Abstract

Autothermal configurations are recognized as a novel concept in process intensification. The main objective of this study is modeling and optimization of the innovative radial flow configuration with dual-functionality of a well-mixed catalyst pattern for enhancement of olefin production. In this novel structure, namely, an autothermal radial flow reactor (AT-RFR), the hydrogen drain-off mechanism in hydrogenation-dehydrogenation reaction is applied. Since the olefin formation reaction is equilibrium-limited, the thermodynamic equilibrium is displaced by using of auxiliary hydrogenation reaction of nitrobenzene in well-mixed catalyst configuration. Moreover, the necessary heat for heavy paraffin dehydrogenation is supplied by the catalytic hydrogenation of nitrobenzene to aniline. Subsequently, the NSGA-II algorithm is used for multiobjective optimization of this configuration. Olefin production rate and selectivity are maximized as two objective functions. The Shannon’s Entropy, LINMAP and TOPSIS methods as three decision making approaches are used to select the final solution of Pareto front. The optimization results have shown that olefin and aniline production rate enhanced about 41.1 and 23.77 ton/day, respectively, based on Shannon’s Entropy methods compared with the nonoptimized configuration. In addition, selectivity of olefin is increased 15.14% in optimized configuration.

中文翻译：

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集成自热径向流反应器中的热和氢管理策略，可提高烯烃的生产量

摘要

自热配置被认为是过程强化中的一个新颖概念。这项研究的主要目的是对具有良好混合催化剂模式的双重功能的创新径向流构型进行建模和优化，以提高烯烃的产量。在这种新颖的结构即自热径向流反应器（AT-RFR）中，应用了氢化-脱氢反应中的氢排出机制。由于烯烃的形成反应受到平衡的限制，因此在充分混合的催化剂构型中，通过使用硝基苯的辅助氢化反应可以置换热力学平衡。此外，重链烷烃脱氢所需的热量是通过硝基苯催化氢化为苯胺来提供的。随后，NSGA-II算法用于此配置的多目标优化。烯烃的生产率和选择性最大化是两个目标函数。使用Shannon的熵，LINMAP和TOPSIS方法作为三种决策方法来选择Pareto前沿的最终解。优化结果表明，与非优化构型相比，基于香农的熵方法，烯烃和苯胺的生产率分别提高了约41.1和23.77吨/天。此外，在优化配置中，烯烃的选择性提高了15.14％。优化结果表明，与非优化构型相比，基于香农的熵方法，烯烃和苯胺的生产率分别提高了约41.1和23.77吨/天。此外，在优化配置中，烯烃的选择性提高了15.14％。优化结果表明，与非优化构型相比，基于香农的熵方法，烯烃和苯胺的生产率分别提高了约41.1和23.77吨/天。此外，在优化配置中，烯烃的选择性提高了15.14％。