EVALUATION OF THE CAPACITY OF APRON FEEDERS USED IN CRUSHING–SCREENING PLANTS BY RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL INTELLIGENCE METHODS

Abstract

In this study, the capacity (Q) of Apron feeders is investigated through response surface methodology (RSM) and some artificial intelligence methods. In this regard, a comprehensive field survey is performed to compile quantitative data on the common working conditions of Apron feeders used in the Turkish Mining Industry (TMI). Based on the collected data, RSM analyses are performed to reveal the factors affecting the Q of Apron feeders. Accordingly, hopper width (B), the height of the material layer conveyed (D), conveyor speed (V), and fill factor (φ) are determined to be the most critical factors for the Q. Several interaction and contour plots are presented to observe the variations in the Q values. Moreover, several predictive models are also introduced to estimate the Q of apron feeders based on artificial intelligence methods such as multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN). The performance of the established predictive models is assessed based on scatter plots, and it is found that the predictive model based on RSM methodology provides relatively better results than the ones found on soft computing-based predictive models. The presented predictive models can be reliably used to estimate the Q of Apron feeders with high capacity. However, crushing–screening plant designers should be careful when using established predictive models for assessing low-capacity Apron feeders. Based on the findings obtained, the present study demonstrates the applicability of RSM methodology and several artificial intelligence methods for evaluating the Q of Apron feeders.

Keywords

• Apron feeders
• Crushing-screening plant
• Response surface methodology
• Artificial intelligence
• Mining industry

APRON BESLEYİCİ KAPASİTESİNİN YÜZEY TEPKİ YÖNTEMİ VE BAZI YAPAY ZEKA YÖNTEMLERİ İLE DEĞERLENDİRİLMESİ

Abstract

Bu çalışmada Apron besleyicilerin kapasitesi (Q), yüzey tepki yöntemi (RSM) ve bazı yapay zekâ yöntemleriyle araştırılmıştır. Bu bağlamda, Türk Madencilik Sektöründe (TMI) kullanılan Apron besleyicilerin yaygın çalışma koşullarına ilişkin niceliksel verilerin toplanması amacıyla kapsamlı bir saha araştırması yapılmıştır. Toplanan bu verilere göre, Apron besleyicilerin Q değerini etkileyen değiştirgelerin ortaya konması için RSM analizleri gerçekleştirilmiştir. Buna göre, besleyici hazne genişliği (B), taşınan malzemenin bant üzerindeki yüksekliği (D), konveyör hızı (V) ve doluluk faktörü (φ), Q değeri için en önemli faktörler olarak belirlenmiştir. Q değerlerindeki gözlemlemek için çeşitli etkileşim ve kontur grafikleri sunulmuştur. Ayrıca, apron besleyicilerin Q değerini tahmin için, çok değişkenli uyarlamalı regresyon analizi (MARS), uyarlamalı ağ tabanlı bulanık mantık çıkarım sistemi (ANFIS) ve yapay sinir ağları (ANN) gibi bazı yapay zekâ yöntemlerine dayılı bazı tahmin modelleri tanıtılmıştır. Kurulan tahmin modellerinin performansı dağılım grafiklerine göre değerlendirilmiş ve RSM metodolojisine dayalı tahmin modelinin, yapay zekâ tabanlı tahmin modellerine göre nispeten daha iyi sonuçlar sağladığı bulunmuştur. Sunulan tahmin modelleri, yüksek kapasiteli Apron besleyicilerin Q değerini tahmin etmek için güvenilir bir şekilde kullanılabilir. Ancak kırma-eleme tesisi tasarımcıları, düşük kapasiteli Apron besleyicileri değerlendirmek için sunulan tahmin modellerini kullanırken dikkatli olmalıdır. Elde edilen bulgulara dayanarak, bu çalışma, Apron besleyicilerinin Q değerini değerlendirmek için RSM metodolojisinin ve çeşitli yapay zekâ yöntemlerinin uygulanabilirliğini göstermiştir.

Keywords

• Apron besleyiciler
• Kırma – eleme tesisi
• Yüzey tepki yöntemi
• Yapay zekâ
• Madencilik endüstrisi

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