Economic MPC with terminal cost and application to an oilsand primary separation vessel

Wastewater treatment is an integral component in the sustainable development of our society. Optimal control and operation is critical to the efficiency and economics of a wastewater treatment plant. In this work, we apply economic model predictive control (EMPC) to a wastewater treatment plant and compare its performance with two commonly used control methods. Specifically, we take advantage of the benchmark simulation model no. 1 provided by the International Water Association to simulate a biological wastewater treatment plant. A computationally efficient EMPC developed recently is adopted in this work to optimize the effluent quality and operating cost directly. The performance of the EMPC is compared with a proportional-integral (PI) control scheme and a regular tracking model predictive control (MPC) scheme from different perspectives including effluent quality and operating cost. The simulation results demonstrate that EMPC has the potential to significantly improve effluent quality and reduce operating cost simultaneously compared with PI and MPC schemes.

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“…The three schemes considered are as follows: (I) PI control, (II) tracking MPC, and (III) economic MPC with terminal cost developed in ref 17.…”

Section: Description Of Control Methodsmentioning

confidence: 99%

Economic Model Predictive Control of Wastewater Treatment Processes

Zeng

Liu

2015

Ind. Eng. Chem. Res.

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“…Recently, economic MPC (EMPC), which is formulated with a stage cost representing the process economics, has been proposed which merges economic considerations with feedback control [23][24][25][26] (see, also, the survey papers 27,28 and the references therein). The use of a general stage cost introduces additional complexity in assessing closed-loop stability.…”

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confidence: 99%

Economic model predictive control of nonlinear time‐delay systems: Closed‐loop stability and delay compensation

Ellis

Christofides

2015

AIChE Journal

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in Wiley Online Library (wileyonlinelibrary.com) Closed-loop stability of nonlinear time-delay systems under Lyapunov-based economic model predictive control (LEMPC) is considered. LEMPC is initially formulated with an ordinary differential equation model and is designed on the basis of an explicit stabilizing control law. To address closed-loop stability under LEMPC, first, we consider the stability properties of the sampled-data system resulting from the nonlinear continuous-time delay system with state and input delay under a sample-and-hold implementation of the explicit controller. The steady-state of this sampled-data closed-loop system is shown to be practically stable. Second, conditions such that closed-loop stability, in the sense of boundedness of the closedloop state, under LEMPC are derived. A chemical process example is used to demonstrate that indeed closed-loop stability is maintained under LEMPC for sufficiently small time-delays. To cope with performance degradation owing to the effect of input delay, a predictor feedback LEMPC methodology is also proposed. The predictor feedback LEMPC design employs a predictor to compute a prediction of the state after the input delay period and an LEMPC scheme that is formulated with a differential difference equation (DDE) model, which describes the time-delay system, initialized with the predicted state. The predictor feedback LEMPC is also applied to the chemical process example and yields improved closed-loop stability and economic performance properties. V C 2015 American Institute of Chemical Engineers AIChE J, 61: 4152-4165, 2015 Keywords: economic model predictive control, nonlinear time-delay systems, process control, process economics, process optimization IntroductionWithin the context of control of chemical process systems, time-delays resulting from computation and communication delays are enviable albeit these time-delays may be small and insignificant depending on the magnitude of the time-delay relative to the time-constants of the process dynamics. From a modeling perspective, time-delays are often employed to describe and/or to approximate the dynamics of transportation of material through the process system (e.g., flow through pipes), control actuator dynamics, measurement sensor dynamics, and high-order dynamic behavior. Thus, robustness with respect to closed-loop stability and performance of control systems to time-delays is an important consideration. Moreover, many chemical process systems have significant nonlinear behavior owing to complex reaction mechanisms, Arrhenius reaction-rate dependence on temperature, and thermodynamic relationships which adds additional complexity in considering the closed-loop behavior of the system resulting from a nonlinear time-delay system under a control law.Dynamic models of systems that involve nonlinearities and time-delays are systems of nonlinear differential difference equations (DDEs). Systems described by DDEs are fundamentally different than systems described by ordinary differential equat...

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“…This means that, in EMPC, an economic cost function or objective is directly optimized instead of tracking a predetermined set-point [13,14]. To date, EMPC has had some successful applications in different processes [15][16][17][18][19]. In the studies of Zeng et al [15] and Liu et al [16], for example, EMPC was applied to improve the effluent quality of a waste water treatment process and the operation of an oil sands primary separation vessel, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…To date, EMPC has had some successful applications in different processes [15][16][17][18][19]. In the studies of Zeng et al [15] and Liu et al [16], for example, EMPC was applied to improve the effluent quality of a waste water treatment process and the operation of an oil sands primary separation vessel, respectively. Idris et al [17] applied EMPC to a continuous catalytic distillation process whileMendoza-Serrano et al [18] and Touretzky et al [19] applied EMPC to temperature control in buildings.…”

Section: Introductionmentioning

confidence: 99%

Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control

Decardi‐Nelson

Liu

2018

Processes

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Abstract:To reduce CO 2 emissions from power plants, electricity companies have diversified their generation sources. Fossil fuels, however, still remain an integral energy generation source as they are more reliable compared to the renewable energy sources. This diversification as well as changing electricity demand could hinder effective economical operation of an amine-based post-combustion CO 2 capture (PCC) plant attached to the power plant to reduce CO 2 emissions. This is as a result of large fluctuations in the flue gas flow rate and unavailability of steam from the power plant. To tackle this problem, efficient control algorithms are necessary. In this work, tracking and economic model predictive controllers are applied to a PCC plant and their economic performance is compared under different scenarios. The results show that economic model predictive control has a potential to improve the economic performance and energy efficiency of the amine-based PCC process up to 6% and 7%, respectively, over conventional model predictive control.

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Economic MPC with terminal cost and application to an oilsand primary separation vessel

Cited by 27 publications

References 37 publications

Economic Model Predictive Control of Wastewater Treatment Processes

Economic Model Predictive Control of Wastewater Treatment Processes

Economic model predictive control of nonlinear time‐delay systems: Closed‐loop stability and delay compensation

Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control

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