پیش‌بینی توان کشی آسیای نیمه خود شکن با شبکه عصبی مصنوعی شعاعی بر اساس مولفه‌های اصلی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی معدن و متالورژی، دانشگاه صنعتی امیرکبیر، تهران، ایران

2 دانشکده مهندسی، دانشگاه کاشان، کاشان، ایران

چکیده

ارائه مدل های آسیای نیمه خودشکن برای پیش بینی کارآیی آن یکی از ابزارهای مفید برای طراحی بهتر مدار خردایش است. هرچند پیش از این مدل های آسیای نیمه خودشکن زیادی ارائه شده است ولی در اکثر آنها پیش بینی کارآیی آسیا در مقیاس صنعتی انجام نشده است. توان کشی آسیای نیمه خودشکن تاثیر موثری بر کارآیی آسیا دارد؛ بنابراین در این مطالعه، مدل جدیدی بر اساس ترکیب شبکه عصبی مصنوعی شعاعی و مولفه های اصلی برای پیش بینی توانکشی آسیای نیمه خود شکن ارائه شده است. پارامترهای رطوبت بار اولیه، دبی بار اولیه، وزن بار داخل آسیا، درصد جامد بار اولیه، دبی آب ورودی و خروجی به آسیا و اندیس کار انتخاب و تاثیر آن بر توانکشی آسیا بررسی شد. نتایج نشان داد که مدل ترکیبی شبکه عصبی مصنوعی و مولفه های اصلی آموزش یافته با R = 0/8456و RMSE = 68/0752قابلیت استفاده برای پیش بینی توان کشی آسیای نیمه خودشکن در مقیاس صنعتی را دارد. نتایج آنالیز حساسیت نشان داد که تمامی پارامترهای ورودی به مدل تاثیر معناداری بر خروجی دارند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Perdition of Semi-autogenous mill Power Using Radial Artificial Neural Network Based on Principal Component

نویسندگان [English]

  • F.S. Hoseinian 1
  • B. Rezai 1
  • S. Soltani-Mohammadi 2
1 Department of Mining & Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran
2 Department of Mining Engineering, University of Kashan, Kashan, Iran
چکیده [English]

Providing of semi-autogenous (SAG) mill models for prediction of its effectiveness is one of the most useful tools for better design of grinding circuit. Many SAG mill models have been presented in the literature, but in most of them have not been predicted the mill performance in industrial scale. Semi-autogenous mill power has an effective impact on the mill performance. So in this study, a new model based on combination of radial artificial neural network and principal component is presented to predict semi-autogenous mill power. The feed moisture, mass flowrate, mill load cell weight, SAG mill solid percent, inlet and outlet water to the SAG mill and work index selected as input variables and evaluated the effect of them on the mill power. The results showed that the trained hybrid model of artificial neural network and principal component with R=0.8456 and RMSE= 68.0752 can be used to predict the semi-autogenous mill power in industrial scale. The sensitivity analysis results showed that all model input parameters had a significant effect on the output.

کلیدواژه‌ها [English]

  • Semi-Autogenous Mill
  • Mill Power
  • Radial Artificial Neural Network
  • Principal Component

اصل مقاله

Principal Component

مراجع

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نشریه مهندسی عمران امیرکبیر
دوره 50، شماره 3
مرداد و شهریور 1397
صفحه 511-518

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