Journal
APPLIED SCIENCES-BASEL
Volume 13, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/app13179826
Keywords
briquetting; machine learning; compressive resistance; groundnut shells; quality of briquette
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Briquetting is a pre-treatment method for producing uniform raw materials that are easy to process, transport, and store. Determining briquette quality is difficult and costly, but this paper presents an easy and inexpensive machine learning methodology for determining quality parameters of briquette samples. The best estimate is achieved using Extra Trees regression model with R2 and MAPE values of 0.76 and 0.0799, respectively.
Briquetting is considered one of the pre-treatment methods available to produce raw materials of uniform size and moisture content that are easy to process, transport, and store. The quality of briquettes in terms of density and strength depends on the physical and chemical properties of the raw material and the briquetting conditions. However, determining briquette quality is difficult, very costly, and requires long laboratory studies. In this paper, an easy, inexpensive, and fast methodology based on machine learning for the determination of quality parameters of briquette samples is presented. Compressive resistance, one of the most important briquette quality parameters, was estimated by machine learning methods, considering particle size, material moisture, applied pressure value, briquette density, shatter index, and tumbler index. Extra Trees, Random Forest, and Light Gradient Boosting regression models were used. The best estimate is seen in the Extra Trees regression model. The R2 and MAPE values are 0.76 and 0.0799, respectively.
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