Dynamic multi-objective optimization control for wastewater treatment process

Qiao, Junfei; Zhang, Wei

doi:10.1007/s00521-016-2642-8

Cited by 69 publications

(21 citation statements)

References 22 publications

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“…The influence degree of each burden surface feature on production indicators is calculated by Eqs. (16) and (17) and the results are demonstrated in Fig. 9.…”

Section: ) Generation Of Corrected Values Based On Feedback Compensamentioning

confidence: 91%

“…Recently, the requirement of energy-saving production and high-quality products for the complex industrial process has risen significantly. Operational optimization methodology for the process production indicators has been successfully applied to some other industrial processes except BF ironmaking process, including flotation process [14], hematite grinding process [15], [16], waster treatment [17], etc. The similarity among these tasks is that they use data-based approaches for operational decisions of "black box" processes.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in energy-efficient state. In the proposed scheme, production indicators prediction models are firstly developed using kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multi-objective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which, an improved association rules mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

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“…The influence degree of each burden surface feature on production indicators is calculated by Eqs. (16) and (17) and the results are demonstrated in Fig. 9.…”

Section: ) Generation Of Corrected Values Based On Feedback Compensamentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

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“…where a n is a weight connecting input to the nth hidden node, b n is the bias of the nth hidden node, β is the hidden layer-to-output layer weight vector. Substituting the approximate solution (14) into 7and boundary conditions (8), we can obtain an equation system of weights β, and the new equation system is…”

Section: Legendre Basis Function Neural Network For Solving Odesmentioning

confidence: 99%

“…With the development of artificial intelligence and computer technology, more and more researchers have developed a keen interest in neural network methods. Neural networks have been used in many fields such as pattern recognition [11], graphics processing [12], risk assessment [13], control systems [14], forecasting [15][16][17][18], and classification [19], showing wide application prospects. Based on the advantages of neural network methods, the use of neural network function approximation capabilities [20][21][22] has led to the development of a number of adopted neural network model for solving differential equations.…”

Section: Introductionmentioning

confidence: 99%

“…. , n, suppose that the approximate solution of one-dimensional differential equations is given by(14). If we have any m + 1 distinct samples (x, f), for any a n , b n randomly chosen from any intervals of R, respectively, according to any continuous probability distribution, then the hidden layer output matrix H of Legendre network is invertible and Hβ -T = 0.Proof According to Legendre network, for any m + 1 arbitrary and distinct samples (x, f), with x = [x 0 , .…”

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confidence: 99%

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A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods

2018

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This paper develops a Legendre neural network method (LNN) for solving linear and nonlinear ordinary differential equations (ODEs), system of ordinary differential equations (SODEs), as well as classic Emden-Fowler equations. The Legendre polynomial is chosen as a basis function of hidden neurons. A single hidden layer Legendre neural network is used to eliminate the hidden layer by expanding the input pattern using Legendre polynomials. The improved extreme learning machine (IELM) algorithm is used for network weights training when solving algebraic equation systems, and several algorithm steps are summed up. Convergence was analyzed theoretically to support the proposed method. In order to demonstrate the performance of the method, various testing problems are solved by the proposed approach. A comparative study with other approaches such as conventional methods and latest research work reported in the literature are described in detail to validate the superiority of the method. Experimental results show that the proposed Legendre network with IELM algorithm requires fewer neurons to outperform the numerical algorithm in the latest literature in terms of accuracy and execution time.

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Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system

Wang

Zhao

Qiao

2021

Intl J Robust & Nonlinear

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The wastewater treatment is an effective method for alleviating the shortage of water resources. In this article, a data‐driven iterative adaptive tracking control approach is developed to improve the control performance of the dissolved oxygen concentration and the nitrate nitrogen concentration in the nonlinear wastewater treatment plant. First, the model network is established to obtain the steady control and evaluate the new system state. Then, a nonquadratic performance functional is provided to handle asymmetric control constraints. Moreover, the new costate function and the tracking control policy are derived by using the dual heuristic dynamic programming algorithm. In the present control scheme, two neural networks are constructed to approximate the costate function and the tracking control law. Finally, the feasibility of the proposed algorithm is confirmed by applying the designed strategy to the wastewater treatment plant.

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Dynamic multi-objective optimization control for wastewater treatment process

Cited by 69 publications

References 22 publications

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods

Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system

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