Harnessing Ligno Cellulose and Cellulose Derivative Residues for Sustainable Biomethanation With Effect of Different Transformation in Rsm Optimization

Harnessing Ligno Cellulose and Cellulose Derivative Residues for Sustainable Biomethanation With Effect of Different Transformation in Rsm Optimization | 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 Harnessing Ligno Cellulose and Cellulose Derivative Residues for Sustainable Biomethanation With Effect of Different Transformation in Rsm Optimization p Kanakasabai, Ahmed Said Salim Qahoor Al Mahri, S Sivamani, Noor Mohammed Said Qahoor, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4058906/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 Biogas technology stands out as a viable energy alternative in rural areas, acclaimed for being an exemplary appropriate technology that addresses the fundamental need for fuel. By utilizing discarded and lingo cellulose derivatives such as potato peel waste sourced from vegetable markets, this technology harnesses energy in the form of biogas enriched with a high methane content. The anaerobic bacteria play a pivotal role in converting and peel wastes into biogas through a synergistic process. Crucial considerations for the biomethanation process encompass process parameters like substrate concentration, substrate and cell mass concentration. Simultaneously, kinetic parameters such as maximum specific growth rate, kinetic constant, and ultimate methane yield take precedence in the anaerobic digestion process for efficient biogas production. This study endeavors to explore the anaerobic reactions of potato and potato peel wastes within a semi-batch digester. Variations in substrate concentrations and different substrates significantly impact biogas production, leading to the development of a mathematical interpretation of the biomethanation process. Between 33.16 and 38.68 MJ/Nm 3 of biogas is the energy yield obtained from this procedure. Through a meticulous mathematical analysis of experimental data, model equations correlating ultimate methane yield with diverse substrate concentrations and loading have been formulated. Biogas Potato Potato peel Biomethanation Anaerobic processes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 INTRODUCTION Amid increasing concerns about environmental degradation and the exhaustion of conventional energy sources, there has been a global effort in recent years to identify sustainable and environmentally friendly energy alternatives. Organic waste biomethanization has emerged as a focal point in this quest for sustainable energy, drawing significant attention due to its potential. The generation of fruit waste occurs at various stages, including harvesting, transportation, storage, marketing, and processing (Pawlik 2023),( Sadh, 2023) (Chukwuma, 2023) (Ebrahimian, 2023) (Harada, 1996). These waste materials, owing to their inherent characteristics and composition, tend to decompose rapidly, leading to unpleasant odors. Therefore, there is an urgent need to develop efficient waste treatment technologies for managing fruit waste (Ebrahimian, 2023) (Blonskaja, 2003) (Chen, 1980) (Hashimoto, 1981) (Standard Method, 1975), not only to produce biofuel but also to mitigate greenhouse gas emissions. The successful production of biogas relies on a sophisticated microbiological process, where ligno cellulose and cellulose waste undergoing treatment serves as a substrate for a diverse range of microorganisms (Sitorus, 2013 ) (Saikat Banerjee, 2004 ). A greater diversity in the composition of the organic material enhances the availability of growth components, fostering a wider spectrum of organisms thriving in the system. According to the Food and Agricultural Organization (FAO), the estimated percentage of fruit and vegetable waste at various stages of the food supply chain is 15% during agricultural production (Arelli, 2023), 9% in post-harvest handling and storage (Sanchez, 1985), 25% in processing and packaging (Sweeney, 1991 ) and 10% in distribution, with consumption accounting for 7% (Boopathi, 1988). Primarily, solid wastes, particularly from traditional markets, including fruit and vegetable residues, are often disposed of in municipal landfills or dumping sites, posing environmental challenges (Deressa, 2015). Due to their inherent characteristics, these wastes degrade easily, emitting foul odors. Given their high moisture and organic content, biological treatments like anaerobic digestion are more suitable for these wastes than methods like incineration and composting (Goyal, 1996) . Biogas, a combustible gas produced through the anaerobic degradation of organic material by bacteria in sealed, oxygen-deprived conditions, is prevalent in digester organic cesspools and sanitary landfills (Pavi, 2017). The flammable biogas generated from organic waste is predominantly composed of methane CH 4 and CO 2 . The production process involves two key steps: the preparation of raw materials and the anaerobic biodegradation process facilitated by microorganisms to sustain methane gas (Elgarahy, 2023) (Sagagi,2009). Potato residues contain cellulose have become a promising substrate for biomethanation among other organic wastes due to their high organic content and widespread availability (Masebinu, 2018). Beyond providing a sustainable energy source, the production of biogas from potato waste aids the fruit processing industry in more effectively managing its waste, reducing the environmental impact of organic waste disposal (Arelli, 2023) (Neto, 2021). While potato biomethanation holds great potential, several challenges need to be addressed, including optimizing process parameters, increasing biogas yield, and reducing inhibitory factors that may hinder efficient biogas production (Bouallagui, 2003). Understanding the complex biochemical mechanisms involved in potato waste biomethanation is crucial for designing effective and financially sustainable biogas production systems. This study aims to explore the key variables influencing the biomethanation of potato waste, focusing on enhancing biogas yield, streamlining the process, and devising solutions to overcome negative consequences (Garcia-Calderon, 1998) (Knol,. 1978). The goal is to support the sustainable use of potato waste for renewable energy production, contributing to a more sustainable and environmentally friendly future through a comprehensive examination of the biomethanation process. EXPERIMENTAL DETAILS The experimental procedures will be executed using a semi-batch digester that has been designed and assembled. This setup comprises a one-liter glass conical flask with a feed intake orifice, a gas exit nozzle, and a pressure measuring nozzle. Positioned on a hot plate with temperature control, the digester ensures a consistent temperature for the waste product undergoing digestion. A U-tube manometer, connected to the pressure measuring nozzle, has one end exposed to the atmosphere. Thermometer wells integrated into the digester allow the insertion of thermometers to monitor the temperature of the feed slurry. The pressure of the generated gas is assessed by the manometer. To maintain uniform agitation of the slurry at a controlled stirrer speed, the digester is equipped with a magnetic stirrer and a motor featuring a speed-controlling regulator. Figure 1 depicts a schematic diagram of the digester setup. Potato and peel wastes, characterized as outlined in Table 1 , ar.e utilized for the anaerobic digestion process. One liter of slurry, composed of potato and peel wastes with a specified substrate concentration, is introduced into the digester, along with a 1% mixed culture serving as an inoculum. The inoculum is prepared by dissolving cow dung in distilled water to maintain a pH level between 6.8 and 7.2. This mixture undergoes incubation at 35°C for seven days under anaerobic conditions and is stored in the incubator at 0°C.Experiments are conducted over a retention period of 17–21 days, varying substrate concentrations within the 30°C range. Since it has been established that there are no additional components in the biogas, the produced biogas at different retention days is collected, measured, and subjected to analysis using a gas analyzer [10] to determine methane and carbondioxide concentrations. RESULTS AND DISCUSSION Using 10, 20, and 30% of potato as well as potato peel waste at 30 ° C digestion temperature and controlling the pH in the range of 6.8 to 7.2 for a retention period of 14 days, experimental studies on the biomethanation of potato as well as potato peel wastes have been conducted in a semi-batch digester. The experimental and data-analysis results have been graphically depicted in Figures 2, 3, 4, 5, 6, 7, and 8, as well as on Tables 1 to 5. The relationship between the hydraulic retention time (which is measured in seconds) and the volume of biogas yield (measured in millilitres) for various concentrations of potato and potato peel substrate is shown in the graph, Figures 2 and 3. It seems to show the results of an experiment in the production of biogas with varying quantities of potato and potato peel as the substrate. The length of time the substrate is kept in the biogas production system is shown by the x-axis, which stands for the hydraulic retention time. Extended periods of retention typically facilitate enhanced digestion and, as a result, increased production of biogas. The volume of biogas yield, represented by the y-axis, shows how much biogas was produced over the designated retention period. Various lines or data points on the graph most likely correspond to different substrate concentrations of potatoes and potato peel. These lines or points can shed light on how the concentration of the substrate made of potatoes and potato peels affects the amount of biogas produced. Higher substrate concentrations typically result in higher biogas production—that is, until they reach an optimal concentration, after which additional increases may not increase biogas yield proportionately because of constraints on microbial activity and substrate availability, among other things. By examining the data on the graph, one can gain important knowledge about the ideal circumstances for producing biogas from potato and potato peel substrate. This data can be used by researchers to calculate the ideal substrate concentration and hydraulic retention duration to produce the most biogas possible. Additionally, this data can be used for further research and development in the field of renewable energy production and waste management. Figures 2, 3, 4, and 5 show that as substrate concentration rises to 20% potato and potato peel concentration, the volume of biogas yield, cumulative biogas yields, and cumulative methane yield—a significant component of biogas—all generally increase. The ideal substrate concentration varies depending on the run, though. Among the range of parameters tested, it has been noted that the highest yields of biogas and methane have been achieved at potato peel concentrations of 10% and 20%. Depending on the substrate concentration and digestion temperature, 67% of the biogas produced is made up of methane and the remaining 35% is carbon dioxide. Biogas has an energy yield that ranges from 33.16 to 38.68 MJ/Nm 3 . For retention times ranging from 21 to 27 days, an increase in biogas yield as well as methane yield has been observed for all substrates and substrate concentrations during the experiment preview. Additional experimental observations show that biogas generation began on the second day in each case, that the maximum yield of biogas and methane was found on the seventh or tenth day of retention time, that the yield gradually decreased due to the decay of the bacterial growth, and that there was no discernible biogas generation after 27 days of retention time, when the bacterial action had essentially stopped. Within the range of variables tested, as illustrated in figures 6 and 7, the cumulative methane yield in millilitres varies nonlinearly with inverse retention time in day-1. Based on this, the ultimate methane yield (B o ) has been calculated at x=0 at various concentrations of apples and apple peels. The analysis of mathematics reveals that the generalised equation that fits the curves can be written as B = A x 3 + C x 2 + D x 1 + B o and B = A x 2 + C x 1 + B o for potato concentration as well as for potato peel concentration where A, C and D are the co-efficient whose values are dependent on substrate concentration (Scano, 2014) (Viswanath, 1992). The equation which fits in the graphs are given in Table 1, It has been further observed from figures 7 and 9 that [B/(Bo-B)] demonstrates a linear connection to (retention time) for different substrate concentration. Generalized correlation, which is provided as equation (1), can be used to illustrate the equation that fit these curves, x=S+R.B/(Bo-B)…(1) where the co-efficient S and R depend on substrate concentration, concentration of cell mass in addition to process kinetics (Kumar, 2023). The values of S and R for various substrate concentrations are given in table 4 and 5. Consequently, it has been discovered that the kinetic approach to equation suggested by Chen and Hashimoto (Chen, 1980) (Hashimoto, 1981) as T=1/µm + k/µm. B/(Bo-B)...(2) is appropriate for semi-batch digester running with potato and potato peel wastes. Nevertheless, in contrast to equation 2, the maximum specific growth rate (µm) in addition to kinetic parameter (k) can be evaluated from the intercepts and slopes of the graphs of figure 7 and 9 and therefore, S represents 1/ µm and R represents k/ µm. Table 4 and 5 show the variation of and k with change in potato and potato peel concentration respectively. It has been observed from the Table 5 and 6 that µm shows non-linear relationship for a given potato and potato peel concentration and that it increases with potato and potato peel concentration reaching a maximum value at 20% concentration after which it decreases. Kinetic parameter k varies linearly with potato and potato peel concentration and it decreases with increase in potato and potato peel concentration within the range of the concentration experimented with. Experimental design Using Design-Expert software (ver 10, Stat-Ease, Inc., USA), the generation of biogas from cellulose as well as lingo cellulose waste was optimised under a range of operational conditions. The study selected two independent factors, namely hydraulic retention time as well as substrate concentration, while the dependent variables were the average biogas production (Vijin Prabhu et al. 2020) (Jain and Mattiason 1998) (Chandra et al. 2012) (Shanmugam and Horan 2009). A list of all coded as well as real values is included in Tables 6A, 7A, and 8A, along with the variance analysis. For the double Central Composite Design Matrix with two independent factors and experimental responses for biogas yield (B) independent variables 6C, 7C, and 8C, thirteen tests were conducted. For the current example, regression equations were created using the least squares method, as demonstrated in Eq (1). B = a o + a 1 T+ a 2 C + a 3 .T.C + a 4 .T 2 + a 5 .C 2 …..(1) where C (substrate concentration) and T (hydraulic retention time) are independent variables, and B (expected response) and the coefficients ao, a1, a2, a3, a4, and a5 are present. The individual and cumulative impacts of the input data on the responses are described by the graphical illustrations for these equations. The relationship among estimates of parameters and how those estimates affect the responses is ascertained by using these equations, which are also referred to as response surfaces. Comparative study of power transformation Any data analysis must include the transformation of the response. If the magnitude of the response (predicted values) determines the error (residuals), then transformation is required. To determine whether the statistical presumptions that underpin the data analysis are met, Design-Expert offers a wide range of diagnostic capabilities. The residuals' normality is examined using the normal plot. If a pattern appears in the residuals versus predicted response values plot, it will suggest an issue. Response transformation won't really change anything unless the ratio of the maximum response to the least response is very high. The suggested transformation from the power family will be provided by the RSM methodology. Depending on the kind of response, one must apply one of the two non-power law transformations: arcsin-sqrt for proportions and logit for bounded data. When proportional data is available, the RSM plot frequently suggests a square-root transformation; for bounded data, it suggests a log transformation. The power function can be used to describe most data transformations; power provides a scale that satisfies the statistical model's equal variance requirement. A response transformation's appropriate selection depends on statistical analysis and/or subject-matter expertise. As long as the data are positive, the power transformation permits transformation to any power in the –3 to +3 range. To keep the data from having powers of negative numbers, you can add a constant. When an observation's standard deviation is proportionate to the mean raised to a certain power, scaling the observation by that power yields a scale that satisfies the ANOVA's equal variance requirement. The Diagnostics plots include the RSM to assist you in selecting the proper power transformation. No transformation of linear regression model Using data from all experimentation, a plain quadratic model with RSM optimization without transformation was generated in this present scope of study and used to determine all types of variable outcomes. It describes the appropriate observations, including the average biogas production (B). Equations (2) are used to express the results of the quadratic modeling of B, where T is the cleavage time. The correlation coefficient R of the different responses is determined by using the method of regression analysis of the present data detected by statistical software. The model's determined and evaluated the F-value of 15.43, which indicates that it is a significant model. The likelihood that a large F-value is the result of little amount of noise which is only 0.12%. The model phrases are considered to be significant if the P-value becomes less than or equal to 0.05. A2 as well as B2 are significant model terms in this illustration. The model in terms aren't consider to be significant if the values are greater than or equal to 0.1. Model reducibility can help the model fitness if it contains a large number of unnecessary or unused terms (apart from those required to support properly the hierarchy). As is typically expected, the adjusting R² of 0.8574 is not considered to be as close to the estimated R² of 0.4084; that is, the difference is not considered to be larger than 0.2. This can point to a substantial blocking impact or a potential issue with your data or model. Outliers, corresponding transformation, model reduction, etc. Validation experiments ought to be used to test any empirical model. Adeq Precision quantifies the ratio of signal to noise. Ideally, the ratio should be higher than 4. A sufficient signal is indicated by the ratio of 9.413. The one in question can be navigated with in design mode. The coefficient estimate displays the expected change as a consequence for every single change of a factor's value when all other variables are held constant. The point of intersection of an orthogonal design is the total average reaction of all the kits. This average is modified by factors according to factor configurations. The VIFs are not considered to be 1 if the factors appear orthogonal; multi-collinearity is indicated by VIFs greater than 1, where a higher VIF indicates a stronger factor correlation. VIF values less than 10 are generally accepted. Final equation in terms of actual factor is B = -65.41+ 12.88T+ 10.93.C - 4.5x10 -6 .T.C - 0.64.T 2 - 0.27.C 2 …..(2) At particular levels of each factor, predictions can be made using the true factors equation. In this case, the levels ought to be stated in each factor's original units. Since the coefficients have been adjusted by the measurement units of all variables as well as the point where they intersect is not in the centre portion, the models may not utilized to evaluate the other factors in comparison. Power transformation of linear regression model In this study, RSM optimisation using power transformation was utilised to create a quadratic model utilising data from all the experiments, which was then utilised to forecast every result from the variables. After statistical software has observed the data, regression analysis is used to determine the correlation coefficient R for the different responses. The model's determined and evaluated the F-value of 15.43 specifies that it is considered significant. There is only a 0.12% chance that the generated noise would be the reason of the F-value which is high. Model terminology with the significant P-values less than 0.05 are deemed to be significant. In this situation, A2 as well as B2 are considered to be significant model terms. As one might normally expect, there is a difference of more than 0.2 between the adjusted R2 of 0.8574 as well as the predicted R2 of 0.4084. This might indicate a big block effect or a possible problem with your model or data. It is important to consider model reduction, response transformation, outliers, as well as other issues. Any empirical model should be tested using confirmation runs. Adeq Precision determines the signal-to-noise ratio. The ratio ought to be greater than 4. A ratio of 9.413 indicates that the signal is strong enough. This model can be used to navigate the design space. The coefficients reflect modifications made in relation to that average, as determined by the factor settings. When the factors are orthogonal, the VIF is 1. Multi-colinearity is indicated by VIFs larger than 1, which indicate stronger factor correlation. In general, VIFs of no more than ten are acceptable. Final equation in terms of actual factor is B = -65.4+ 12.87.T+ 10.92.C - 4.5x10 -16 .T.C - 0.643.T 2 - 0.26.C 2 …..(2) Square root (SQRT) transformation of linear regression model In this study, the quadratic model was developed using RSM optimisation as well as sqrt transformation, as well as it was used to predict all of the variables' outcomes. The regression analysis of the data collected by statistical software yields the correlation coefficient R for various responses. The Model F-value of 10.75 indicates that the model is significant. There is only a 0.35% chance which an F-value that big will occur due to noise. P-values of less than 0.0500 indicate that model terms are significant. In this case, A² is an important model term. Values above 0.1 indicate that the model the context are not significant. If there are many insignificant model terms (excluding those necessary to support hierarchy), reducing them may improve the model. The Estimated R² of 0.1805 differs from the Adjusted R² of 0.8024 by more than 0.2. This might suggest a large block effect or an issue with the model and/or data. Model reduction, response transformation, outliers, and so on are all important considerations. All empirical hypotheses should be tested through confirmation runs. Adeq Precision measures the signal-to-noise ratio. A ratio greater than four is preferred. Your ratio of 8.042 indicates a good signal. This model can help navigate the design space. Final equation in terms of actual factor is B = -4.03 + 1.27.T+ 0.8.C – 1.8x10 -17 .T.C - 0.063.T 2 - 0.02.C 2 …..(2) Analysis Transformations are frequently used in linear models, as well as because linear models are somewhat an essential component of RSM, the use of transformed data represents an effort to obtain the most effective technique as well as model conformance. The article investigates how the power transformation affects the RSM suggested by second-order models. The response surface algorithm has been enhanced by including linear, power, as well as SQRT transformations in the model variable responses before analysing the implications of using different methods to estimate the model's parameters. Figure 9 shows the desirability and Contour plot of biogas yield (B) for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression. It is reveals from the plot that the desirability is more for without transformation in this case which is 0.8. Figure 10 shows the surface plot of biogas yield (B) for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression. It is reveals from the plot that the better result is given by without transformation in this case. However, the equation which fits the surface is given in Eq (1) The following is a straightforward presentation of this analysis for parameter estimation: Because each of the three estimation methods linear, power, as well as SQRT transformation has a different structure applied to it, the outcomes of the three methods which are well-known for estimating parameters did not match. CONCLUSION There has been much focus on the energy potential of Potato as well as Potato peel waste. The current study offers helpful information about the productive use of such wastes using a semi-batch digestion process to produce biogas in the thermophillic and mesophillic temperature range. It also shows that substrate concentration has a significant impact on both the methane content and the biogas yield. Within the range of these experimental parameters, a 20% substrate concentration produced the maximum methane and total biogas yields. It has also shown that, for a range of Potato and peel concentrations, the variation of the kinetic parameter with temperature exhibits a linear relationship, but the variation of the maximum specific growth rate (µm) with Potato and peel concentration shows non-linear behaviour. The development of helpful generalised correlations between Potato and Potato peel concentration and both the maximum specific growth rate and the kinetic parameter has resulted from the mathematical analyses of the experimental data. In case of different transformation of RSM optimization linear without transformation gives the better result with 94.46 cc biogas yield for the substrate concentration 20.336% and time 14.91 days. The evaluation of the second-order model, for example, reveals that this improvement is not consistent with statistical theory, indicating that the evaluation of the second-order model constructed from the original data yields superior results than the examination of the second-order model of the data that was transformed. This observation supports the power transformation standpoint that it is not a requirement that power transformations be perfectly fitting for every stage of the RSM. Optimisation steps that require raising the degree of the ascent orientation to the optimal surface produced by the algorithms can be reduced by using transformations. Declarations Declarations Declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper. the results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration by another publisher. all of the material is owned by the authors and/or no permissions are required. ETHICAL APPROVAL This is an observational study. We will not intentionally engage in or participate in any form of malicious harm to another person or animal. So we confirmed that no ethical approval is required. FUNDING Funded by TRC Oman and UTAS Salalah Author Contribution "A.B. and C.D. wrote the main manuscript text and E. prepared all figures. All authors reviewed the manuscript." ACKNOWLEDGEMENT We would like to acknowledge TRC Oman and UTAS Salalah for funding this project. 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(2014). Biogas from anaerobic digestion of fruit and vegetable wastes: Experimental results on pilot-scale and preliminary performance evaluation of a full-scale power plant. Energy conversion and management, 77, 22–30. Shanmugam P, Horan NJ (2009) Optimising the biogas production from leather fleshing waste by co-digestion with MSW. Bioresour Technol 100:4117–4120. https://doi.org/10.1016/J.BIORTECH.2009.03.052 Sitorus, B., & Panjaitan, S. D. (2013). Biogas recovery from anaerobic digestion process of mixed fruit-vegetable wastes. Energy Procedia, 32 , 176–182. Sweeney, D. W., & Graetz, D. A. (1991). Application of distillery waste anaerobic digester effluent to St. Augustinegrass. Agriculture, ecosystems & environment, 33(4), 341–351. Vijin Prabhu A, Manimaran R, Antony Raja S, Jeba P (2020) Biogas production from anaerobic co-digestion of Prosopis juliflora pods with water hyacinth, dry leaves, and cow manure. Energy Sources, Part A Recover Util Environ Eff 42:375–386. https://doi.org/10.1080/15567036.2019.1587084 Viswanath, P., Devi, S. S., & Nand, K. (1992). Anaerobic digestion of fruit and vegetable processing wastes for biogas production. Bioresource technology, 40 (1), 43–48. Zhou, J., Yang, J., Yu, Q., Yong, X., Xie, X., Zhang, L., … Jia, H. (2017). Different organic loading rates on the biogas production during the anaerobic digestion of rice straw: A pilot study. Bioresource technology, 244, 865–871. Tables Table 1. Correlation equation Substrate Concentration in percent(v/v) Equation Ultimate methane yield in ml Potato 10% B = -2789.4x3 + 4783.8x2 - 2736.9x + 534.12 534.12 20% -4220.4x3 + 7448.7x2 - 3865.9x + 655.31 655.31 30% B = 4033.6x3 + 5331.8x2 - 4425.4x + 763.93 763.93 Potato peel 10% B = 5785.8x2 - 2895.8x + 324.68 324.68 20% B = 4106.2x2 - 3271.9x + 635.99 635.99 30% B = 26408x2 - 13219x + 1474.5 1474.5 Table-2. Characteristics of Potato waste Parameters Results Parameters Results Parameters Results B.O.D. (10% solution) C.O.D. (10% solution) pH of the solution Specific gravity TDS 483.34 kg/Cu.m 690.428 kg/Cu.m 6.35 1.22 251 kg/Cu.m Proximate analysis:(by weight) Ash Moisture Volatile matter Fixed carbon 6% 70.6% 17% 6.4% Non volatile solid (by weight) CHN analysis: (dry basis, by weight). Total carbon Hydrogen Nitrogen 12.4% 28.49% 4.46% 0.6% Table-3. Characteristics of Potato peel waste Parameters Results Parameters Results Parameters Results B.O.D. (10% solution) C.O.D. (10% solution) pH of the solution Specific gravity TDS 576.12 kg/Cu.m 822.86 kg/Cu.m 6.7 1.29 333 kg/Cu.m Proximate analysis:(by weight) Ash Moisture Volatile matter Fixed carbon 3.73% 65.22% 18.18% 12.87% Non volatile solid (by weight) CHN analysis: (dry basis, by weight). Total carbon Hydrogen Nitrogen 16.6% 57.3% 8.97% 1.2% Table 4. Table of maximum specific growth rate and kinetic parameter for different Potato concentration % Potato concentration 1/Um k/Um Um k 10 2.67 5.23 0.37 1.95 20 2.08 6.86 0.47 3.28 30 3.53 6.29 0.28 1.78 Table 5. Table of maximum specific growth rate and kinetic parameter for different Potato peel concentration % Potato peel concentration 1/Um k/Um Um k 10 5.21 10.34 0.19 1.98 20 2.54 5.96 0.39 2.34 30 5.22 10.51 0.19 2.01 Table 6A. Analysis of Variance of the quadratic model for no transformation of linear regression Source Sum of Squares df Mean Square F-value p-value Model 31270.19 5 6254.04 15.43 0.0012 significant A-TIME 0.0000 1 0.0000 0.0000 1.0000 B-CONCENTRATION 25.00 1 25.00 0.0617 0.8110 AB 0.0000 1 0.0000 0.0000 1.0000 A² 28828.80 1 28828.80 71.12 < 0.0001 B² 5024.46 1 5024.46 12.40 0.0097 Residual 2837.50 7 405.36 Lack of Fit 2837.50 3 945.83 Pure Error 0.0000 4 0.0000 Cor Total 34107.69 12 Table 6B. Statistics of fitness data for no transformation of linear regression Std. Dev. 20.13 R² 0.9168 Mean 53.85 Adjusted R² 0.8574 C.V. % 37.39 Predicted R² 0.4084 Adeq Precision 9.4129 Table 6C. Estimation of coefficients in terms of coded factor for no transformation of linear regression Factor Coefficient Estimate df Standard Error 95% CI Low 95% CI High VIF Intercept 110.00 1 9.00 88.71 131.29 A-TIME 0.0000 1 7.12 -16.83 16.83 1.0000 B-CONCENTRATION 1.77 1 7.12 -15.06 18.60 1.0000 AB 0.0000 1 10.07 -23.80 23.80 1.0000 A² -64.37 1 7.63 -82.43 -46.32 1.02 B² -26.87 1 7.63 -44.93 -8.82 1.02 Table 7A. Analysis of Variance of the quadratic model for power transformation of linear regression Source Sum of Squares df Mean Square F-value p-value Model 31270.19 5 6254.04 15.43 0.0012 significant A-TIME 0.0000 1 0.0000 0.0000 1.0000 B-CONCENTRATION 25.00 1 25.00 0.0617 0.8110 AB 0.0000 1 0.0000 0.0000 1.0000 A² 28828.80 1 28828.80 71.12 < 0.0001 B² 5024.46 1 5024.46 12.40 0.0097 Residual 2837.50 7 405.36 Lack of Fit 2837.50 3 945.83 Pure Error 0.0000 4 0.0000 Cor Total 34107.69 12 Table 7B. Statistics of fitness data for power transformation of linear regression Std. Dev. 20.13 R² 0.9168 Mean 53.85 Adjusted R² 0.8574 C.V. % 37.39 Predicted R² 0.4084 Adeq Precision 9.4129 Table 7C. Estimation of coefficients in terms of coded factor for power transformation of linear regression Factor Coefficient Estimate df Standard Error 95% CI Low 95% CI High VIF Intercept 110.00 1 9.00 88.71 131.29 A-TIME 0.0000 1 7.12 -16.83 16.83 1.0000 B-CONCENTRATION 1.77 1 7.12 -15.06 18.60 1.0000 AB 0.0000 1 10.07 -23.80 23.80 1.0000 A² -64.37 1 7.63 -82.43 -46.32 1.02 B² -26.87 1 7.63 -44.93 -8.82 1.02 Table 8A. Analysis of Variance of the quadratic model for SQRT transformation of linear regression Source Sum of Squares df Mean Square F-value p-value Model 288.21 5 57.64 10.75 0.0035 significant A-TIME 0.0000 1 0.0000 0.0000 1.0000 B-CONCENTRATION 0.0834 1 0.0834 0.0156 0.9042 AB 0.0000 1 0.0000 0.0000 1.0000 A² 278.39 1 278.39 51.91 0.0002 B² 27.78 1 27.78 5.18 0.0570 Residual 37.54 7 5.36 Lack of Fit 37.54 3 12.51 Pure Error 0.0000 4 0.0000 Cor Total 325.75 12 Table 8B. Statistics of fitness data for SQRT transformation of linear regression Std. Dev. 2.32 R² 0.8848 Mean 5.37 Adjusted R² 0.8024 C.V. % 43.16 Predicted R² 0.1805 Adeq Precision 8.0417 Table 8C. Estimation of coefficients in terms of coded factor for SQRT transformation of linear regression Factor Coefficient Estimate df Standard Error 95% CI Low 95% CI High VIF Intercept 10.49 1 1.04 8.04 12.94 A-TIME 0.0000 1 0.8188 -1.94 1.94 1.0000 B-CONCENTRATION 0.1021 1 0.8188 -1.83 2.04 1.0000 AB 0.0000 1 1.16 -2.74 2.74 1.0000 A² -6.33 1 0.8780 -8.40 -4.25 1.02 B² -2.00 1 0.8780 -4.07 0.0780 1.02 Additional Declarations No competing interests reported. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4058906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280726955,"identity":"4c5680d2-1fd4-4ce0-b924-a4e0d5f78f23","order_by":0,"name":"p Kanakasabai","email":"","orcid":"","institution":"University of Technology and Applied Science, Salalah, Oman","correspondingAuthor":false,"prefix":"","firstName":"p","middleName":"","lastName":"Kanakasabai","suffix":""},{"id":280726956,"identity":"0786ccb6-53ce-48c5-9308-7f58f146d745","order_by":1,"name":"Ahmed Said Salim Qahoor Al Mahri","email":"","orcid":"","institution":"University of 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18:00:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4058906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4058906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53064796,"identity":"1fdea458-976e-4f2d-93b7-949ead9456bd","added_by":"auto","created_at":"2024-03-20 07:58:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31054,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of Semi batch digester set-up\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/e611a51294537b9e6cee4592.jpg"},{"id":53064777,"identity":"9d3d16c8-8e08-474e-abd4-ce1171f0090c","added_by":"auto","created_at":"2024-03-20 07:58:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153092,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of Volume of biogas yield in ml against hydraulic retention time in s for different substrate (Potato) concentration.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/db34001aca5776485320bc0a.jpg"},{"id":53064788,"identity":"2427e6a2-13b1-456d-b12d-079aa9e6ddfd","added_by":"auto","created_at":"2024-03-20 07:58:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":157110,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of Volume of biogas yield in ml against hydraulic retention time in s for different substrate (Potato peel) concentration.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/8629e95f58c41fbe5908b8e1.jpg"},{"id":53064779,"identity":"a54cca4f-401f-44e9-8c79-4f9895844b81","added_by":"auto","created_at":"2024-03-20 07:58:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155228,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of cumulative volume of biogas yield in ml against hydraulic retention time in s for different substrate (Potato) concentration.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/cfc42de793998197057d0103.jpg"},{"id":53064797,"identity":"d6c9d3b4-b7a3-4d61-ace2-da1b2692b5b2","added_by":"auto","created_at":"2024-03-20 07:58:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138685,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of cumulative volume of biogas yield in ml against hydraulic retention time in s for different substrate (Potato peel) concentration.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/193e6d2c856bbccd976c4a7d.jpg"},{"id":53064778,"identity":"d59f9a6c-590f-4e5f-8ad4-9894c0b4d14c","added_by":"auto","created_at":"2024-03-20 07:58:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186345,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of cumulative volume of methane yield in ml against inverse retention time in 1/s for different substrate (Potato) concentration.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/1f909da9d854ba1be652b95b.jpg"},{"id":53064783,"identity":"34ecab00-c3c4-4599-907b-26175b26c502","added_by":"auto","created_at":"2024-03-20 07:58:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":141021,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of hydraulic retention time in s against B/(Bo-B) for different substrate (Potato) concentration.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/8fbd2c25c96ab0cf30d1f23a.jpg"},{"id":53064782,"identity":"477da4d9-1456-4a73-a9aa-60d78160f91c","added_by":"auto","created_at":"2024-03-20 07:58:30","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":184939,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of hydraulic retention time in s against B/(Bo-B) for different substrate (Potato peel) concentration.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/0d03b767f176445018650518.jpg"},{"id":53064792,"identity":"4b3868ad-5f48-4499-848c-265176c73019","added_by":"auto","created_at":"2024-03-20 07:58:31","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":151799,"visible":true,"origin":"","legend":"\u003cp\u003ePlot of hydraulic retention time in s against B/(Bo-B) for different substrate (Potato peel) concentration.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/3a89fb4e816cb52a7f22fa68.jpg"},{"id":53064781,"identity":"e7fee0c3-eff4-4912-84bf-885e735cbbee","added_by":"auto","created_at":"2024-03-20 07:58:30","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":162978,"visible":true,"origin":"","legend":"\u003cp\u003eDesirability and Contour plot of biogas yield (B) for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/83ff3442a5fcbddb5d5955c1.jpg"},{"id":53064780,"identity":"d03145ea-d40a-498e-98bb-f8a6cb8df2f3","added_by":"auto","created_at":"2024-03-20 07:58:30","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":243251,"visible":true,"origin":"","legend":"\u003cp\u003eSurface plot of biogas yield (B) for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/c59574f1f62c165dd3a0aec8.jpg"},{"id":53064791,"identity":"9c292d4e-6055-462d-86f2-5307a01436b5","added_by":"auto","created_at":"2024-03-20 07:58:31","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":92908,"visible":true,"origin":"","legend":"\u003cp\u003eSolution of Optimization for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/6e41815e4a72a986bca1b5c5.jpg"},{"id":53747488,"identity":"fdd257e7-3b3d-4481-87de-a7364dcc974b","added_by":"auto","created_at":"2024-03-29 18:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1230662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4058906/v1/372fd80c-9c12-46d7-865e-04044479fd12.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHarnessing Ligno Cellulose and Cellulose Derivative Residues for Sustainable Biomethanation With Effect of Different Transformation in Rsm Optimization\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAmid increasing concerns about environmental degradation and the exhaustion of conventional energy sources, there has been a global effort in recent years to identify sustainable and environmentally friendly energy alternatives. Organic waste biomethanization has emerged as a focal point in this quest for sustainable energy, drawing significant attention due to its potential. The generation of fruit waste occurs at various stages, including harvesting, transportation, storage, marketing, and processing (Pawlik 2023),( Sadh, 2023) (Chukwuma, 2023) (Ebrahimian, 2023) (Harada, 1996). These waste materials, owing to their inherent characteristics and composition, tend to decompose rapidly, leading to unpleasant odors. Therefore, there is an urgent need to develop efficient waste treatment technologies for managing fruit waste (Ebrahimian, 2023) (Blonskaja, 2003) (Chen, 1980) (Hashimoto, 1981) (Standard Method, 1975), not only to produce biofuel but also to mitigate greenhouse gas emissions.\u003c/p\u003e \u003cp\u003eThe successful production of biogas relies on a sophisticated microbiological process, where ligno cellulose and cellulose waste undergoing treatment serves as a substrate for a diverse range of microorganisms (Sitorus, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (Saikat Banerjee, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). A greater diversity in the composition of the organic material enhances the availability of growth components, fostering a wider spectrum of organisms thriving in the system. According to the Food and Agricultural Organization (FAO), the estimated percentage of fruit and vegetable waste at various stages of the food supply chain is 15% during agricultural production (Arelli, 2023), 9% in post-harvest handling and storage (Sanchez, 1985), 25% in processing and packaging (Sweeney, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and 10% in distribution, with consumption accounting for 7% (Boopathi, 1988). Primarily, solid wastes, particularly from traditional markets, including fruit and vegetable residues, are often disposed of in municipal landfills or dumping sites, posing environmental challenges (Deressa, 2015). Due to their inherent characteristics, these wastes degrade easily, emitting foul odors. Given their high moisture and organic content, biological treatments like anaerobic digestion are more suitable for these wastes than methods like incineration and composting (Goyal, 1996) .\u003c/p\u003e \u003cp\u003eBiogas, a combustible gas produced through the anaerobic degradation of organic material by bacteria in sealed, oxygen-deprived conditions, is prevalent in digester organic cesspools and sanitary landfills (Pavi, 2017). The flammable biogas generated from organic waste is predominantly composed of methane CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. The production process involves two key steps: the preparation of raw materials and the anaerobic biodegradation process facilitated by microorganisms to sustain methane gas (Elgarahy, 2023) (Sagagi,2009).\u003c/p\u003e \u003cp\u003ePotato residues contain cellulose have become a promising substrate for biomethanation among other organic wastes due to their high organic content and widespread availability (Masebinu, 2018). Beyond providing a sustainable energy source, the production of biogas from potato waste aids the fruit processing industry in more effectively managing its waste, reducing the environmental impact of organic waste disposal (Arelli, 2023) (Neto, 2021).\u003c/p\u003e \u003cp\u003eWhile potato biomethanation holds great potential, several challenges need to be addressed, including optimizing process parameters, increasing biogas yield, and reducing inhibitory factors that may hinder efficient biogas production (Bouallagui, 2003). Understanding the complex biochemical mechanisms involved in potato waste biomethanation is crucial for designing effective and financially sustainable biogas production systems. This study aims to explore the key variables influencing the biomethanation of potato waste, focusing on enhancing biogas yield, streamlining the process, and devising solutions to overcome negative consequences (Garcia-Calderon, 1998) (Knol,. 1978). The goal is to support the sustainable use of potato waste for renewable energy production, contributing to a more sustainable and environmentally friendly future through a comprehensive examination of the biomethanation process.\u003c/p\u003e"},{"header":"EXPERIMENTAL DETAILS","content":"\u003cp\u003eThe experimental procedures will be executed using a semi-batch digester that has been designed and assembled. This setup comprises a one-liter glass conical flask with a feed intake orifice, a gas exit nozzle, and a pressure measuring nozzle. Positioned on a hot plate with temperature control, the digester ensures a consistent temperature for the waste product undergoing digestion. A U-tube manometer, connected to the pressure measuring nozzle, has one end exposed to the atmosphere. Thermometer wells integrated into the digester allow the insertion of thermometers to monitor the temperature of the feed slurry. The pressure of the generated gas is assessed by the manometer. To maintain uniform agitation of the slurry at a controlled stirrer speed, the digester is equipped with a magnetic stirrer and a motor featuring a speed-controlling regulator. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts a schematic diagram of the digester setup. Potato and peel wastes, characterized as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, ar.e utilized for the anaerobic digestion process. One liter of slurry, composed of potato and peel wastes with a specified substrate concentration, is introduced into the digester, along with a 1% mixed culture serving as an inoculum. The inoculum is prepared by dissolving cow dung in distilled water to maintain a pH level between 6.8 and 7.2. This mixture undergoes incubation at 35\u0026deg;C for seven days under anaerobic conditions and is stored in the incubator at 0\u0026deg;C.Experiments are conducted over a retention period of 17\u0026ndash;21 days, varying substrate concentrations within the 30\u0026deg;C range. Since it has been established that there are no additional components in the biogas, the produced biogas at different retention days is collected, measured, and subjected to analysis using a gas analyzer [10] to determine methane and carbondioxide concentrations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eUsing 10, 20, and 30% of potato as well as potato \u0026nbsp;peel waste at 30\u003csup\u003e\u0026deg;\u003c/sup\u003eC digestion temperature and controlling the pH in the range of 6.8 to 7.2 for a retention period of 14 days, experimental studies on the biomethanation of \u0026nbsp;potato as well as potato peel wastes have been conducted in a semi-batch digester. The experimental and data-analysis results have been graphically depicted in Figures 2, 3, 4, 5, 6, 7, and 8, as well as on Tables 1 to 5.\u003c/p\u003e\n\u003cp\u003eThe relationship between the hydraulic retention time (which is measured in seconds) and the volume of biogas yield (measured in millilitres) for various concentrations of potato and potato peel substrate is shown in the graph, Figures 2 and 3. It seems to show the results of an experiment in the production of biogas with varying quantities of potato and potato peel as the substrate. The length of time the substrate is kept in the biogas production system is shown by the x-axis, which stands for the hydraulic retention time. Extended periods of retention typically facilitate enhanced digestion and, as a result, increased production of biogas. The volume of biogas yield, represented by the y-axis, shows how much biogas was produced over the designated retention period.\u003c/p\u003e\n\u003cp\u003eVarious lines or data points on the graph most likely correspond to different substrate concentrations of potatoes and potato peel. These lines or points can shed light on how the concentration of the substrate made of potatoes and potato peels affects the amount of biogas produced. Higher substrate concentrations typically result in higher biogas production\u0026mdash;that is, until they reach an optimal concentration, after which additional increases may not increase biogas yield proportionately because of constraints on microbial activity and substrate availability, among other things.\u003c/p\u003e\n\u003cp\u003eBy examining the data on the graph, one can gain important knowledge about the ideal circumstances for producing biogas from potato and potato peel substrate. This data can be used by researchers to calculate the ideal substrate concentration and hydraulic retention duration to produce the most biogas possible. Additionally, this data can be used for further research and development in the field of renewable energy production and waste management.\u003c/p\u003e\n\u003cp\u003eFigures 2, 3, 4, and 5 show that as substrate concentration rises to 20% potato and potato peel concentration, the volume of biogas yield, cumulative biogas yields, and cumulative methane yield\u0026mdash;a significant component of biogas\u0026mdash;all generally increase. The ideal substrate concentration varies depending on the run, though. Among the range of parameters tested, it has been noted that the highest yields of biogas and methane have been achieved at potato peel concentrations of 10% and 20%. Depending on the substrate concentration and digestion temperature, 67% of the biogas produced is made up of methane and the remaining 35% is carbon dioxide. Biogas has an energy yield that ranges from 33.16 to 38.68 MJ/Nm\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; For retention times ranging from 21 to 27 days, an increase in biogas yield as well as methane yield has been observed for all substrates and substrate concentrations during the experiment preview. Additional experimental observations show that biogas generation began on the second day in each case, that the maximum yield of biogas and methane was found on the seventh or tenth day of retention time, that the yield gradually decreased due to the decay of the bacterial growth, and that there was no discernible biogas generation after 27 days of retention time, when the bacterial action had essentially stopped.\u003c/p\u003e\n\u003cp\u003eWithin the range of variables tested, as illustrated in figures 6 and 7, the cumulative methane yield in millilitres varies nonlinearly with inverse retention time in day-1. Based on this, the ultimate methane yield (B\u003csub\u003eo\u003c/sub\u003e) has been calculated at x=0 at various concentrations of apples and apple peels. The analysis of mathematics reveals that the generalised equation that fits the curves can be written as B = A x\u003csup\u003e3\u003c/sup\u003e + C x\u003csup\u003e2\u003c/sup\u003e + D x\u003csup\u003e1\u003c/sup\u003e + B\u003csub\u003eo\u003c/sub\u003e and B = A x\u003csup\u003e2\u003c/sup\u003e + C x\u003csup\u003e1\u003c/sup\u003e + B\u003csub\u003eo\u003c/sub\u003e for potato concentration as well as for potato peel concentration where A, C and D are the co-efficient whose values are dependent on substrate concentration (Scano, 2014) (Viswanath, 1992). The equation which fits in the graphs are given in Table 1,\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;It has been further observed from figures 7 and 9 that [B/(Bo-B)] demonstrates a linear connection to (retention time) for different substrate concentration. Generalized correlation, which is provided as equation (1), can be used to illustrate the equation that fit these curves, x=S+R.B/(Bo-B)\u0026hellip;(1) where the co-efficient S and R depend on substrate concentration, concentration of cell mass in addition to process kinetics (Kumar, 2023). The values of S and R for various substrate concentrations are given in table 4 and 5.\u003c/p\u003e\n\u003cp\u003eConsequently, it has been discovered that the kinetic approach to equation suggested by Chen and Hashimoto (Chen, 1980) (Hashimoto, 1981) \u0026nbsp;as T=1/\u0026micro;m + k/\u0026micro;m. B/(Bo-B)...(2) is appropriate for semi-batch digester running with potato and potato peel wastes. Nevertheless, in contrast to equation 2, the maximum specific growth rate (\u0026micro;m) in addition to kinetic parameter (k) can be evaluated from the intercepts and slopes of the graphs of figure 7 and 9 and therefore, S represents 1/ \u0026micro;m and R represents k/ \u0026micro;m.\u003c/p\u003e\n\u003cp\u003eTable 4 and 5 show the variation of and k with change in potato and potato peel concentration respectively. It has been observed from the Table 5 and 6 that \u0026micro;m shows non-linear relationship for a given potato and potato peel concentration and that it increases with potato and potato peel concentration reaching a maximum value at 20% concentration after which it decreases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKinetic parameter k varies linearly with potato and potato peel concentration and it decreases with increase in potato and potato peel concentration within the range of the concentration experimented with.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExperimental design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing Design-Expert software (ver 10, Stat-Ease, Inc., USA), the generation of biogas from cellulose as well as lingo cellulose waste was optimised under a range of operational conditions. The study selected two independent factors, namely hydraulic retention time as well as substrate concentration, while the dependent variables were the average biogas production (Vijin Prabhu et al. 2020) (Jain and Mattiason 1998) (Chandra et al. 2012) (Shanmugam and Horan 2009). A list of all coded as well as real values is included in Tables 6A, 7A, and 8A, along with the variance analysis. For the double Central Composite Design Matrix with two independent factors and experimental responses for biogas yield (B) independent variables 6C, 7C, and 8C, thirteen tests were conducted. For the current example, regression equations were created using the least squares method, as demonstrated in Eq (1).\u003c/p\u003e\n\u003cp\u003eB = a\u003csub\u003eo\u003c/sub\u003e + a\u003csub\u003e1\u003c/sub\u003eT+ a\u003csub\u003e2\u003c/sub\u003eC + a\u003csub\u003e3\u003c/sub\u003e.T.C + a\u003csub\u003e4\u003c/sub\u003e.T\u003csup\u003e2\u003c/sup\u003e + a\u003csub\u003e5\u003c/sub\u003e.C\u003csup\u003e2\u003c/sup\u003e \u0026hellip;..(1)\u003c/p\u003e\n\u003cp\u003ewhere C (substrate concentration) and T (hydraulic retention time) are independent variables, and B (expected response) and the coefficients ao, a1, a2, a3, a4, and a5 are present. The individual and cumulative impacts of the input data on the responses are described by the graphical illustrations for these equations. The relationship among estimates of parameters and how those estimates affect the responses is ascertained by using these equations, which are also referred to as response surfaces.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative study of power transformation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAny data analysis must include the transformation of the response. If the magnitude of the response (predicted values) determines the error (residuals), then transformation is required. To determine whether the statistical presumptions that underpin the data analysis are met, Design-Expert offers a wide range of diagnostic capabilities. The residuals\u0026apos; normality is examined using the normal plot. If a pattern appears in the residuals versus predicted response values plot, it will suggest an issue. Response transformation won\u0026apos;t really change anything unless the ratio of the maximum response to the least response is very high.\u003c/p\u003e\n\u003cp\u003eThe suggested transformation from the power family will be provided by the RSM methodology. Depending on the kind of response, one must apply one of the two non-power law transformations: arcsin-sqrt for proportions and logit for bounded data. When proportional data is available, the RSM plot frequently suggests a square-root transformation; for bounded data, it suggests a log transformation. The power function can be used to describe most data transformations; power provides a scale that satisfies the statistical model\u0026apos;s equal variance requirement. A response transformation\u0026apos;s appropriate selection depends on statistical analysis and/or subject-matter expertise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs long as the data are positive, the power transformation permits transformation to any power in the \u0026ndash;3 to +3 range. To keep the data from having powers of negative numbers, you can add a constant. When an observation\u0026apos;s standard deviation is proportionate to the mean raised to a certain power, scaling the observation by that power yields a scale that satisfies the ANOVA\u0026apos;s equal variance requirement. The Diagnostics plots include the RSM to assist you in selecting the proper power transformation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo transformation of linear regression model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from all experimentation, a plain quadratic model with RSM optimization without transformation was generated in this present scope of study and used to determine all types of variable\u0026nbsp;outcomes.\u0026nbsp;It\u0026nbsp;describes the appropriate observations, including the average biogas production (B). Equations (2) are used to express the results of the quadratic modeling of B, where T is the cleavage time.\u0026nbsp;The correlation coefficient R of the different responses is determined by using the method of regression analysis of the present data detected by statistical software.\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s determined and evaluated the F-value of 15.43, which indicates that it is a significant model. The likelihood that a large F-value is the result of little amount of noise which is only 0.12%. The model phrases are considered to be significant if the P-value becomes less than or equal to 0.05. A2 as well as B2 are significant model terms in this illustration. The model in terms aren\u0026apos;t consider to be significant if the values are greater than or equal to 0.1. Model reducibility can help the model fitness if it contains a large number of unnecessary or unused terms (apart from those required to support properly the hierarchy). As is typically expected, the adjusting R\u0026sup2; of 0.8574 is not considered to be as close to the estimated R\u0026sup2; of 0.4084; that is, the difference is not considered to be larger than 0.2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis can point to a substantial blocking impact or a potential issue with your data or model. Outliers, corresponding transformation, model reduction, etc. Validation experiments ought to be used to test any empirical model. Adeq Precision quantifies the ratio of signal to noise. Ideally, the ratio should be higher than 4. A sufficient signal is indicated by the ratio of 9.413. The one in question can be navigated with in design mode.\u003c/p\u003e\n\u003cp\u003eThe coefficient estimate displays the expected change as a consequence for every single change of a factor\u0026apos;s value when all other variables are held constant. The point of intersection of an orthogonal design is the total average reaction of all the kits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis average is modified by factors according to factor configurations. The VIFs are not considered to be 1 if the factors appear orthogonal; multi-collinearity is indicated by VIFs greater than 1, where a higher VIF indicates a stronger factor correlation. VIF values less than 10 are generally accepted.\u003c/p\u003e\n\u003cp\u003eFinal equation in terms of actual factor is\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB = -65.41+ 12.88T+ 10.93.C - 4.5x10\u003csup\u003e-6\u003c/sup\u003e .T.C - 0.64.T\u003csup\u003e2\u003c/sup\u003e - 0.27.C\u003csup\u003e2\u003c/sup\u003e \u0026hellip;..(2)\u003c/p\u003e\n\u003cp\u003eAt particular levels of each factor, predictions can be made using the true factors equation. In this case, the levels ought to be stated in each factor\u0026apos;s original units. Since the coefficients have been adjusted by the measurement units of all variables as well as the point where they intersect is not in the centre portion, the models may not utilized to evaluate the other factors in comparison.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower transformation of linear regression model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, RSM optimisation using power transformation was utilised to create a quadratic model utilising data from all the experiments, which was then utilised to forecast every result from the variables. After statistical software has observed the data, regression analysis is used to determine the correlation coefficient R for the different responses.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;The model\u0026apos;s determined and evaluated the F-value of 15.43 specifies that it is considered significant. There is only a 0.12% chance that the generated noise would be the reason of the \u0026nbsp;F-value which is \u0026nbsp;high. Model terminology with the significant P-values less than 0.05 are deemed to be significant. In this situation, A2 as well as B2 are considered to be significant model terms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs one might normally expect, there is a difference of more than 0.2 between the adjusted R2 of 0.8574 as well as the predicted R2 of 0.4084. This might indicate a big block effect or a possible problem with your model or data. It is important to consider model reduction, response transformation, outliers, as well as other issues. Any empirical model should be tested using confirmation runs. Adeq Precision determines the signal-to-noise ratio. The ratio ought to be greater than 4. A ratio of 9.413 indicates that the signal is strong enough. This model can be used to navigate the design space.\u003c/p\u003e\n\u003cp\u003eThe coefficients reflect modifications made in relation to that average, as determined by the factor settings. When the factors are orthogonal, the VIF is 1. Multi-colinearity is indicated by VIFs larger than 1, which indicate stronger factor correlation. In general, VIFs of no more than ten are acceptable.\u003c/p\u003e\n\u003cp\u003eFinal equation in terms of actual factor is\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB = -65.4+ 12.87.T+ 10.92.C - 4.5x10\u003csup\u003e-16\u003c/sup\u003e .T.C - 0.643.T\u003csup\u003e2\u003c/sup\u003e - 0.26.C\u003csup\u003e2\u003c/sup\u003e \u0026hellip;..(2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSquare root (SQRT) transformation of linear regression model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, the quadratic model was developed using RSM optimisation as well as sqrt transformation, as well as it was used to predict all of the variables\u0026apos; outcomes. The regression analysis of the data collected by statistical software yields the correlation coefficient R for various responses.\u003c/p\u003e\n\u003cp\u003eThe Model F-value of 10.75 indicates that the model is significant. There is only a 0.35% chance which an F-value that big will occur due to noise. P-values of less than 0.0500 indicate that model terms are significant. In this case, A\u0026sup2; is an important model term. Values above 0.1 indicate that the model the context are not significant. If there are many insignificant model terms (excluding those necessary to support hierarchy), reducing them may improve the model.\u003c/p\u003e\n\u003cp\u003eThe Estimated R\u0026sup2; of 0.1805 differs from the Adjusted R\u0026sup2; of 0.8024 by more than 0.2. This might suggest a large block effect or an issue with the model and/or data. Model reduction, response transformation, outliers, and so on are all important considerations. All empirical hypotheses should be tested through confirmation runs. Adeq Precision measures the signal-to-noise ratio. A ratio greater than four is preferred. Your ratio of 8.042 indicates a good signal. This model can help navigate the design space.\u003c/p\u003e\n\u003cp\u003eFinal equation in terms of actual factor is\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB = -4.03 + 1.27.T+ 0.8.C \u0026ndash; 1.8x10\u003csup\u003e-17\u003c/sup\u003e .T.C - 0.063.T\u003csup\u003e2\u003c/sup\u003e - 0.02.C\u003csup\u003e2\u003c/sup\u003e \u0026hellip;..(2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTransformations are frequently used in linear models, as well as because linear models are somewhat an essential component of RSM, the use of transformed data represents an effort to obtain the most effective technique as well as model conformance. The article investigates how the power transformation affects the RSM suggested by second-order models. The response surface algorithm has been enhanced by including linear, power, as well as SQRT transformations in the model variable responses before analysing the implications of using different methods to estimate the model\u0026apos;s parameters.\u003c/p\u003e\n\u003cp\u003eFigure 9 shows the desirability and Contour plot of\u0026nbsp;biogas yield (B)\u0026nbsp;for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression. It is reveals from the plot that the desirability is more for without transformation in this case which is 0.8.\u0026nbsp;Figure 10 shows the surface plot of\u0026nbsp;biogas yield (B)\u0026nbsp;for (A) No transformation (B) power transformation (C) SQRT transformation of linear regression. It is reveals from the plot that the better result is given by without transformation in this case. However, the equation which fits the surface is given in Eq (1)\u003c/p\u003e\n\u003cp\u003eThe following is a straightforward presentation of this analysis for parameter estimation: Because each of the three estimation methods linear, power, as well as SQRT transformation has a different structure applied to it, the outcomes of the three methods which are well-known for estimating parameters did not match.\u0026nbsp;\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThere has been much focus on the energy potential of Potato as well as Potato peel waste. The current study offers helpful information about the productive use of such wastes using a semi-batch digestion process to produce biogas in the thermophillic and mesophillic temperature range. It also shows that substrate concentration has a significant impact on both the methane content and the biogas yield. Within the range of these experimental parameters, a 20% substrate concentration produced the maximum methane and total biogas yields.\u003c/p\u003e \u003cp\u003eIt has also shown that, for a range of Potato and peel concentrations, the variation of the kinetic parameter with temperature exhibits a linear relationship, but the variation of the maximum specific growth rate (\u0026micro;m) with Potato and peel concentration shows non-linear behaviour. The development of helpful generalised correlations between Potato and Potato peel concentration and both the maximum specific growth rate and the kinetic parameter has resulted from the mathematical analyses of the experimental data.\u003c/p\u003e \u003cp\u003eIn case of different transformation of RSM optimization linear without transformation gives the better result with 94.46 cc biogas yield for the substrate concentration 20.336% and time 14.91 days. The evaluation of the second-order model, for example, reveals that this improvement is not consistent with statistical theory, indicating that the evaluation of the second-order model constructed from the original data yields superior results than the examination of the second-order model of the data that was transformed. This observation supports the power transformation standpoint that it is not a requirement that power transformations be perfectly fitting for every stage of the RSM. Optimisation steps that require raising the degree of the ascent orientation to the optimal surface produced by the algorithms can be reduced by using transformations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003eDeclare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper. the results/data/figures in this manuscript have not been published elsewhere, nor are they under consideration by another publisher. all of the material is owned by the authors and/or no permissions are required.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eETHICAL APPROVAL\u003c/h2\u003e \u003cp\u003eThis is an observational study. We will not intentionally engage in or participate in any form of malicious harm to another person or animal. So we confirmed that no ethical approval is required.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e \u003cp\u003eFunded by TRC Oman and UTAS Salalah\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"A.B. and C.D. wrote the main manuscript text and E. prepared all figures. All authors reviewed the manuscript.\"\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e \u003cp\u003eWe would like to acknowledge TRC Oman and UTAS Salalah for funding this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArelli, Vijayalakshmi, Naveen Kumar Mamindlapelli, and Gangagni Rao Anupoju. \"Influence of solids concentration on microbial diversity and methane yield in the anaerobic digestion of rice husk.\" Bioresource Technology Reports 22 (2023): 101455.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee, S., \u0026amp; Biswas, G. K. (2004). Studies on biomethanation of distillery wastes and its mathematical analysis. Chemical Engineering Journal, 102(2), 193\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlonskaja, V.; Menert, A.; Vilu, R.; Use of two-stage anaerobic treatment for distillery waste, Advances in Environmental Research, 7, 3, (2003), 671\u0026ndash;678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoopathi, R.; Larsen, V.F. and Senior, E.; Performance of anaerobic baffled reactor (ABR) in treating distillery wastes water from a scotch whisky factory, Biomass, 16, 2, (1988), 133\u0026ndash;143.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouallagui, H., Cheikh, R. B., Marouani, L., \u0026amp; Hamdi, M. (2003). Mesophilic biogas production from fruit and vegetable waste in a tubular digester. 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Bioresource technology, \u003cem\u003e40\u003c/em\u003e(1), 43\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, J., Yang, J., Yu, Q., Yong, X., Xie, X., Zhang, L., \u0026hellip; Jia, H. (2017). Different organic loading rates on the biogas production during the anaerobic digestion of rice straw: A pilot study. Bioresource technology, 244, 865\u0026ndash;871.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Correlation equation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.693430656934307%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcentration in percent(v/v)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.24087591240876%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEquation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUltimate methane yield in ml\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.693430656934307%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePotato\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" valign=\"top\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.24087591240876%\" valign=\"top\"\u003e\n \u003cp\u003eB = -2789.4x3 + 4783.8x2 - 2736.9x + 534.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\" valign=\"top\"\u003e\n \u003cp\u003e534.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.623376623376622%\" valign=\"top\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.917748917748916%\" valign=\"top\"\u003e\n \u003cp\u003e-4220.4x3 + 7448.7x2 - 3865.9x + 655.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.458874458874458%\" valign=\"top\"\u003e\n \u003cp\u003e655.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.623376623376622%\" valign=\"top\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.917748917748916%\" valign=\"top\"\u003e\n \u003cp\u003eB = 4033.6x3 + 5331.8x2 - 4425.4x + 763.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.458874458874458%\" valign=\"top\"\u003e\n \u003cp\u003e763.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.693430656934307%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003ePotato peel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.445255474452555%\" valign=\"top\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"41.24087591240876%\" valign=\"top\"\u003e\n \u003cp\u003eB = 5785.8x2 - 2895.8x + 324.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.62043795620438%\" valign=\"top\"\u003e\n \u003cp\u003e324.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.623376623376622%\" valign=\"top\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.917748917748916%\" valign=\"top\"\u003e\n \u003cp\u003eB = 4106.2x2 - 3271.9x + 635.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.458874458874458%\" valign=\"top\"\u003e\n \u003cp\u003e635.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.623376623376622%\" valign=\"top\"\u003e\n \u003cp\u003e30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.917748917748916%\" valign=\"top\"\u003e\n \u003cp\u003eB = 26408x2 - 13219x + 1474.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.458874458874458%\" valign=\"top\"\u003e\n \u003cp\u003e1474.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable-2. Characteristics of Potato waste\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.922480620155039%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.41860465116279%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.217054263565892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410852713178294%\" valign=\"top\"\u003e\n \u003cp\u003eB.O.D. (10% solution)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC.O.D. (10% solution)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epH of the solution\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSpecific gravity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTDS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.922480620155039%\" valign=\"top\"\u003e\n \u003cp\u003e483.34 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e690.428 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.35\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e251 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.41860465116279%\" valign=\"top\"\u003e\n \u003cp\u003eProximate analysis:(by weight)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAsh \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMoisture\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVolatile matter\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFixed carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e70.6%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.217054263565892%\" valign=\"top\"\u003e\n \u003cp\u003eNon volatile solid (by weight)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCHN analysis: (dry basis, by weight).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal carbon\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHydrogen\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e12.4%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28.49%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.46%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table-3. Characteristics of Potato peel waste\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"516\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.922480620155039%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.41860465116279%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.217054263565892%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.410852713178294%\" valign=\"top\"\u003e\n \u003cp\u003eB.O.D. (10% solution)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC.O.D. (10% solution)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003epH of the solution\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSpecific gravity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTDS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.922480620155039%\" valign=\"top\"\u003e\n \u003cp\u003e576.12 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e822.86 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e333 kg/Cu.m\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.41860465116279%\" valign=\"top\"\u003e\n \u003cp\u003eProximate analysis:(by weight)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAsh \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMoisture\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eVolatile matter\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFixed carbon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3.73%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e65.22%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18.18%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.217054263565892%\" valign=\"top\"\u003e\n \u003cp\u003eNon volatile solid (by weight)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCHN analysis: (dry basis, by weight).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eTotal carbon\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHydrogen\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNitrogen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.015503875968992%\" valign=\"top\"\u003e\n \u003cp\u003e16.6%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57.3%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.97%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 4. Table of maximum specific growth rate and kinetic parameter for different Potato concentration\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"419\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.014319809069214%\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Potato concentration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.37708830548926%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1/Um\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.899761336515514%\"\u003e\n \u003cp\u003e\u003cstrong\u003ek/Um\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13842482100239%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.570405727923628%\"\u003e\n \u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.014319809069214%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.37708830548926%\" valign=\"top\"\u003e\n \u003cp\u003e2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.899761336515514%\" valign=\"top\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13842482100239%\" valign=\"top\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.570405727923628%\" valign=\"top\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.014319809069214%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.37708830548926%\" valign=\"top\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.899761336515514%\" valign=\"top\"\u003e\n \u003cp\u003e6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13842482100239%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.570405727923628%\" valign=\"top\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.014319809069214%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.37708830548926%\" valign=\"top\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.899761336515514%\" valign=\"top\"\u003e\n \u003cp\u003e6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.13842482100239%\" valign=\"top\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.570405727923628%\" valign=\"top\"\u003e\n \u003cp\u003e1.78\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\u003eTable 5. Table of maximum specific growth rate and kinetic parameter for different Potato peel concentration\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"508\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.834645669291337%\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Potato peel concentration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.30708661417323%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1/Um\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.881889763779526%\"\u003e\n \u003cp\u003e\u003cstrong\u003ek/Um\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.913385826771652%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.062992125984252%\"\u003e\n \u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.834645669291337%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.30708661417323%\" valign=\"top\"\u003e\n \u003cp\u003e5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.881889763779526%\" valign=\"top\"\u003e\n \u003cp\u003e10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.913385826771652%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.062992125984252%\" valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.834645669291337%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.30708661417323%\" valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.881889763779526%\" valign=\"top\"\u003e\n \u003cp\u003e5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.913385826771652%\" valign=\"top\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.062992125984252%\" valign=\"top\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.834645669291337%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.30708661417323%\" valign=\"top\"\u003e\n \u003cp\u003e5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.881889763779526%\" valign=\"top\"\u003e\n \u003cp\u003e10.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.913385826771652%\" valign=\"top\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.062992125984252%\" valign=\"top\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6A. Analysis of Variance of the quadratic model for no transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31270.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6254.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28828.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28828.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5024.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5024.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2837.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e405.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2837.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e945.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34107.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6B. Statistics of fitness data for no transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.4129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6C. Estimation of coefficients in terms of coded factor\u0026nbsp;for no transformation of linear regression\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Low\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI High\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-64.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-82.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-46.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-26.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-44.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7A. Analysis of Variance of the quadratic model for power transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31270.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6254.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28828.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28828.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5024.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5024.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2837.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e405.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2837.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e945.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34107.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7B. Statistics of fitness data for power transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.4129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7C. Estimation of coefficients in terms of coded factor\u0026nbsp;for power transformation of linear regression\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Low\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI High\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e88.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e131.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-64.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-82.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-46.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-26.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-44.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8A. Analysis of Variance of the quadratic model for SQRT transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum of Squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eF-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e288.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esignificant\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.9042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e278.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e278.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidual\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLack of Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePure Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCor Total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e325.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8B. Statistics of fitness data for SQRT transformation of linear regression\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdeq Precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 8C. Estimation of coefficients in terms of coded factor\u0026nbsp;for SQRT transformation of linear regression\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient Estimate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Error\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI Low\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI High\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e12.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA-TIME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB-CONCENTRATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"Biogas, Potato, Potato peel, Biomethanation, Anaerobic processes","lastPublishedDoi":"10.21203/rs.3.rs-4058906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4058906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBiogas technology stands out as a viable energy alternative in rural areas, acclaimed for being an exemplary appropriate technology that addresses the fundamental need for fuel. By utilizing discarded and lingo cellulose derivatives such as potato peel waste sourced from vegetable markets, this technology harnesses energy in the form of biogas enriched with a high methane content. The anaerobic bacteria play a pivotal role in converting and peel wastes into biogas through a synergistic process. Crucial considerations for the biomethanation process encompass process parameters like substrate concentration, substrate and cell mass concentration. Simultaneously, kinetic parameters such as maximum specific growth rate, kinetic constant, and ultimate methane yield take precedence in the anaerobic digestion process for efficient biogas production. This study endeavors to explore the anaerobic reactions of potato and potato peel wastes within a semi-batch digester. Variations in substrate concentrations and different substrates significantly impact biogas production, leading to the development of a mathematical interpretation of the biomethanation process. Between 33.16 and 38.68 MJ/Nm\u003csup\u003e3\u003c/sup\u003e of biogas is the energy yield obtained from this procedure. Through a meticulous mathematical analysis of experimental data, model equations correlating ultimate methane yield with diverse substrate concentrations and loading have been formulated.\u003c/p\u003e","manuscriptTitle":"Harnessing Ligno Cellulose and Cellulose Derivative Residues for Sustainable Biomethanation With Effect of Different Transformation in Rsm Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 07:58:15","doi":"10.21203/rs.3.rs-4058906/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":"a4152a20-039d-4801-84ef-c7fd1ab4f705","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-29T18:01:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 07:58:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4058906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4058906","identity":"rs-4058906","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}