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Integrated process design, scheduling, and control using multiparametric programming
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DOI：10.1016/j.compchemeng.2019.03.004.png)
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Abstract
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A unified theory and framework for the integration of process design, control, and scheduling based on a single high fidelity model is presented. The framework features (i) a mixed-integer dynamic optimization (MIDO) formulation with design, scheduling, and control considerations, and (ii) a multiparametric optimization strategy for the derivation of offline/explicit maps of optimal receding horizon policies. Explicit model predictive control schemes are developed as a function of design and scheduling decisions, and similarly design dependent scheduling policies are derived accounting for the closed-loop dynamics. Inherent multi-scale gap issues are addressed by an offline design dependent surrogate model. The proposed framwork is illustrated by two example problems, a system of two continuous stirred tank reactor, and a small residential combined heat and power (CHP) network. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords:
Enterprise-wide optimization
Integration
Multi-parametric programming
Process scheduling
Model predictive control
Process design
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