scholarly journals Mathematical modeling of a cement raw-material blending process using a neural network

2016 ◽  
Vol 50 (4) ◽  
pp. 485-490 ◽  
Author(s):  
Aysun Egrisogut Tiryaki ◽  
Recep Kozan ◽  
Nurettin Gokhan Adar
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Raw Material ◽  
Blending Process ◽  
Cement Raw Material

scholarly journals Modeling and Optimization of Cement Raw Materials Blending Process

2012 ◽  
Vol 2012 ◽  
pp. 1-30 ◽  
Author(s):  
Xianhong Li ◽  
Haibin Yu ◽  
Mingzhe Yuan
Keyword(s):  
Interior Point Method ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Raw Materials ◽  
Raw Material ◽  
Constraint Optimization ◽  
Nonlinear Constraint ◽  
Blending Process ◽  
Ratio Optimization ◽  
Cement Raw Material

This paper focuses on modelling and solving the ingredient ratio optimization problem in cement raw material blending process. A general nonlinear time-varying (G-NLTV) model is established for cement raw material blending process via considering chemical composition, feed flow fluctuation, and various craft and production constraints. Different objective functions are presented to acquire optimal ingredient ratios under various production requirements. The ingredient ratio optimization problem is transformed into discrete-time single objective or multiple objectives rolling nonlinear constraint optimization problem. A framework of grid interior point method is presented to solve the rolling nonlinear constraint optimization problem. Based on MATLAB-GUI platform, the corresponding ingredient ratio software is devised to obtain optimal ingredient ratio. Finally, several numerical examples are presented to study and solve ingredient ratio optimization problems.

Prediction of raw meal fineness in the grinding process of cement raw material: A two-dimensional convolutional neural network prediction method

2020 ◽  
pp. 095965182096544
Author(s):  
Gang Liu ◽  
Zhiyong Ouyang ◽  
Xiaochen Hao ◽  
Xin Shi ◽  
Lizhao Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Raw Material ◽  
Grinding Process ◽  
Time Varying ◽  
Two Dimensional ◽  
Time Varying Delay ◽  
Coupling Time ◽  
Varying Delay ◽  
Cement Raw Material

Raw meal fineness is the percentage content of 80 µm sieving residue after the cement raw material is ground. The accurate prediction of raw meal fineness in the vertical mill system is very helpful for the operator to control the vertical mill. However, due to the complexity of the industrial environment, the process variables have coupling, time-varying delay and nonlinear characteristics in the grinding process of cement raw material. At present, few people pay attention to the coupling characteristics among variables, thus solving this problem is particularly important in raw meal fineness prediction. In this article, we propose a two-dimensional convolutional neural network method that is used to predict raw meal fineness during the grinding process of raw material. Convolutional neural network has strong feature extraction capabilities and does not require manual feature selection. The two-dimensional convolution kernels are used to extract the coupling, time-varying delay and nonlinear features among variables, especially the coupling features. In addition, two important parameters P and L of two-dimensional convolutional neural network model are optimized, respectively. The optimized model solves the problems of coupling, time-varying delay and nonlinearity among variables. Our two-dimensional convolutional neural network model is proved to be very effective by comparing with the state-of-the-art methods.

Cement raw material quality analysis using laser-induced breakdown spectroscopy

2016 ◽  
Vol 31 (12) ◽  
pp. 2384-2390 ◽  
Author(s):  
Hualiang Yin ◽  
Zongyu Hou ◽  
Lei Zhang ◽  
Xiangjie Zhang ◽  
Zhe Wang ◽  
...  
Keyword(s):  
Raw Material ◽  
Quality Analysis ◽  
Laser Induced Breakdown Spectroscopy ◽  
Standardization Method ◽  
Material Quality ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Cement Raw Material

The capability of LIBS analysis of the cement raw material is improved by using a new spectrum standardization method.

Identification of Wool and Cashmere Based on Texture Analysis

2015 ◽  
Vol 671 ◽  
pp. 385-390 ◽  
Author(s):  
Sen Lin Yuan ◽  
Kai Lu ◽  
Yue Qi Zhong
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Texture Analysis ◽  
Texture Feature ◽  
Digital Camera ◽  
Texture Image ◽  
Feasible Method ◽  
Microscopic Images ◽  
Texture Parameters ◽  
Network Experiment

In order to separate wool from cashmere efficiently, an identification method based on texture analysis was proposed in this paper. The microscopic images captured by CCD digital camera were preprocessed as the texture image. Improved Tamura texture feature were employed to analyzing the final texture images and to attaining the texture parameters. Through a large number of samples, the mathematical modeling was completed by using neural network. Experiment results indicate that texture analysis can be a feasible method to identify cashmere and wool.

Self-tuning adaptive control of cement raw material blending

Automatica ◽  
1978 ◽  
Vol 14 (6) ◽  
pp. 525-532 ◽  
Author(s):  
László Keviczky ◽  
Jenö Hetthéssy ◽  
Miklós Hilger ◽  
János Kolostori
Keyword(s):  
Adaptive Control ◽  
Raw Material ◽  
Self Tuning ◽  
Cement Raw Material
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