Data‐Driven Modeling of Biodiesel Production Using Artificial Neural Networks,Chemical Engineering & Technology

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Data‐Driven Modeling of Biodiesel Production Using Artificial Neural Networks
Chemical Engineering & Technology ( IF 1.8 ) Pub Date : 2021-02-23 , DOI: 10.1002/ceat.202000434
Anitha Mogilicharla ₁ , P Swapna Reddy ₂

Affiliation

Data‐driven modeling of biodiesel production was developed by simultaneous transesterification and esterification of rapeseed oil and myristic acid with methanol, without catalyst or with different amounts of sulfated zirconia catalyst. An artificial neural network (ANN)‐based model was created with experimental literature data. The input data, i.e., reaction time, catalyst, temperature, and methanol‐to‐oil ratio, and output data, i.e., total fatty acid methyl ester and oleic acid methyl ester, were considered to develop the model. Multiple input single output (MISO) ANN architecture was taken to predict the above targeted two output parameters. The proposed ANN model is computationally efficient and works reasonably well when tested on biodiesel production for solving the MISO model.

中文翻译：

使用人工神经网络的生物柴油生产数据驱动建模

通过将菜籽油和肉豆蔻酸与甲醇同时酯交换和酯化，无需催化剂或使用不同量的硫酸化氧化锆催化剂，即可开发出数据驱动的生物柴油生产模型。使用实验文献数据创建了基于人工神经网络（ANN）的模型。考虑输入数据，即反应时间，催化剂，温度和甲醇/油比，以及输出数据，即总脂肪酸甲酯和油酸甲酯，以建立模型。采用多输入单输出（MISO）ANN架构来预测上述目标两个输出参数。所提出的人工神经网络模型计算效率高，并且在用于解决MISO模型的生物柴油生产测试中可以很好地工作。

更新日期：2021-04-20

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