Genetic algorithm applied to study of the economic viability of alcohol production from Cassava root from 2002 to 2013

被引:31
作者
Campos Benvenga, Marco Antonio [1 ]
Henriques Librantz, Andre Felipe [1 ]
Curvelo Santana, Jose C. [1 ]
Tambourgi, Elias Basile [2 ]
机构
[1] Univ Nove de Julho UNINOVE, Ind Engn Grad Program, Ave Francisco Matarazzo 612, BR-05001100 Sao Paulo, SP, Brazil
[2] Univ Estadual Campinas, Sch Chem Engn FEQ, Ave Albert Einstein 500,6066, BR-13084970 Campinas, SP, Brazil
基金
巴西圣保罗研究基金会;
关键词
Economical analysis; Cassava alcohol; Genetic algorithms; Process optimization; Carbon credits; ETHANOL-PRODUCTION; ALPHA-AMYLASE; CARBON CREDIT; SUSTAINABILITY; OPTIMIZATION; HYDROLYSIS; SYSTEMS; COTTON; PRICE; BEER;
D O I
10.1016/j.jclepro.2015.11.051
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This work aimed to investigate the economical and environmental viability of alcohol production from cassava root from 2002 to 2013 using genetic algorithms technique, a meta-heuristic technique inspired on biological natural selection process. Amylases from Aspergillus niger have been used in starch hydrolysis processes and Saccharomyces cerevisiae yeast in fermentation process. The carbon credits obtained of alcohol production and biomass from cassava residues were used to reduce cost production. The hydrolysis occurred at temperatures ranging from 30 to 60 degrees C, with starch concentrations from 8.0 to 22 g/L. Results showed the best conditions for starch hydrolysis at 23.4 g/L, 61.9 degrees C and 111.0 min, where an yield of 84% was achieved. In this condition, though in the presence of yeast, the hydrolysis yield is close 100% and the ethanol yield was 88%, after fermentation process. An alcohol cost between 0.04 and 0.62 US$/L has been found for the studied period. In addition, the company acquires 7.8 billion of carbon credits, is environment friendly, and improves its image in the society. Results show that this meta heuristic optimization technique could be useful for improvement of industrial alcohol production. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:483 / 494
页数:12
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