Dynamic models of dense medium coal preparation using sensitivity analysis and reinforcement prey predator optimization

Dense Medium Coal Preparation (DMCP) is currently the main method for coal washing in China.Its dynamic model is the foundation of characteristic analysis,optimization and control.Therefore,an accurate dynamic model is the key to achieve intelligent coal preparation.Motivated by this goal,the mechanism knowledge,statistical analysis and artificial intelligence methods were integrated to construct a knowledge data driven dynamic model for DMCP process.Firstly,using the material balance principle,a mechanism model including slurry mixing process,dense medium separation process and recovery process was established.Furthermore,low discrepancy Sobol’ sequence was used in the variance based Sobol’ sensitivity analysis to analyze the sensitivity of mechanism model parameters,thereby finding the key model parameters that played a leading role on the model,such as the slurry separation ratio and the ash ratio constant in the overflow and underflow.In this way,only the important model parameters should be optimized and the unimportant model parameters could be assigned within a certain range,which would reduce the calculation cost in the subsequent optimization algorithm.Meanwhile,to improve the accuracy of model parameter optimization,a Reinforcement Prey Predator Optimization (RPPO) algorithm was proposed with the idea of reinforcement learning.The RPPO algorithm can make search agent adaptively adjust the step size by learn the historical information,which solves the local optimum problem caused by the constant step size.In the experimental study,the ranges of model parameters were firstly set according to the process knowledge and engineering experience.Then the unimportant model parameters were selected randomly from this ranges and fixed,while the key model parameters were optimized using RPPO algorithm according to the practical data.The results show that the RPPO algorithm can improve the search ability for the optimal key model parameters,making the model outputs closest to the actual measurement data.The root mean square error of the proposed model and the standard deviation of probability density are smaller than the other models,proving the effectiveness and accuracy of the proposed model.This study provides important model foundation for the intelligent system research and implementation of DMCP process.