Distributed Model Predictive Control of an Amine Gas Sweetening Plant

Moharir, Manjiri; Pourkargar, Davood Babaei; Almansoori, Ali; Daoutidis, Pródromos

doi:10.1021/acs.iecr.8b01291

Cited by 21 publications

(11 citation statements)

References 46 publications

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“…Recently, Moharir et al 107,108 introduced a digraph representation for distributed parameter systems (control systems in partial differential equations), and applied the modularitybased community detection to the distributed model predictive control of a gas sweetening plant and a benchmark process with tubular reactors and flash separators. These works also demonstrated computational advantages of community-based decompositions.…”

Section: Modularity-based Decomposition For Distributed Control and Optimizationmentioning

confidence: 99%

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

2019

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Section: Modularity-based Decomposition For Distributed Control and Optimizationmentioning

confidence: 99%

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

2019

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“…Real‐world networks are complex and large scale, represent very interacting entities, and generate diverse types of data. Biological networks, 1 power networks, 2 controlled chemical processes, 3‐6 and many others 7‐9 are examples of complex networks. Moreover, modern manufacturing plants are increasingly integrated, 10,11 leading to structural and computational complexities.…”

Section: Introductionmentioning

confidence: 99%

An efficient algorithm for community detection in complex weighted networks

et al. 2021

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Community detection decomposes large‐scale, complex networks “optimally” into sets of smaller sub‐networks. It finds sub‐networks that have the least inter‐connections and the most intra‐connections. This article presents an efficient community detection algorithm that detects community structures in a weighted network by solving a multi‐objective optimization problem. The whale optimization algorithm is extended to enable it to handle multi‐objective optimization problems with discrete variables and to solve the problems on parallel processors. To this end, the population's positions are discretized using a transfer function that maps real variables to discrete variables, the initialization steps for the algorithm are modified to prevent generating unrealistic connections between variables, and the updating step of the algorithm is redefined to produce integer numbers. To identify the community configurations that are Pareto optimal, the non‐dominated sorting concept is adopted. The proposed algorithm is tested on the Tennessee Eastman process and several benchmark community‐detection problems.

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“…A new distributed model predictive control method presented in [12] coordinates each decomposed subsystem controller through the global performance indicators, also considers the interaction between subsystems, which improves the system global performance. In [13], an amine gas sweetening plant under the DMPC is studied. For linear discrete systems, a novel distributed predictive control algorithm [14] is presented, where each subsystem only needs its neighbor state variable reference trajectory, which reduces the transmission of information.…”

Section: Introductionmentioning

confidence: 99%

Design of Distributed Fractional Order PID Type Dynamic Matrix Controller for Large-Scale Process Systems

Teng

2020

IEEE Access

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As a typical representative of distributed model predictive control, distributed dynamic matrix control (DDMC) is able to satisfy the basic control requirements for large-scale systems. However, the constraints and disturbances in actual industrial process usually lead to the slow set-point target tracking, large overshoot and weak anti-interference ability of the system. Therefore, the relevant requirements may not be met for some complex industrial processes. The existing distributed PID type dynamic matrix control (PID-DDMC) method can improve the control performance, but it maybe not accurate enough in some cases. Based on this background, this paper introduces fractional order PID (FOPID) into distributed dynamic matrix control, and proposes a distributed fractional order PID type dynamic matrix control (FOPID-DDMC) algorithm. To compare with the conventional PID control, it expands the control and parameter setting range of the controller, and makes the control effect of the controller more accurate. Furthermore, the coupling effect among subsystems is dispelled by adopting the Nash optimal theory, and information interaction between the subsystems through network communication is realized, thereby, completing the optimization of the whole large-scale system. Finally, through a numerical simulation example and a level-temperature control process, the feasibility of the proposed algorithm is demonstrated by comparing with the traditional DDMC and PID-DDMC.

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Distributed Model Predictive Control of an Amine Gas Sweetening Plant

Cited by 21 publications

References 46 publications

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology

An efficient algorithm for community detection in complex weighted networks

Design of Distributed Fractional Order PID Type Dynamic Matrix Controller for Large-Scale Process Systems

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