کاربرد یادگیری ماشین برخط در پیش‌بینی میزان هدررفت Al2O3 و NaOH در فرایند سیلیس زدایی مجتمع آلومینای جاجرم

نوع مقاله : علمی-پژوهشی

نویسندگان

دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران

چکیده

در این پژوهش از ابزارهای یادگیری ماشین استفاده شد تا اثر عامل‌های گوناگون بر هدر رفت سود کاستیک (NaOH) و آلومینا (Al2O3) و افزایش آلودگی سدیم کربنات (Na2CO3) در مرحله نخست فرایند بایر مورد بررسی قرار گیرد. پارامترهای مورد بررسی در این مطالعه عبارت‌اند از غلظت Na2Oc، Na2Ou، Na2Ot و Al2O3 در فاز محلول خروجی به واحد آسیای تر است. عامل‌های مورد بررسی نیز شامل شدت جریان جرمی و آنالیز شیمیایی ترکیب‌های گوناگون موجود در بوکسیت و آهک مصرفی، شدت جریان و آنالیز شیمیایی محلول سدیم آلومینات ورودی و آنالیز شیمیایی محلول سدیم آلومینات خروجی از واحد به صورت روزانه برای 3016 روز متوالی بوده است. در این مطالعه از دو روش کاهش گرادیان برخط فوریه (FOGD) و کاهش گرادیان برخط نیستروم (NysGD) برای مدل‌سازی فرایند یادشده استفاده شد. نتیجه‌ها نشان‌دهنده دقت و سرعت یادگیری بیش‌تر روش FOGD نسبت به روش NysGD است. این نتیجه‌ها نشان می‌دهند می‌توان با استفاده از دو روش با دقت بالایی فرایند مورد بررسی را مدل‌سازی نمود.

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موضوعات

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