模型预测控制
脂肪酸甲酯
生物柴油
酯交换
迭代学习控制
生物柴油生产
脂肪酸
最优控制
计算机科学
过程(计算)
间歇式反应器
可再生能源
批处理
控制(管理)
化学
工程类
数学优化
数学
有机化学
甲醇
催化作用
电气工程
人工智能
操作系统
程序设计语言
作者
Nikita Gupta,Riju De,Hariprasad Kodamana,Sharad Bhartiya
标识
DOI:10.1021/acsomega.2c04255
摘要
To harness energy security and reduce carbon emissions, humankind is trying to switch toward renewable energy resources. To this extent, fatty acid methyl esters, also known as biodiesel, are popularly used as a green fuel. Fatty acid methyl esters can be produced by a batch transesterification reaction between vegetable oil and alcohol. Being a batch process, fatty acid methyl esters production is beset with issues such as uncertainties and unsteady state behavior, and therefore, adequate process control measures are necessitated. In this study, we have proposed a novel two-tier framework for the control of the fatty acid methyl esters production process. The proposed approach combines the constrained batch-to-batch iterative learning control technique and explicit model predictive control to obtain the desired concentration of the fatty acid methyl esters. In particular, the batch-to-batch iterative learning control technique is used to generate reactor temperature set-points, which is further utilized to obtain an optimal coolant flow rate by solving a quadratic objective cost function, with the help of explicit model predictive control. Our simulation results indicate that the fatty acid methyl esters concentration trajectory converges to the desired batch trajectory within four batches for uncertainty in activation energy and six batches for uncertainty in both inlet concentration of triglyceride and in activation energy even in the presence of process disturbances. The proposed approach was compared to the heuristic-based approach and constraint iterative learning control approach to showcase its efficacy.
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