Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Rivera, Elmer Ccopa; Rabelo, Sarita Cândida; Garcia, Danielle dos Santos; Filho, Rubens Maciel; Costa, Aline Carvalho da

doi:10.1002/jctb.2391

Cited by 53 publications

(38 citation statements)

References 32 publications

(24 reference statements)

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“…temperature, pH, enzyme and substrate loadings and a combination of these parameters) that provided the most valuable information for apple pomace hydrolysis were selected for the development of the ANN model. The selection of the most appropriate parameters for ANN modelling is considered of paramount importance for prediction of the hydrolysis process (Puig-Arnavat et al 2013;Rivera et al 2010). The constructed ANN was assessed for its accuracy for generalisation and predictive ability using R 2 value and MSE values for the outputs.…”

Section: Annmentioning

confidence: 99%

“…6), further confirming the successful training and predictive ability of the developed ANN. It has been reported in literature that ANNs are flexible as new data can be added anytime giving fitting (Bhotmange and Shastri 2011;O'Dwyer et al 2008;Rivera et al 2010;Sousa et al 2011;Wang et al 2011).…”

Section: Annmentioning

confidence: 99%

“…Enzyme loadings below and above the optima resulted in lower sugar concentrations (''Effect of enzyme loading''). Minimum enzyme loading is important for cost-effective biofuel production (Lynd et al 2008;Rivera et al 2010;Zhang et al 2010). …”

Section: Annmentioning

confidence: 99%

“…It has been reported that ANN needs fewer experiments, is not affected by experimental design and is flexible as it can predict values from the unseen/unmeasured data (Rivera et al 2010;Wang et al 2011;Zhang et al 2010). However, the limitation of ANN is that it cannot explain the interactive mechanism between the enzyme and the substrate (Brown et al 2010;Sousa et al 2011;Wang et al 2011).…”

Section: Annmentioning

confidence: 99%

“…ANN was also successfully used to model the effect of pretreatment conditions (temperature, time, solid content and sulphuric acid concentration) on glucose production from biomass (Sousa et al 2011). Glucose yield from sugarcane bagasse using different loadings of cellulase and b-glucosidase was predicted by Rivera et al (2010). Zhang et al (2010) used ANN to better predict glucose concentration by considering three hydrolysis parameters; substrate concentration, cellulase concentration and hydrolysis time.…”

Section: Introductionmentioning

confidence: 99%

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Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

et al. 2017

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The enzymatic degradation of lignocellulosic biomass such as apple pomace is a complex process influenced by a number of hydrolysis conditions. Predicting optimal conditions, including enzyme and substrate concentration, temperature and pH can improve conversion efficiency. In this study, the production of sugar monomers from apple pomace using commercial enzyme preparations, Celluclast 1.5L, Viscozyme L and Novozyme 188 was investigated. A limited number of experiments were carried out and then analysed using an artificial neural network (ANN) to model the enzymatic hydrolysis process. The ANN was used to simulate the enzymatic hydrolysis process for a range of input variables and the optimal conditions were successfully selected as was indicated by the R 2 value of 0.99 and a small MSE value. The inputs for the ANN were substrate loading, enzyme loading, temperature, initial pH and a combination of these parameters, while release profiles of glucose and reducing sugars were the outputs. Enzyme loadings of 0.5 and 0.2 mg/g substrate and a substrate loading of 30% were optimal for glucose and reducing sugar release from apple pomace, respectively, resulting in concentrations of 6.5 g/L glucose and 28.9 g/L reducing sugars. Apple pomace hydrolysis can be successfully carried out based on the predicted optimal conditions from the ANN.

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Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

et al. 2017

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Engineered thermostable fungal cellulases exhibit efficient synergistic cellulose hydrolysis at elevated temperatures

Trudeau

Lee

Arnold

2014

Biotech & Bioengineering

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A major obstacle to using widely available and low-cost lignocellulosic feedstocksto produce renewable fuels and chemicals is the high cost and low efficiency of the enzyme mixtures used to hydrolyze cellulose to fermentable sugars. One possible solution entails engineering current cellulases to function efficiently at elevated temperatures in order to boost reaction rates and exploit several other advantages of a higher temperature process. Here we describe the creation of the most stable reported fungal endoglucanase, a derivative of Hypocrea jecorina (anamorph Trichoderma reesei) Cel5A, by combining stabilizing mutations identified using consensus design, chimera studies, and structure-based computational methods. The engineered endoglucanase has an optimal temperature that is 17 °C higher than wild type H. jecorina Cel5A, and hydrolyzes 1.5 times as much cellulose over 60 h at its optimum temperature compared to the wild type enzyme at its optimal temperature.This enzyme complements previously-engineeredhighly-active, thermostable variants of the fungal cellobiohydrolases Cel6A and Cel7A in a thermostable cellulase mixture that hydrolyzes cellulose synergistically at an optimum temperature of 70 °C over 60 h.The thermostable mixture produces three times as much total sugar as the bestmixture ofthewild type enzymes operating at its optimum temperature of 60 °C, clearly demonstrating the advantage of higher-temperature cellulose hydrolysis.

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Novel Aspergillus hemicellulases enhance performance of commercial cellulases in lignocellulose hydrolysis

Shin

Chen

2011

Biotechnology Progress

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A novel hemicellulase-producing fungal strain was isolated from a local soil sample. The organism is identified as Aspergillus fumigatus based on ribosomal RNA analyses. The Aspergillus strain, designated as 2NB, produces both enzymes acting on xylan backbone (xylanase and β-xylosidase), and those acting on side chains (or accessory enzymes) notably α-arabinofuranosidase and acetyl-xylan esterase. The Asperigillus hemicellulases are characterized as having relatively low xylanase and β-xylosidase activities but high side chain removal activities. The activity ratio of side-chain acting enzymes to xylanase is higher than that of the Multifect enzyme, a commercial hemicellulase product. The potential of the novel hemicellulases in lignocelluloses bioprocessing was demonstrated with alkaline-pretreated switchgrass as lignocellulose substrate with hemicellulase supplemented with a ratio of xylanase activity to filter paper unit of 2:1. Supplement of Aspergillus hemicellulases to commercial cellulases significantly enhanced the hydrolysis of lignocellulose, achieving a 94% hydrolysis yield based on reducing sugar measurement, compared to 60% when no hemicellulase or 75% when Multifect enzyme was used under otherwise identical conditions. The significant improvement resulting from supplementing a hemicellulase mix with high side-chain removal activities suggests the importance of accessory hemicellulases in lignocellulose processing.

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Enzymatic hydrolysis of sugarcane bagasse for bioethanol production: determining optimal enzyme loading using neural networks

Cited by 53 publications

References 32 publications

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

Using an artificial neural network to predict the optimal conditions for enzymatic hydrolysis of apple pomace

Engineered thermostable fungal cellulases exhibit efficient synergistic cellulose hydrolysis at elevated temperatures

Novel Aspergillus hemicellulases enhance performance of commercial cellulases in lignocellulose hydrolysis

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