Machine learning frameworks to accurately predict coke reactivity index

Precisely forecasting coke reactivity index (CRI) plays a critical role in the metallurgical industry, as it enables optimization of coke quality, leading to cost-effective production and efficient resource utilization. In this research, several machine learning predictive models based on extra trees, decision tree, support vector machine, random forest, multilayer perceptron artificial neural network, K-nearest neighbors, convolutional neural network, ensemble learning, and adaptive boosting using a dataset gathered from a coke plant are developed to predict CRI. To minimize overfitting in each algorithm, K-fold cross-validation methodology is employed during the training phase. The efficacy of each algorithm is visually represented through graphical methods and quantitatively evaluated using performance metrics. The findings indicate that maximum fluidity and mean maximum reflectance (MMR) exhibit a direct correlation with CRI while being indirectly relevant to moisture content, ash content, sulfur content, basicity index, plastic layer thickness, and MMR. Among the various predictive models evaluated, the random forest model emerged as the most accurate tool, according to the performance metrics of R -squared, mean square error, and average absolute relative error (%), with numerical values of 0.958, 3.718, and 2.545%, respectively, for the total datapoints. The developed tool can be easily used to accurately estimate CRI without needing experimental or field data reliably.