Synergistic and Intelligent Process Optimization: First Results and Open Challenges

Abstract

Data science has become an important research topic across scientific disciplines. In Process Systems Engineering, one attempt to create true value from process data is to use it proactively to improve the quality and accuracy of production planning as often a schedule based on statistical average data is outdated already when reaching the plant floor. Thus, due to the hierarchical planning structures, it is difficult to quickly adapt a schedule to changing conditions. This challenge has also been investigated in integration of scheduling and control studies (Touretzky et al. AIChE J. 2017, 63 (66), 1959−1973. The project SINGPRO investigated the merging of big data platforms, machine learning, and data analytics with process planning and scheduling optimization. The goal was to create online, reactive, and anticipative tools for more sustainable and efficient operation. In this article, we discuss selected outcomes of the project and reflect the topic of combining optimization and data science in a broader scope.

Integration of planning, scheduling, and control: A review and new perspectives

“…[ 127 ] As a result, there is an increasing interest in a synergistic combination of big data platforms, ML, and data analytics with process planning and scheduling optimization. [ 128 ] Moreover, RL holds the potential as an efficient support or alternative to the classical MPC. [ 129 ] Therefore, iPSC models could be obtained using a proper combination of ML and mathematical programming methods.…”