Soft sensor for apparent degree of calcination in NSP cement production line

Yuan, Zhugang; Liu, Hui

doi:10.1109/iccae.2010.5451912

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…Artificial neural networks (ANN) and support vector machines (SVM) are two examples of machine learning techniques that have demonstrated their efficacy in this area. For calculating the apparent degree of calcination, Gang and Hui [64] created a model utilizing a least Squares support vector radial basis function (RBF) kerneled machine (LSSVM). The temperature and pressure of the furnace, the calciner's outlet temperature and pressure, the temperature of the tertiary air, and the quantity of cement raw that was laid off were all inputs into the model.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Boakye,

Fenton,

Simske

2023

JMMP

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This study uses machine learning methods to model different stages of the calcination process in cement, with the goal of improving knowledge of the generation of CO2 during cement manufacturing. Calcination is necessary to determine the clinker quality, energy needs, and CO2 emissions in a cement-producing facility. Due to the intricacy of the calcination process, it has historically been challenging to precisely anticipate the CO2 produced. The purpose of this study is to determine a direct association between CO2 generation from the manufacture of raw materials and the process factors. In this paper, six machine learning techniques are investigated to explore two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. CO2 molecular composition (dry basis) sensitivity analysis uses over 6000 historical manufacturing health data points as input variables, and the results are used to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models is examined, and the models are then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable has the biggest impact on CO2 emissions, the significance of the other factors is also assessed.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Algorithm to Predict CO2 Using a Cement Manufacturing Historic Production Variables Dataset: A Case Study at Union Bridge Plant, Heidelberg Materials, Maryland

Boakye,

Fenton,

Simske

2023

JMMP

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“…A. Process of Soft Measurement First of all, auxiliary variables should be chosen according to the production process, and then some data preprocessing should be made [3,4]. Based on the processed data, a fuzzy controller is designed to achieve the goal of a soft measuring.…”

Section: Design Of the Soft Measurement Systemmentioning

confidence: 99%

“…The rule table is shown in TABLEI. The weighted average method is used to make the ambigurious value clear as (4). ( )…”

Section: Design Of Fuzzy Controllermentioning

confidence: 99%

The Application of Fuzzy Reasoning in Cement Particle Size Soft Sensor

Luan

Meng

Shen

2013

AMM

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In the cement production process, cement quality is the most important indicator. One way to measure the quality of the cement is the cement particle size. Only the cement particle size is symmetrical and the specific surface area could reach the national standard that this kind of cement is qualified products. In actual production, the specific surface area is tested in laboratory through experiment. This process takes a long time, and the instantaneous value of specific surface area can not be got. This paper, auxiliary variables coming from the practical production site are used to get an instantaneous value through fuzzy control algorithm and other rules. VC++ 6.0 is used to program this algorithm. After a long period of actual operation, this method is proved to be effective, the instantaneous specific surface area is got and the precision is acceptable. This could guide the operator to change parameters of the equipment timely. This could be widely used and is easy to spread.

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