Multiscale process systems engineering—analysis and design of chemical and energy systems from molecular design up to process optimization

Abstract

As indicated by Grossmann and Westerberg [1], a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place (see Fig. 1). The first step of multiscale process modeling is to connect the molecular level with the phase level, where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level information. Typically, quantum chemical (QC) computation, molecular simulation, and equations of state are used to provide such predictions. Recently, due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools, data-driven descriptor models have been developed and widely used for property predictions [2]. With the properties of the system, it is then possible to derive the constitutive relations (e.g., kinetics and phase equilibria) and implement them into the mass, energy, and momentum conservations of each process unit. Taking into account the connections between different units, one can finally scale the system up toward the process level where process modeling, intensification, and optimization are performed to maximize the economic and environmental performances of the process. It is clear that domain knowledge (e.g., chemical engineering) as well as data science and systems engineering methods are essential for an adequate description or modeling of a

Decision Making for Control of the Gasoline Fraction Hydrotreating Process in a Fuzzy Environment

“…Based on the processing of statistical data, expert information and the method of building fuzzy models [5,41], the structural identification of models describing the quality of hydrogenate depending on the input, mode parameters was carried out x i , i = 1, 5 in the form of the following fuzzy multiple regression equations:…”