Process Optimization Continued

Process Optimization is undergoing a paradigm shift. In the past, good knowledge about the process, its components, and clear objectives for the optimization were a sufficient starting point for working with a process simulator. Today, process optimization comes from many different directions: Modular plants, autonomous plants, smart maintenance, surrogate modeling, metaheuristics, and cluster computing. New enablers for process optimization are digitization, which results in the Digital Twin and collaborative workflows, and machine learning, which allows to build gray-box models combining rigorous thermodynamic simulation and plant data. This session focuses on the methods and tools available for process optimization.