Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control

Ouyang, Minggao; Wang, Yunlong

doi:10.1155/2020/4519428

Cited by 6 publications

(4 citation statements)

References 28 publications

(25 reference statements)

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“…Their approach effectively achieves temperature control by guiding the CSTR to explore the A3C network and learn a control law through a motivating function, thus demonstrating the capability of the A3C algorithm for temperature regulation in CSTR systems. In addressing temperature control challenges in continuously stirred tank reactor (CSTR) processes, Ouyang, Wang, Wu, and Lin propose an innovative approach [41]. They introduce an Improved Sparrow Search Algorithm (ISSA) to optimize PID parameters, enhancing control performance.…”

Section: Continuous Stirred Tank Reactor (Cstr)mentioning

confidence: 99%

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Ahmad

2023

Preprint

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Machine Learning (ML) has brought about a transformative era in reactor operations, reshaping monitoring, control, and optimization strategies. This survey comprehensively explores the diverse spectrum of ML applications in industrial reactors. From real-time sensor analysis ensuring reactor functionality to adaptive control algorithms ensuring stability, ML's impact is profound and multifaceted. The benefits of ML are equally evident in optimization, encompassing performance trend prediction, proactive maintenance, and fine-tuning of operations for enhanced efficiency. This review identifies dominant ML techniques, operations stages receptive to ML integration, core data sources, and critical input-output parameters. Aimed at both academics and practitioners, this exploration enriches reactor technologies, unlocking their full potential through insights driven by ML.

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Section: Continuous Stirred Tank Reactor (Cstr)mentioning

confidence: 99%

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Ahmad

2023

Preprint

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“…Control techniques using feedback linearization established for linear control have been adapted to control nonlinear systems [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

et al. 2021

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This paper tackles the control problem of nonlinear disturbed polynomial systems using the formalism of output feedback linearization and a subsequent sliding mode control design. This aims to ensure the asymptotic stability of an unstable equilibrium point. The class of systems under investigation has an equivalent Byrnes–Isidori normal form, which reveals stable zero dynamics. For the case of modeling uncertainties and/or process dynamic disturbances, conventional feedback linearizing control strategies may fail to be efficient. To design a robust control strategy, meta-heuristic techniques are synthesized with feedback linearization and sliding mode control. The resulting control design guarantees the decoupling of the system output from disturbances and achieves the desired output trajectory tracking with asymptotically stable dynamic behavior. The effectiveness and efficiency of the designed technique were assessed based on a benchmark model of a continuous stirred tank reactor (CSTR) through numerical simulation analysis.

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“…The development of linear ADRC 13 substantially aided the industrial use of ADRC. [14][15][16][17] ADRC has been used for a long time to study the control of large inertial systems, 18 such as chemical production process, 19 fully distributed automatic generation control, 20 distributed parameter system, [21][22][23][24] main steam temperature control. 25 In their work, 26 the authors achieved almost feedback-linearization by employing a low-power extended observer.…”

mentioning

confidence: 99%

“…31 Because the responses induced by the SST system's higher order dynamics are sluggish, a modified ADRC 32 has been proposed to improve the control performance. By enhancing the tracking differentiator with tunable parameters, the control structure and the ESO achieved an enhanced ADRC 19 and realized the optimal control of the systems with nonlinear and large delays.…”

mentioning

confidence: 99%

Robust compensation for active disturbance rejection control of a class of high‐order systems with large time delay: Simulation and experimental studies

Liu,

Wu,

Dai

et al. 2023

Intl J Robust & Nonlinear

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Rejection of unknown disturbances and uncertainties in high‐order nonlinear systems with large delays is an important control problem. Active disturbance rejection control (ADRC) solves disturbances and uncertainties in a simple and effective way. In engineering, low‐order ADRC is often used to control high‐order large‐delay systems because of its ease of implementation and simplicity of operation. However, this leads to the problem that the low‐order ADRC has a limited ability to configure the high‐order nonlinear systems. The desired dynamics and the closed‐loop dynamics are almost always very biased. For a class of high‐order nonlinear systems with delay, a bias feedback compensation (BFC) approach to ADRC is suggested. This strategy works by adding a feedback loop based on the desired dynamics over the ADRC loop. The BFC strategy can improve the poor performance of low‐order ADRC for higher‐order nonlinear systems. In addition, a synthesis parameter tuning procedure of BFC‐ADRC is proposed in this article. Then, the stability problem is analyzed in this article. Finally, high‐fidelity simulation studies on a 1000 MW power plant for superheater steam temperature control and experiments on three interacting water tanks for level control are performed to verify the tracking, disturbance rejection, and robustness of the control strategy. Both simulation and experiments show that the method is highly robust and significantly improves both disturbance rejection and tracking performance.

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Optimization and Realization of the Continuous Reactor with Improved Automatic Disturbance Rejection Control

Cited by 6 publications

References 28 publications

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Advances in Machine Learning for Monitoring, Control, and Optimization of Temperature of Reactors

Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

Robust compensation for active disturbance rejection control of a class of high‐order systems with large time delay: Simulation and experimental studies

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