Prediction of carbonisation time in byproduct recovery coke ovens through gooseneck temperature measurement

Abstract

Precise prediction of the end of carbonisation possesses intangible benefits in the coke making process. The coke ovens in Tata Steel measure the raw gas temperature (at the gooseneck arrangement in the oven top) to identify the end of coking. Based on the gooseneck temperature profile, the carbonisation time is divided into active carbonisation time (ACT) and soaking time. As the soaking time is varied between 45 min and 1.5 h as per the need, the current study focusses on developing a mathematical model to predict the ACT given the coal blend properties and the operating conditions of the oven. Different statistical methods ranging from linear regression to artificial neural network (ANN) have been used to arrive at a robust model. Piece-wise linear regression and ANN have been found to out-perform the other statistical techniques. However, the ANN model is preferred in terms of the predictability of unseen data.