关键词：
Algorithms
Hydrocarbons
Genetics
Chemical reactions
Optimization
摘要：
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 industrialscales, 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 m related fields. Hence, there definitely exists the need to predict a priori optimum reaction conditions in a computationally demanding multivanable 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.
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