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مقایسه روش های طبقه بندی، شبکه عصبی مصنوعی و رگرسیون چندمتغیره در برآورد بازیابی فلز از بلوک کانسنگ | ||
| نشریه مهندسی منابع معدنی | ||
| مقاله 2، دوره 5، شماره 2 - شماره پیاپی 16، تیر 1399، صفحه 21-41 اصل مقاله (1.44 M) | ||
| نوع مقاله: علمی-پژوهشی | ||
| شناسه دیجیتال (DOI): 10.30479/jmre.2019.10997.1284 | ||
| نویسندگان | ||
| جواد غلام نژاد* 1؛ رضا لطفیان2؛ یوسف میرزائیان لرد کیوان3 | ||
| 1دانشیار، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| 2دانشجوی دکتری، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| 3استادیار، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| تاریخ دریافت: 26 خرداد 1398، تاریخ بازنگری: 03 دی 1398، تاریخ پذیرش: 18 آذر 1398 | ||
| چکیده | ||
| با توجه به نقش بازیابی در محاسبه ارزش اقتصادی بلوک کانسنگ و تأثیر مقدار این ارزش بر محاسبات طراحی و برنامه‌ریزی تولید معدن، تعیین بازیابی فلز از بلوک کانسنگ ارسالی به کارخانه فرآوری، از اهمیت بالایی برخوردار است. هدف از این پژوهش، بررسی قابلیت برآورد بازیابی بلوک کانسنگ به‌صورت کیفی و با روشهای مبتنی بر طبقه‌بندی داده‌ها از مجموعه روشهای داده‌کاوی و به‌صورت کمّی، با دو روش رگرسیون چندمتغیره و مدل هوشمند شبکه عصبی، بر اساس داده‌های آنالیز خوراک ورودی کارخانه است. برای نیل به این هدف، معدن مس میدوک مورد مطالعه قرار گرفت و با استفاده از 58 نمونه آنالیزشده عیار خوراک کارخانه، شامل عیارهای Cu، CuOو CuS و میزان بازیابی عنصر Cu در محصول نهایی، فرآیند پیش‌بینی بازیابی کل ذخیره به‌صورت کیفی با روشهای طبقه‌بندی درخت تصمیم، قانون بیز و الگوریتم نزدیک‌ترین همسایه انجام شد. برای برآورد کمّی میزان بازیابی ذخیره، مدل رگرسیون چندمتغیره و شبکه عصبی مصنوعی برای شاخص‌های عیاری مذکور و میزان بازیابی بین 47 نمونه از 58 نمونه برقرار شد و توسط 11 نمونه آنالیزشده آزمایشی، مدل‌های به‌دست‌آمده اعتبارسنجی شدند. معیارهای میانگین خطا و جذر میانگین مربعات خطا در مدل رگرسیونی به ترتیب 021702/0 و 024972/0 و در مدل شبکه عصبی مصنوعی به ترتیب 015753/0 و 021404/0 محاسبه شدند. بنابراین مدل شبکه عصبی مصنوعی به‌عنوان ابزار دقیق‌تری در پیش‌بینی بازیابی نسبت به مدل رگرسیون چندمتغیره عمل می‌کند. نتایج آنالیز حساسیت این مدل نشان داد، عیار Cu مهم‌ترین عامل و عیار CuO و CuS نیز به ترتیب، دیگر عوامل تاثیرگذار بر تغییرات بازیابی هستند. | ||
| کلیدواژه‌ها | ||
| بازیابی؛ طبقه‌بندی؛ رگرسیون چندمتغیره؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Comparison of Artificial Neural Networks and Multivariate Linear Regression Classification Techniques in Metal Recovery Estimation | ||
| نویسندگان [English] | ||
| J. Gholamnejad1؛ R. Lotfian2؛ Y. Mirzaeian Lord Keivan3 | ||
| 1Associate Professor, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| 2Ph.D. Student, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| 3Assistant Professor, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| چکیده [English] | ||
| Due to the role of recovery in calculating the economic value of ore blocks and the impact of the block's economic value on the design calculations of the final pit and production planning, determination of the amount of metal recovery from the ore material sent to the processing plant is very important. The aim of this study is to investigate the capability of estimating the recovery rate of ore in qualitative manner with three methods based on data classification from data mining techniques and quantitatively using multivariate regression and artificial neural networks. Hence, the Miduk copper mine was studied using 58 analyzed samples of the feed of the plant, including Cu, CuO and CuS grades, and the recovery rate of Cu in the final product of the plant. The process of predicting the total recovery of the reserve was made qualitatively by decision tree method, classification based on Bayes rule and k-nearest neighbor (kNN) classification algorithm. For quantitative estimation of recovery, multivariate regression and artificial neural network models were established between the mentioned grade parameters and recovery rates (For 47 samples of 58 samples) and with the 11 additional analyzed samples, the obtained models were validated. The coefficient of (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) in the regression model were 0.77, 0.027722 and 0.029722, respectively, and in the artificial neural network model, 0.82, 0.015753 and 0.024040, respectively. Therefore, the artificial neural networks model acts as a more accurate tool for predicting recovery versus the multivariable regression model. The results of sensitivity analysis of artificial neural network model showed that Cu grade is the most important factor and grade of CuO and CuS, respectively, as well as other factors influencing the changes in recovery rate. | ||
| کلیدواژه‌ها [English] | ||
| Recovery, Classification, Multivariate regression, Artificial neural network | ||
| مراجع | ||
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