Using artificial neural networks for the intelligent estimation of selectivity index and metallurgical responses of a sample coal bioflotation by rhamnolipid biosurfactants

ABSTRACT

The present study investigated the effects of rhamnolipid (RL) biosurfactant on the flotation performance of a coal sample. The significance of the effect of RL on the system responses, including ash content, coal recovery, yield and separation efficiency, and process kinetics and selectivity, was statistically assessed using One-Way Analysis of Variance (ANOVA) methodology. The ANOVA results revealed that adding RL to the system significantly challenged the studied metallurgical aspects. Briefly, RL biosurfactant depressed coal flotation through physical interaction with the surface of coal particles through chemical bonding between the carboxyl group in the RL structure with those on the surface of coal particles. The potential mechanisms involved were schematically proposed. The correlation between the condition of RL addition and process responses was modeled using the Artificial Neural Network (ANN) approach. Rested in the mean squared error (MSE), root mean squared error (RMSE), and percentage error as the measures of model accuracy, the Levenberg-Marquardt algorithm (LMA) with [7–16-1] structure was found to be the most reliable algorithm to predict the process response. As evidenced, the correlation coefficient values of test data were 98.09%, 96.93%, 98.37%, 98.46%, 99.50%, and 97.37% for ash content, coal recovery, yield, separation efficiency, the rate constant, and selectivity index, respectively. These values confirmed that the process could be simulated using the ANN method with an appropriate structure.

KEYWORDS:

• Coal
• rhamnolipid biosurfactant
• bioflotation
• simulation
• artificial neural network

Additional information

Notes on contributors

Alireza Gholami

Alireza Gholami received B.Sc. in Mining Engineering from Higher Education Complex of Zarand in 2018 and now is a senior in M.Sc. in Mineral Processing at Tarbiat Modares University. He is interested in process automation and mineral processing methods and working for Talavaran Pars Faravar Company Ltd. as a mineral processing engineer. His research interests include hydrometallurgy and flotation of valuable minerals, applications of data mining and machine learning in mineral processing, and mineral processing plant design and optimization.

Hamid Khoshdast

Hamid Khoshdast received Ph.D degree in Mineral Processing and Bioprocessing from Shahid Bahonar University of Kerman in 2012. He is working as Assistant Professor in the Department of Mining Engineering at Higher Education Complex of Zarand. He has ten years of work experience in the field of Academic and Industry. His research interests are mineral bio- and processing, waste water bio- and treatment, and process development, optimizations and simulation.