Process Simulation for the Design and Scale Up of Heterogeneous Catalytic Process: Kinetic Modelling Issues

• Main Authors: Antonio Tripodi, Matteo Compagnoni, Rocco Martinazzo, Gianguido Ramis, Ilenia Rossetti
• Format: Article
• Language: English
• Published: MDPI AG 2017-05-01
• Series: Catalysts
• Subjects: process simulation; kinetic modelling; ammonia; methanol; bioethanol; steam reforming; ethylene
• Online Access: http://www.mdpi.com/2073-4344/7/5/159

Process simulation represents an important tool for plant design and optimization, either applied to well established or to newly developed processes. Suitable thermodynamic packages should be selected in order to properly describe the behavior of reactors and unit operations and to precisely define phase equilibria. Moreover, a detailed and representative kinetic scheme should be available to predict correctly the dependence of the process on its main variables. This review points out some models and methods for kinetic analysis specifically applied to the simulation of catalytic processes, as a basis for process design and optimization. Attention is paid also to microkinetic modelling and to the methods based on first principles, to elucidate mechanisms and independently calculate thermodynamic and kinetic parameters. Different case studies support the discussion. At first, we have selected two basic examples from the industrial chemistry practice, e.g., ammonia and methanol synthesis, which may be described through a relatively simple reaction pathway and the relative available kinetic scheme. Then, a more complex reaction network is deeply discussed to define the conversion of bioethanol into syngas/hydrogen or into building blocks, such as ethylene. In this case, lumped kinetic schemes completely fail the description of process behavior. Thus, in this case, more detailed—e.g., microkinetic—schemes should be available to implement into the simulator. However, the correct definition of all the kinetic data when complex microkinetic mechanisms are used, often leads to unreliable, highly correlated parameters. In such cases, greater effort to independently estimate some relevant kinetic/thermodynamic data through Density Functional Theory (DFT)/ab initio methods may be helpful to improve process description.