Abstract Biodiesel are fatty acid methyl esters (FAME), which can be produced by the transesterification reaction of vegetable oils with methanol. A batch transesterification process is often associated with model uncertainties and unmeasured disturbances, which may create a detrimental effect on the batch end FAME yield due to plant-model mismatch. Therefore, batch-to-batch iterative learning control (ILC) is necessary to track the desired reference FAME profile under such process variations. This work demonstrates a constrained quadratic programming problem (QPP) based batch-to-batch ILC framework for optimizing the endpoint FAME concentration by controlling the hot water flow profile passing through the reactor jacket under uncertainty. Parametric uncertainties are modeled separately in two case studies, which involve different batch transesterification models differing in the state variables. Case study 1 considers uncertainty in the apparent activation energy and brings out a comparative study between a QPP based ILC and a heuristics based approach. The comparison is shown based on the tracking performance of the ILC in terms of reduction in the batch end tracking error and total root mean square error of the same. Batch-to-batch ILC is superior as it produces faster convergence of the tracking error by saving 6 batches as compared to the heuristics approach. Case study 2 involves the implementation of constrained QPP based ILC algorithm on a proposed 54-state detailed batch transesterification model of canola oil, where uncertainty is modeled as the change in the input triglyceride composition from the base case. The desired reference FAME concentration profile is tracked in 9 batches for fixed uncertainty whereas it takes 15 batches to achieve the stochastic convergence under stochastic disturbance.

中文翻译：

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不确定条件下批量酯交换过程的约束迭代学习控制

摘要 生物柴油是脂肪酸甲酯（FAME），可通过植物油与甲醇的酯交换反应生产。批量酯交换过程通常与模型不确定性和无法测量的干扰有关，由于工厂模型不匹配，这可能对批量最终 FAME 产量产生不利影响。因此，需要批次间迭代学习控制 (ILC) 来跟踪此类过程变化下所需的参考 FAME 配置文件。这项工作展示了基于约束二次规划问题 (QPP) 的批次间 ILC 框架，用于通过控制在不确定情况下通过反应器夹套的热水流量分布来优化终点 FAME 浓度。参数不确定性在两个案例研究中分别建模，其中涉及状态变量不同的不同批次酯交换模型。案例研究 1 考虑了表观活化能的不确定性，并对基于 QPP 的 ILC 和基于启发式的方法进行了比较研究。比较是基于 ILC 的跟踪性能在批次结束跟踪误差和总均方根误差的减少方面显示的。Batch-to-batch ILC 是优越的，因为与启发式方法相比，它通过节省 6 个批次来产生更快的跟踪误差收敛。案例研究 2 涉及在提出的 54 状态详细的菜籽油批量酯交换模型上实施基于约束 QPP 的 ILC 算法，其中不确定性被建模为输入甘油三酯成分与基本情况的变化。