PREDICTING SCREENING AND CLASSIFICATION PRODUCTS VIA THE PSEUDORANDOM NUMBER SELECTION ROUTINE

Year 2022, Volume: 61 Issue: 1, 25 - 30, 07.03.2022

Mahmut Camalan

https://doi.org/10.30797/madencilik.947610

Abstract

Screening and classification are performed for the separation of particles by their sizes. There are empirical, phenomenological, and numerical models for predicting the size distributions of screening/classification products. This paper introduces a new algorithm for the same purpose, which partially mimics phenomenological and numerical models. The algorithm iteratively selects the monosize fractions with pre-defined probabilities, then carries particle masses from the selected fractions either to the oversize or undersize product. The applicability of the algorithm was validated against the product size distributions of some industrial-scale screening/classification operations provided in the literature. The results show that the algorithm is predictive if each particle has a selection probability proportional to the mass of its monosize fraction and some power of its diameter. Results also suggest that vibrating screens can provide the sharpest size separation.

Keywords

Screening, Classification, Pseudorandom Number Generation, Particle Selection, Algorithm

Predicting screening/classification products via the pseudorandom number selection routine

Abstract

Eleme ve sınıflandırma, tanelerin boyuna göre ayrılması için kullanılmaktadır. Eleme/sınıflandırma ürünlerinnin tane boyu dağılımlarını belirlemek için ampirik, yarı-ampirik ve nümerik modeller vardır. Bu makale, aynı amaçla kullanılan, kısmen yarı-ampirik ve nümerik modellere benzeyen bir algoritma sunmaktadır. Algoritma dar tane aralıklarını önceden belirlenmiş olasılıklarla seçmekte; daha sonra seçilen dar tane aralıklarından tane kütlelerini iri veya ince ürüne taşımaktadır. Algoritmanın uygulanabilirliği, bazı endüstriyel ölçekli eleme/sınıflandırma işlemlerinin ürün tane boyutlarına karşı doğrulanmıştır. Sonuçlar, tanelerin seçilme olasılığının taneyi içeren dar tane aralığının kütlesine ve tane çapının bazı kuvvetine orantılı olduğunda algoritmanın doğruya yakın tahmin yapabileceğini göstermektedir. Sonuçlar ayrıca titreşimli eleklerin en keskin tane ayrımını yapabileceğini tavsiye etmektedir.

Keywords

Eleme, Sınıflandırma, Yalancı rastgele sayı üretme, Tane seçimi, Algoritma

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