Optimization of Hydrolysis in Ethanol Production from Sugarcane Bagasse

Optimization of Hydrolysis in Ethanol Production from Sugarcane Bagasse | 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Article Optimization of Hydrolysis in Ethanol Production from Sugarcane Bagasse NIGUS WORKU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2004225/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 This research involved optimizing acid hydrolysis in the development of ethanol, a promising alternative energy source for restricted crude oil, from lignocellulosic materials (Sugarcane Bagasse). The conversion of Sugarcane Bagasse to ethanol can mainly be accomplished through three process steps: pretreatment of Sugarcane Bagasse for the removal of lignin and hemicellulose, acid hydrolysis of pretreated Sugarcane Bagasse for the conversion of cellulose into sugar reduction (glucose) and fermentation of sugars into ethanol using anaerobic Saccharomyces cerevisiae . The effects of parameters (factors) in the hydrolysis step were investigated and the optimum combination of parameters values (temperature, time, and acid concentration) was set by experimentation. A factorial design of three-factors-at-two-level with a replica of two (2 3 = 8, 8*2 = 16) was applied to the hydrolysis step to investigate the effect of hydrolysis parameters on the response variable (ethanol yield) using Design-Expert® 13 software. Renewable Resources Ethanol Fermentation Hydrolysis Sugarcane Bagasse Optimization Figures Figure 1 Figure 2 Figure 3 Article highlights Ethanol production from sugarcane bagasse by dilute-acid hydrolysis. Reduced 1 st generation ethanol from sugarcane leads to larger amounts of surplus bagasse. Ethanol yields from the hydrolysates were similar to fermentation of the glucose solution. 1. Introduction The use of bioethanol can reduce our dependence on fossil fuels while reducing net emissions of carbon dioxide, the main greenhouse gas [ 1 ]. The feedstock used for biofuels has been categorized into three major groups, cellulose biomass, sugar, and starchy crops, and oil-producing plants. Interest is currently focused on the first group also referred to as a biofuel of the second generation. This is because there are conflicts between food production for human and animal consumption in the second and third groups [ 2 ]. Brazil and the US together accounted for about 60.0% of the world ethanol production exploiting sugarcane and corn, respectively [ 3 ]. However, using these food crops for ethanol production may raise concerns about food security environmental degradation debate, and other issues. Fortunately, there is a growing interest worldwide to develop new and cheaper carbohydrate sources for the production of bio-ethanol [ 4 ]. The most attractive feedstock source is the lignocellulosic biomass, from which ethanol or other chemical agents can be produced via scarification and fermentation [ 5 ]. Lignocellulosic biomass is the primary and most abundant organic material on the earth which makes it the most promising resource for alternative energy. Among the available lignocellulosic feedstocks, Sugarcane Bagasse s are receiving a renewed interest due to their high growth rate and better reduction of carbon footprint compared to an equivalent area of woody plants [ 6 ]. The high overall cost of the cellulosic biofuel supply chain (CBSC) is the principal explanation for this enormous difference between the target and actual output. Researchers and industrial societies have made efforts to reduce the cost of industrialization of cellulosic biofuels using different approaches to tackle this issue, including supply chain optimization and management. A well-planned supply chain can help to promote the adoption of cellulosic biofuel since it has the great potential to enhance economic viability. A typical CBSC consists of five entities: biomass collector, biomass inventory, biorefinery, biofuel storage, and end-product distributor [ 7 ]. Many studies have investigated CBSC design and optimization considering multiple aspects, including location selection, feedstock uncertainty, economic performance, transportation, financial risk, and energy consumption [ 8 ]– [ 10 ]. A number of lignocellulose pre-treatment technologies existed in both laboratory scales and as pilot plants, such as dilute acid, flow-through, ammonia fiber explosion, ammonia recycle percolation, lime, steam explosion, and organosolv (OS) pre-treatment which have suffered from relatively low sugar yields, severe reaction conditions, large capital investment, or high processing costs. Recently, a novel fractionating recalcitrant lignocellulose technology under modest reaction conditions was developed. Based on this technology, three components that existed in lignocellulosic materials will be separated for further use. The cellulose component will be used for ethanol via enzyme scarification and fermentation [ 11 ]–[ 13 ]. Another barrier or challenge is the absence of robust organisms for ethanol production. Currently, different recombinant strains have been engineered to produce ethanol from lignocellulosic biomass, such as genetically engineered Saccharomyces cerevisiae , Escherichia coli , Klebsiella oxytoca , and Zymomonas mobilis , which provide a basis for constructing an industrially suitable engineered strain on cellulose ethanol industrialization. Among all these strains, Z. mobilis used historically in tropical areas to make alcoholic beverages from plant sap, showed fast growth rates and high specific ethanol production compared with S. cerevisiae . The advantages that Z. mobilis holds over traditional yeast processes have led to more economical methods of producing ethanol. However, its narrow spectrum of fermentable carbohydrates has limited its use, especially for fuel ethanol production from lignocellulosic materials [ 14 ], [ 15 ]. The exploitation of natural energy resources and the increasing cost of raw materials drive the search for renewable energy sources. Bioethanol is known as an important renewable bioenergy source that may be used to reduce greenhouse gases (GHG) and dependency on fossil fuels. Thus, bioethanol is regarded as a more environmentally friendly fuel than gasoline. Sugarcane bagasse (SCB) is a potentially renewable resource that may be used to produce bioethanol, which is one of the largest cellulosic agro-industrial by-products. Over the last decade, many efforts have been made to achieve maximum hydrolysis and saccharification efficiency to obtain higher yields of fermentable sugar and ethanol from SCB [ 16 ]. Therefore, this study investigates Sugarcane Bagasse as the source of carbon and the optimization of the medium for ethanol production. The design expert was employed to screen the effects of different medium ingredients on ethanol yield in order to perform this study and further optimization of the medium was carried out using the response surface methodology. 2. Experimental 2.1. Equipment and Chemicals Polyethylene plastic bags were used to store the sample, a grinding machine was used for grinding and homogenizing the samples. Measuring cylinders (Duran, Germany) and deionizer (type 04/05, Italy) were used to remove ions from water. Vessels, hydrometer, pH-meter, vertical autoclave, cutting mill, autoclavable bioreactor, shaker, funnel, sieves, digital balances, vacuum filter, rotary evaporator, and sulfuric acid (70% Spectrosol, BDH, England), sodium hydroxide (30%, Riedel de Haen), yeast extract, urea, dextrose sugar, Mg SO 4 ·7H 2 O and yeast ( Saccharomyces cerevisiae ) were used for the experiments. 2.2. Sample Preparation Sugarcane Bagasse was collected from Sugarcane Bagasse selling market in Bahirdar; it was collected in plastic bags and dispatched to the laboratory for further work. 10 kg of Sugarcane Bagasse was taken and it was cut by knife into pieces of about 3–5 cm in length for ease of drying and grinding. Sample drying was carried out in the oven at 378 K. After drying, the samples were weighed and crushed in the cutting mill (D51820007W). The maximum particle size of the ground mixed sample was 2 mm. The sample with particle size larger than 2 mm was ground over and over again until all particle size was 2 mm (Clean the sieves of the sieve shaker using a cleaning brush if any particles are struck in the openings, record the weight of each sieve, and receiving pan, dry the specimen in the oven for 3–4 min to get the dried specimen (ignore, if the specimen is already dried), weigh the specimen and record its weight. Finally, the sieves were arranged in the following order: the smaller openings sieve in the bottom and larger openings sieve on the top using BS410 Standard sieves (0.1 g accuracy balance). The sample was then kept at a temperature of 298 K until the next stage of the experiment. 2.2.1. Steam pretreatment The powder Sugarcane Bagasse was treated at a temperature of 393 K. First, the Sugarcane Bagasse powder was treated and it was fed in batches, every batch contains 100 g of screened Sugarcane Bagasse powder with a 5:1 (v/w) ratio of water to the sample with 0.75% of sulfuric acid, the pressure 202.65 kPa was constant during the treatment process for all the batches. The retention time for every batch was half an hour similar to the other batches [ 17 ]. Finally, the samples were kept in a Vertical autoclave (“Tempo”) for the given pretreatment time and pretreatment temperature and were allowed to cool. 2.2.2. Hydrolysis The three-parameter-two-level (2 3 = 8, 8*2 = 16) factorial design was applied to the hydrolysis step of the experimentation. The hydrolysis experiments for ethanol production and optimization were conducted in a completely randomized design using Design-Expert® 13 software. 100 g of grinding Sugarcane Bagasse chips were used for each experiment and the factors for hydrolysis time were (15 and 45 min), hydrolysis temperature (363 and 383 K), and acid concentration (1 and 5%) each at two-level and two replicas. 2.2.3. Fermentation Before fermentation, there was pH adjustment and sterilization of the reactor then the clear solution went to fermentation. The fermentation was carried out under anaerobic conditions at a temperature of 303 K and at 150 rpm for 3 days. Before conducting fermentation, we have to prepare the media for the yeast. We needed a favorable condition for yeast growth or the required amount of nutrients. For preparing 200 ml of media, we mixed sugar (dextrose) in the amount of 20 g, yeast extract (0.4 g), urea (2.0 g), prepared water (200 ml), and Mg SO 4 ·7H 2 O (2.0 g). 2.2.4. Distillation In this experiment, the separation was done by rotary evaporator at a temperature of 358 K. Finally, the data was analyzed by Design-Expert® software. The significance of the result was set from the analysis of variance (ANOVA). 3. Results And Discussion Statistical analysis of the experimental results temperature, acid concentration and time hydrolysis are the key variables in this process and the resulting data using Design-Expert® software (Table 1 ). As well as the design summary for three variables and a 2-level factorial design (Table 2 ). Table 1 The resulting data using Design-Expert® software Run Time, min Temperature, K Acid concentration, %, v/v Yield, % 1 15.00 363 5.00 43.2 2 15.00 363 1.00 37.6 3 45.00 383 5.00 37.7 4* 15.00 363 5.00 39.5 5 45.00 363 1.00 41.4 6 15.00 383 5.00 42.3 7 45.00 383 1.00 43.3 8 15.00 383 1.00 37.0 9* 15.00 363 1.00 38.6 10* 45.00 363 1.00 43.3 11* 15.00 383 1.00 38.6 12 45.00 363 5.00 38.0 13* 15.00 383 5.00 39.5 14* 45.00 363 5.00 38.0 15* 45.00 383 5.00 35.0 16* 45.00 383 1.00 41.4 Note: *replica Table 2 Design summary for three variables and 2-level factorial design Design Summary Study Type Factorial Initial design 2 Level Factorial Center Points 0 Design Model Quadratic Polynomial Runs 16 Blocks No Blocks In order to determine whether or not the quadratic polynomial model is significant for the experiments, it was necessary to conduct an analysis of variance. The probability (p-values) values were used as a tool to check the significance of each coefficient, which also indicated the interaction strength of each parameter. The smaller the p-values are, the bigger the significance of the corresponding coefficient. The results of statistical analysis including the estimated values of factors’ coefficients, interactive terms, F-value, and p-values are shown in Table 3 . The larger magnitude of the F-value and the smaller magnitude of the p-value indicate more significance of the corresponding coefficient. Acid concentrations in the linear and quadratic polynomial models are highly significant for the yield of ethanol (p < 0.05). Among the interactive terms, only the interaction between time and acid concentration was highly significant. Table 3 Analysis of variance for quadratic polynomial model Source Sum of squares df Mean squares F Value p-value p > F Model 76.94 7 10.99 4.44 0.0164* A-time 0.20 1 0.20 0.082 0.0821 B-Temperature 1.44 1 1.44 0.58 0.0675 C-acid concentration 4.00 1 4.00 7.62 0.0393 AB 0.20 1 0.20 0.082 0.7821 AC 69.72 1 69.72 28.17 0.0007 BC 0.81 1 0.81 0.33 0.5830 ABC 0.56 1 0.56 0.23 0.6463 Pure Error 19.80 8 2.48 Cor Total 96.74 15 Note: *significant Using the designed experimental data (Table 3 ) the quadratic polynomial model for ethanol production from Sugarcane Bagasse by the dilute acid hydrolysis was retreated and shown as below: Final equation in terms of coded factors: Ethanol yield = + 39.65 + 0.11A – 0.30B – 0.50C – 0.11AB – 2.09AC – 0.23BC – 0.19ABC……(1) Final equation in terms of actual factors: Ethanol yield = + 34.16250 + 0.22167·time – 0.063750·temperature + 2.38750·acid concentration + 3.50000E -003 ·time·temperature – 0.028333·time·acid concentration + 0.033750·temperature·acid concentration – 2.50000E -003 ·time·temperature·acid concentration………………………………………..….. (2) Table 4 Actual versus model predicted for ethanol yields Diagnostics case statistics Internal External studentized residual Run order Standard value Actual value Predicted value Residual 1 37.60 38.10 -0.50 -0.449 -0.426 2 2 38.60 38.10 0.50 0.449 0.426 9 3 41.40 41.40 -0.95 0.854 -0.838 5 4 43.30 43.35 0.95 0.854 0.838 10 5 37.00 37.80 -0.80 -0.719 -0.696 8 6 38.00 37.80 0.80 0.719 0.696 11 7 43.30 42.35 0.95 0.854 0.838 7 8 41.40 42.35 -0.95 -0.854 -0.838 16 9 43.2 41.35 1.85 1.663 1.923 1 10 39.50 41.35 -1.85 -1.663 -1.923 4 11 38.00 38.00 0.000 0.000 0.000 12 12 38.00 38.00 0.000 0.000 0.000 14 13 39.50 40.90 -1.40 -1.259 -1.315 13 14 42.30 40.90 1.40 1.259 1.315 6 15 37.70 36.35 1.35 1.214 1.257 3 16 35.00 36.35 -1.35 -1.214 -1.257 15 The actual versus predicted values using the model in the above equation are tabulated in Table 5 . Regression analysis based on the coded variables on the experimental data was performed, and coefficients of the second-order models were calculated. Substitution of coefficients calculated and response variables in the above equation resulted in the empirical equations for ethanol yields. To see how the quadratic polynomial model satisfies the assumptions of the analysis of variance (ANOVA) in the experiments, plots in Table 4 were analyzed in Fig. 1 in terms of normal plots of residuals and residual versus predicted values. The normal probability plot (Fig. 1 ) indicates the residuals following a normal distribution, in the case of this experiment the points in the plots show fit into a straight line indicating that the quadratic polynomial model satisfies the assumptions analysis of variance (ANOVA). 3.1. Optimization The optimization hydrolysis criteria for ethanol production from Sugarcane Bagasse dilute acid are summarized as follows (Table 5 ). The optimum possible solutions for acid hydrolysis are determined by various factors and the ethanol yield has a complex relationship with independent variables that include first, second and third-order polynomials and may have more than one limit point. The best way of expressing the effect of any parameter on the yield within the experimental space under investigation was to produce response surface plots of the equation. The three-dimensional response surfaces, contours and interactions were plotted in Figs. 2 and 3. Table 5 Optimum possible solution Solutions number Time, min Temperature, K Acid concentration, % Yield, % Desirability 1 17.03 363.00 1.00 38.6757 0.887 Selected 2 17.18 363.00 1.00 38.7165 0.887 3 16.82 363.00 1.00 38.6171 0.887 4 16.88 363.00 1.01 38.6435 0.886 5 16.35 363.00 1.00 38.4833 0.886 6 16.39 363.00 1.01 38.5087 0.886 7 16.15 363.00 1.00 38.4266 0.886 8 18.05 363.00 1.00 38.9652 0.885 9 17.41 363.11 1.00 38.7808 0.885 10 15.82 363.00 1.04 383841 0.884 11 15.90 363.00 1.09 38.4786 0.883 12 18.63 363.13 1.00 39.1261 0.882 13 15.00 363.00 1.05 38.1764 0.882 14 19.64 363.16 1.00 39.3818 0.844 Table 6. Optimization criteria for optimum ethanol yield Name Goal Lower limit Upper limit Time Minimize 15 45 Temperature Minimize 363 383 Acid Concen. Minimize 1 5 Yield Maximize 35 43.3 4. Conclusions The probability (p-values) values and normal probability plot indicate the quadratic polynomial model satisfying ANOVA assumptions and the model was considered to be accurate and reliable for predicting the yield of ethanol from Sugarcane Bagasse using dilute acid hydrolysis. The resulting data were analyzed using Design-Expert® 13 to determine the effects of hydrolysis parameters and optimization in ethanol production. Optimization hydrolysis of ethanol production from Sugarcane Bagasse using dilute acid hydrolysis was carried out. A 2-level factorial design method was used to optimize the production of ethanol from Sugarcane Bagasse. The probability (p-value) of 0.0164 demonstrates a high significance for the regression model. The yield of ethanol of 38.76% was obtained when optimum conditions were: hydrolysis time of 17.03 min, hydrolysis temperature of 363 K, and acidic concentration of 1%. Validation experiments verified the availability and the accuracy of the model with a desirability of 88.7%. Declarations Compliance with ethical standards Conflicts of interest: The authors declare no conflict of interest. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements : Our sincere thanks to Dr. Abrham Bayeh Institute of Technology University of Gondar and Mr. Gedefaw Asmare, Bahirdar Institute of Technology. References J. Li, F. Xiong, M. Fan, and Z. Chen, “The role of nonfood bioethanol production in neutralizing China’s transport carbon emissions: An integrated life cycle environmental-economic assessment,” Energy Sustain. 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Chae, “Chapter 2 - Production of Bioethanol From Sugarcane Bagasse: Current Approaches and Perspectives,” in Applied Microbiology and Bioengineering , P. Shukla, Ed. Academic Press, 2019, pp. 21–42. M. W. Dong, “9 - How to be More Successful with HPLC Analysis: Practical Aspects in HPLC Operation,” in Handbook of Pharmaceutical Analysis by HPLC , vol. 6, S. Ahuja and M. W. Dong, Eds. Academic Press, 2005, pp. 255–271. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-2004225","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132118980,"identity":"3a242e62-6a62-4984-9a7a-933ca1e72639","order_by":0,"name":"NIGUS WORKU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZhBhYCNnf7wBxLAgVktBmjHDmQMgLRLEWvXhcGLDjQQQiwgt5uzs1yR/GBw2Zpz5/OqGHwUSDPzt3Ql4tVg285RJ8xikyzFL55Td7AE6TOLM2Q14tRgc5kmTZjCwNmaTzkm7wQPUYiCRS1gL0GHMiT2SZ9Ju/iFOC/sxCR4D58QZEuzHbhNrC7M1j0GasQFPDtttGQMJHsJ+OX/84c0ff2zkDNiPP7v5Bsjgb+/Fr4WBgccAhcFDQDkIsD9AZ4yCUTAKRsEoQAUAGKRD7bQ5elUAAAAASUVORK5CYII=","orcid":"","institution":"University of Gondar","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"NIGUS","middleName":"","lastName":"WORKU","suffix":""}],"badges":[],"createdAt":"2022-08-27 10:16:26","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-2004225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2004225/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25862426,"identity":"2200d668-e627-43c6-9991-8fa40bac1fce","added_by":"auto","created_at":"2022-08-30 19:14:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6074,"visible":true,"origin":"","legend":"\u003cp\u003eNormal plots of residual\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2004225/v1/7d6822e20c09305df6e9e56a.png"},{"id":25861360,"identity":"dafa609a-4b82-4e1c-bac9-518f8d54485e","added_by":"auto","created_at":"2022-08-30 19:09:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10916,"visible":true,"origin":"","legend":"\u003cp\u003eOptimization of contours in ethanol yield\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2004225/v1/abdefacd39b1d09806686747.png"},{"id":25861362,"identity":"8d657501-986b-4cff-af8c-717f060bdcd4","added_by":"auto","created_at":"2022-08-30 19:09:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20245,"visible":true,"origin":"","legend":"\u003cp\u003eSurface of possible optimum \u003cem\u003es\u003c/em\u003eolution\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2004225/v1/ea8a39838178c4be298423c7.png"},{"id":25862427,"identity":"de2781c1-3cf0-4b44-894f-da143fa6df36","added_by":"auto","created_at":"2022-08-30 19:14:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":310296,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2004225/v1/d22d7065-f0db-4d12-b88b-3e33210db24a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eOptimization of Hydrolysis in Ethanol Production from Sugarcane Bagasse \u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","fulltext":[{"header":"Article highlights","content":"\u003cul\u003e\n \u003cli\u003eEthanol production from sugarcane bagasse by dilute-acid hydrolysis.\u003c/li\u003e\n \u003cli\u003eReduced 1\u003csup\u003est\u003c/sup\u003e generation ethanol from sugarcane leads to larger amounts of surplus bagasse.\u003c/li\u003e\n \u003cli\u003eEthanol yields from the hydrolysates were similar to fermentation of the glucose solution.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe use of bioethanol can reduce our dependence on fossil fuels while reducing net emissions of carbon dioxide, the main greenhouse gas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The feedstock used for biofuels has been categorized into three major groups, cellulose biomass, sugar, and starchy crops, and oil-producing plants. Interest is currently focused on the first group also referred to as a biofuel of the second generation. This is because there are conflicts between food production for human and animal consumption in the second and third groups [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBrazil and the US together accounted for about 60.0% of the world ethanol production exploiting sugarcane and corn, respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, using these food crops for ethanol production may raise concerns about food security environmental degradation debate, and other issues. Fortunately, there is a growing interest worldwide to develop new and cheaper carbohydrate sources for the production of bio-ethanol [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The most attractive feedstock source is the lignocellulosic biomass, from which ethanol or other chemical agents can be produced \u003cem\u003evia\u003c/em\u003e scarification and fermentation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLignocellulosic biomass is the primary and most abundant organic material on the earth which makes it the most promising resource for alternative energy. Among the available lignocellulosic feedstocks, Sugarcane Bagasse s are receiving a renewed interest due to their high growth rate and better reduction of carbon footprint compared to an equivalent area of woody plants [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe high overall cost of the cellulosic biofuel supply chain (CBSC) is the principal explanation for this enormous difference between the target and actual output. Researchers and industrial societies have made efforts to reduce the cost of industrialization of cellulosic biofuels using different approaches to tackle this issue, including supply chain optimization and management. A well-planned supply chain can help to promote the adoption of cellulosic biofuel since it has the great potential to enhance economic viability. A typical CBSC consists of five entities: biomass collector, biomass inventory, biorefinery, biofuel storage, and end-product distributor [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Many studies have investigated CBSC design and optimization considering multiple aspects, including location selection, feedstock uncertainty, economic performance, transportation, financial risk, and energy consumption [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u0026ndash; [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA number of lignocellulose pre-treatment technologies existed in both laboratory scales and as pilot plants, such as dilute acid, flow-through, ammonia fiber explosion, ammonia recycle percolation, lime, steam explosion, and organosolv (OS) pre-treatment which have suffered from relatively low sugar yields, severe reaction conditions, large capital investment, or high processing costs. Recently, a novel fractionating recalcitrant lignocellulose technology under modest reaction conditions was developed. Based on this technology, three components that existed in lignocellulosic materials will be separated for further use. The cellulose component will be used for ethanol \u003cem\u003evia\u003c/em\u003e enzyme scarification and fermentation [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother barrier or challenge is the absence of robust organisms for ethanol production. Currently, different recombinant strains have been engineered to produce ethanol from lignocellulosic biomass, such as genetically engineered \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eKlebsiella oxytoca\u003c/em\u003e, and \u003cem\u003eZymomonas mobilis\u003c/em\u003e, which provide a basis for constructing an industrially suitable engineered strain on cellulose ethanol industrialization. Among all these strains, \u003cem\u003eZ. mobilis\u003c/em\u003e used historically in tropical areas to make alcoholic beverages from plant sap, showed fast growth rates and high specific ethanol production compared with \u003cem\u003eS. cerevisiae\u003c/em\u003e. The advantages that \u003cem\u003eZ. mobilis\u003c/em\u003e holds over traditional yeast processes have led to more economical methods of producing ethanol. However, its narrow spectrum of fermentable carbohydrates has limited its use, especially for fuel ethanol production from lignocellulosic materials [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe exploitation of natural energy resources and the increasing cost of raw materials drive the search for renewable energy sources. Bioethanol is known as an important renewable bioenergy source that may be used to reduce greenhouse gases (GHG) and dependency on fossil fuels. Thus, bioethanol is regarded as a more environmentally friendly fuel than gasoline. Sugarcane bagasse (SCB) is a potentially renewable resource that may be used to produce bioethanol, which is one of the largest cellulosic agro-industrial by-products. Over the last decade, many efforts have been made to achieve maximum hydrolysis and saccharification efficiency to obtain higher yields of fermentable sugar and ethanol from SCB [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study investigates Sugarcane Bagasse as the source of carbon and the optimization of the medium for ethanol production. The design expert was employed to screen the effects of different medium ingredients on ethanol yield in order to perform this study and further optimization of the medium was carried out using the response surface methodology.\u003c/p\u003e"},{"header":"2. Experimental","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Equipment and Chemicals\u003c/h2\u003e \u003cp\u003ePolyethylene plastic bags were used to store the sample, a grinding machine was used for grinding and homogenizing the samples. Measuring cylinders (Duran, Germany) and deionizer (type 04/05, Italy) were used to remove ions from water. Vessels, hydrometer, pH-meter, vertical autoclave, cutting mill, autoclavable bioreactor, shaker, funnel, sieves, digital balances, vacuum filter, rotary evaporator, and sulfuric acid (70% Spectrosol, BDH, England), sodium hydroxide (30%, Riedel de Haen), yeast extract, urea, dextrose sugar, Mg SO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO and yeast (\u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e) were used for the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample Preparation\u003c/h2\u003e \u003cp\u003eSugarcane Bagasse was collected from Sugarcane Bagasse selling market in Bahirdar; it was collected in plastic bags and dispatched to the laboratory for further work. 10 kg of Sugarcane Bagasse was taken and it was cut by knife into pieces of about 3\u0026ndash;5 cm in length for ease of drying and grinding. Sample drying was carried out in the oven at 378 K. After drying, the samples were weighed and crushed in the cutting mill (D51820007W). The maximum particle size of the ground mixed sample was 2 mm. The sample with particle size larger than 2 mm was ground over and over again until all particle size was 2 mm (Clean the sieves of the sieve shaker using a cleaning brush if any particles are struck in the openings, record the weight of each sieve, and receiving pan, dry the specimen in the oven for 3\u0026ndash;4 min to get the dried specimen (ignore, if the specimen is already dried), weigh the specimen and record its weight. Finally, the sieves were arranged in the following order: the smaller openings sieve in the bottom and larger openings sieve on the top using BS410 Standard sieves (0.1 g accuracy balance). The sample was then kept at a temperature of 298 K until the next stage of the experiment.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Steam pretreatment\u003c/h2\u003e \u003cp\u003eThe powder Sugarcane Bagasse was treated at a temperature of 393 K. First, the Sugarcane Bagasse powder was treated and it was fed in batches, every batch contains 100 g of screened Sugarcane Bagasse powder with a 5:1 (v/w) ratio of water to the sample with 0.75% of sulfuric acid, the pressure 202.65 kPa was constant during the treatment process for all the batches. The retention time for every batch was half an hour similar to the other batches [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Finally, the samples were kept in a Vertical autoclave (\u0026ldquo;Tempo\u0026rdquo;) for the given pretreatment time and pretreatment temperature and were allowed to cool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Hydrolysis\u003c/h2\u003e \u003cp\u003eThe three-parameter-two-level (2\u003csup\u003e3\u003c/sup\u003e = 8, 8*2\u0026thinsp;=\u0026thinsp;16) factorial design was applied to the hydrolysis step of the experimentation. The hydrolysis experiments for ethanol production and optimization were conducted in a completely randomized design using Design-Expert\u0026reg; 13 software. 100 g of grinding Sugarcane Bagasse chips were used for each experiment and the factors for hydrolysis time were (15 and 45 min), hydrolysis temperature (363 and 383 K), and acid concentration (1 and 5%) each at two-level and two replicas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Fermentation\u003c/h2\u003e \u003cp\u003eBefore fermentation, there was pH adjustment and sterilization of the reactor then the clear solution went to fermentation. The fermentation was carried out under anaerobic conditions at a temperature of 303 K and at 150 rpm for 3 days. Before conducting fermentation, we have to prepare the media for the yeast. We needed a favorable condition for yeast growth or the required amount of nutrients. For preparing 200 ml of media, we mixed sugar (dextrose) in the amount of 20 g, yeast extract (0.4 g), urea (2.0 g), prepared water (200 ml), and Mg SO\u003csub\u003e4\u003c/sub\u003e\u0026middot;7H\u003csub\u003e2\u003c/sub\u003eO (2.0 g).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Distillation\u003c/h2\u003e \u003cp\u003eIn this experiment, the separation was done by rotary evaporator at a temperature of 358 K. Finally, the data was analyzed by Design-Expert\u0026reg; software. The significance of the result was set from the analysis of variance (ANOVA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results And Discussion","content":"\u003cp\u003eStatistical analysis of the experimental results temperature, acid concentration and time hydrolysis are the key variables in this process and the resulting data using Design-Expert\u0026reg; software (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As well as the design summary for three variables and a 2-level factorial design (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe resulting data using Design-Expert\u0026reg; software\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime, min\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemperature, K\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcid concentration, %, v/v\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYield, %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *replica\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDesign summary for three variables and 2-level factorial design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDesign Summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactorial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial design\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 Level Factorial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCenter Points\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesign Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuadratic Polynomial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRuns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Blocks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn order to determine whether or not the quadratic polynomial model is significant for the experiments, it was necessary to conduct an analysis of variance. The probability (p-values) values were used as a tool to check the significance of each coefficient, which also indicated the interaction strength of each parameter. The smaller the p-values are, the bigger the significance of the corresponding coefficient.\u003c/p\u003e \u003cp\u003eThe results of statistical analysis including the estimated values of factors\u0026rsquo; coefficients, interactive terms, F-value, and p-values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The larger magnitude of the F-value and the smaller magnitude of the p-value indicate more significance of the corresponding coefficient. Acid concentrations in the linear and quadratic polynomial models are highly significant for the yield of ethanol (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the interactive terms, only the interaction between time and acid concentration was highly significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance for quadratic polynomial model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003cp\u003ep\u0026nbsp;\u0026gt;\u0026nbsp;F\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0164*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-Temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-acid concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCor Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the designed experimental data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) the quadratic polynomial model for ethanol production from Sugarcane Bagasse by the dilute acid hydrolysis was retreated and shown as below:\u003c/p\u003e \u003cp\u003eFinal equation in terms of coded factors:\u003c/p\u003e \u003cp\u003eEthanol yield\u0026thinsp;=\u0026thinsp;+\u0026thinsp;39.65\u0026thinsp;+\u0026thinsp;0.11A \u0026ndash; 0.30B \u0026ndash; 0.50C \u0026ndash; 0.11AB \u0026ndash; 2.09AC \u0026ndash; 0.23BC \u0026ndash; 0.19ABC\u0026hellip;\u0026hellip;(1)\u003c/p\u003e \u003cp\u003eFinal equation in terms of actual factors:\u003c/p\u003e \u003cp\u003eEthanol yield\u0026thinsp;=\u0026thinsp;+\u0026thinsp;34.16250\u0026thinsp;+\u0026thinsp;0.22167\u0026middot;time \u0026ndash; 0.063750\u0026middot;temperature\u0026thinsp;+\u0026thinsp;2.38750\u0026middot;acid concentration\u0026thinsp;+\u0026thinsp;3.50000E\u003csup\u003e-003\u003c/sup\u003e\u0026middot;time\u0026middot;temperature \u0026ndash; 0.028333\u0026middot;time\u0026middot;acid concentration\u0026thinsp;+\u0026thinsp;0.033750\u0026middot;temperature\u0026middot;acid concentration \u0026ndash; 2.50000E\u003csup\u003e-003\u003c/sup\u003e\u0026middot;time\u0026middot;temperature\u0026middot;acid concentration\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;.. (2)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActual versus model predicted for ethanol yields\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDiagnostics case statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInternal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExternal studentized residual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRun order\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eActual value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePredicted value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eResidual\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe actual \u003cem\u003eversus\u003c/em\u003e predicted values using the model in the above equation are tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Regression analysis based on the coded variables on the experimental data was performed, and coefficients of the second-order models were calculated. Substitution of coefficients calculated and response variables in the above equation resulted in the empirical equations for ethanol yields.\u003c/p\u003e \u003cp\u003eTo see how the quadratic polynomial model satisfies the assumptions of the analysis of variance (ANOVA) in the experiments, plots in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e were analyzed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e in terms of normal plots of residuals and residual versus predicted values. The normal probability plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicates the residuals following a normal distribution, in the case of this experiment the points in the plots show fit into a straight line indicating that the quadratic polynomial model satisfies the assumptions analysis of variance (ANOVA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Optimization\u003c/h2\u003e \u003cp\u003eThe optimization hydrolysis criteria for ethanol production from Sugarcane Bagasse dilute acid are summarized as follows (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The optimum possible solutions for acid hydrolysis are determined by various factors and the ethanol yield has a complex relationship with independent variables that include first, second and third-order polynomials and may have more than one limit point. The best way of expressing the effect of any parameter on the yield within the experimental space under investigation was to produce response surface plots of the equation. The three-dimensional response surfaces, contours and interactions were plotted in Figs.\u0026nbsp;2 and 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOptimum possible solution\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSolutions number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTime, min\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eTemperature, K\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eAcid concentration, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eYield, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDesirability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.6757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.887 Selected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.7165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.6171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.6435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.4833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.5087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.4266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e18.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.9652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.7808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e383841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.4786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e18.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.1261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.1764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e363.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.3818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e\u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable 6. Optimization criteria for optimum ethanol yield\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eName\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGoal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLower limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eUpper limit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAcid Concen.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMinimize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMaximize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe probability (p-values) values and normal probability plot indicate the quadratic polynomial model satisfying ANOVA assumptions and the model was considered to be accurate and reliable for predicting the yield of ethanol from Sugarcane Bagasse using dilute acid hydrolysis. The resulting data were analyzed using Design-Expert\u0026reg; 13 to determine the effects of hydrolysis parameters and optimization in ethanol production. Optimization hydrolysis of ethanol production from Sugarcane Bagasse using dilute acid hydrolysis was carried out. A 2-level factorial design method was used to optimize the production of ethanol from Sugarcane Bagasse. The probability (p-value) of 0.0164 demonstrates a high significance for the regression model. The yield of ethanol of 38.76% was obtained when optimum conditions were: hydrolysis time of 17.03 min, hydrolysis temperature of 363 K, and acidic concentration of 1%. Validation experiments verified the availability and the accuracy of the model with a desirability of 88.7%.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur sincere thanks to Dr. Abrham Bayeh Institute of Technology University of Gondar and Mr. Gedefaw Asmare, Bahirdar Institute of Technology.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJ. 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Academic Press, 2005, pp.\u0026nbsp;255\u0026ndash;271.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University Of Gondar","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":"Ethanol, Fermentation, Hydrolysis, Sugarcane Bagasse, Optimization","lastPublishedDoi":"10.21203/rs.3.rs-2004225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2004225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research involved optimizing acid hydrolysis in the development of ethanol, a promising alternative energy source for restricted crude oil, from lignocellulosic materials (Sugarcane Bagasse). The conversion of Sugarcane Bagasse to ethanol can mainly be accomplished through three process steps: pretreatment of Sugarcane Bagasse for the removal of lignin and hemicellulose, acid hydrolysis of pretreated Sugarcane Bagasse for the conversion of cellulose into sugar reduction (glucose) and fermentation of sugars into ethanol using anaerobic \u003cem\u003eSaccharomyces cerevisiae\u003c/em\u003e. The effects of parameters (factors) in the hydrolysis step were investigated and the optimum combination of parameters values (temperature, time, and acid concentration) was set by experimentation. A factorial design of three-factors-at-two-level with a replica of two (2\u003csup\u003e3\u003c/sup\u003e = 8, 8*2\u0026thinsp;=\u0026thinsp;16) was applied to the hydrolysis step to investigate the effect of hydrolysis parameters on the response variable (ethanol yield) using Design-Expert\u0026reg; 13 software.\u003c/p\u003e","manuscriptTitle":"Optimization of Hydrolysis in Ethanol Production from Sugarcane Bagasse","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-30 19:09:41","doi":"10.21203/rs.3.rs-2004225/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":"d7b3ea2e-87bc-49a2-9936-a5ac81587a9a","owner":[],"postedDate":"August 30th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":15091571,"name":"Renewable Resources"}],"tags":[],"updatedAt":"2022-08-30T19:09:41+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-30 19:09:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2004225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2004225","identity":"rs-2004225","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}