scholarly journals Soft Sensor Modeling Method Based on SPA-GWO-SVR for Marine Protease Fermentation Process

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhu Li ◽  
Khalil Ur Rehman ◽  
Liu Wenhui ◽  
Faiza Atique
Keyword(s):  
Real Time ◽  
Prediction Accuracy ◽  
Fermentation Process ◽  
Soft Sensor ◽  
Projection Algorithm ◽  
Critical Parameters ◽  
Support Vector ◽  
Sensor Model ◽  
Factor C ◽  
Sensor Modeling

The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process.

Intelligent Control System Based on SVM Combined with Soft Sensor for Microbiological Fermentation

2010 ◽  
Vol 20-23 ◽  
pp. 1185-1191
Author(s):  
Jin Hai Wu ◽  
Tao Cen
Keyword(s):  
Variable Selection ◽  
Fermentation Process ◽  
Information Criterion ◽  
Soft Sensor ◽  
Support Vector ◽  
Intelligent Control System ◽  
Two Factors ◽  
Novel Method ◽  
Input Variables ◽  
Sensor Modeling

The accuracy of SVM in fermentation process is mainly impacted by two factors input variable selection and parameter setting in SVM training procedures. In this paper, a novel method is proposed to solve the problem． The selection problem of SVM parameters and input variables is considered as a compound optimization problem. A new compound optimal objective function based on Akaike information criterion is constructed. In this paper, we propose a new method of soft sensor constructed with generalized support vector machine for microbiological fermentation. Experiment results demonstrate this method is an effective approach for parameter selection and input variable selection and has good performance for soft sensor modeling in microorganism fermentation process.

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.

scholarly journals Soft Sensor Model Based on IBA-LSSVM for Photosynthetic Bacteria Fermentation Process

2021 ◽  
Vol 25 (2) ◽  
pp. 145-158
Author(s):  
Khalil Ur Rehman ◽  
◽  
Xiang Lin Zhu ◽  
Bo Wang ◽  
Muhammad Shahzad ◽  
...  
Keyword(s):  
Real Time ◽  
Photosynthetic Bacteria ◽  
Differential Evolution Algorithm ◽  
Bat Algorithm ◽  
Soft Sensor ◽  
Learning Ability ◽  
Support Vector ◽  
Model Based ◽  
Sensor Model ◽  
Improved Model

It is difficult to measure the key biological process variables of photosynthetic bacteria fermentation in real-time, and offline measurement has a large time lag and cannot meet the needs of real-time optimization control. In this paper, a soft sensor model based on least square support vector machine with an improved bat algorithm (IBA-LSSVM) was proposed. The velocity equation of the bat algorithm (BA) was improved and the random variation operation in differential evolution algorithm was introduced into BA algorithm. Thus, the diversity of the population can be increased, and the global and local searching ability of the BA algorithm can be enhanced. Furthermore, the IBA-LSSVM soft sensor model was established for the living cell concentration and compared with BA-LSSVM soft sensor model. Finally, the simulation results show that the improved model was the better learning ability and prediction performance than BA-LSSVM, the measurement error is 0.1358. The improved model could provide accurate guidance for the photosynthetic bacteria fermentation control optimization. This model has certain practical value.

scholarly journals Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Weili Xiong ◽  
Wei Zhang ◽  
Dengfeng Liu ◽  
Baoguo Xu
Keyword(s):  
Fermentation Process ◽  
Soft Sensor ◽  
Least Square ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Fermentation Processes ◽  
Numerical Examples ◽  
Effectiveness And Efficiency ◽  
Sensor Modeling ◽  
Penicillin Fermentation

Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM), the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.

SOFT SENSOR MODELING FOR VACUUM OIL PURIFICATION MACHINE BASED ON LSSVM

2008 ◽  
Vol 07 (01) ◽  
pp. 141-144 ◽  
Author(s):  
CHUAN LI ◽  
SHILONG WANG ◽  
XIANMING ZHANG ◽  
LING KANG ◽  
JIANJUN MIN
Keyword(s):  
Rough Sets ◽  
Soft Sensor ◽  
Industrial Processes ◽  
Support Vector ◽  
Rough Sets Theory ◽  
Sensor Model ◽  
Oil Purification ◽  
Sets Theory ◽  
Primary Variable ◽  
Sensor Modeling

According to the secondary variables acquired from industrial processes, a Least Squares Support Vector Machine (LSSVM) based model is proposed for the primary variable soft sensing. The Rough Sets Theory is firstly employed to compress values and attributes of the secondary variables. Then the LSSVM is delivered for the primary variable nonlinear estimating. The method is applied for the vacuum oil purification machine. The moisture content in oil, a hard-to-be-measured primary variable, is computed from the soft sensor model. The result shows that the proposed method features a faster and more precise approximation ability.

scholarly journals Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7635
Author(s):  
Bo Wang ◽  
Xingyu Wang ◽  
Mengyi He ◽  
Xianglin Zhu
Keyword(s):  
Pichia Pastoris ◽  
Least Squares ◽  
Fermentation Process ◽  
Model Simulation ◽  
Weighted Least Squares ◽  
Soft Sensor ◽  
Modeling Method ◽  
Local Model ◽  
Support Vector ◽  
Sensor Modeling

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

scholarly journals Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-26 ◽  
Author(s):  
Wei Xie ◽  
Jie-sheng Wang ◽  
Cheng Xing ◽  
Sha-Sha Guo ◽  
Meng-wei Guo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Soft Sensor ◽  
Support Vector ◽  
Structure Parameters ◽  
Generalization Ability ◽  
Sensor Model ◽  
Learning Machine

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

Outlier Detection for Soft-Sensor Modeling Data Based on k-Nearest Neighbor

2012 ◽  
Vol 468-471 ◽  
pp. 2504-2509
Author(s):  
Qiang Da Yang ◽  
Zhen Quan Liu
Keyword(s):  
Outlier Detection ◽  
Nearest Neighbor ◽  
Detection Method ◽  
Soft Sensor ◽  
K Nearest Neighbor ◽  
Sensor Model ◽  
Sensor Technique ◽  
On Line ◽  
Modeling Data ◽  
Sensor Modeling

The on-line estimation of some key hard-to-measure process variables by using soft-sensor technique has received extensive concern in industrial production process. The precision of on-line estimation is closely related to the accuracy of soft-sensor model, while the accuracy of soft-sensor model depends strongly on the accuracy of modeling data. Aiming at the special character of the definition for outliers in soft-sensor modeling process, an outlier detection method based on k-nearest neighbor (k-NN) is proposed in this paper. The proposed method can be realized conveniently from data without priori knowledge and assumption of the process. The simulation result and practical application show that the proposed outlier detection method based on k-NN has good detection effect and high application value.

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