Machine Learning And Data-driven Techniques For Complex Industrial Processes

This Special Issue aims to address the state-of-the-art advances in research and applications of machine learning and data-driven techniques for complex industrial processes. Topics of interest include, but are not limited to: data-driven methods in chemical processes, production scheduling & optimization, process control & optimization, process modeling, production design, process control, optimization and scheduling, and digital twins for smart manufacturing. Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website.