Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

Wang, Jie Sheng; Han, Shuang; Guo, Qiu

doi:10.1007/s00500-013-1068-9

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…where is the output signal of the network output layer, (1) is the output signal of the network middle layer, (1) = [ (1) 1 , (1) 2 , (1) 3 , ∧] and (0) is the input signal of the network input layer, and (2) = [ (2) 0 , (2) 1 , (2) 2 , . .…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…(3) Give the input signals and make the controller output signal correspond to the input signal of the neural decoupling module. (4) According to the selected samples and time interval, adopt (26) to calculate the output of the neural network , (1) , (0) , and values of the process variables changed with time (set = 0 at the begin of calculation).…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…(5) According to (30) and (31), calculate the weight variances of the output layer and middle layer and update weights (1) and (2) . 6Replete steps (4) and 5until reaching the specified iterations to determine (2) and (1) at the end.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…With VCM as a raw material, the suspension method to produce polyvinyl chloride (PVC) resin is a kind of typical intermittent chemical production process [1]. The traditional polymerization monomer conversion ratio is controlled to the scope 85%-90%.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Gao

Yang

Wang

2014

Mathematical Problems in Engineering

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PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN) is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO) subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature). Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Gao

Yang

Wang

2014

Mathematical Problems in Engineering

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“…Among them, the conversion rate and conversion velocity of VCM are the most important factors affecting the quality of PVC. The different VCM conversion has a certain impact on the molecular weight of PVC resin, thermal stability, porosity, the residues of VCM, the absorptivity of plasticizers, and processing liquidity [2]. The conversion rate of VCM is a key factor affecting the thermal aging property of the resin.…”

Section: Introductionmentioning

confidence: 99%

Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

2017

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For predicting the conversion velocity of the vinyl chloride monomer (VCM) in the polymerization process of polyvinylchloride (PVC), an improved Group Method of Data Handling- (GMDH-) type neural network soft-sensor model is proposed. After analyzing the technique of PVC manufacturing process, the auxiliary variables for setting up the soft-sensor model are selected and the experimental data are normalized. Because the internal standard of the original GMDH-type neural cannot solve the problem of multiple-collinearity problem and the useful variables tend to be prematurely eliminated in the modeling process, a hybrid method combining the regression analysis method and the least squares method is proposed to solve the multiple-collinearity problem. On the same time, by adopting some optimization experiences in genetic algorithm (GA), the generational crossover combination variables method is proposed to solve the shortcoming of useful variable being eliminated prematurely. The simulation results show that the proposed soft-sensor model can significantly improve the prediction accuracy of economic and technical indicators in the PVC polymerization process and can meet the real time control requirements of polymerization reactor production process.

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Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm

2021

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Echo state networks based predictive model of vinyl chloride monomer convention velocity optimized by artificial fish swarm algorithm

Cited by 8 publications

References 14 publications

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Soft-Sensor Modeling of PVC Polymerizing Process Based on F-GMDH-Type Neural Network Algorithm

Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm

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