Article3 Biotech2021
Optimization of process parameters for bio-enzymatic and enzymatic saccharification of waste broken rice for ethanol production using response surface methodology and artificial neural network-genetic algorithm.
Article in 3 Biotech, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 33 citations in OpenAlex.
- Valorization of Rice By-Products Through Conventional and Emerging Extraction Technologies: Trends, Challenges, and Applications.Comprehensive reviews in food science and food safety · 2026Review
- Optimizing brown rice liquefaction and saccharification using response surface methodology for grain ethanol production.Scientific reports · 2026Article
- Statistical Optimization of Process Parameters and Purification of Uricase Using IsolateIndian journal of microbiology · 2025Article
- Effect of pretreatment strategies on halophyteFrontiers in bioengineering and biotechnology · 2023Article
- Bioethanol production from3 Biotech · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Reducible sugar solution has been produced from waste broken rice by a novel saccharification process using a combination of bio-enzyme (bakhar) and commercial enzyme (α-amylase). The reducible sugar solution thus produced is a promising raw material for the production of bioethanol using the fermentation process. Response surface methodology (RSM) and Artificial neural network-genetic algorithm (ANN-GA) have been used separately to optimize the multivariable process parameters for maximum yield of the total reducing sugar (TRS) in saccharification process. The maximum yield (0.704 g/g) of TRS is predicted by the ANN-GA model at a temperature of 93 °C, saccharification time of 250 min, 6.5 pH and 1.25 mL/kg of enzyme dosages, while the RSM predicts the maximum yield of 0.7025 g/g at a little different process conditions. The fresh experimental validation of the said model predictions by ANN-GA and RSM is found to be satisfactory with the relative mean error of 2.4% and 3.8% and coefficients of determination of 0.997 and 0.996.
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Registered trials
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