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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects Volume 39, 2017 - Issue 12
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Articles

Prediction of grinding behavior of low-grade coal based on its moisture loss by neural networks

Mahmut AltınerDepartment of Mining Engineering, Çukurova University, Adana, TurkeyCorrespondence[email protected]
ORCID Iconhttp://orcid.org/0000-0002-7428-5999View further author information
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Yusuf KuvvetliDepartment of Industrial Engineering, Çukurova University, Adana, TurkeyView further author information
Pages 1250-1257 | Published online: 22 May 2017

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Alireza Gholami & Hamid Khoshdast. (2020) Using artificial neural networks for the intelligent estimation of selectivity index and metallurgical responses of a sample coal bioflotation by rhamnolipid biosurfactants. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 0:0, pages 1-19.
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Yongbin Liu, Xing Wang, Tingdan Tan, Xiaoxun Ma & Long Xu. (2020) Prediction of direct coal liquefaction residue catalytic gasification based on back propagation neural network. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 0:0, pages 1-16.
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Articles from other publishers (1)

Hamid Khoshdast, Alireza Gholami, Ahmad Hassanzadeh, Tomasz Niedoba & Agnieszka Surowiak. (2021) Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms. Materials 14:11, pages 2880.
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