化学反应工程中的“优化”：以甲烷氧化偶联为例,Industrial & Engineering Chemistry Research

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化学反应工程中的“优化”：以甲烷氧化偶联为例
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-06-19 , DOI: 10.1021/acs.iecr.8b01424
Yousef Mohammadi ₁ , Alexander Penlidis ₂

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从理论和实践的角度来看，包括化学，生物和大分子反应在内的反应体系的最优化都非常重要。尽管已经常规检查了几种经典的确定性和随机建模和模拟方法，以了解和控制从实验室到工业规模的反应系统，但几乎所有方法都解决了同一问题（即，如何从任何给定的反应输入集中预测反应输出变量）。能够将预设的所需反应输出适当地连接到相应输入的有效，通用的数学工具的开发和应用一直是相关领域专家的理想目标。因此，绝对需要在计算上需要的多变量空间中预测先验最佳反应条件，以将化学和生物反应保持在最佳条件下，同时满足预设的所需目标。作为一种新颖而强大的解决方案，我们在此引入一种功能强大的计算工具，该工具能够通过动力学蒙特卡洛（KMC）模拟方法的合并，同时模拟和优化[即“优化”复杂的化学，生物学和大分子反应，并遗传算法的多目标版本（NSGA-II）]。作为具有挑战性的化学反应工程系统的一个例子，KMC和NSGA-II协同作用以优化甲烷的氧化偶联（OCM），已清楚地证明了该方法的出色功能。毫无疑问，提出的新型杂交技术非常强大，可以解决化学，生物学和大分子反应工程中许多未解决的优化问题。

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“Optimulation” in Chemical Reaction Engineering: Oxidative Coupling of Methane as a Case Study

The optimization of reacting systems, including chemical, biological, and macromolecular reactions, is of great importance from both theoretical and practical standpoints. Even though several classical deterministic and stochastic modeling and simulation approaches have been routinely examined to understand and control reacting systems from lab- to industrial-scales, almost all tackle the same problem (i.e., how to predict reaction outputs from any given set of reaction input variables). Development and application of an effective and versatile mathematical tool capable of appropriately connecting preset desired reaction outputs to corresponding inputs have always been the ideal goal for experts in related fields. Hence, there definitely exists the need to predict a priori optimum reaction conditions in a computationally demanding multivariable space for both keeping the chemical and biological reactions in optimal conditions and at the same time satisfying preset desired targets. As a novel and powerful solution, we hereby introduce a robust and functional computational tool capable of simultaneously simulating and optimizing [i.e., “optimulating” intricate chemical, biological, and macromolecular reactions via the amalgamation of the Kinetic Monte Carlo (KMC) simulation approach and the multiobjective version of genetic algorithms (NSGA-II)]. The synergistic interplay of KMC and NSGA-II for the optimulation of oxidative coupling of methane (OCM) as an example of a challenging chemical reaction engineering system has clearly demonstrated the outstanding capabilities of the proposed method. Undoubtedly, the proposed novel hybridized technique is very powerful and can address a variety of unsolved optimization questions in chemical, biological, and macromolecular reaction engineering.

更新日期：2018-06-20

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