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              <text>Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method</text>
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              <text>Artificial neural network; Bamboo; Bioethanol; Non-dominated sorting genetic algorithm-II; Saccharification</text>
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              <text>The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration.  2023, The Author(s), under exclusive licence to Springer Nature B.V.</text>
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              <text>Kiran K.; Jose N.; Rout A.; Ravikumar R.; Hebbar G.S.</text>
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              <text>Environment, Development and Sustainability</text>
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              <text>Springer Science and Business Media B.V.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/s10668-023-04209-6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/s10668-023-04209-6&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178188635&amp;amp;doi=10.1007%2Fs10668-023-04209-6&amp;amp;partnerID=40&amp;amp;md5=4485290ba54702aea8d792ccc0d5285b" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178188635&amp;amp;doi=10.1007%2fs10668-023-04209-6&amp;amp;partnerID=40&amp;amp;md5=4485290ba54702aea8d792ccc0d5285b&lt;/a&gt;</text>
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              <text>ISSN: 1387585X; CODEN: EDSNB</text>
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              <text>Kiran K., Mechanical and Automobile Engineering Department, School of Engineering and Technology, CHRIST University, Bengaluru, 560074, India; Jose N., Mechanical and Automobile Engineering Department, School of Engineering and Technology, CHRIST University, Bengaluru, 560074, India; Rout A., Mechanical and Automobile Engineering Department, School of Engineering and Technology, CHRIST University, Bengaluru, 560074, India; Ravikumar R., Mechanical and Automobile Engineering Department, School of Engineering and Technology, CHRIST University, Bengaluru, 560074, India; Hebbar G.S., Mechanical and Automobile Engineering Department, School of Engineering and Technology, CHRIST University, Bengaluru, 560074, India</text>
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