ABSTRACT
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Process models are used extensively in the design and analysis of chemical processes and process equipment. Such models are either sets of differential or algebraic equations that theoretically describe the features of a process system of interest to the designer, or heuristic or self-learning models that have been developed from process data. Process models enable the prediction of the system’s performance and thus enhance the understanding of the system while reducing the need for extensive experimental efforts. Models are derived to predict performance of a chemical process at steady state, dynamic behavior of a process, flow patterns inside process equipment, or even physical properties at a molecular level. The mathematical complexity of a model depends greatly upon its purpose, which determines the level of detail that is required to be captured by the model, the size of the system that is to be modeled, and the length scales to be considered. Process models can be developed with the aim of simulating the performance of a given system or of exploiting degrees of freedom to determine optimal choices for process design and operation. Optimization models offer the advantage that they incorporate decision-making capabilities, whereas simulation models enable the testing of systems for which there are no degrees of freedom, i.e., systems for which all design and operational decisions have been made by the engineer.
