Ironmaking Process Parameter Analysis and Optimization for the Blast Furnace

2009 ◽  
Vol 620-622 ◽  
pp. 21-24
Author(s):  
Shuang Ping Yang ◽  
Yong Hui Song ◽  
Liu Hua Xin
Keyword(s):  
Optimal Operation ◽  
Partial Least Square ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Evaluation Indicator ◽  
Least Square Regression ◽  
Calculation Results ◽  
Set Up ◽  
Reasonable Description

With practical data of the BF ironmaking from Jiuquan Iron&Steel Cooperation Ltd. (JISC), taking the quality of pig iron as evaluation indicator, mathematical models based on the least square regression and partial least square regression were set up respectively by co-relation analysis of feeding-to-product interval of the BF processing. The calculation results showed that the reasonable description can be obtained by the partial least square regression model; and 10 of 29 parameters with obvious impact on the BF operation were listed accordingly. Meanwhile, an optimal group of parameters was found by genetic algorism calculation method. The optimal index of the group was 99.13％. This study is beneficial to the improvement of feeding adjustment and optimal operation of BF ironmaking.

Mechanical Parameter Analysis of Rock Material via PLSR Model

2011 ◽  
Vol 467-469 ◽  
pp. 1826-1831 ◽  
Author(s):  
Zao Bao Liu ◽  
Wei Ya Xu ◽  
Fei Xu ◽  
Lin Wei Wang
Keyword(s):  
Regression Model ◽  
Principal Component ◽  
Partial Least Square ◽  
Rock Material ◽  
Least Square ◽  
Parameter Analysis ◽  
Partial Least Square Regression ◽  
Mechanical Parameter ◽  
Least Square Regression

Mechanical parameter analysis is a complicated issue since it is influenced by many factors. Closely related with the influencing factors of compressibility coefficients of rock material (sandstone), this article first introduces the way to process partial least square regression (PLSR) analysis. The process of carrying out PLSR is divided into six steps as for analysis and prediction of the regression model, which are data preparation, principle collection, regression model for first principle component, secondary principle analysis, establishment of final regression model and number determination of principal component l. And then introduces PLSR for application of analysis and prediction of compressibility coefficients with 30 experiment samples. Seven prediction samples are carried out by PLSR with the training process of 30 samples. The result shows PLSR has good accuracy in prediction under the condition that the model is properly deprived based on certain experimental samples. Finally, some conclusions are made for further study on both mechanical parameters and partial least square regression method.

Modeling of Piezoelectric Ceramics Based on Partial Least-Square Regression Method

2010 ◽  
Vol 139-141 ◽  
pp. 13-16
Author(s):  
Xue Jun Zhu ◽  
Zhi Wen Zhu
Keyword(s):  
Strain Curve ◽  
Piezoelectric Ceramics ◽  
Significance Test ◽  
Regression Method ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Hysteresis Phenomenon ◽  
Least Square Regression ◽  
Set Up

In this paper, a kind of piezoelectric ceramics model based on hysteretic nonlinear theory has been developed. Van de Pol nonlinear difference item was introduced to interpret the hysteresis phenomenon of the voltage-strain curve of piezoelectric ceramics. The coupling relationship between voltage and stress was obtained in partial least-square regression method to describe the driftage phenomenon of the voltage-strain curve in different stress. Based on above, the final relationship among strain, stress and voltage was set up. The results of significance test showed that the new model could describe the hysteresis characteristics of piezoelectric ceramics in different stress well. The new piezoelectric ceramics model considers the effect of stress, and is easy to be analyzed in theory, which is helpful to vibration control.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

2020 ◽  
Vol 27 (35) ◽  
pp. 43439-43451 ◽  
Author(s):  
Jianfeng Yang ◽  
Yumin Duan ◽  
Xiaoni Yang ◽  
Mukesh Kumar Awasthi ◽  
Huike Li ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Co2 Exchange ◽  
Apple Orchard ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Square Regression

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 36 (06) ◽  
Author(s):  
NGUYEN MINH QUANG ◽  
TRAN NGUYEN MINH AN ◽  
NGUYEN HOANG MINH ◽  
TRAN XUAN MAU ◽  
PHAM VAN TAT
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stability Constants ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Structure Property ◽  
Least Square Regression ◽  
Thiosemicarbazone Complexes ◽  
Artificial Neural

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

Modeling of Shape Memory Alloy Based on Hysteretic Nonlinear Theory

2010 ◽  
Vol 44-47 ◽  
pp. 537-541 ◽  
Author(s):  
Zhi Wen Zhu ◽  
Jin Wang ◽  
Jia Xu
Keyword(s):  
Nonlinear Theory ◽  
Significance Test ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Hysteresis Phenomenon ◽  
Final Model ◽  
Stress Curve ◽  
Strain Stress ◽  
Set Up

In this paper, a kind of SMA model based on hysteretic nonlinear theory was developed. Von del Pol nonlinear difference item was introduced to interpret the hysteresis phenomenon of strain-stress curve of SMA. The coupling relationship between strain and temperature was obtained in partial least-square regression method to describe the variation of stiffness with temperature. Based on above, the final relationship among strain, stress and temperature was set up. The result of significance test shows that the final model can describe the characteristics of SMA in different temperature well. The new SMA model broadens the region of temperature, and is easy to be analyzed in theory, which is helpful to application of SMA in engineering fields.

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