This paper demonstrates how the design and control of processes described by large-scale, complex, mixed-integerdynamic models can be simultaneously optimised in the face of time-varying disturbances and parametric u...
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This paper demonstrates how the design and control of processes described by large-scale, complex, mixed-integerdynamic models can be simultaneously optimised in the face of time-varying disturbances and parametric uncertainties. A rigorously modelled distillation example is used for this purpose, where the number of trays, feed location, column diameter, surface areas of the heat exchangers and tuning parameters of the controllers are selected in order to minimise the total annualised cost of the system, while satisfying a large number of feasibility constraints. (C) 2000 Elsevier Science Ltd. All rights reserved.
We present a detailed dynamic model for a reactive distillation system, based on which we develop design-dependent explicit optimal control strategies. A mixed-integer dynamic optimization formulation is then proposed...
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We present a detailed dynamic model for a reactive distillation system, based on which we develop design-dependent explicit optimal control strategies. A mixed-integer dynamic optimization formulation is then proposed integrating design, control and operational components, the solution of which allows us to derive explicit closed-loop strategies that maintain stable and operable conditions in the presence of process disturbances. The simultaneous approach is illustrated with the design of a methyl tert-butyl ether reactive distillation system example as a part of the developed model library in the RAPID SYNOPSIS project. (C) 2020 Elsevier Ltd. All rights reserved.
Volatile electricity prices make demand response attractive for processes that can modulate their production rate. However, if nonlinear dynamic processes must be scheduled simultaneously with their local multi-energy...
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Volatile electricity prices make demand response attractive for processes that can modulate their production rate. However, if nonlinear dynamic processes must be scheduled simultaneously with their local multi-energy system, the resulting scheduling optimization problems often cannot be solved in real time. For single-input single-output processes, the problem can be simplified without sacrificing feasibility by dynamic ramping constraints that define a derivative of the production rate as the ramping degree of freedom. In this work, we extend dynamic ramping constraints to flat multi-input multi-output processes by a coordinate transformation that gives the true nonlinear ramping limits. Approximating these ramping limits by piecewise affine functions gives a mixed-integer linear formulation that guarantees feasible operation. As a case study, dynamic ramping constraints are derived for a heated reactor-separator process that is subsequently scheduled simultaneously with its multi-energy system. The dynamic ramping formulation bridges the gap between rigorous process models and simplified process representations for real-time scheduling.
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