Integrated deep learning-production planning-economic model predictive control framework for large-scale processes. A fluid catalytic cracker-fractionator case study

In this paper, we seek a tighter coordination between the production planning and process control layers in the process decision-making hierarchy, with the purpose of reducing the economic gap between planned and realized production. We introduce deep learning-based frameworks that support a stochastic production planning (PP) formulation that integrates economic model predictive control (EMPC) and PP via a feedback mechanism that relays the effect of process disturbances (traditionally dealt with by the control layer) to the planning layer.Our framework demonstrates superior economic performance, promising solution times (potentially allow-ing industrial implementation) and a close match of planned and realized process economics compared to a traditional industrial linear and non-integrated EMPC-PP benchmark.