Accelerating process control and optimization via machine learning: A review

The design and operation of chemical processes depend on An alternative approach is to accelerate the solution process decisions spanning a wide range of scales, from the molecular itself by 1) selecting a solution strategy (algorithm selection) up to the enterprise-wide, and constrained by multiple physical and 2) tuning it (algorithm configuration) such that a desired and chemical phenomena [1, 2, 3, 4]. Process control and optimization performance function like solution time is minimized. The acceleration methods provide a systematic framework to identify is usually achieved by exploiting some underlying the best possible decisions in designing and operating a process, property of the decision-making problem. An example is the subject to constraints that emerge from physics or design case of structured decision-making problems, where the structure and operational considerations. Over the last few decades, there can be used as the basis of decomposition-based optimization have been significant advances in both theory and algorithm development algorithms, which are usually faster than monolithic algorithms regarding the control of nonlinear and constrained for large-scale problems [24]. Although this approach process systems [5, 6, 7, 8, 9, 10], as well as the solution of does not compromise solution quality, selecting and tuning a broad classes of optimization problems [11, 12, 13, 14, 15].