Abstract
A dynamic model of the hydrogenation of benzene to cyclohexane reaction in a real-life industrial reactor is elaborated. Transformations of the model leading to satisfactory results are presented and discussed. Operating conditions accepted in the simulations are identical to those observed in the chemical plant. Under those conditions, some components of the reaction mixture vanish, and the diffusion coefficients of the components vary along the reactor (they are strongly concentration-dependent). We came up with a final reactor model predicting with reasonable accuracy the reaction mixture’s outlet composition and temperature profile throughout the process. Additionally, the model enables the anticipation of catalyst activity and the remaining deactivated catalyst lifetime. Conclusions concerning reactor operation conditions resulting from the simulations are presented as well. Since the model provides deep insight into the process of simulating, it allows us to make knowledge-based decisions. It should be pointed out that improvements in the process run, related to operating conditions, or catalyst application, or both on account of the high scale of the process and its expected growth, will remarkably influence both the profits and environmental protection.
Highlights
Mathematical models of full processes, single apparatus, devices, etc. are presented in many or even the majority of recently published scientific papers
The paper presents a way of obtaining a mathematical model of a large-scale, sophisticated industrial process
The resulting reactor model of the hydrogenation of benzene predicts well the results obtained in practice
Summary
Mathematical models of full processes, single apparatus, devices, etc. are presented in many or even the majority of recently published scientific papers. Mathematical models of full processes, single apparatus, devices, etc. Are presented in many or even the majority of recently published scientific papers. There are presented as experimental and theoretical, sophisticated, and simple models. They can differ from each other in terms of precision, complexity, application range, and many other features. The diversity of models presented in the literature is understood: modeling, which provides tools that can improve understanding of the process, allows increasing profits or decreasing costs. The most frequently published models are developed to describe small-scale processes. Models created and successfully tested on a commercial scale are only exceptionally presented. There are many reasons for such a situation. We shall concentrate on two aspects: mathematical model structure and experiment conduction
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