(4b) Digital Twins & Smart Manufacturing –Where Data Analytics Meet Modeling & Optimization

Modeling and Optimization constitute core competencies of process systems and chemical engineering fields, and play pivotal role in the analysis, simulation, design and operational activities that we perform daily in order to improve the performance and sustainability of energy and manufacturing processes and supply chains. The re-emergence of AI and Big Data revolution that we are all witnessing at the moment, also provide an opportunity and a platform toward real-time decision-making. While often perceived as different fields, in the context of Smart Manufacturing, Data Analytics and Modeling/Optimization can in fact be viewed as ‘two sides of the same coin’.

In this lecture, we will attempt to elucidate this argument through the lenses of hybrid modeling and digital twins, which bring together the advantages and power of high-fidelity modeling & optimization and AI/machine learning technologies and tools, and their synergy. A central theme will be that the evolution/revolution in data-driven methods in the context of process modeling and automation is becoming a critical component in advancing the energy & manufacturing transition agenda toward clean energy, increased process/energy efficiency, sustainability and resilience, and ultimately economic and societal prosperity.