Prediction of coke quality using adaptive neurofuzzy inference system

Abstract

The production rate, quality and cost of hot metal production through the blast furnace route depend mainly on the quality of the metallurgical coke. Coke reactivity index and coke strength after reaction (CSR) are the most important parameters used for the assessment of the high temperature properties of coke. Many coke plants and blast furnaces around the world use CSR as a specification just as important as cold strength, size and chemistry. The present work aims to fulfil the need for a model that will predict the coke CSR from coal blend characteristics. In this work, the functional relationship between the coal blend properties (ash, volatile matter, average vitrinite reflectance, crucible swelling number, total reactives, vitrinite distribution V ₉–V ₁₃ and basicity index) and the corresponding coke CSR has been mapped using an adaptive neurofuzzy inference system (ANFIS). The ANFIS model is formulated with different sets of coal blend properties as input variable, and the singular value decomposition and QR factorisation based techniques have been employed for model reduction. It has been found that the developed ANFIS model predicts the CSR with reasonable accuracy.