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Genetic programming approach to predict a model acidolysis system

机译:遗传编程方法预测模型酸解系统

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This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,3-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modelling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modelling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models.
机译:本文建立了固定化的sn-1,3特异性脂肪酶催化三油酸和棕榈酸的酸解模型。开发了基因表达编程(GEP),它是基于遗传编程(GP)的模型的扩展,用于预测该反应的主要反应产物(1-棕榈酰-2,3-油酰甘油( POO),1,3-二棕榈酰-2-油酰基甘油(POP)和三油精(OOO),以底物比(SR),反应温度(T)和反应时间(t)作为输入参数,预测模型为能够预测反应的进展,平均标准误差(MSE)小于1.0,R值为0.978;还提出了明确的GEP模型公式;使用GEP在酸解反应建模中取得了相当好的性能。将所提出的GEP模型与神经网络(NN)模型进行了比较,两者之间的观察结果完全一致,统计数据和散点图表明,新的GEP公式可以替代实验模型。

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