Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

da Silva Pereira Andrea; Pinheiro Alvaro Daniel Teles; Rocha Maria Valderez Ponte; Goncalves Luciana Rocha B.; Cartaxo Samuel Jorge Marques

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Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

机译：Hybrid神经网络建模和粒子群优化从腰果苹果汁改善乙醇生产

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A hybrid neural model (HNM) and particle swarm optimization (PSO) was used to optimize ethanol production by a flocculating yeast, grown on cashew apple juice. HNM was obtained by combining artificial neural network (ANN), which predicted reaction specific rates, to mass balance equations for substrate (S), product and biomass (X) concentration, being an alternative method for predicting the behavior of complex systems. ANNs training was conducted using an experimental set of data of X and S, temperature and stirring speed. The HNM was statistically validated against a new dataset, being capable of representing the system behavior. The model was optimized based on a multiobjective function relating efficiency and productivity by applying the PSO. Optimal estimated conditions were: S-0 = 127 g L-1, X-0 = 5.8 g L-1, 35 degrees C and 111 rpm. In this condition, an efficiency of 91.5% with a productivity of 8.0 g L-1 h(-1)was obtained at approximately 7 h of fermentation.

机译：杂交神经模型（HNM）和粒子群优化（PSO）用于通过絮凝酵母优化乙醇生产，在腰果苹果汁上生长。通过将预测反应特定速率的人工神经网络（ANN）组合来获得HNM，以对底物，产物和生物质（X）浓度的质量平衡方程，是预测复杂系统行为的替代方法。使用实验组的X和S，温度和搅拌速度进行ANNS训练。 HNM对新数据集进行统计验证，能够代表系统行为。通过应用PSO，基于多目标函数对效率和生产率进行优化。最佳估计条件是：S-0 = 127g L-1，X-0 = 5.8g L-1,35℃和111 rpm。在这种情况下，在约7小时的发酵约7小时内获得91.5％的效率为8.0gl-1 h（-1）。

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《Bioprocess and Biosystems Engineering》 |2021年第2期|329-342|共14页
作者
da Silva Pereira Andrea; Pinheiro Alvaro Daniel Teles; Rocha Maria Valderez Ponte; Goncalves Luciana Rocha B.; Cartaxo Samuel Jorge Marques;
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作者单位

Univ Fed Ceara Chem Engn Dept Campus Pici Bloco 709 BR-6045576 Fortaleza CE Brazil;

Univ Fed Rural Semi Arido Engn & Technol Dept Mossoro RN Brazil;

Univ Fed Ceara Chem Engn Dept Campus Pici Bloco 709 BR-6045576 Fortaleza CE Brazil;

Univ Fed Ceara Chem Engn Dept Campus Pici Bloco 709 BR-6045576 Fortaleza CE Brazil;

Univ Fed Ceara Chem Engn Dept Campus Pici Bloco 709 BR-6045576 Fortaleza CE Brazil;

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正文语种 eng
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关键词
Ethanol production; Cashew apple juice; Artificial neural network (ANN); Hybrid neural model (HNM); Particle swarm optimization (PSO);

机译：乙醇生产;腰果汁;人工神经网络（ANN）;杂交神经模型（HNM）;粒子群优化（PSO）;

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2. Efficiency of tannase enzyme for degradation of tannin from cashew apple juice: Modeling and optimization of process using artificial neural network and response surface methodology [J] . Abdullah S., Pradhan Rama Chandra, Aflah Muhammed, Journal of food process engineering . 2020,第10期

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4. A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli [C] . Mee K. Lee, Mohd Saberi Mohamad, Yee Wen Choon, International Conference on Practical Applications of Computational Biology Bioinformatics . 2020

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5. Explorations in swarm algorithms: Hybrid particle swarm optimization and adaptive culture model algorithms. [D] . Noel, Mathew M. 2005

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6. A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification [O] . Guixiang Li, Zhongwei Tan, Weikang Xu, 2021

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7. Improved hybrid particle swarm optimized wavelet neural network for modeling the development of fluid dispensing for electronic packaging [O] . Ling, SH, Iu, HHC, Leung, FHF, 2008

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Hybrid neural network modeling and particle swarm optimization for improved ethanol production from cashew apple juice

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