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• We present a novel framework for completely integrated scheduling and model predictive control of chemical processes. • The controller behavior is represented fully and explicitly by including the MPC KKT conditions directly in the scheduling formulation. • Two case studies demonstrate that schedules obtained using knowledge of closed-loop dynamics lead to increased profit compared with "open-loop" scheduling approaches. Competitive global market conditions and the availability of real-time pricing data call for agile and flexible operations in the chemical process industries. As the operating paradigm shifts, process scheduling decisions may consider a shorter time scale, and interactions with the process control layer become significant. The integration of these two decision-making layers is an active area of research. In this work, we propose a novel framework for the integration of production scheduling and linear model predictive control. Our approach explicitly represents the closed-loop response of the system by embedding the KKT conditions of the controller in a nonlinear programming formulation of the optimal scheduling problem. We present two case studies which demonstrate that control-informed scheduling leads to superior realized performance compared with a hierarchical, sequential decision-making structure. [ABSTRACT FROM AUTHOR]

• A framework for integrated scheduling and control of chemical processes. • Data-driven Hammerstein–Wiener models are used to represent schedule-relevant variables. • Parametric representations of Hammerstein–Wiener models are derived to reduce computational effort. • Two case studies demonstrate significant reduction in solution times compared with prior works. Global competition and increasingly complex product slates and supply chains motivate a continuous drive towards enterprise-wide optimization and integrated decision-making in the chemical process industries. Integration of production scheduling and process control poses particular challenges: the resulting optimization problems tend to be high-dimensional and nonlinear, calling for development of new computational methods. In this work, we propose a novel modeling framework for integrated scheduling and control. We build on existing methods which use data-driven Hammerstein–Wiener models to represent the dynamics of (scheduling-relevant) process variables. This model structure is leveraged to reduce the size of the scheduling optimization problem, by identifying parsimonious parametric representations of the underlying dynamics. The advantages of the approach are demonstrated on two case studies, in which the computational effort is shown to be significantly reduced compared to existing methods, while still capturing the relevant process dynamics. [ABSTRACT FROM AUTHOR]