Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

Xie, Yingbo; Wang, Ding; Qiao, Junfei

doi:10.1007/s11431-021-1960-7

Cited by 28 publications

(2 citation statements)

References 51 publications

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“…However, in practice, controllers usually require the ability to concurrently achieve multiple performance indices that often conflict. Thus, multiobjective (MO) control designs to simultaneously achieve multiple objectives have been proposed [9][10][11][12][13][14][15][16]. In general, MO controller problems (MOCP) can be classified into two classes: MOCPs with functional constraints and MOCPs with dynamical system constraints.…”

Section: Introductionmentioning

confidence: 99%

A grid‐based searching algorithm for observer‐based multiobjective control of T–S fuzzy stochastic jump‐diffusion systems

Hwang

Chiu

2023

IET Control Theory & Appl

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Most studies on a multiobjective optimal control problem (MOCP) with nonlinear stochastic jump-diffusion system (NSJDS) constraints assume the state vector is available, but in practice, this is not necessarily the case. The observer-based MOCP is worthy of further research. Furthermore, the hybrid multiobjective differential evolution algorithm (HMODEA) is usually employed to help solve MOCPs with dynamical system constraints, and such problems often have a higher computational burden. To address these two issues, a grid-based front-squeezing searching algorithm (GBFSA) is proposed to solve the observer-based MOCP with NSJDS. Takagi-Sugeno (T-S) fuzzy model is used to approximate the NSJDS and convert the problem into an MOCP with linear matrix inequality (LMI) constraints. Then, the GBFSA efficiently searches for the Pareto front by merging the LMIs and the squeezing theorem. To automatically select a preferred Pareto controller, the minimum Manhattan distance (MMD) approch is applied. Mathematical proofs are given to show that the obtained Pareto optimal controller can concurrently stabilize the associated NSJDS and achieve the desired performance indices. In addition, computational complexity and convergence analysis are also provided.

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Section: Introductionmentioning

confidence: 99%

A grid‐based searching algorithm for observer‐based multiobjective control of T–S fuzzy stochastic jump‐diffusion systems

Hwang

Chiu

2023

IET Control Theory & Appl

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“…So, many researchers proposed some multivariable control methods. [18][19][20] For the multivariable control of WWTP, Qiao et al 21 proposed a robust PID controller method, which used genetic algorithm to solve robustness-constrained optimization problems, and this method can realize the control of DO concentration and substrate concentration under various disturbances. However, because of the time-varying characteristic of the WWTP model and the uncertainty of the reaction process, there are some problems when using PID control method, such as poor modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

Long short-term memory-based modeling and controlling method for wastewater treatment process

Yang

She

Wu³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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Considering the characteristics of large time variation, strong coupling, large time delay, and serious interference of wastewater treatment system, this article proposes an offline modeling and online controlling method based on long short-term memory network to improve multivariable control and prediction accuracy under disturbance. First, a prediction model of dissolved oxygen and nitrate nitrogen concentrations is established, and the stability of this long short-term memory-based modeling method is proven via the limitation of learning rate. Second, based on the prediction model, a multivariable long short-term memory-based controller is designed to improve the control accuracy by gradient descent algorithm, and the stability of the long short-term memory-based controller is proved by Lyapunov principle. Finally, based on the Benchmark Simulation Model No.1, the simulation experiments of long short-term memory-based modeling and controlling method, the default proportion–integration control method (proposed in the Benchmark Simulation Model No.1), and model predictive control method are conducted and compared, respectively. The results show the long short-term memory-based method owns both better approximating and controlling performance, and compared with the default proportion–integration and model predictive control, the long short-term memory-based controller reduces the integral squared error of dissolved oxygen and nitrate nitrogen concentrations by more than 94% and 80%, respectively.

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The co-removal of PCDD/Fs by SCR system in a full-scale municipal solid waste incinerator: Migration-transformation and decomposition pathways

Wang

Yan

Xie

et al. 2022

Sci. China Technol. Sci.

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Dynamic multi-objective intelligent optimal control toward wastewater treatment processes

Cited by 28 publications

References 51 publications

A grid‐based searching algorithm for observer‐based multiobjective control of T–S fuzzy stochastic jump‐diffusion systems

A grid‐based searching algorithm for observer‐based multiobjective control of T–S fuzzy stochastic jump‐diffusion systems

Long short-term memory-based modeling and controlling method for wastewater treatment process

The co-removal of PCDD/Fs by SCR system in a full-scale municipal solid waste incinerator: Migration-transformation and decomposition pathways

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