Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machin...

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Bibliographic Details
Published in:Materials 2021-11, Vol.14 (23), p.7232
Main Authors: Anton, Costel, Curteanu, Silvia, Lisa, Cătălin, Leon, Florin
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Cost control
Environmental protection
Exhaust emission
Gases
Kilns
Laboratories
Machine learning
Manufacturing
Mechanical properties
Multiple objective analysis
Municipal solid waste
Neural networks
Optimization
Pareto optimization
Pollutants
Productivity
Pulp & paper mills
Raw materials
Regression models
Shear strength
Software
VOCs
Volatile organic compounds
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Summary:Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.
ISSN:1996-1944
1996-1944
DOI:10.3390/ma14237232