Soft-sensing model of oxygen content based on data fusion

2005 ◽  
Author(s):  
Ji-Zhen Liu ◽  
Zheng Zhao ◽  
De-Liang Zeng ◽  
Yan-Qiao Chen
Keyword(s):  
Data Fusion ◽  
Oxygen Content ◽  
Soft Sensing ◽  
Sensing Model

Soft-Sensing Model of Deformation of Welded Steel Structure Based on FLS-SVM and its Application

2014 ◽  
Vol 628 ◽  
pp. 152-156
Author(s):  
Ji Ping Lei ◽  
Jian Mei Chen
Keyword(s):  
Relative Error ◽  
Welding Current ◽  
Steel Structure ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Practical Application ◽  
Soft Sensing ◽  
Welded Steel ◽  
Welded Structure ◽  
Sensing Model

To effectively achieve rapid and high-precision measurements of the deformation of steel welded structure, multiple sets of the actual experimental data of steel welded structure are used as the samples, the soft-sensing model of deformation of welded steel structure, which uses the welding current I, the welding voltage U, the welding speed v and the flow of gas qm as arguments, is established by fuzzy least squares support vector machine, and adaptive genetic algorithm is used to optimize the number of positive gasification rules c and the parameters of kernel function σ, training, testing and practical application results show, the optimization of 200 steps, the training relative error which become saturated is 2.43%, the testing relative error is less than 2.45%.

scholarly journals System Control Scheme and Design Idea of Soft Sensing Model for Heavy Metal Ion Concentration

2021 ◽  
Vol 2033 (1) ◽  
pp. 012193
Author(s):  
Tianqi Wang ◽  
Yucheng Long ◽  
Xingchen Wang ◽  
San Zhang ◽  
Haiming Wang
Keyword(s):  
Heavy Metal ◽  
Metal Ion ◽  
Ion Concentration ◽  
Heavy Metal Ion ◽  
System Control ◽  
Soft Sensing ◽  
Control Scheme ◽  
Sensing Model ◽  
Design Idea

scholarly journals Study of emulsion explosive quality assessment system based on soft measurement and multilevel fuzzy evaluation

2018 ◽  
Vol 189 ◽  
pp. 05003
Author(s):  
Yuesheng Wang ◽  
Chenqi Huang
Keyword(s):  
Quality Assessment ◽  
Detonation Velocity ◽  
Evaluation System ◽  
Assessment System ◽  
Fuzzy Evaluation ◽  
Emulsion Explosives ◽  
Soft Sensing ◽  
Soft Measurement ◽  
Sensing Model

Aiming at the inaccuracy and blindness of the traditional detection and assessment methods for the quality of emulsion explosives, a method for evaluating the quality of emulsion explosives based on soft measurement and multilevel fuzzy evaluation is proposed. The soft-sensing model of BP neural network can predict the online unmeasured performance indicators of detonation velocity and detonation online. The multilevel fuzzy evaluation method establishes the reliable multilevel fuzzy evaluation system based on the key parameters of the production process and expert experience. Experiments show that this soft-sensing model have made the convergence quickly and the accuracy highly,and can accurately predicts the detonation velocity and brisance of emulsion explosives online. In the last part, the design of quality assessment system can provide a new idea for solving the point of quality blindness detection and assessment.

scholarly journals Soft sensing of coal quality

2015 ◽  
Vol 19 (1) ◽  
pp. 231-242 ◽  
Author(s):  
Zheng Zhao ◽  
Deliang Zeng ◽  
Yong Hu ◽  
Shan Gao
Keyword(s):  
System Optimization ◽  
Limited Range ◽  
Exhaust Gas ◽  
Coal Quality ◽  
Soft Sensing ◽  
Free Basis ◽  
Modeling Process ◽  
Sensing Model ◽  
Mechanism Modeling ◽  
Lower Limits

This study presents a soft sensing model of coal quality for utility boilers. This model is based on the coal quality information obtained from exhaust gas. The mechanism modeling method combined with data driving theory is used in the modeling process. The procedure for solving the nonlinear equations for coal quality applies the inner loop iteration of dry ash-free basis of S (Sdaf) and outside loop iteration of dry ash-free basis of N (Ndaf) within a limited range, and dry ash-free basis of C (Cdaf) is searched from the entire range during outside loop iteration. The upper and lower limits of Ndaf are defined according to the NOX content in the exhaust gas, thereby solving the iterative initial value selection problem. Finally, the effectiveness of the proposed method is verified via several simulations and comparisons, the results show that this method is credible and effective and it can be used in power plant for control system optimization.

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