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首页> 外文期刊>International journal of green energy >Multi-objective optimization of a novel crude lipase-catalyzed fatty acid methyl ester (FAME) production using low-order polynomial and Kriging models
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Multi-objective optimization of a novel crude lipase-catalyzed fatty acid methyl ester (FAME) production using low-order polynomial and Kriging models

机译：新型粗脂酶催化脂肪酸甲酯（FAME）生产的多目标优化使用低级多项式和克里格模型生产

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摘要

In this paper, conventional response surface methodology (RSM) based on low-order polynomials and an alternative Kriging-based method are used for the model-based single and multi-objective optimization of fatty-acid methyl ester (FAME) production catalyzed by a novel crude lipase from the yeast Cryptococcus diffluens (D44). The coefficient of determination for the two modeling approaches was calculated as 0.97 for the Kriging method, and 0.86 for RSM; showing a more reliable representation of experimental data by Kriging. Both models were used to perform single (maximizing FAME titer and temporal productivity separately) and multi-objective (maximizing FAME titer and temporal productivity simultaneously) optimizations of four important operating conditions (reaction time and temperature; amount of crude enzyme; and volume of methanol used). In all cases, the highest temperature considered (60 degrees C) gave the best results. A reduction of reaction time in half was seen to be necessary to achieve optimum productivity compared to titer, when the two objectives were considered separately. The observed trade-off between the two objectives was quantified via multi-objective optimization using Pareto-front analysis.
机译：在本文中，基于低阶多项式的常规响应表面方法（RSM）和替代的基于Kriging的方法用于脂肪酸甲酯（DAME）生产的基于模型的单一和多目标优化。来自酵母碱基的新型粗脂肪酶 - 来自酵母裂解物（D44）。对于Kriging方法计算两个建模方法的测定系数为0.97，为RSM为0.86;通过Kriging表示更可靠的实验数据表示。两种模型用于单独（分别最大化的名称滴度和时间生产率）和多目标（最大化的名称滴度和时态生产率）优化四种重要的操作条件（反应时间和温度;粗酶的量;和甲醇的体积和体积的甲醇用过的）。在所有情况下，考虑的最高温度（60摄氏度）给出了最佳结果。当两种目标被分开考虑时，观察到反应时间减少了一半的反应时间以实现最佳的生产率。通过使用静脉正面分析通过多目标优化量化了两个目标之间的观察到的权衡。

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