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

Ekin Köken

Makale detayları

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

Yazarlar:

Ekin Köken

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Maden Tasarımı | İşletme ve Ekonomisi | Maden Mühendisliği (Diğer)

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 1, pp.142-151

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321784

pISSN:

2149-3596

Anahtar kelimeler:

Özet:

 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, crushingscreening 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.

 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, crushingscreening 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.

 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, crushingscreening 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.

 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.

Makale detayları

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

Yazarlar:

Ekin Köken

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Maden Tasarımı | İşletme ve Ekonomisi | Maden Mühendisliği (Diğer)

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 1, pp.142-151

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321784

pISSN:

2149-3596

Apron feeders

Crushing-screening plant

Response surface methodology

Artificial intelligence

Mining industryApron besleyiciler

Kırma – eleme tesisi

Yüzey tepki yöntemi

Yapay zekâ

Madencilik endüstrisi

Sayı

Makale detayları

Yazarlar: Ekin Köken

Yayıncı: Mugla Sitki Kocman University

Ana Başlıklar: Maden Tasarımı | İşletme ve Ekonomisi | Maden Mühendisliği (Diğer)

Dergi: Mugla Journal of Science and Technology Vol.10 Issue 1, pp.142-151

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001321784

pISSN: 2149-3596

Sayı

Yazarlar:

Ekin Köken

Yayıncı:

Mugla Sitki Kocman University

Ana Başlıklar:

Maden Tasarımı | İşletme ve Ekonomisi | Maden Mühendisliği (Diğer)

Dergi:

Mugla Journal of Science and Technology Vol.10 Issue 1, pp.142-151

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001321784

pISSN:

2149-3596