A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon

The phenomenon of Coal–Oil agglomeration for recovering the coal fines by agitating the coal-water slurries in oil is often practised by coal industry to ensure a safe and healthy environment. Experimental procedure for implementing this phenomenon is complex which involves three main mechanisms: crushing, ultimate and proximate analysis. Past studies have often focused on studying this phenomenon by the application of statistical modelling based on response surface designs. The response surface designs hold an assumption of pre-definition of the model structure, which may introduce uncertainty in the predictive ability of the model. Alternatively, the computational intelligence approach of Genetic programming (GP) that evolves the explicit models automatically can be used. However, the effective functioning of GP is often affected by its nature of producing the models of complex size. Therefore, this work develops a hybrid computational intelligence approach namely, Support vector regression-GP (SVR-GP) to study the coal-oil agglomeration phenomenon. Experimental studies based on five inputs, namely, oil dosage, agitation speed, agglomeration time, temperature, and pH are used to measure the organic matter recovery (OMR (%)) from the coal water slurries. A hybrid computational intelligence approach of SVR-GP is proposed in formulating the relationship between OMR (%) and the five inputs. The performance comparison and validation of the SVR-GP model is done based on the coefficient of determination, root mean square error and mean absolute percentage error. 2-D and 3-D surface analysis based on parametric and sensitivity approach is then conducted on the proposed model to find the relevant relationships between OMR (%) and inputs. The findings suggest that the pH of coal slurry has a significant effect on the OMR (%) and hence is important for reducing coal waste generation and promoting a cleaner environment.