Application of Artificial Neural Networks for Evaluating Pressure Filtration of Coal Refuse Slurries

Gireesh S.S. Raman, Mark S. Klima

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Bench-scale pressure filtration testing was performed to evaluate the dewatering characteristics of two coal refuse slurries, which were collected from thickener underflow streams of two coal preparation plants. Pressure filtration provides an opportunity to produce drier solids (filter cake) that can be stacked or mixed with the coarse refuse and improved water (filtrate) recovery that can be reused in the plant. This paper examines the effects of some of the major influencing variables such as pressure, slurry pH, feed solids concentration, fines fraction of solids in the slurry, filtration time, and temperature on dewatering thickener underflow slurry. Experimental results indicated that the overall filtrate flux increased with increase in pressure and temperature while it decreased with increase in fines fraction, pH, filtration time, and solids concentration. A total of 82 experimental results were used to develop a feed forward back-propagating artificial neural network (ANN) model. The model had R2 values over 0.9 for both the training and the testing datasets, indicating the goodness of fit. Sensitivity analysis performed using the ANN model indicated that filtration time and pH were the most significant variables influencing filtrate flux.

Original languageEnglish (US)
Pages (from-to)47-53
Number of pages7
JournalMineral Processing and Extractive Metallurgy Review
Issue number1
StatePublished - Jan 2 2017

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • Geotechnical Engineering and Engineering Geology
  • Mechanical Engineering
  • Economic Geology


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