scholarly journals Adaptive Soft-Sensor Modeling of SMB Chromatographic Separation Process Based on Dynamic Fuzzy Neural Network and Moving Window Strategy

2021 ◽  
Vol 54 (12) ◽  
pp. 657-671
Author(s):  
Dan Wang ◽  
Jie-Sheng Wang ◽  
Shao-Yan Wang ◽  
Cheng Xing
Keyword(s):  
Neural Network ◽  
Chromatographic Separation ◽  
Fuzzy Neural Network ◽  
Soft Sensor ◽  
Separation Process ◽  
Moving Window ◽  
Fuzzy Neural ◽  
Sensor Modeling

Designing a Soft Sensor with the Weighted Fuzzy Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 472-475 ◽  
Author(s):  
Yan Sun ◽  
Ming Ma
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Soft Sensor ◽  
Soft Sensors ◽  
Sensor Model ◽  
Modeling Process ◽  
Inferential Model ◽  
Fuzzy Neural ◽  
Process Output ◽  
Sensor Modeling

Soft sensors are algorithms capable of estimate the process output that can not be measured directly in real time. A data-driven soft sensor is an inferential model developed from process observations. In this paper, the soft sensor modeling process based on the weighted fuzzy neural network was discussed. The proposed algorithm based on genetic algorithm and particle swarm optimization could obtain a near-optimal structure of fuzzy neural network, and the numerical experiments show that the soft sensor model has good performance.

Soft sensor modelling of propylene conversion based on a Takagi-Sugeno fuzzy neural network optimized with independent component analysis and mutual information

2018 ◽  
Vol 41 (3) ◽  
pp. 737-748 ◽  
Author(s):  
Shuting Liu ◽  
Xianwen Gao ◽  
Wenhai Qi ◽  
Shumei Zhang
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Independent Component Analysis ◽  
Fuzzy Neural Network ◽  
Component Analysis ◽  
Soft Sensor ◽  
Independent Component ◽  
Fuzzy Neural ◽  
Propylene Conversion ◽  
Sensor Modelling

Propylene conversion is important to economic efficiency in the production of acrylic acid. Hence, the online measurement of propylene conversion is becoming more and more important. The current measurement method is mainly uses an offline meteorological chromatography analyser, which is difficult to measure accurately in real time. A soft sensor modelling method of propylene conversion based on Takagi-Sugeno (T-S) fuzzy neural network optimized by independent component analysis and mutual information is proposed in this paper. Firstly, fast independent component analysis-based denoising strategy is developed to remove the noise in the measurement of variables influenced by propylene conversion. Then, a mutual information-based variable selection method is proposed to select the key variables from multitudinous variables to reduce the influence of weak correlation. Finally, a T-S fuzzy neural network algorithm is employed to forecast the propylene conversion in the process of propylene oxidation. Simulation results show that the proposed soft sensor modelling method has better prediction accuracy and generalization ability. The method of this paper is obvious and effective.

Soft Sensor Modeling of Biological Fermentation Process Based on tPSO-FNN

2011 ◽  
Vol 464 ◽  
pp. 482-486
Author(s):  
Li Huang ◽  
Yu Kun Sun ◽  
Xiao Fu Ji ◽  
Yong Hong Huang ◽  
Tian Yan Du
Keyword(s):  
Fermentation Process ◽  
Fuzzy Neural Network ◽  
Soft Sensor ◽  
Dynamic Coupling ◽  
Swarm Optimization ◽  
Model Based ◽  
Sensor Model ◽  
Fuzzy Neural ◽  
On Line ◽  
Sensor Modeling

Biological fermentation process is a complex nonlinear dynamic coupling process. As it is very difficult to measure the key biological parameters on line, the process control is unavailable to industrial production in time. In this respect, however, soft sensing can solve the above problem. To overcome some drawbacks of PSO and FNN, such as falling into local minimum occasionally and slow convergence speed, the extremum disturbed particle swarm optimization (tPSO) algorithm is proposed and then combined with fuzzy neural network (FNN) to optimize the network parameters. Furthermore, the tPSO-FNN is applied in the soft sensor modeling of lysine biological fermentation. Experiment results show that the model proposed could measure the key parameters. And the soft sensor model based on tPSO-FNN has higher precision and better performance than the model based on FNN.

Application of Fuzzy Neural Network in Chromatographic Separation

2021 ◽  
pp. 193-200
Author(s):  
Chao-Fan Xie ◽  
Lu-Xiong Xu ◽  
Ruobin Wang ◽  
Fuquan Zhang
Keyword(s):  
Neural Network ◽  
Chromatographic Separation ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural
Sign in / Sign up

Export Citation Format

Share Document