Rule-Based Intelligent System for Variable Importance Measurement and Prediction of Ash Fusion Indexes

摘要

Ash fusion temperatures [AFTs: initial deformation temperature (IDT), softening temperature (ST), and fluid temperature (FT)] are standard keys to estimate behavior of ash oxide for using coal and controlling the slag making at boilers. In this study, the modeling of AFTs based on ash oxide contents for 6537 U.S. coal samples have been investigated by a rule based intelligent system (RBIS). Variable importance measurements (VIMs) of RBIS through the database, indicated that Al2O3 contents in coal samples have the highest importance for prediction of AFTs. The RBIS model based on various rules was generated for predictions of IDT, ST, and FT. A comparison between RBIS and other typical predictive models [linear regression, genetic algorithm neural network (GA-NN), and multilayer perceptron trained by back-propagation algorithm (MLP-BP)] was implemented to assess the capability of this purposed predictive model. Results indicated that RBIS can quite satisfactory predict AFTs, where R-2 for IDT, ST, and FT for the testing stage of models was over 0.82 and differences between actual and RBIS-predicted values for over 80% of data were less than 100 degrees C. These comprehensive results indicated that the RIBS method can be used for the industry sector to model AFT of coal samples and predict their fouling behavior before feeding them to boilers. Moreover, outcomes of this investigation are introducing RBIS as a powerful method for modeling other complicated problems in coal geology, fuel, and energy sectors.