Estimation of Compressive Resistance of Briquettes Obtained from Groundnut Shells with Different Machine Learning Algorithms

Briquetting is considered one of the pre-treatment methods available to produce raw materials uniform size and moisture content that are easy process, transport, store. The quality briquettes in terms density strength depends on physical chemical properties material briquetting conditions. However, determining briquette difficult, very costly, requires long laboratory studies. In this paper, an easy, inexpensive, fast methodology based machine learning for determination parameters samples presented. Compressive resistance, most important parameters, was estimated by methods, considering particle size, moisture, applied pressure value, density, shatter index, tumbler index. Extra Trees, Random Forest, Light Gradient Boosting regression models were used. best estimate seen Trees model. R2 MAPE values 0.76 0.0799, respectively.