A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data

Energy ◽  
2017 ◽  
Vol 124 ◽  
pp. 284-294 ◽  
Author(s):  
You Lv ◽  
Feng Hong ◽  
Tingting Yang ◽  
Fang Fang ◽  
Jizhen Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Fluidized Bed ◽  
Dynamic Model ◽  
Circulating Fluidized Bed ◽  
Support Vector ◽  
Temperature Prediction ◽  
Bed Temperature ◽  
Fluidized Bed Boilers ◽  
Operational Data

Sensor Dynamic Modeling Based on LS-SVM and NGA

2008 ◽  
Vol 381-382 ◽  
pp. 439-442
Author(s):  
Qi Wang ◽  
Zhi Gang Feng ◽  
K. Shida
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Least Squares ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Support Vector ◽  
Local Minima ◽  
Highly Nonlinear ◽  
Sharing Function

Least squares support vector machine (LS-SVM) combined with niche genetic algorithm (NGA) are proposed for nonlinear sensor dynamic modeling. Compared with neural networks, the LS-SVM can overcome the shortcomings of local minima and over fitting, and has higher generalization performance. The sharing function based niche genetic algorithm is used to select the LS-SVM parameters automatically. The effectiveness and reliability of this method are demonstrated in two examples. The results show that this approach can escape from the blindness of man-made choice of LS-SVM parameters. It is still effective even if the sensor dynamic model is highly nonlinear.

The Temperature Prediction in Blast Furnace Base on Fuzzy Least Squares Support Vector Machine

2013 ◽  
Vol 336-338 ◽  
pp. 566-569
Author(s):  
Qing Xin Zhang ◽  
Yong Tao ◽  
Zhan Bo Cui
Keyword(s):  
Support Vector Machine ◽  
Blast Furnace ◽  
Least Squares ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Furnace Temperature ◽  
Temperature Prediction ◽  
Data Description ◽  
Simulation Results

The temperature prediction in blast furnace loses accuracy or Forecasts failure when the temperatures change is at normal levels and obvious. This paper introduces fuzzy membership of samples basing on support vector data description and the fuzzy least squares support vector machine to forecast the blast furnace temperature. Then the simulation was done by using the forecast samples and the model after training by MATLAB. Comparing the simulation results of LS-FSVM with LS-SVM, the model basing on LS-FSVM enhances anti-jamming ability. The accuracy of the temperature prediction in blast furnace promotes significantly when the temperature of blast furnace fluctuates.

scholarly journals Comprehensive Simulator (CSFMB) Applied to Circulating Fluidized Bed Boilers and Gasifiers

2008 ◽  
Vol 2 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Marcio L. de Souza-Santos
Keyword(s):  
Fluidized Bed ◽  
Fluidized Beds ◽  
Circulating Fluidized Bed ◽  
Scaling Up ◽  
Simulation Program ◽  
Circulating Fluidized Beds ◽  
Simulation Results ◽  
Fluidized Bed Boilers ◽  
Operational Data ◽  
Industrial Units

A comprehensive simulation program of bubbling fluidized beds (CSFB or CSFMB) has been able to reproduce operations of circulating fluidized beds. The main considerations that allow such application as well comparisons between simulation results and real operational data are shown. In addition, criteria for scaling-up pilot operations to industrial units are illustrated.

Q-learning System Based on Cooperative Least Squares Support Vector Machine

2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Learning System ◽  
Support Vector ◽  
Q Learning

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

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