of neurons in the hidden layer; i is the number of input variables; k is the number of output variables; α is a constant ranging from 1 to 10. The accuracy assessment of ANN modeling and predictions was carried out with the root means square error (RMSE) calculated with Eq. 5 . $$RMSE=\sqrt{\frac{1}{m}{\sum }_{h=1}^{m}\left({y}_{pre}^{\left(h\right)}-{y{\prime }}_{exp}^{\left(h\right)}\right)}$$ 5 where \({y}_{pre}^{\left(h\right)}\) and \({y{\prime }}_{exp}^{\left(h\right)}\) represent the predicted value obtained from the ANN model and the experimental value, respectively; m is the number of samples for ANN modeling. The relative importance of the four input variables on each output variable was evaluated via Garson's equation in Eq. 6 . $${I}_{i}=\frac{\sum _{j=1}^{n}\left(\frac{\left|L{W}_{j,i}\right|}{\sum _{i=1}^{i=4}\left|L{W}_{j,i}\right|}\times \left|L{W}_{k,j}\right|\right)}{\sum _{i=1}^{i=4}\left\{\sum _{j=1}^{n}\left(\frac{\left|L{W}_{j,i}\right|}{\sum _{i=1}^{i=4}\left|L{W}_{j,i}\right|}\times \left|L{W}_{k,j}\right|\right)\right\}}\times 100\%$$ 6 where I i is the relative importance of the i th input variable on the output variable; IW j,i is the net weight from i th input variable to j th neuron in the hidden layer; LW k,j is the net weight from the j th neuron in the hidden layer to the k th output variable. Table 2 Selection of model parameters for the development of ANN Model parameters Specifications Number of input variables 4 ( t , T , C p , C h ) Number of output variables 1 ( C , R y or R l ) Network algorithm Resilient backpropagation with backtracking Activation functions Logistic, ReLU, or Tanh Number of iterations 5–1000 Number of hidden layers 1 Number of neurons in hidden layers 1–13 Error function RMSE Threshold of the error function 0.02 Table 2 summarizes the key parameters of the ANN model; its activation functions, number of iterations, and number of neurons in hidden layers still need to be determined within the provided range. As the flowchart of ANN modeling depicted in Fig. 2 , the entire datasets for ANN training and validation (see Table S1 ) were divided as training and validation with a ratio of 70:30. The ANN models are created following the sequence of choosing an activation function, determining iterations, and selecting the number of neurons in the hidden layer. RMSE is regarded as the essential indicator in comparing NP and TP. The trained ANN model was tested using the testing dataset (see Tabel S2 ) to evaluate its accuracy in the actual application. For a better understanding of the roles of each condition in PHP pretreatment, the values of RI are also calculated with the final created ANN model. 2.5 Statistical analysis The raw data digitization, preprocessing, and formula calculations (such as component recovery, removal, and RMSE values) are conducted in Excel (Microsoft, USA). One-way analysis of variance (ANOVA) was carried out with OriginPro (OriginLab Corp., USA). The data was displayed as "means ± SD" and compared differences among means using Fisher’s Least Significant Difference (LSD) method at the significance level of "ns", P > 0.05, * P < 0.05, ** P < 0.01, and *** P < 0.001. 3. Results And Discussion 3.1 Pretreatment of WS under various conditions Four essential conditions, including pretreatment time, temperature, and the concentration of H 3 PO 4 and H 2 O 2 that dominate the efficiency of PHP pretreatment, were selected as the control variables for designing the single-factor experiment based on the earlier work (Wang et al., 2014 ). The characteristics of the recovered cellulose-enriched fraction (CEF) of WS after PHP pretreatment are depicted in Fig. 3 a. The lignin removal (L- R l ) shows a strong regularity along with the changes of temperature ( T axis) and H 3 PO 4 (or H 2 O 2 ) concentration ( C p or C h axis). The substantial delignification of WS occurs in the conditions of higher T and C h . Meanwhile, L- R l is significantly decreased when the C h is increasing to 100% or when the C p is nearing 0%. The L- R l is not revealing evident changes as the pretreatment time ( t ) rises. Figure 3 b shows the distribution of hemicellulose removal (H- R l ) under various PHP conditions. The H- R l sharply increases to 100% when the t and T increase. The aforementioned results indicate that T , C p , and C h may dominate the lignin removal and hemicellulose is sensitive to t and T in PHP pretreatment. Lignin and hemicellulose form a protective physical barrier to general valorization applications. Therefore, their removal is a critical indicator of pretreatment efficiency assessment (Ohgren et al., 2007 ). The fundamental goal of pretreatment is to separate cellulose for more straightforward utilization. Thus, its yield and purity are major assessment factors that should be studied carefully (Tang et al., 2019 ). In the current study, two associated indicators, the cellulose content (C- C ) and cellulose recovery (C- R y ) were determined as visualized in Fig. 3 c and Fig. 3 d, respectively. The C- C is comparably low under the shorter t or the lower T , and C- C is likewise low under excessive C p or C h , which may be caused by the existence of lignin and hemicellulose in CEF under low pretreatment intensity. Although the C- C is under severe pretreatment conditions (high temperature or long time), the C- R y is significantly reduced owing to oxidative degradation and acid hydrolysis. In summary, assessing PHP pretreatment efficiency is complicated work, which should account for the effect of barrier component removal and cellulose recovery. A more targeted pretreatment approach is always constructive in biomass valorization, which would help minimize energy and chemical consumption and achieve multi-stage utilization of all biomass components (Wagle et al., 2022 ). Establishing an accurate and reliable evaluation model to predict pretreatment efficiency is the cardinal procedure for realizing targeted separation or utilization of biomass. This study adopts the emerging Artificial Neural Network (ANN) technology for modeling to establish this model. Four pretreatment conditions ( t , T , C p , and C h ) and four obtained results (C- C , C- R y , H- R l , and L- R l ) are selected as the ANN model's input and output variables. 3.2 Identify key parameters and training/validation for the ANN model Technically, the ANN model could be specified by three entities: interconnections, activation functions, and learning rules (Sadiq et al., 2019 ). The current employed ANN model is a multilayer feed-forward network using the algorithm of resilient backpropagation with backtracking (see Fig. 1 and Table 2 ). Therefore, the major effort is to find a befitting activation function. Activation functions sit at the foundation of deep neural networks allowing them to learn arbitrarily complicated mappings. Without any activation, a neural network will only be able to learn a linear relation between input and the desired output (Goyal et al., 2020 ). Three well-known activation functions used in data science are evaluated to validate their adaptability to PHP pretreatment, including the rectified linear units (ReLU) and the family of sigmoid functions such as logistic and tangent hyperbolic (Tanh). As shown in Fig. 4 a, the RMSE value was applied to assess the accuracy of ANN training. For PHP pretreatment, the training accuracy of the Tanh activation function is considerably superior to Logistic for C- C , C- R y at the level of P < 0.01 or H- R l , L- R l at the level of P < 0.001. Moreover, Tanh also has a lower RMSE value than ReLU when choosing C- C ( P < 0.05) and L- R l ( P 0.05). Based on the above results, it is evident that the Tanh activation function is a good alternative option in ANN modeling for PHP pretreatment. Figure 4 b displays the RMSE values for the varied number of iterations. As the iterations rise, their RMSE drop to a stable state (≥ 550 iterations). Coefficient of variation (CV) is applied to estimate the variations between several output variables (subplot). The number of iterations required to discover an optimum solution for a certain accuracy substantially impacts the total computing efforts and the performance of an algorithm. An improved ANN model should require less computation and fewer iterations (She, 2014 ). Therefore, in the current experiment, 900 iterations are suitable for the ANN model considering its accuracy and computational quantity. Studies have proven that even if the training data includes adequate information, too many neurons in the hidden layer would increase the training time, making it harder to obtain the desired effect (Panchal et al., 2011 ). Choosing an appropriate number of hidden layer neurons is critical. The RMSE of training/validation for 4 ANN models with the varied numbers of neurons under 900 iterations are exhibited in Fig. 4 c. The range of neuron numbers (1–13) is confirmed by an empirical equation (Eq. 4 ). As the iteration times increase, the RMSE of ANN training steadily decreases until reaching a stable state (the demarcation is 8th, 8th, 6th, and 11th for C- C , C- R y , H- R l , and L- R l , respectively). But its RMSE for ANN validation differs with varied neuron numbers. Fewer neurons in the hidden layer would lead to underfitting, whereas too many neurons might result in overfitting for the ANN model. To prevent these problems, a fundamental principle of "the minimal RMSE value of the minimum neurons" is applied to estimate the number of neurons for each output variable. The final choice is 9 neurons for C- C , 10 neurons for C- R y and H- R l , and 12 neurons for L- R l . Figure 5 compares the experimental results and predicted results obtained from the optimized parameters. All data are well fitted with an R 2 of 0.9648 to 0.9957, which suggests the trained ANN model has great prediction accuracy. 3.3 Prediction and relative importance of input variables The optimum ANN structure was established following numerical experiments with training and validation datasets (the entire datasets see Table S1 ). A new group set of the experiment (see Table 1 ) was conducted to validate the efficacy of the trained ANN model (the acquired experimental data for testing see Table S2 ). The fitting relationships between the predicted and experimental values of C- C , C- R y , H- R l , and L- R l are presented in Fig. 6 a. The slope of the fit curve ( S ) and its correlation coefficient ( R 2 ) are the two main metrics for evaluating the accuracy of the trained ANN model (Luo et al., 2021 ). Both the S and R 2 reaching 1.00 for one output variable means the ANN model is accurate in predicting this specific variable. As shown in Fig. 6 a, a dotted line ( x = y , slop is 1.00) is highlighted for simple comparison. The S values and R 2 values of C- C , H- R l , and L- R l are in the range of 0.96–1.07 and 0.9917–0.9989, respectively. Whereas the S and R 2 of C- C are 0.63 and 0.8070, respectively. Based on the exhibited modeling performance in Fig. 6 a, it could be concluded that the trained ANN model is an effective tool for predicting the efficiency of PHP pretreatment (the optimized parameters of the ANN models see Table S3 ). Table 3 Weights and biases of the hidden and output layers used in the developed ANN model Output variables Node, j Weights and biases of the Hidden Layer Output Layer IW j,1 IW j,2 IW j,3 IW j,4 b 1,j LW 1,j b 2,k Cellulose content (C- C ) 1 0.3546 2.7478 -11.0701 -0.5659 -0.3355 -0.2664 -0.3497 2 1.6277 -0.0323 -0.9088 -0.0853 0.9050 0.7899 3 0.9275 3.6886 1.4694 -6.3371 -0.0393 0.3410 4 0.7444 2.1048 -0.2320 -4.1909 -0.7666 -0.5319 5 -2.1959 -4.5648 2.5710 0.7984 0.0911 -0.3441 6 -0.0613 -0.2332 -0.0097 -0.0675 -0.7729 -0.9569 7 0.4559 0.0191 -1.3029 -0.5046 1.4831 -1.8032 8 -3.9905 5.5148 -3.0977 1.3431 1.6675 0.2117 9 5.8116 -8.9853 -0.7405 11.6301 2.1622 0.1680 Cellulose recovery (C- R y ) 1 -0.3055 2.7450 0.2364 -1.9260 -2.0054 -0.7894 -0.6025 2 0.0717 -1.7365 1.4491 1.2504 -0.1772 1.2644 3 -4.4021 -38.9738 -9.6011 3.3957 0.4542 0.3247 4 -8.8985 -9.3384 7.2843 -0.5127 2.5725 -1.0121 5 0.0010 0.1714 14.0232 0.5292 0.7064 -0.4126 6 16.1069 0.1492 15.0702 1.0173 0.3760 0.7551 7 5.8893 1.4854 -7.3563 9.2947 0.8378 -0.6200 8 -15.0383 -1.6843 0.9099 -0.3390 0.5161 0.7589 9 0.1446 2.0196 0.4183 -2.8676 0.6540 1.2090 10 -0.9127 0.4690 0.8830 0.3745 1.2096 1.2523 Hemicellulose removal (H- R l ) 1 0.8395 -18.1163 -0.6642 -1.0810 1.0422 -0.5711 1.9256 2 -5.1863 -17.8053 -1.3774 -0.5058 0.8781 -2.8093 3 -0.7045 19.8830 -0.0230 0.5135 -0.3585 0.1859 4 -5.1471 -17.4140 -1.5684 17.4996 1.2024 -0.9612 5 -0.5778 19.4753 -0.5377 0.3716 0.0425 2.4185 6 5.1469 21.4395 0.4276 -18.9849 0.0606 0.5129 7 -1.3705 19.6821 -1.5558 -1.0160 1.5824 0.7250 8 -3.5546 18.3374 8.0666 1.1083 2.1260 0.2486 9 0.2569 -20.9335 -0.0926 0.1512 0.4714 -1.3870 10 0.4681 -18.0798 0.3928 -0.2164 0.6000 -0.2335 Lignin removal (L- R l ) 1 -0.0528 -1.0832 -4.2216 1.3689 -1.1109 -1.1378 -0.2323 2 8.5965 -6.8695 -0.0166 -16.5425 0.6172 0.2271 3 -0.2676 -0.8281 1.3845 0.4985 -0.8440 -2.1040 4 -0.7472 -0.1430 -0.1792 0.5669 0.9663 1.1065 5 -2.6061 -5.2002 -0.9199 3.2301 -0.0599 -0.5832 6 -0.0201 0.1240 -0.5098 0.1887 0.9007 1.4053 7 -0.5294 4.6038 0.2552 -14.3484 0.3497 -0.4417 8 -1.1109 25.3572 1.3814 15.8228 -0.2981 -0.3107 9 0.2910 0.7686 0.6973 -0.6124 0.5303 -2.5000 10 -0.4854 0.1353 0.8392 2.0677 -1.2080 -1.2096 11 -9.3648 -1.3826 -1.1954 0.5778 0.2294 -0.2585 12 -3.4432 -1.1727 1.1569 -2.2897 0.3578 0.3301 Weights and biases are the learnable parameters of the ANN model, and a weight decides how much influence the input will have on the output. Bias is an additional parameter used to adjust the output and the weighted sum of the inputs to the neuron. The weights ( IW j,i and LW k,j ) and biases ( b 1,j and b 2,k ) from 4 input variables ( t , T , C p , and C h ) to 1 output variable (each of C- C , C- R y , H- R l , and L- R l ) were recorded in Table 3 . Relative importance ( RI ) is an essential indication for explicitly describing each input variable's influence on the output variable, and it was calculated using the Garson equation as represented in Fig. 6 b. For the C- C , the RI of C p is 31.8%, which is higher than the other conditions. The t , T , and C h shared virtually equally RI of 23.3%, 22.6%, and 22.3%, respectively. For the C- R y , the RI of T (27.1%) and C p (27.0%) are higher than t (24.6%) and C h (21.3%). Significant contrast of RI proportions occurs in H- R l , where the RI of T is 78.6%, which is significantly higher than the other pretreatment conditions. For the L- R l , the C p and C h have higher significance than T and t . The above results indicated that the concentration of H 3 PO 4 and H 2 O 2 (total RI for each output variable is 48.3–62.6%) has a dominant effect on the cellulose and lignin component of WS in PHP pretreatment. Moreover, different output variables that have various RI values may reflect a more complicated synergistic effect of H 3 PO 4 and H 2 O 2 . That suggests the method adopted for adjusting the concentration ratio of H 3 PO 4 /H 2 O 2 (dilute 85% phosphoric acid with 30% hydrogen peroxide) might still have a significant potential for improvement. According to Fig. 6 b, pretreatment time ( RI is 9.5–24.6% for each output variable) appears to be not the key factor dominating the efficiency of PHP pretreatment. Therefore, further efforts should be performed in the following investigation to reveal the composition-efficacy relationship of H 3 PO 4 / H 2 O 2 and to modify the pretreatment method depending on the present research. 4. Conclusions In this paper, the feasibility of employing an artificial neural network (ANN) to predict pretreatment efficiency was studied. Experimentally verified that Tangent hyperbolic (Tanh) is an appropriate activation function for modeling in this experiment. Each trained ANN model contains four conditions of PHP pretreatment as input variables, one hidden layer, and one selected output variable. The specific network that trained for cellulose content (C- C ), cellulose recovery (C- R y ), hemicellulose removal (H- R l ), and lignin removal (L- R l ) owned 9, 10, 10, and 12 neurons, respectively. Their modeling accuracy for validation ( R 2 ) could reach 0.9648–9957 after 900 iterations. And the R 2 of testing datasets is 0.8070–0.9989, which implies an optimum prediction efficiency of the proposed ANN models. The relative importance of four pretreatment conditions to C- C , C- R y , H- R l , and L- R l was also investigated to give fresh insights for fine-tuning PHP pretreatment. In summary, considering the fitting accuracy of the predicted and experimental results, models based upon ANN are beneficial for predicting the efficiency of PHP pretreatment from the pretreatment conditions. Declarations Acknowledgments The authors are grateful to the China Scholarship Council (CSC, 201906910047) and the University of Calgary for supporting this study. Authors' contributions QW: Methodology, Investigation, Writing - Original Draft, Visualization; JG. H: Writing – Review & Editing, Investigation, Supervision, Funding acquisition; LZ: Formal analysis; Mei Huang: Visualization; DT: Data Curation; YM. Z: References; SH. D: Project administration; FS: Conceptualization, Writing – Review & Editing, Supervision, Funding acquisition; XQ. Z: Supervision. All authors read and approved the final manuscript. Funding The National Natural Science Foundation of China (21978183), and the Science & Technology Department of Sichuan Province (2022YFH0065, 2022YFN0027, 2021ZYD0099). Availability of data and materials Data available within the article or its supplementary materials. Ethics approval and consent to participate This article does not contain any studies with human participants or animals performed by any of the authors. All authors have read and agreed the ethics for publishing the manuscript. Consent for publication All authors approved the consent for publishing the manuscript to Bioresources and Bioprocessing. Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper. References Bhange VP, Bhivgade UV, & Vaidya AN (2017). Artificial neural network modeling in pretreatment of garden biomass for lignocellulose degradation. Waste and Biomass Valorization, 10(6): 1571-1583. https://doi.org/10.1007/s12649-017-0163-z Goyal M, Goyal R, Venkatappa Reddy P, & Lall B (2020). Activation functions. In W Pedrycz & S-M Chen (Eds.), Deep learning: algorithms and applications (pp. 1-30). Switzerland: Springer Cham https://doi.org/10.1007/978-3-030-31760-7_1 Hosseini Koupaie E, Dahadha S, Bazyar Lakeh AA, Azizi A, & Elbeshbishy E (2019). Enzymatic pretreatment of lignocellulosic biomass for enhanced biomethane production-a review. J Environ Manag, 233: 774-784. https://doi.org/10.1016/j.jenvman.2018.09.106 Kartal F, & Özveren U (2021). An improved machine learning approach to estimate hemicellulose, cellulose, and lignin in biomass. Carbohydrate Polymer Technologies and Applications, 2(25): 100148. https://doi.org/10.1016/j.carpta.2021.100148 Lei M, Shen F, Hu JG, Zhao L, Huang M, Zou JM, Tian D, Yang G, Zeng YM, & Deng SH (2022). A novel way to facilely degrade organic pollutants with the tail-gas derived from PHP (phosphoric acid plus hydrogen peroxide) pretreatment of lignocellulose. J Hazard Mater, 424(Pt B): 127517. https://doi.org/10.1016/j.jhazmat.2021.127517 Liu Z, Wan X, Wang Q, Tian D, Hu J, Huang M, Shen F, & Zeng Y (2021). Performances of a multi-product strategy for bioethanol, lignin, and ultra-high surface area carbon from lignocellulose by PHP (phosphoric acid plus hydrogen peroxide) pretreatment platform. Renewable & Sustainable Energy Reviews, 150: 111503. https://doi.org/10.1016/j.rser.2021.111503 Luo HZ, Gao L, Liu Z, Shi YJ, Xie F, Bilal M, Yang RL, & Taherzadeh MJ (2021). Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling. Bioresources and Bioprocessing, 8(1): 134. https://doi.org/10.1186/s40643-021-00488-x Luterbacher JS, Martin Alonso D, & Dumesic JA (2014). Targeted chemical upgrading of lignocellulosic biomass to platform molecules. Green Chem, 16(12): 4816-4838. https://doi.org/10.1039/c4gc01160k Ohgren K, Bura R, Saddler J, & Zacchi G (2007). Effect of hemicellulose and lignin removal on enzymatic hydrolysis of steam pretreated corn stover. Bioresour Technol, 98(13): 2503-2510. https://doi.org/10.1016/j.biortech.2006.09.003 Panchal G, Ganatra A, Kosta YP, & Panchal D (2011). Behaviour analysis of multilayer perceptronswith multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2): 332-337. https://doi.org/10.7763/ijcte.2011.V3.328 Pereira LMS, Milan TM, & Tapia-Blácido DR (2021). Using response surface methodology (RSM) to optimize 2G bioethanol production: a review. Biomass Bioenergy, 151: 106166. https://doi.org/10.1016/j.biombioe.2021.106166 Qiu JW, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, Deng SH, & Zhang J (2018). Bioethanol production from wheat straw by phosphoric acid plus hydrogen peroxide (PHP) pretreatment via simultaneous saccharification and fermentation (SSF) at high solid loadings. Bioresour Technol, 268: 355-362. https://doi.org/10.1016/j.biortech.2018.08.009 Qiu JW, Wang Q, Shen F, Yang G, Zhang YZ, Deng SH, Zhang J, Zeng YM, & Song C (2017). Optimizing phosphoric acid plus hydrogen peroxide (PHP) pretreatment on wheat straw by response surface method for enzymatic saccharification. Appl Biochem Biotechnol, 181(3): 1123-1139. https://doi.org/10.1007/s12010-016-2273-7 Rashid T, Ali Ammar Taqvi S, Sher F, Rubab S, Thanabalan M, Bilal M, & ul Islam B (2021). Enhanced lignin extraction and optimisation from oil palm biomass using neural network modelling. Fuel, 293: 120485. https://doi.org/10.1016/j.fuel.2021.120485 Sadiq R, Rodriguez MJ, & Mian HR (2019). Empirical models to predict disinfection by-products (DBPs) in drinking water: an updated review. In J Nriagu (Ed.), Encyclopedia of environmental health (pp. 324-338). Berkeley, United States: Elsevier https://doi.org/10.1016/b978-0-12-409548-9.11193-5 She YX (2014). Nature-inspired optimization algorithms. (2nd ed.). Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/c2019-0-03762-4 Sluiter A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D, & Crocker D Determination of structural carbohydrates and lignin in biomass.(2010). https://www.nrel.gov/docs/gen/fy13/42618.pdf Accessed 27 August.2022. Sunphorka S, Chalermsinsuwan B, & Piumsomboon P (2017). Application of artificial neural network for kinetic parameters prediction of biomass oxidation from biomass properties. J Energy Inst, 90(1): 51-61. https://doi.org/10.1016/j.joei.2015.10.007 Tang S, Dong Q, Fang Z, & Miao ZD (2019). Complete recovery of cellulose from rice straw pretreated with ethylene glycol and aluminum chloride for enzymatic hydrolysis. Bioresour Technol, 284: 98-104. https://doi.org/10.1016/j.biortech.2019.03.100 Tian D, Chen YY, Shen F, Luo MY, Huang M, Hu JG, Zhang YZ, Deng SH, & Zhao L (2021). Self-generated peroxyacetic acid in phosphoric acid plus hydrogen peroxide pretreatment mediated lignocellulose deconstruction and delignification. Biotechnol Biofuels, 14(1): 224. https://doi.org/10.1186/s13068-021-02075-w Tocco D, Carucci C, Monduzzi M, Salis A, & Sanjust E (2021). Recent developments in the delignification and exploitation of grass lignocellulosic biomass. ACS Sustainable Chemistry & Engineering, 9(6): 2412-2432. https://doi.org/10.1021/acssuschemeng.0c07266 Tsekos C, Tandurella S, & de Jong W (2021). Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks. J Anal Appl Pyrolysis, 157: 105180. https://doi.org/10.1016/j.jaap.2021.105180 Wagle A, Angove MJ, Mahara A, Wagle A, Mainali B, Martins M, Goldbeck R, & Raj Paudel S (2022). Multi-stage pre-treatment of lignocellulosic biomass for multi-product biorefinery: a review. Sustainable Energy Technologies and Assessments, 49: 101702. https://doi.org/10.1016/j.seta.2021.101702 Walczak S, & Cerpa N (2001). Artificial neural networks. In RA Meyers (Ed.), Encyclopedia of physical science and technology (pp. 631-645). Berkeley: Elsevier https://doi.org/10.1016/b0-12-227410-5/00837-1 Wan X, Yao FP, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, & Deng SH (2019). Pretreatment of wheat straw with phosphoric acid and hydrogen peroxide to simultaneously facilitate cellulose digestibility and modify lignin as adsorbents. Biomolecules, 9(12): 844. https://doi.org/10.3390/biom9120844 Wang Q, Hu JG, Shen F, Mei ZL, Yang G, Zhang YZ, Hu YD, Zhang J, & Deng SH (2016). Pretreating wheat straw by the concentrated phosphoric acid plus hydrogen peroxide (PHP): Investigations on pretreatment conditions and structure changes. Bioresour Technol, 199: 245-257. https://doi.org/10.1016/j.biortech.2015.07.112 Wang Q, Tian D, Hu JG, Shen F, Yang G, Zhang YZ, Deng SH, Zhang J, Zeng YM, & Hu YD (2018). Fates of hemicellulose, lignin and cellulose in concentrated phosphoric acid with hydrogen peroxide (PHP) pretreatment. RSC Advances, 8(23): 12714-12723. https://doi.org/10.1039/c8ra00764k Wang Q, Wang ZH, Shen F, Hu JG, Sun FB, Lin LL, Yang G, Zhang YZ, & Deng SH (2014). Pretreating lignocellulosic biomass by the concentrated phosphoric acid plus hydrogen peroxide (PHP) for enzymatic hydrolysis: evaluating the pretreatment flexibility on feedstocks and particle sizes. Bioresour Technol, 166: 420-428. https://doi.org/10.1016/j.biortech.2014.05.088 Yang J, Huang Y, Xu HY, Gu DY, Xu F, Tang JT, Fang C, & Yang Y (2020). Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks. Food Chem, 313: 126138. https://doi.org/10.1016/j.foodchem.2019.126138 Yao FP, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, Deng SH, & Zhang J (2019). Recycling solvent system in phosphoric acid plus hydrogen peroxide pretreatment towards a more sustainable lignocellulose biorefinery for bioethanol. Bioresour Technol, 275: 19-26. https://doi.org/10.1016/j.biortech.2018.12.040 Supplementary Files Graphicalabstract2.png SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2083176","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":138781056,"identity":"f706e675-7fd0-4920-b568-07c2f3fde24a","order_by":0,"name":"Qing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBADHgYG5gNAWkKGFC1sCSAtPCRZZADVSwDIu/cYfi74ZS1jzr/m86sbNRY8DOyHj27Ap8XwzBlj6Zl96TyWM95us845BnQYT1raDbxaZuRukObtOcxjcOPsNuMcNqAWCR4zQlo2/4ZoOfPMOOcfEVrkJXK3SfP8AGo538P8OLeNCC0GPOe/WfM2pANtYTNjzu2T4GEj5Bf59rbk2zx/rO0Nzh9+/DnnW50cP/vhY/htOQAkGNuYgZGYwCYBEmHDpxxsSwOI/APUwn+A+QMh1aNgFIyCUTAyAQABdkh2aHJB1QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-2538-4798","institution":"Sichuan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Wang","suffix":""},{"id":138781057,"identity":"a6ce3b1e-f001-424c-871f-9fdbe0279a18","order_by":1,"name":"Jinguang Hu","email":"","orcid":"","institution":"University of Calgary","correspondingAuthor":false,"prefix":"","firstName":"Jinguang","middleName":"","lastName":"Hu","suffix":""},{"id":138781058,"identity":"a2c504f1-25bd-4b6c-bef2-86003c973169","order_by":2,"name":"Li Zhao","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhao","suffix":""},{"id":138781059,"identity":"2361273d-9304-4aa3-9ceb-759475cda658","order_by":3,"name":"Mei Huang","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Huang","suffix":""},{"id":138781060,"identity":"cc502478-9339-447c-a10e-e417599d8cf4","order_by":4,"name":"Dong Tian","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Tian","suffix":""},{"id":138781061,"identity":"f6c2af2a-2811-4fc3-ab69-0b3809dd6996","order_by":5,"name":"Yongmei Zeng","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yongmei","middleName":"","lastName":"Zeng","suffix":""},{"id":138781062,"identity":"e1b1d36a-0fd2-4dab-8aaa-7905e41ba6ad","order_by":6,"name":"Shihuai Deng","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Shihuai","middleName":"","lastName":"Deng","suffix":""},{"id":138781063,"identity":"0e5c03ba-7f6b-44cf-b48c-927753f91446","order_by":7,"name":"Fei Shen","email":"","orcid":"https://orcid.org/0000-0001-5693-170X","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Shen","suffix":""},{"id":138781064,"identity":"c1325bc0-35cf-4a40-8817-292db7990089","order_by":8,"name":"Xinquan Zhang","email":"","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xinquan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2022-09-20 05:25:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2083176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2083176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27041314,"identity":"bde27352-b38c-49bd-8020-9c554a3be552","added_by":"auto","created_at":"2022-09-27 16:44:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367356,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of PHP pretreatment and the development of the ANN model. (a) Experimental work demonstrating the procedure and data collection for PHP pretreatment; (b) Topology of a three-layer ANN model for predicting cellulose content and recovery after PHP pretreatment. \u003cem\u003eb1,j\u003c/em\u003e, the bias of inputs; \u003cem\u003eb2,k\u003c/em\u003e, the bias of outputs; \u003cem\u003eIWj,i\u003c/em\u003e, weights of inputs to hidden layers; \u003cem\u003eLWk,j\u003c/em\u003e, weights of neurons in hidden layers to output layers.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/8de2c66450f57267623eb780.jpg"},{"id":27041798,"identity":"3e68b9a4-aed0-4886-8e43-22b785acafed","added_by":"auto","created_at":"2022-09-27 16:49:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213555,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the ANN modeling approach. NP, network performance; TP, target network performance; \u003cem\u003eC\u003c/em\u003e, component content; \u003cem\u003eRy\u003c/em\u003e, component recovery; \u003cem\u003eRl\u003c/em\u003e, component removal.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/290566812d8334452db164a1.jpg"},{"id":27041797,"identity":"1f5cb1fd-4939-4ca8-b0bc-460afbbb5fb2","added_by":"auto","created_at":"2022-09-27 16:49:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277197,"visible":true,"origin":"","legend":"\u003cp\u003eComponents characteristics of the obtained CEF under various PHP pretreatment conditions. (a) L-\u003cem\u003eRl\u003c/em\u003e, lignin removal; (b) H-\u003cem\u003eRl\u003c/em\u003e, hemicellulose removal; (c) C-\u003cem\u003eC\u003c/em\u003e, cellulose content; (d) C-\u003cem\u003eRy\u003c/em\u003e, cellulose recovery. The parameters of each PHP condition are normalized in [0, 1] to draw a ternary contour plot—the color mapping referring to an actual percentage of component content, recovery, or removal. And for clarity demonstration, each evaluation index is exhibited by two ternary contour plots, and the abscissa is the concentration of H3PO4 or H2O2, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/777768044507f0c5b7d4be04.jpg"},{"id":27041316,"identity":"df499c11-89c7-4295-853c-793408254ee1","added_by":"auto","created_at":"2022-09-27 16:44:04","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":166194,"visible":true,"origin":"","legend":"\u003cp\u003eRMSE values of C-\u003cem\u003eC,\u003c/em\u003e C-\u003cem\u003eRy\u003c/em\u003e, H-\u003cem\u003eRl\u003c/em\u003e, and L-\u003cem\u003eRl\u003c/em\u003e for ANN training and validation. (a) RMSE values under 3 activation functions: Logistic (sigmoid), ReLU (rectified linear units), and Tanh (tangent hyperbolicus); (b) RMSE values under different number of iterations; (c) RMSE values with various number of neurons in hidden layer.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/8d0851a9e2b10364e7be9ea6.jpg"},{"id":27041319,"identity":"42995066-9dd8-444d-bd91-5096ff888bae","added_by":"auto","created_at":"2022-09-27 16:44:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":114750,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the experimental results and predicted results derived from the ANN model for (a) C-\u003cem\u003eC,\u003c/em\u003e(b) C-\u003cem\u003eRy\u003c/em\u003e, (c) H-\u003cem\u003eRl\u003c/em\u003e, and (d) L-\u003cem\u003eRl\u003c/em\u003ein the training periods.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/344219df9561053ca0dc4870.jpg"},{"id":27041321,"identity":"cce12a5a-0d77-4bca-a67f-9b5c21553427","added_by":"auto","created_at":"2022-09-27 16:44:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":99839,"visible":true,"origin":"","legend":"\u003cp\u003eTesting the trained ANN model (a); the \u003cem\u003eRI\u003c/em\u003e values of PHP pretreatment conditions on each output variable (b).\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/5dccd5425a137fa62eb5b3bb.jpg"},{"id":28029210,"identity":"04824db7-ed2d-4cb6-b9fa-40b67ad66a36","added_by":"auto","created_at":"2022-10-20 07:54:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":950935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/49962d8d-a90e-4237-8393-564ad676a1d7.pdf"},{"id":27041317,"identity":"e2fb4435-c952-4fda-9a7f-dad35e1e29ad","added_by":"auto","created_at":"2022-09-27 16:44:05","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":110262,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract2.png","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/fed96e368d6b79d142cbcb81.png"},{"id":27041320,"identity":"907a1607-fe3c-4d9b-a8d6-0dae448ab420","added_by":"auto","created_at":"2022-09-27 16:44:05","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":229696,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2083176/v1/dfe2b052be05d3fe9e8ac700.docx"}],"financialInterests":"","formattedTitle":"Prediction of phosphoric acid plus hydrogen peroxide (PHP) pretreatment efficiency using artificial neural network modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe fast growth of human civilization relies on the consumption of fossil resources such as petroleum, coal, and natural gas, which generate major issues including environmental pollution and climate change (Rashid et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such a challenging scenario leads to an increasing interest in alternative and renewable energy sources (Hosseini Koupaie et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lignocellulosic biomass is the world's most abundant renewable organic carbon-based resource, and it is regarded as the most potential alternative to fossil resources (Luterbacher et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Lignocellulosic biomass consists mostly of cellulose, hemicellulose, and lignin. Three components do not exist individually but are strongly related by physical forces and chemical interactions to produce a 3D cross-linked structure. Hemicellulose and lignin fill in the cellulose chains to strengthen the mechanical strength of the plant's cell wall via hydrogen bonding and ester linkages (Tocco et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, it is tough to extract each component from lignocellulosic biomass adequately.\u003c/p\u003e \u003cp\u003eOur earlier work developed an effective biomass pretreatment method entitled phosphoric acid plus hydrogen peroxide (PHP) pretreatment (Wang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The systematic investigation revealed that PHP pretreatment could handle diverse softwoods, hardwoods, straws, and herbs with gentle processing conditions and minimal grinding needs to obtain satisfactory lignocellulose deconstruction. Under normal pretreatment conditions, the final recovered cellulose enriched-fraction (CEF) held a cellulose recovery of 92%, while 83.7% lignin and 100% hemicellulose were removed. It can harvest 29.1\u0026ndash;32.6 g glucose from 100 g wheat straw by enzyme hydrolysis (Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, with the simultaneous saccharification and fermentation (SSF) process at 15.3% solid loading for 120 h, it could harvest 15.5 g of ethanol from 100 g of wheat straw (Qiu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). PHP pretreatment might also separate the hemicellulose and lignin from biomass to manufacture high-value products, such as oligosaccharides or supercapacitors (Liu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Besides, the oxidative tail-gas from PHP pretreatment may be applied to produce 68.0\u0026ndash;98.3% methyl blue degradation (Lei et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recycle experiment indicated that 86.0% phosphoric acid could be recovered after \u0026ge;\u0026thinsp;11 cycles of pretreatment (Yao et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe previous research has explained the transformation mechanism of major components and the formation of multiple oxidation systems during PHP pretreatment, which is of great significance for the in-depth development of the PHP method (Tian et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, component separation is crucial in most application situations, which highlights the importance of conducting optimization experiments to boost the component separation efficiency, notably the separation of cellulose. Response surface methodology (RSM) is a regularly used strategy for response surface mapping to the region of interest, response optimization, and operation condition selection (Pereira et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Efforts have been conducted to choose PHP pretreatment conditions by RSM with Box-Behnken design, and notable improvement has been realized (Qiu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, both lignocellulose deconstruction and lignin/hemicellulose degradation processes are complicated and non-linear. They are tough to predict using RSM, particularly with limited experimental groups. Hence, from our perspective, PHP pretreatment conditions may optimize even more for the difference, dependent on the purpose of their applications.\u003c/p\u003e \u003cp\u003eNowadays, a powerful prediction tool named Artificial Neural Network (ANN) has been applied in numerous study domains because of its modeling capabilities, even if limited experimental data is available (Rashid et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ANN technology is inspired by the operating mode of the human brain and nervous system, which comprises many neurons in multiple layers. A neuron may link to all or a subset of the neurons in the subsequent layer, with these connections simulating the brain's synaptic connections (Walczak \u0026amp; Cerpa, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). For this reason, ANN may learn from complicated, linear, and non-linear systems that approximate non-linear without any previous fitting function specified (Rashid et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A recent study collected a total of 482 samples, and it evaluated the primary pyrolysis products of lignocellulosic biomass via ANN modeling, which successfully achieved the best possible results over different reactor systems, conditions, and biomass for the solid, liquid, and gaseous pyrolysis product yields (Tsekos et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of biomass was proposed, which implies that the ANN model may predict the pretreatment efficiency with constrained circumstances and groups of pretreatment (Luo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the applications of the ANN model in biomass component estimation (Kartal \u0026amp; \u0026Ouml;zveren, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), kinetic parameters prediction of biomass oxidation (Sunphorka et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and pretreatment for lignocellulose degradation (Bhange et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were studied. And these applications completely highlight the potential of the ANN model in biomass valorization.\u003c/p\u003e \u003cp\u003eThe current study seeks to create an ANN model for assessing PHP pretreatment efficiency. Herein, four significant factors (time, temperature, concentration of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) that impact PHP pretreatment were utilized as input variables. The content and recovery of cellulose were regarded as the output variables. Furthermore, the research also evaluates the relative importance of those pretreatment conditions for achieving the pretreatment target (recovering cellulose and removing hemicellulose or lignin) by evaluating the neural net weights in the created ANN model. This study would provide a novel approach for evaluating and improving lignocellulosic biomass pretreatment.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Materials and reagents\u003c/h2\u003e\n \u003cp\u003eWheat straw (WS) is typical lignocellulosic biomass with a large annual output, which is employed as the raw material in this study. WS was collected on the farm of Sichuan Agricultural University in Chengdu, China. It was air-dried and milled through a 40-mesh sieve (\u0026le; 0.45 mm) before PHP pretreatment. All reagents (analytically pure) were provided by Sigma-Aldrich unless mentioned elsewhere.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 PHP pretreatment\u003c/h2\u003e\n \u003cp\u003eTo carry out the PHP pretreatment, use 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e reagent to dilute 85% H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e reagent to prepare the PHP solution. Then, add WS to the PHP solution with a solid/liquid ratio of 1:10 (w/w) in a 250 mL screw-cap bottle and mix them completely. The mixture was shaken at the specified pretreatment temperature and reaction time at a 160 r/min rotation speed. Add 1.0 L of deionized water to halt the pretreatment process when it reaches the predetermined time. Finally, the treated WS is filtered and washed to a neutral pH to obtain the cellulose-enriched fraction (CEF), then stored at \u0026minus;\u0026thinsp;20\u0026deg;C for further use. The design of PHP pretreatment conditions is given in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePHP pretreatment conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePurpose\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e (h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e (\u0026deg;C)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANN training and validation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u0026ndash;7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANN testing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u0026ndash;7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: The comprehensive test was applied in the experimental design, and the collected raw data for ANN training, validation, and testing are presented in \u003cstrong\u003eTable S1\u003c/strong\u003e and \u003cstrong\u003eTable S2\u003c/strong\u003e, respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Analytical methods\u003c/h2\u003e\n \u003cp\u003eThe main components, including cellulose, hemicellulose, and lignin of WS material and CEF, were analyzed according to the NREL (National Renewable Energy Lab of the US) method (Sluiter et al., 2010). Wherein the hydrolysate sugars were separated by a Shodex SH1011 column at 60\u0026deg;C using the 0.05 mol/L H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e mobile phase at a flow rate of 0.8 mL/min. The separated sugars were quantified using an Agilent 1260 Infinity Ⅱ HPLC system with a G7162A differential refractive index detector. The solid recovery (SR) of WS after pretreatment was estimated according to the equation\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Taba\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSR (%)\u0026thinsp;=\u0026thinsp;\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e \u0026times; 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003ewhere \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the dry weight of WS (herein, \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.00 g), and \u003cem\u003em\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e is the dry weight of the recovered CEF after PHP pretreatment.\u003c/p\u003e\n \u003cp\u003eThe cellulose recovery (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e) and component (hemicellulose or lignin) removal (\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) after PHP pretreatment was calculated following the equation:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tabb\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e (%)\u0026thinsp;=\u0026thinsp;SR \u0026times; (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e) \u0026times; 100%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e (%)\u0026thinsp;=\u0026thinsp;100% \u0026minus; \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e represent the related component content in WS material and CEF, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 ANN modeling\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea depicts the flow diagram of PHP pretreatment. The pretreatment time (\u003cem\u003et\u003c/em\u003e), pretreatment temperature (\u003cem\u003eT\u003c/em\u003e), concentration of phosphoric acid (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), and concentration of hydrogen peroxide (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e) are the primary parameters that impact the efficiency of PHP pretreatment. After pretreatment, the main components of the produced CEF, including the solid recovery, cellulose/hemicellulose/lignin content, and the associated components\u0026rsquo; recovery/removal, would be simultaneously analyzed (the results see \u003cstrong\u003eTable S1\u003c/strong\u003e). The ANN model was developed using the Neural Network Fitting program (version 1.33, based on the neuralnet library in R software) in OriginPro 2022b (OriginLab Corp., USA).\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb, the ANN model proposed in the current study is a multiple-layer neural network with interconnected neurons arranged into three layers of input, hidden, and output layers, consisting of 4 input variables, 1 hidden layer with \u003cem\u003en\u003c/em\u003e neurons, and 1 output variable.\u003c/p\u003e\n \u003cp\u003eThe number of neurons (\u003cem\u003en\u003c/em\u003e) in the hidden layer was determined according to an empirical equation Eq. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (Yang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$n\\le \\sqrt{i+k}+\\alpha$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003en\u003c/em\u003e is the number of neurons in the hidden layer; \u003cem\u003ei\u003c/em\u003e is the number of input variables; \u003cem\u003ek\u003c/em\u003e is the number of output variables; \u003cem\u003e\u0026alpha;\u003c/em\u003e is a constant ranging from 1 to 10.\u003c/p\u003e\n \u003cp\u003eThe accuracy assessment of ANN modeling and predictions was carried out with the root means square error (RMSE) calculated with Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$RMSE=\\sqrt{\\frac{1}{m}{\\sum }_{h=1}^{m}\\left({y}_{pre}^{\\left(h\\right)}-{y{\\prime }}_{exp}^{\\left(h\\right)}\\right)}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y}_{pre}^{\\left(h\\right)}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y{\\prime }}_{exp}^{\\left(h\\right)}\\)\u003c/span\u003e\u003c/span\u003e represent the predicted value obtained from the ANN model and the experimental value, respectively; \u003cem\u003em\u003c/em\u003e is the number of samples for ANN modeling.\u003c/p\u003e\n \u003cp\u003eThe relative importance of the four input variables on each output variable was evaluated via Garson\u0026apos;s equation in Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$${I}_{i}=\\frac{\\sum _{j=1}^{n}\\left(\\frac{\\left|L{W}_{j,i}\\right|}{\\sum _{i=1}^{i=4}\\left|L{W}_{j,i}\\right|}\\times \\left|L{W}_{k,j}\\right|\\right)}{\\sum _{i=1}^{i=4}\\left\\{\\sum _{j=1}^{n}\\left(\\frac{\\left|L{W}_{j,i}\\right|}{\\sum _{i=1}^{i=4}\\left|L{W}_{j,i}\\right|}\\times \\left|L{W}_{k,j}\\right|\\right)\\right\\}}\\times 100\\%$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the relative importance of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e input variable on the output variable; \u003cem\u003eIW\u003c/em\u003e\u003csub\u003e\u003cem\u003ej,i\u003c/em\u003e\u003c/sub\u003e is the net weight from \u003cem\u003ei\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e input variable to \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e neuron in the hidden layer; \u003cem\u003eLW\u003c/em\u003e\u003csub\u003e\u003cem\u003ek,j\u003c/em\u003e\u003c/sub\u003e is the net weight from the \u003cem\u003ej\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e neuron in the hidden layer to the \u003cem\u003ek\u003c/em\u003e\u003csup\u003eth\u003c/sup\u003e output variable.\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSelection of model parameters for the development of ANN\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecifications\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of input variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (\u003cem\u003et\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of output variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (\u003cem\u003eC\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetwork algorithm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResilient backpropagation with backtracking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eActivation functions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogistic, ReLU, or Tanh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of iterations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of hidden layers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of neurons in hidden layers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreshold of the error function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the key parameters of the ANN model; its activation functions, number of iterations, and number of neurons in hidden layers still need to be determined within the provided range. As the flowchart of ANN modeling depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the entire datasets for ANN training and validation (see \u003cstrong\u003eTable S1\u003c/strong\u003e) were divided as training and validation with a ratio of 70:30. The ANN models are created following the sequence of choosing an activation function, determining iterations, and selecting the number of neurons in the hidden layer. RMSE is regarded as the essential indicator in comparing NP and TP. The trained ANN model was tested using the testing dataset (see \u003cstrong\u003eTabel S2\u003c/strong\u003e) to evaluate its accuracy in the actual application. For a better understanding of the roles of each condition in PHP pretreatment, the values of \u003cem\u003eRI\u003c/em\u003e are also calculated with the final created ANN model.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe raw data digitization, preprocessing, and formula calculations (such as component recovery, removal, and RMSE values) are conducted in Excel (Microsoft, USA). One-way analysis of variance (ANOVA) was carried out with OriginPro (OriginLab Corp., USA). The data was displayed as \u0026quot;means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026quot; and compared differences among means using Fisher\u0026rsquo;s Least Significant Difference (LSD) method at the significance level of \u0026quot;ns\u0026quot;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, * \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.1 Pretreatment of WS under various conditions\u003c/h2\u003e\n \u003cp\u003eFour essential conditions, including pretreatment time, temperature, and the concentration of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e that dominate the efficiency of PHP pretreatment, were selected as the control variables for designing the single-factor experiment based on the earlier work (Wang et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The characteristics of the recovered cellulose-enriched fraction (CEF) of WS after PHP pretreatment are depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea. The lignin removal (L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) shows a strong regularity along with the changes of temperature (\u003cem\u003eT\u003c/em\u003e axis) and H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e (or H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e axis). The substantial delignification of WS occurs in the conditions of higher \u003cem\u003eT\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e. Meanwhile, L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e is significantly decreased when the \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e is increasing to 100% or when the \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is nearing 0%. The L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e is not revealing evident changes as the pretreatment time (\u003cem\u003et\u003c/em\u003e) rises. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the distribution of hemicellulose removal (H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) under various PHP conditions. The H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e sharply increases to 100% when the \u003cem\u003et\u003c/em\u003e and \u003cem\u003eT\u003c/em\u003e increase. The aforementioned results indicate that \u003cem\u003eT\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e may dominate the lignin removal and hemicellulose is sensitive to \u003cem\u003et\u003c/em\u003e and \u003cem\u003eT\u003c/em\u003e in PHP pretreatment. Lignin and hemicellulose form a protective physical barrier to general valorization applications. Therefore, their removal is a critical indicator of pretreatment efficiency assessment (Ohgren et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe fundamental goal of pretreatment is to separate cellulose for more straightforward utilization. Thus, its yield and purity are major assessment factors that should be studied carefully (Tang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the current study, two associated indicators, the cellulose content (C-\u003cem\u003eC\u003c/em\u003e) and cellulose recovery (C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e) were determined as visualized in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed, respectively. The C-\u003cem\u003eC\u003c/em\u003e is comparably low under the shorter \u003cem\u003et\u003c/em\u003e or the lower \u003cem\u003eT\u003c/em\u003e, and C-\u003cem\u003eC\u003c/em\u003e is likewise low under excessive \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e, which may be caused by the existence of lignin and hemicellulose in CEF under low pretreatment intensity. Although the C-\u003cem\u003eC\u003c/em\u003e is under severe pretreatment conditions (high temperature or long time), the C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e is significantly reduced owing to oxidative degradation and acid hydrolysis. In summary, assessing PHP pretreatment efficiency is complicated work, which should account for the effect of barrier component removal and cellulose recovery.\u003c/p\u003e\n \u003cp\u003eA more targeted pretreatment approach is always constructive in biomass valorization, which would help minimize energy and chemical consumption and achieve multi-stage utilization of all biomass components (Wagle et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Establishing an accurate and reliable evaluation model to predict pretreatment efficiency is the cardinal procedure for realizing targeted separation or utilization of biomass. This study adopts the emerging Artificial Neural Network (ANN) technology for modeling to establish this model. Four pretreatment conditions (\u003cem\u003et\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e) and four obtained results (C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) are selected as the ANN model\u0026apos;s input and output variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.2 Identify key parameters and training/validation for the ANN model\u003c/h2\u003e\n \u003cp\u003eTechnically, the ANN model could be specified by three entities: interconnections, activation functions, and learning rules (Sadiq et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The current employed ANN model is a multilayer feed-forward network using the algorithm of resilient backpropagation with backtracking (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, the major effort is to find a befitting activation function. Activation functions sit at the foundation of deep neural networks allowing them to learn arbitrarily complicated mappings. Without any activation, a neural network will only be able to learn a linear relation between input and the desired output (Goyal et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThree well-known activation functions used in data science are evaluated to validate their adaptability to PHP pretreatment, including the rectified linear units (ReLU) and the family of sigmoid functions such as logistic and tangent hyperbolic (Tanh). As shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea, the RMSE value was applied to assess the accuracy of ANN training. For PHP pretreatment, the training accuracy of the Tanh activation function is considerably superior to Logistic for C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e at the level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 or H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e at the level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Moreover, Tanh also has a lower RMSE value than ReLU when choosing C-\u003cem\u003eC\u003c/em\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as output variables, whereas it does not have a significant difference in C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e and H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Based on the above results, it is evident that the Tanh activation function is a good alternative option in ANN modeling for PHP pretreatment.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb displays the RMSE values for the varied number of iterations. As the iterations rise, their RMSE drop to a stable state (\u0026ge;\u0026thinsp;550 iterations). Coefficient of variation (CV) is applied to estimate the variations between several output variables (subplot). The number of iterations required to discover an optimum solution for a certain accuracy substantially impacts the total computing efforts and the performance of an algorithm. An improved ANN model should require less computation and fewer iterations (She, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, in the current experiment, 900 iterations are suitable for the ANN model considering its accuracy and computational quantity.\u003c/p\u003e\n \u003cp\u003eStudies have proven that even if the training data includes adequate information, too many neurons in the hidden layer would increase the training time, making it harder to obtain the desired effect (Panchal et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). Choosing an appropriate number of hidden layer neurons is critical. The RMSE of training/validation for 4 ANN models with the varied numbers of neurons under 900 iterations are exhibited in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ec. The range of neuron numbers (1\u0026ndash;13) is confirmed by an empirical equation (Eq. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). As the iteration times increase, the RMSE of ANN training steadily decreases until reaching a stable state (the demarcation is 8th, 8th, 6th, and 11th for C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, respectively). But its RMSE for ANN validation differs with varied neuron numbers. Fewer neurons in the hidden layer would lead to underfitting, whereas too many neurons might result in overfitting for the ANN model. To prevent these problems, a fundamental principle of \u0026quot;the minimal RMSE value of the minimum neurons\u0026quot; is applied to estimate the number of neurons for each output variable. The final choice is 9 neurons for C-\u003cem\u003eC\u003c/em\u003e, 10 neurons for C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e and H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and 12 neurons for L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e. Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e compares the experimental results and predicted results obtained from the optimized parameters. All data are well fitted with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of 0.9648 to 0.9957, which suggests the trained ANN model has great prediction accuracy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Prediction and relative importance of input variables\u003c/h2\u003e\n \u003cp\u003eThe optimum ANN structure was established following numerical experiments with training and validation datasets (the entire datasets see \u003cstrong\u003eTable S1\u003c/strong\u003e). A new group set of the experiment (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) was conducted to validate the efficacy of the trained ANN model (the acquired experimental data for testing see \u003cstrong\u003eTable S2\u003c/strong\u003e). The fitting relationships between the predicted and experimental values of C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e are presented in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea. The slope of the fit curve (\u003cem\u003eS\u003c/em\u003e) and its correlation coefficient (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) are the two main metrics for evaluating the accuracy of the trained ANN model (Luo et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Both the \u003cem\u003eS\u003c/em\u003e and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e reaching 1.00 for one output variable means the ANN model is accurate in predicting this specific variable. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, a dotted line (\u003cem\u003ex\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ey\u003c/em\u003e, slop is 1.00) is highlighted for simple comparison. The \u003cem\u003eS\u003c/em\u003e values and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values of C-\u003cem\u003eC\u003c/em\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e are in the range of 0.96\u0026ndash;1.07 and 0.9917\u0026ndash;0.9989, respectively. Whereas the \u003cem\u003eS\u003c/em\u003e and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of C-\u003cem\u003eC\u003c/em\u003e are 0.63 and 0.8070, respectively. Based on the exhibited modeling performance in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, it could be concluded that the trained ANN model is an effective tool for predicting the efficiency of PHP pretreatment (the optimized parameters of the ANN models see \u003cstrong\u003eTable S3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWeights and biases of the hidden and output layers used in the developed ANN model\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutput variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNode, j\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eWeights and biases of the Hidden Layer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOutput Layer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eIW\u003c/span\u003e\u003csub\u003e\u003cstrong\u003ej,1\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eIW\u003c/span\u003e\u003csub\u003e\u003cstrong\u003ej,2\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eIW\u003c/span\u003e\u003csub\u003e\u003cstrong\u003ej,3\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eIW\u003c/span\u003e\u003csub\u003e\u003cstrong\u003ej,4\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eb\u003c/span\u003e\u003csub\u003e\u003cstrong\u003e1,j\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eLW\u003c/span\u003e\u003csub\u003e\u003cstrong\u003e1,j\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eb\u003c/span\u003e\u003csub\u003e\u003cstrong\u003e2,k\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eCellulose content\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(C-\u003c/strong\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eC\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.0701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.6886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.3371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.1909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.1959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.5648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.8032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.9905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.5148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.0977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.9853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eCellulose recovery\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(C-\u003c/strong\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eR\u003c/span\u003e\u003csub\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003ey\u003c/span\u003e\u003c/sub\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.9260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.0054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.7365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.4021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-38.9738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.6011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.8985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.3384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.0232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.1069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.0702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.3563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.0383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.6843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.8676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemicellulose removal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(H-\u003c/strong\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eR\u003c/span\u003e\u003csub\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003el\u003c/span\u003e\u003c/sub\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.1163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.1863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.8053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.8093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.8830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.1471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.4140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.4753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.4395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.9849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.6821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.5558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.5546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-20.9335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.0798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eLignin removal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(L-\u003c/strong\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eR\u003c/span\u003e\u003csub\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003el\u003c/span\u003e\u003c/sub\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.0832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.2216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.8695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.5425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.8440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.1040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.7472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.6061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.3484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.3572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.6124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.4854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.2096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.3648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.3826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.2585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.4432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.2897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWeights and biases are the learnable parameters of the ANN model, and a weight decides how much influence the input will have on the output. Bias is an additional parameter used to adjust the output and the weighted sum of the inputs to the neuron. The weights (\u003cem\u003eIW\u003c/em\u003e\u003csub\u003e\u003cem\u003ej,i\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eLW\u003c/em\u003e\u003csub\u003e\u003cem\u003ek,j\u003c/em\u003e\u003c/sub\u003e) and biases (\u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003e1,j\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eb\u003c/em\u003e\u003csub\u003e\u003cem\u003e2,k\u003c/em\u003e\u003c/sub\u003e) from 4 input variables (\u003cem\u003et\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e) to 1 output variable (each of C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) were recorded in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Relative importance (\u003cem\u003eRI\u003c/em\u003e) is an essential indication for explicitly describing each input variable\u0026apos;s influence on the output variable, and it was calculated using the Garson equation as represented in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb. For the C-\u003cem\u003eC\u003c/em\u003e, the \u003cem\u003eRI\u003c/em\u003e of \u003cem\u003eC\u003c/em\u003e\u003csub\u003ep\u003c/sub\u003e is 31.8%, which is higher than the other conditions. The \u003cem\u003et\u003c/em\u003e, \u003cem\u003eT\u003c/em\u003e, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e shared virtually equally \u003cem\u003eRI\u003c/em\u003e of 23.3%, 22.6%, and 22.3%, respectively. For the C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, the \u003cem\u003eRI\u003c/em\u003e of \u003cem\u003eT\u003c/em\u003e (27.1%) and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e (27.0%) are higher than \u003cem\u003et\u003c/em\u003e (24.6%) and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e (21.3%). Significant contrast of \u003cem\u003eRI\u003c/em\u003e proportions occurs in H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, where the \u003cem\u003eRI\u003c/em\u003e of \u003cem\u003eT\u003c/em\u003e is 78.6%, which is significantly higher than the other pretreatment conditions. For the L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, the \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e have higher significance than \u003cem\u003eT\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e. The above results indicated that the concentration of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (total \u003cem\u003eRI\u003c/em\u003e for each output variable is 48.3\u0026ndash;62.6%) has a dominant effect on the cellulose and lignin component of WS in PHP pretreatment. Moreover, different output variables that have various \u003cem\u003eRI\u003c/em\u003e values may reflect a more complicated synergistic effect of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. That suggests the method adopted for adjusting the concentration ratio of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e/H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (dilute 85% phosphoric acid with 30% hydrogen peroxide) might still have a significant potential for improvement. According to Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb, pretreatment time (\u003cem\u003eRI\u003c/em\u003e is 9.5\u0026ndash;24.6% for each output variable) appears to be not the key factor dominating the efficiency of PHP pretreatment. Therefore, further efforts should be performed in the following investigation to reveal the composition-efficacy relationship of H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e / H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and to modify the pretreatment method depending on the present research.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn this paper, the feasibility of employing an artificial neural network (ANN) to predict pretreatment efficiency was studied. Experimentally verified that Tangent hyperbolic (Tanh) is an appropriate activation function for modeling in this experiment. Each trained ANN model contains four conditions of PHP pretreatment as input variables, one hidden layer, and one selected output variable. The specific network that trained for cellulose content (C-\u003cem\u003eC\u003c/em\u003e), cellulose recovery (C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e), hemicellulose removal (H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e), and lignin removal (L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e) owned 9, 10, 10, and 12 neurons, respectively. Their modeling accuracy for validation (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) could reach 0.9648\u0026ndash;9957 after 900 iterations. And the \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of testing datasets is 0.8070\u0026ndash;0.9989, which implies an optimum prediction efficiency of the proposed ANN models. The relative importance of four pretreatment conditions to C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e was also investigated to give fresh insights for fine-tuning PHP pretreatment. In summary, considering the fitting accuracy of the predicted and experimental results, models based upon ANN are beneficial for predicting the efficiency of PHP pretreatment from the pretreatment conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the China Scholarship Council (CSC, 201906910047) and the University of Calgary for supporting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQW: Methodology, Investigation, Writing - Original Draft, Visualization; JG. H: Writing \u0026ndash; Review \u0026amp; Editing, Investigation, Supervision, Funding acquisition; LZ: Formal analysis; Mei Huang: Visualization; DT: Data Curation; YM. Z: References; SH. D: Project administration; FS: Conceptualization, Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Funding acquisition; XQ. Z: Supervision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Natural Science Foundation of China (21978183), and the Science \u0026amp; Technology Department of Sichuan Province (2022YFH0065, 2022YFN0027, 2021ZYD0099).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available within the article or its supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors. All authors have read and agreed the ethics for publishing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the consent for publishing the manuscript to Bioresources and Bioprocessing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBhange VP, Bhivgade UV, \u0026amp; Vaidya AN (2017). Artificial neural network modeling in pretreatment of garden biomass for lignocellulose degradation. Waste and Biomass Valorization, 10(6): 1571-1583. https://doi.org/10.1007/s12649-017-0163-z\u003c/li\u003e\n\u003cli\u003eGoyal M, Goyal R, Venkatappa Reddy P, \u0026amp; Lall B (2020). Activation functions. In W Pedrycz \u0026amp; S-M Chen (Eds.), Deep learning: algorithms and applications (pp. 1-30). Switzerland: Springer Cham https://doi.org/10.1007/978-3-030-31760-7_1\u003c/li\u003e\n\u003cli\u003eHosseini Koupaie E, Dahadha S, Bazyar Lakeh AA, Azizi A, \u0026amp; Elbeshbishy E (2019). Enzymatic pretreatment of lignocellulosic biomass for enhanced biomethane production-a review. J Environ Manag, 233: 774-784. https://doi.org/10.1016/j.jenvman.2018.09.106\u003c/li\u003e\n\u003cli\u003eKartal F, \u0026amp; Özveren U (2021). An improved machine learning approach to estimate hemicellulose, cellulose, and lignin in biomass. Carbohydrate Polymer Technologies and Applications, 2(25): 100148. https://doi.org/10.1016/j.carpta.2021.100148\u003c/li\u003e\n\u003cli\u003eLei M, Shen F, Hu JG, Zhao L, Huang M, Zou JM, Tian D, Yang G, Zeng YM, \u0026amp; Deng SH (2022). A novel way to facilely degrade organic pollutants with the tail-gas derived from PHP (phosphoric acid plus hydrogen peroxide) pretreatment of lignocellulose. J Hazard Mater, 424(Pt B): 127517. https://doi.org/10.1016/j.jhazmat.2021.127517\u003c/li\u003e\n\u003cli\u003eLiu Z, Wan X, Wang Q, Tian D, Hu J, Huang M, Shen F, \u0026amp; Zeng Y (2021). Performances of a multi-product strategy for bioethanol, lignin, and ultra-high surface area carbon from lignocellulose by PHP (phosphoric acid plus hydrogen peroxide) pretreatment platform. Renewable \u0026amp; Sustainable Energy Reviews, 150: 111503. https://doi.org/10.1016/j.rser.2021.111503\u003c/li\u003e\n\u003cli\u003eLuo HZ, Gao L, Liu Z, Shi YJ, Xie F, Bilal M, Yang RL, \u0026amp; Taherzadeh MJ (2021). Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling. Bioresources and Bioprocessing, 8(1): 134. https://doi.org/10.1186/s40643-021-00488-x\u003c/li\u003e\n\u003cli\u003eLuterbacher JS, Martin Alonso D, \u0026amp; Dumesic JA (2014). Targeted chemical upgrading of lignocellulosic biomass to platform molecules. Green Chem, 16(12): 4816-4838. https://doi.org/10.1039/c4gc01160k\u003c/li\u003e\n\u003cli\u003eOhgren K, Bura R, Saddler J, \u0026amp; Zacchi G (2007). Effect of hemicellulose and lignin removal on enzymatic hydrolysis of steam pretreated corn stover. Bioresour Technol, 98(13): 2503-2510. https://doi.org/10.1016/j.biortech.2006.09.003\u003c/li\u003e\n\u003cli\u003ePanchal G, Ganatra A, Kosta YP, \u0026amp; Panchal D (2011). Behaviour analysis of multilayer perceptronswith multiple hidden neurons and hidden layers. International Journal of Computer Theory and Engineering, 3(2): 332-337. https://doi.org/10.7763/ijcte.2011.V3.328\u003c/li\u003e\n\u003cli\u003ePereira LMS, Milan TM, \u0026amp; Tapia-Blácido DR (2021). Using response surface methodology (RSM) to optimize 2G bioethanol production: a review. Biomass Bioenergy, 151: 106166. https://doi.org/10.1016/j.biombioe.2021.106166\u003c/li\u003e\n\u003cli\u003eQiu JW, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, Deng SH, \u0026amp; Zhang J (2018). Bioethanol production from wheat straw by phosphoric acid plus hydrogen peroxide (PHP) pretreatment via simultaneous saccharification and fermentation (SSF) at high solid loadings. Bioresour Technol, 268: 355-362. https://doi.org/10.1016/j.biortech.2018.08.009\u003c/li\u003e\n\u003cli\u003eQiu JW, Wang Q, Shen F, Yang G, Zhang YZ, Deng SH, Zhang J, Zeng YM, \u0026amp; Song C (2017). Optimizing phosphoric acid plus hydrogen peroxide (PHP) pretreatment on wheat straw by response surface method for enzymatic saccharification. Appl Biochem Biotechnol, 181(3): 1123-1139. https://doi.org/10.1007/s12010-016-2273-7\u003c/li\u003e\n\u003cli\u003eRashid T, Ali Ammar Taqvi S, Sher F, Rubab S, Thanabalan M, Bilal M, \u0026amp; ul Islam B (2021). Enhanced lignin extraction and optimisation from oil palm biomass using neural network modelling. Fuel, 293: 120485. https://doi.org/10.1016/j.fuel.2021.120485\u003c/li\u003e\n\u003cli\u003eSadiq R, Rodriguez MJ, \u0026amp; Mian HR (2019). Empirical models to predict disinfection by-products (DBPs) in drinking water: an updated review. In J Nriagu (Ed.), Encyclopedia of environmental health (pp. 324-338). Berkeley, United States: Elsevier https://doi.org/10.1016/b978-0-12-409548-9.11193-5\u003c/li\u003e\n\u003cli\u003eShe YX (2014). Nature-inspired optimization algorithms. (2nd ed.). Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/c2019-0-03762-4\u003c/li\u003e\n\u003cli\u003eSluiter A, Hames B, Ruiz R, Scarlata C, Sluiter J, Templeton D, \u0026amp; Crocker D Determination of structural carbohydrates and lignin in biomass.(2010). \u003cstrong\u003ehttps://www.nrel.gov/docs/gen/fy13/42618.pdf\u003c/strong\u003e Accessed 27 August.2022.\u003c/li\u003e\n\u003cli\u003eSunphorka S, Chalermsinsuwan B, \u0026amp; Piumsomboon P (2017). Application of artificial neural network for kinetic parameters prediction of biomass oxidation from biomass properties. J Energy Inst, 90(1): 51-61. https://doi.org/10.1016/j.joei.2015.10.007\u003c/li\u003e\n\u003cli\u003eTang S, Dong Q, Fang Z, \u0026amp; Miao ZD (2019). Complete recovery of cellulose from rice straw pretreated with ethylene glycol and aluminum chloride for enzymatic hydrolysis. Bioresour Technol, 284: 98-104. https://doi.org/10.1016/j.biortech.2019.03.100\u003c/li\u003e\n\u003cli\u003eTian D, Chen YY, Shen F, Luo MY, Huang M, Hu JG, Zhang YZ, Deng SH, \u0026amp; Zhao L (2021). Self-generated peroxyacetic acid in phosphoric acid plus hydrogen peroxide pretreatment mediated lignocellulose deconstruction and delignification. Biotechnol Biofuels, 14(1): 224. https://doi.org/10.1186/s13068-021-02075-w\u003c/li\u003e\n\u003cli\u003eTocco D, Carucci C, Monduzzi M, Salis A, \u0026amp; Sanjust E (2021). Recent developments in the delignification and exploitation of grass lignocellulosic biomass. ACS Sustainable Chemistry \u0026amp; Engineering, 9(6): 2412-2432. https://doi.org/10.1021/acssuschemeng.0c07266\u003c/li\u003e\n\u003cli\u003eTsekos C, Tandurella S, \u0026amp; de Jong W (2021). Estimation of lignocellulosic biomass pyrolysis product yields using artificial neural networks. J Anal Appl Pyrolysis, 157: 105180. https://doi.org/10.1016/j.jaap.2021.105180\u003c/li\u003e\n\u003cli\u003eWagle A, Angove MJ, Mahara A, Wagle A, Mainali B, Martins M, Goldbeck R, \u0026amp; Raj Paudel S (2022). Multi-stage pre-treatment of lignocellulosic biomass for multi-product biorefinery: a review. Sustainable Energy Technologies and Assessments, 49: 101702. https://doi.org/10.1016/j.seta.2021.101702\u003c/li\u003e\n\u003cli\u003eWalczak S, \u0026amp; Cerpa N (2001). Artificial neural networks. In RA Meyers (Ed.), Encyclopedia of physical science and technology (pp. 631-645). Berkeley: Elsevier https://doi.org/10.1016/b0-12-227410-5/00837-1\u003c/li\u003e\n\u003cli\u003eWan X, Yao FP, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, \u0026amp; Deng SH (2019). Pretreatment of wheat straw with phosphoric acid and hydrogen peroxide to simultaneously facilitate cellulose digestibility and modify lignin as adsorbents. Biomolecules, 9(12): 844. https://doi.org/10.3390/biom9120844\u003c/li\u003e\n\u003cli\u003eWang Q, Hu JG, Shen F, Mei ZL, Yang G, Zhang YZ, Hu YD, Zhang J, \u0026amp; Deng SH (2016). Pretreating wheat straw by the concentrated phosphoric acid plus hydrogen peroxide (PHP): Investigations on pretreatment conditions and structure changes. Bioresour Technol, 199: 245-257. https://doi.org/10.1016/j.biortech.2015.07.112\u003c/li\u003e\n\u003cli\u003eWang Q, Tian D, Hu JG, Shen F, Yang G, Zhang YZ, Deng SH, Zhang J, Zeng YM, \u0026amp; Hu YD (2018). Fates of hemicellulose, lignin and cellulose in concentrated phosphoric acid with hydrogen peroxide (PHP) pretreatment. RSC Advances, 8(23): 12714-12723. https://doi.org/10.1039/c8ra00764k\u003c/li\u003e\n\u003cli\u003eWang Q, Wang ZH, Shen F, Hu JG, Sun FB, Lin LL, Yang G, Zhang YZ, \u0026amp; Deng SH (2014). Pretreating lignocellulosic biomass by the concentrated phosphoric acid plus hydrogen peroxide (PHP) for enzymatic hydrolysis: evaluating the pretreatment flexibility on feedstocks and particle sizes. Bioresour Technol, 166: 420-428. https://doi.org/10.1016/j.biortech.2014.05.088\u003c/li\u003e\n\u003cli\u003eYang J, Huang Y, Xu HY, Gu DY, Xu F, Tang JT, Fang C, \u0026amp; Yang Y (2020). Optimization of fungi co-fermentation for improving anthraquinone contents and antioxidant activity using artificial neural networks. Food Chem, 313: 126138. https://doi.org/10.1016/j.foodchem.2019.126138\u003c/li\u003e\n\u003cli\u003eYao FP, Tian D, Shen F, Hu JG, Zeng YM, Yang G, Zhang YZ, Deng SH, \u0026amp; Zhang J (2019). Recycling solvent system in phosphoric acid plus hydrogen peroxide pretreatment towards a more sustainable lignocellulose biorefinery for bioethanol. Bioresour Technol, 275: 19-26. https://doi.org/10.1016/j.biortech.2018.12.040\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lignocellulosic biomass, ANN model, Pretreatment efficiency, Prediction, Relative importance","lastPublishedDoi":"10.21203/rs.3.rs-2083176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2083176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCellulose from lignocellulosic biomass is the most promising renewable feedstock which may become a substitute for petrochemical products. However, it is challenging to extract cellulose from biomass because of the structural resistance of lignocellulose. Phosphoric acid plus hydrogen peroxide (PHP) pretreatment is an efficient approach that might be applied to get the cellulose-enriched fraction (CEF) from biomass. This study employed the artificial neural network (ANN) to predict the PHP pretreatment efficiency. The critical conditions, including pretreatment time (\u003cem\u003et\u003c/em\u003e), temperature (\u003cem\u003eT\u003c/em\u003e), H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e), and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e), were employed as input variables for the ANN model to predict the output variables: cellulose content (C-\u003cem\u003eC\u003c/em\u003e), cellulose recovery (C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e), hemicellulose removal (H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e), and lignin removal (L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e). The key parameters of ANN models are selected depending on the root mean square errors (RMSE). ANN models' final optimal topological structure contains one hidden layer with 9, 10, 10, and 12 neurons for C-\u003cem\u003eC\u003c/em\u003e, C-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003ey\u003c/em\u003e\u003c/sub\u003e, H-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, and L-\u003cem\u003eR\u003c/em\u003e\u003csub\u003e\u003cem\u003el\u003c/em\u003e\u003c/sub\u003e, respectively. The actual testing data fit the predicted data with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e of 0.8070\u0026ndash;0.9989. Additionally, we computed the relative importance (\u003cem\u003eRI\u003c/em\u003e) of input variables on output variables using the Garson equation with net weight matrixes. And the results revealed that \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eh\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eRI\u003c/em\u003e 12.0\u0026ndash;62.6%) impacted the effectiveness of PHP pretreatment primarily. \u003cem\u003eT\u003c/em\u003e (\u003cem\u003eRI\u003c/em\u003e 78.6%) dominates the removal efficacy of hemicellulose, and \u003cem\u003et\u003c/em\u003e (\u003cem\u003eRI\u003c/em\u003e 9.5\u0026ndash;24.6%) has less influence compared to the other conditions. The study provides insights into the optimization of biomass pretreatment.\u003c/p\u003e","manuscriptTitle":"Prediction of phosphoric acid plus hydrogen peroxide (PHP) pretreatment efficiency using artificial neural network modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-27 16:44:03","doi":"10.21203/rs.3.rs-2083176/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"558a921e-e7b0-4354-b41d-fb7be823f862","owner":[],"postedDate":"September 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-20T07:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2022-09-27 16:44:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2083176","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2083176","identity":"rs-2083176","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}