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Modified hierarchical strategy for transient performance improvement of the ORC based waste heat recovery system

ENERGY 2022年第PartA期261卷

作者： Shi, Yao Zhang, Zhiming Xie, Lei Wu, Xialai Liu, Xueqin Amy Lu, Shan Su, Hongye Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Huzhou Univ 759 East 2nd Rd Huzhou 313000 Peoples R China Queens Univ Belfast Belfast BT29 5AH North Ireland Inst Intelligence Sci & Engn Shenzhen Polytech 4089 Shahe West Rd Shenzhen Peoples R China

Organic Rankine Cycle (ORC) system outperforms in the low-grade waste heat recovery and has found its wide application in engineering practice. Due to the strong coupling multivariable nature of the ORC power unit, the traditional hierarchical control scheme, which is composed of real-time optimization (RTO) in the upper layer and model predictive control (MPC) in the lower one, is usually utilized in the control of the ORC system for good economic performance. The upper RTO solves an economic objective function in real time and obtains the optimal steady-state setpoints which are sent to the lower MPC layer for tracking. However, the disadvantage of the traditional hierarchical control scheme lies in its neglection to the transient characteristics with the evolution of the ORC system, which will finally lead to poor overall control performance. To address this problem, in this article, a modified hierarchical control strategy which is denoted as RTO-PFFDMC grounded on pseudo feedforward dynamic matrix control (PFFDMC) algorithm is proposed. The novel PFFDMC presented in this article is applied to replace the traditional dynamic matrix control (DMC) in the lower layer and thus achieve the closed-loop reference trajectory of ORC system expected by field engineers as closely as possible without cumbersome adjustment of tuning parameters. Simulations are performed to verify the effectiveness of the proposed strategy on transient performance in comparison with the traditional one.

关键词： Organic Rankine Cycle Waste heat recovery dynamic matrix control Feedforward compensator Closed-loop performance

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Optimal control of pH Value in Wet Flue Gas Desulfurization Process Based on Model Predictive control 33

Optimal Control of pH Value in Wet Flue Gas Desulfurization ...

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33rd Chinese control and Decision Conference (CCDC)

作者： Dong, Jiawei Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Engn Res Ctr Digital Community Beijing 100124 Peoples R China Commun Univ China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9781665440899

In the flue gas desulfurization control system, the pH value of the absorption tower slurry plays a vital role in the desulfurization efficiency of the system. Aiming at the non-linearity and large lag of the slurry pH change process in the wet flue gas desulfurization (WFGD) process, a model predictive control (MPC) method based on the dynamic matrix control (DMC) algorithm is designed to realize the precise tracking control of the slurry pH value. The simulation results show that, compared with the traditional PID control algorithm, the MPC method based on the DMC algorithm has better tracking ability and higher control accuracy for the setting inputs and fluctuations of the slurry pH value. This method could further satisfies the system real-time control requirements. In addition, the model predictive control method can effectively suppress the stochastic interference in the WFGD process, and enhance the stability and anti-interference ability of the system.

关键词： Wet Flue Gas Desulfurization Slurry pH Value Model Predictive control dynamic matrix control

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Dual-mode fast DMC algorithm for the control of ORC based waste heat recovery system

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ENERGY 2022年第PartA期244卷 122664-122664页

作者： Shi, Yao Lin, Runze Wu, Xialai Zhang, Zhiming Sun, Pei Xie, Lei Su, Hongye Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Inst Intelligence Sci & Engn Shenzhen Polytech Shenzhen 518055 Peoples R China Huzhou Univ 759 East 2nd Rd Huzhou 313000 Peoples R China

Organic Rankine Cycle (ORC) system stands out in low-grade waste heat recovery for its outstanding performance. Considering the fluctuating nature of the heat source, model predictive control (MPC) is used to control the ORC systems and has achieved good performance. However, with the increase of the number of variables and the expansion of the control/prediction horizon, MPC will take longer calcu-lation time. This may bring about the incapacity to obtain optimization problem solutions in one sam-pling instant and finally, lead to the failure of the setpoint tracking. To address this problem, in this article, a dual-mode fast dynamic matrix control (FDMC) algorithm grounded on singular value decomposition (SVD) and dynamic matrix control (DMC) is proposed. The presented algorithm features in its calculation speed improvement on the premise of ensuring tracking performance. Two simulations on disturbance rejection and setpoint tracking are performed to verify the effectiveness and rapidity of the proposed algorithm in comparison with the traditional DMC approach.(c) 2021 Elsevier Ltd. All rights reserved.

关键词： dynamic matrix control Organic rankine cycle Waste heat recovery Singular value decomposition Dual-mode

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Greenhouse air temperature predictive control using the dynamic matrix control

Greenhouse air temperature predictive control using the Dyna...

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International Conference on Intelligent control and Information Processing

作者： Z. T. Xu Z. Y. Yao L. J. Chen S. F. Du China Agricultural University

ISBN: (纸本)9781467362481

In order to make an accurate prediction of the greenhouse environment parameters, this paper proposes a controlling method for greenhouse prediction. Targeting that the former greenhouse model is not precise enough, and that the model parameters are difficult to determine, the paper firstly makes an analysis of the technical difficulties, and then proposes a prediction method based on dynamic matrix control. This method is structural simple, stable to control, and highly precise. It can accurately predict the greenhouse environment parameters through a combination of simulation, and then achieve the final goal of controlling those parameters effectively.

关键词： air temperature greenhouses Prediction methods dynamic matrix control highly accurate Predictive control model parameters matrices Standing Orders control Methods

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On the interaction and integration of production planning and (advanced) process control

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COMPUTERS & CHEMICAL ENGINEERING 2020年 133卷

作者： Santander, Omar Betts, Christopher L. Archer, Erin E. Baldea, Michael Univ Texas Austin McKetta Dept Chem Engn Austin TX 78712 USA Marathon Petr Corp San Antonio TX USA Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

Process operations typically follow a hierarchical decision-making structure,where the flow of information is predominantly "downwards:" business functions (planning/scheduling) provide targets for the control layers. Promoting "upwards" information flow has recently received significant consideration, in view of integrating decision-making and creating feedback mechanisms that improve process economics. This work considers the interaction of production planning and process control, motivated by the practical observation that control decisions may have a strong "butterfly" effect on the implementation of a production plan, which may induce a significant gap between planned and realized profit. We trace this issue to the practice of sharing process degrees of freedom as decision variables in both the planning and control problems. A new planning approach is proposed, whereby potentially shared degrees of freedom are reserved for process control, and represented as uncertain parameters in the planning problem. The statistical properties of the parameters are learned from historical process operating data. An extensive case study is presented to support the theoretical developments. (C) 2019 Published by Elsevier Ltd.

关键词： Production planning Integrated planning and control Model predictive control Process performance dynamic matrix control

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A Predictive controller Based on dynamic matrix control for a Non-minimum Phase Robot Manipulator

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2012年第3期10卷 574-581页

作者： Eshaghi, Shima Kharrati, Hamed Badamchizadeh, Mohammad Ali Hasanzadeh, Iraj Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

In this paper a hybrid control strategy is presented based on dynamic matrix control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.

关键词： dynamic matrix control feedback linearization nonlinearity dynamic non-minimum phase behavior predictive control robotic manipulator

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Development of Embedded Temperature Regulator Based on DMC Fast Algorithm

Development of Embedded Temperature Regulator Based on DMC F...

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第34届中国控制与决策会议

作者： Zhen-He Wu Zhong Zhao College of Information Science and Technology Beijing University of Chemical Technology

A fast dynamic matrix control(DMC) algorithm is proposed to satisfy the requirements of the embedded temperature *** exists online high-dimension matrix inversion in the DMC control algorithm,so it is difficult to apply to the embedded temperature regulator with limited computing *** this work,the high-dimension matrix is decomposed into the product of a triangular matrix and its transpose matrix by Cholesky decomposition,and the inversion of the matrix calculation could be calculated *** on the proposed algorithm,an embedded temperature regulator has been ***,the computation cost of the fast algorithm is greatly reduced compared to the traditional DMC *** experimental results have verified that the algorithm can be applied to the embedded temperature regulator and provides better regulation effect than that with the PID control algorithm.

关键词： dynamic matrix control Fast Algorithm Embedded Regulator Temperature control

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Two-time dimensional dynamic matrix control for batch processes with convergence analysis against the 2D interval uncertainty

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JOURNAL OF PROCESS control 2012年第5期22卷 899-914页

作者： Mo, Shengyong Wang, Limin Yao, Yuan Gao, Furong Hong Kong Univ Sci & Technol Dept Chem & Bimol Engn Kowloon Hong Kong Peoples R China Liaoning Shihua Univ Coll Sci Fushun 113001 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu Taiwan Hong Kong Univ Sci & Technol Natl Engn Res Ctr Ind Automat S China Fok Ying Tung Grad Sch Kowloon Hong Kong Peoples R China

A batch process can be treated as a 2-dimentional (2D) system with a time dimension within each batch and a batch dimension from batch to batch. This paper integrates the learning ability of iterative learning control (ILC) into the prediction model of model predictive control (MPC). Based on this integrated model, a 2D dynamic matrix control (2D-DMC) algorithm with a feedback control and an optimal feed-forward control is proposed. The sufficient conditions for exponentially asymptotic and monotonic convergence of the proposed 2D-DMC are established with proof under certain assumptions, in the presence of not only the completely repeatable uncertainties but also the non-repeatable interval uncertainties. The effectiveness of the proposed control scheme is tested through simulation and experimental implementation in the context of injection molding, a typical batch process. The results show that the batch process control performance is significantly improved. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Iterative learning control dynamic matrix control Two-dimensional (2D) system Batch process

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A distributed model predictive control algorithm with the gap metric output feedback decoupling

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COMPUTERS & CHEMICAL ENGINEERING 2021年 145卷 107167-107167页

作者： Wang, Jiaorao Xu, Zuhua Song, Chunyue Yao, Yi Zhao, Jun Zhejiang Univ Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

A distributed model predictive control (DMPC) algorithm with the gap metric output feedback decoupling is proposed. For large-scale systems including strongly coupled subsystems and inaccessible states, we initially address the gap metric output feedback decoupling to weakly decouple the subsystems. The gap metric is used to analyze the similarity between the original subsystems and the decoupled ones so that subsystems' interactions are decreased while their dynamic characteristics are unchanged. With the decoupling, reducing the communication between controllers will not limit control performance. Then a new transmission strategy is developed to improve computational efficiency of DMPC. At each sampling time, controllers communicate with each other only once and solve local control laws based on the previous optimal solutions. Furthermore, the output feedback is weighted as an input compensation to reduce the influence of model mismatch. Case studies on a reactor separator process demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Large-scale systems Distributed model predictive control dynamic matrix control Gap metric Output feedback decoupling

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On Fractional Order Predictive PI controller Design for a MIMO Reactive Distillation Process 21st

On Fractional Order Predictive PI Controller Design for a MI...

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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Edet, Emmanuel Univ Chichester Dept Engn & Design Bognor Regis Campus Bognor Regis England

In this paper, fractional-order predictive PID control scheme is applied in composition control of boiling fractions of a reactive distillation plant set up for the esterification reaction between acetic acid and ethanol. The controller shares similar structural features with Model-based Predictive controller (MPC) as many attractive benefits of dynamic matrix control (DMC) are retained such as constraint handling capability. However, it optimizes a different objective function formulated to achieve a more robust control action. These robust properties make it attractive to multivariable process control applications. Process's state space model is assumed to be available and the model is augmented for prediction of future outputs. Thereafter, a structured cost function is defined which retains the design objective of fractional-order predictive PI controller. Optimisation of this cost function results in realising a near-optimal MIMO controller with reduced input control efforts. Simulation studies using Giwa's reactive distillation column demonstrates better control performance over dynamic matrix control of the same system. It also rejects disturbances, both measured and unknown disturbances, better than dynamic matrix control system under similar conditions. A major contribution of this paper is the development of a MIMO fractional order predictive PI controller for multivariable process control applications such as composition control of a reactive distillation column. Copyright (C) 2020 The Authors.

关键词： Model-based predictive control Fractional order PID control Linear multivariable control Process control dynamic matrix control

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