Prediction of Briquette Deformation Energy via Ensemble Learning Algorithms Using Physico-Mechanical Parameters

Abstract

Briquetting is a compaction technology that has been used for many years to produce raw materials that are uniform in size and moisture content and are easy to process, transport and store. The physical and chemical properties of the raw material and the briquetting conditions also affect the density and strength of the briquettes. Nonetheless, assessing the quality of briquettes is challenging and extremely expensive, and necessitates lengthy laboratory investigations. In this study, a fast, cost-effective, and simple method using machine learning was used to evaluate the quality characteristics of briquette samples. The deformation energy, one of the most important briquette quality parameters, was predicted by machine learning methods, considering specific compression force, moisture content, compression resistance, briquette density, tumbler index, water resistance, shatter index and compression stress. For this purpose, Random Forest, Extreme Gradient Boosting, and CatBoost methods, which are among the ensemble learning methods, were used. The RMSE, MAE, MAPE, and R2 metrics were used to evaluate the models. With respect to the training data, the model created using the Extreme Gradient Boosting method was successful on all the metrics. However, for test data, the best RMSE (15.69), MAPE (0.0146), and R2 (0.9715) were obtained from the model established with the CatBoost method. The best MAE (10.63) was obtained from the model established with the Random Forest method. The metric results and the graphs obtained from the prediction values of the models revealed that machine learning methods were successfully able to predict briquette deformation energy.

Prediction of Leaf Break Resistance of Green and Dry Alfalfa Leaves by Machine Learning Methods

“…In another study, Mostafaeipour et al [71] produced predictions with the value of R 2 :0.953, MSE: 0.0102 and RMSE: 0.1010 with the Extreme Learning Machine model. Kabas et al [72] produced predictions with the value of R 2 :0.9715, MAPE: 0.0146 and RMSE: 15.69 with the CatBoost model, and MAE: 10.63 with the RF model. In this study, while the best RMSE (0.0616) and R 2 (0.96) values for green alfalfa plant were obtained from the model established with the RF method, the best MAE (0.0340) value was obtained from the model established with the ET method.…”