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Open AccessArticle

A Novel Artificial Intelligence Technique to Estimate the Gross Calorific Value of Coal Based on Meta-Heuristic and Support Vector Regression Algorithms

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Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien street, Duc Thang ward, Bac Tu Liem District, Hanoi 100000, Vietnam
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Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien street, Duc Thang ward, Bac Tu Liem District, Hanoi 100000, Vietnam
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Korea
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Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi 100000, Vietnam
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Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi 100000, Vietnam
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Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam
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Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam
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Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz 51368, Iran
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4868; https://doi.org/10.3390/app9224868
Received: 25 September 2019 / Revised: 3 November 2019 / Accepted: 7 November 2019 / Published: 14 November 2019
(This article belongs to the Special Issue Meta-heuristic Algorithms in Engineering)
Gross calorific value (GCV) is one of the essential parameters for evaluating coal quality. Therefore, accurate GCV prediction is one of the primary ways to improve heating value as well as coal production. A novel evolutionary-based predictive system was proposed in this study for predicting GCV with high accuracy, namely the particle swarm optimization (PSO)-support vector regression (SVR) model. It was developed based on the SVR and PSO algorithms. Three different kernel functions were employed to establish the PSO-SVR models, including radial basis function, linear, and polynomial functions. Besides, three benchmark machine learning models including classification and regression trees (CART), multiple linear regression (MLR), and principle component analysis (PCA) were also developed to estimate GCV and then compared with the proposed PSO-SVR model; 2583 coal samples were used to analyze the proximate components and GCV for this study. Then, they were used to develop the mentioned models as well as check their performance in experimental results. Root-mean-squared error (RMSE), correlation coefficient (R2), ranking, and intensity color criteria were used and computed to evaluate the GCV predictive models developed. The results revealed that the proposed PSO-SVR model with radial basis function had better accuracy than the other models. The PSO algorithm was optimized in the SVR model with high efficiency. These should be used as a supporting tool in practical engineering to determine the heating value of coal seams in complex geological conditions. View Full-Text
Keywords: gross calorific value; coal; proximate analyze; artificial intelligence; PSO-SVR gross calorific value; coal; proximate analyze; artificial intelligence; PSO-SVR
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Bui, H.-B.; Nguyen, H.; Choi, Y.; Bui, X.-N.; Nguyen-Thoi, T.; Zandi, Y. A Novel Artificial Intelligence Technique to Estimate the Gross Calorific Value of Coal Based on Meta-Heuristic and Support Vector Regression Algorithms. Appl. Sci. 2019, 9, 4868.

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