Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology
Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology
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DOI：
10.1016/j.susmat.2015.01.001
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发表时间：
2015-04-01
影响因子：
9.6
通讯作者：
Chatterjee, P. K.
中科院分区：
文献类型：
--
作者：
Das, S.;Bhattacharya, A.;Chatterjee, P. K.
响应面法（RSM）通常用于优化影响酶水解的工艺参数。然而，人工神经网络-遗传算法混合模型也可以作为一个有效的选择，主要是非线性多项式系统。本研究比较了这些方法用于水葫芦生物质的酶解以最大化总还原糖（TRS）用于生物乙醇生产。使用9.92（% w/w）底物浓度、49.56 U/g纤维素酶浓度、280.33 U/g木聚糖酶浓度和0.13（% w/w）表面活性剂浓度获得最大TRS（0.5672 g/g）。人工神经网络（ANN）和响应面的平均%误差分别为3.08和4.82，最佳输出的预测百分比误差分别为0.95和1.41，这表明人工神经网络在说明系统的非线性行为方面具有至高无上的地位。水解产物的发酵产生的最大乙醇浓度为10.44克/升，使用树干毕赤酵母，其次是8.24和6.76克/升的休哈塔假丝酵母和酿酒酵母。(C)2015作者由Elsevier B. V.发布。这是CC BY-NC-ND许可下的开放获取文章
Response surface methodology (RSM) is commonly used for optimising process parameters affecting enzymatic hydrolysis. However, artificial neural network-genetic algorithm hybrid model can also serve as an effective option, primarily for non-linear polynomial systems. The present study compares these approaches for enzymatic hydrolysis of water hyacinth biomass tomaximise total reducing sugar (TRS) for bio-ethanol production. Maximum TRS (0.5672 g/g) was obtained using 9.92 (% w/w) substrate concentrations, 49.56 U/g cellulase concentrations, 280.33 U/g xylanase concentrations and 0.13 (% w/w) surfactant concentrations. The average % error for artificial neural networking (ANN) and RSM were 3.08 and 4.82 and the prediction percentage errors in optimum output are 0.95 and 1.41, respectively, which showed the supremacy of ANN in illustrating the non-linear behaviour of the system. Fermentation of the hydrolysate yielded a maximum ethanol concentration of 10.44 g/l using Pichia stipitis, followed by 8.24 and 6.76 g/l for Candida shehatae and Saccharomyces cerevisiae. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license