(91c) Integrated Multiscale Modeling-Simulation (MMS) and Machine Learning (ML)- Based Design and Development of Novel Systems, and Processes

Traditionally, the development of novel systems and technologies is complemented by testing. Experimental tools for testing and examining the results are expensive and their use is time consuming. In recent years, thanks to the rapid growth in the computational speed, it is seen that the use of computer aided methods in the systems’ design phase contributes greatly to the reduction of the cost and time for the entire technology development process. Experimental methods can improve the computational model by incorporating new data, while computational tools can use this advanced model to analyze structure, properties, and optimization by examining a wide set of possible configurations. Moreover, the simultaneous use of computational and experimental tools allows to tackle problems that cannot be solved using theoretical or experimental methods itself. At this context, overcoming the challenges which hinders commercialization of the technology requires hierarchical theoretical and experimental approaches. Thus, in this session, the focus is the comprehensive understanding and application of Integrated Multiscale Modelling-Simulation (MMS) and Machine Learning (ML) approach which provides new insights for acceleration on the commercialization/utilization for the design and development of novel technologies. The practice of the main concepts is demonstrated through an example of process intensification by reactive-separation systems in power generation process (Integrated Gasification Combined Cycle power plant, intensified by hybrid Membrane Reactor/Adsorptive Reactor system) for simultaneous hydrogen production and CO2 Capture.