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Modeling and multi-criteria optimization of an industrial process for continuous lactic acid production

机译：连续生产乳酸的工业过程的建模和多标准优化

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摘要

The key feature of this paper is the optimization of an industrial process for continuous production of lactic acid. For this, a two-stage fermentor process integrated with cell recycling has been mathematically modeled and optimized for overall productivity, conversion, and yield simultaneously. Non-dominated sorting genetic algorithm (NSGA-Ⅱ) was applied to solve the constrained multi-objective optimization problem as it is capable of finding multiple Pareto-optimal solutions in a single run, thereby avoiding the need to use a single-objective optimization several times. Compared with traditional methods, NSGA-Ⅱ could find most of the solutions in the true Pareto-front and its simulation is also very direct and convenient. The effects of operating variables on the optimal solutions are discussed in detail. It was observed that we can make higher profit with an acceptable compromise in a two-stage system with greater efficiency.
机译：本文的关键特征是优化连续生产乳酸的工业工艺。为此，已经对与细胞回收相结合的两阶段发酵罐工艺进行了数学建模，并针对总生产率，转化率和产量同时进行了优化。应用非支配排序遗传算法（NSGA-Ⅱ）来解决约束多目标优化问题，因为它能够在一次运行中找到多个Pareto最优解，从而避免了多次使用单目标优化的需要。次。与传统方法相比，NSGA-Ⅱ可以在真正的Pareto-front中找到大多数解决方案，并且其仿真也非常直接和方便。详细讨论了操作变量对最优解的影响。据观察，我们可以通过在两阶段系统中以可接受的折衷来以更高的效率获得更高的利润。

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