Design of Neural Network Using Genetic Algorithm for Recovery and Grade Prediction of Pilot Plant Flotation Column Concentrate
محل انتشار: دومین همایش صنایع معدنی
سال انتشار: 1391
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,559
فایل این مقاله در 16 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
MININDC02_046
تاریخ نمایه سازی: 18 دی 1391
چکیده مقاله:
Metallurgical performance prediction is a key parameter associated with the characterization of any control process. In fact, it is not possible to have accurate control to many mineralprocessing problems without having accurate prediction value. Attempts have been made to utilize artificial neural networks (ANNs) for prediction of process performance for the completeuse of available control capabilities. Despite of the wide range of applications and flexibility of ANNs, there is still no general framework or procedure through which the appropriate network for a specific task can be designed. Design and structural optimization of neural networks is stillstrongly dependent upon the designer's experience. This is an obvious barrier to the wider applications of neural network. To mitigate this problem, a new method for the auto-design of neural networks was used, based on genetic algorithm (GA). The new proposed method wasevaluated by a case study in pilot plant flotation column at Sarcheshmeh Copper Plant. Design of topology and parameters of the neural networks as decision variables was done using genetic algorithms in order to improve the effectiveness of forecasting when ANN is applied to apermeability predicting problem from well. The chemical reagents dosage, froth height, air, wash water flow rates, gas holdup, Cu grade in the rougher feed, flotation column feed, column tail and final concentrate streams were used to the simulation by GANN. In this work, multi-layer GANNs with Back Propagation (BP) algorithm with 8-17-10-2 arrangement has been applied to predict the Cu grade and recovery, respectively. The correlation coefficient (R) values for the testing sets for Cu grade and recovery were 0.93, 0.93 respectively. The results discussed in this paper indicate that the proposed model can be used to predict the Cu grade and recovery with a reasonable error.
کلیدواژه ها:
نویسندگان
F Nakhaei
Department of Mining & Metallurgical Engineering, Amirkabir University of Technology
M Irannajad
Department of Mining & Metallurgical Engineering, Amirkabir University of Technology
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :