ارزیابی دقت‌ شبکه های عصبی مصنوعی (MLP و RBF) در پیش بینی گرد و غبار کارخانه سیمان سبزوار

نوع مقاله: مقالات پژوهشى اصیل کمی و کیفی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی محیط زیست دانشکده منابع طبیعی، دانشگاه یزد، ایران.

2 دانشیار گروه مهندسی محیط زیست، دانشکده منابع طبیعی، دانشگاه یزد، ایران.

3 استادیار، گروه علوم کامپیوتر، دانشکده ریاضی و علوم کامپیوتر، دانشگاه حکیم سبزواری، ایران.

چکیده

چکیده
زمینه و هدف: مدل­سازی گردو‌غبار می­تواند به عنوان یک ابزار مناسب برای پیش­بینی گردو‌غبار صنایع در آینده و تعیین استراتژی­های کنترل انتشار آلاینده­ها تلقی شود. در این مطالعه از شبکه­های عصبی پرسپترون (MLP) و پایه شعاعی (RBF) به عنوان ابزاری برای پیش­بینی گردو‌غبار خروجی از دودکش اصلی کارخانه سیمان سبزوار واقع در استان خراسان رضوی استفاده شد.
مواد و روش‌ها: در محدوده مطالعاتی مورد نظر، ابتدا میزان غلظت گردو‌غبار خروجی از دودکش اصلی کارخانه سیمان به وسیله اندازه­گیری­های میدانی به‌دست آمد. سپس با به‌کار­گیری پارامتر­های خط تولید (درجه حرارت، سرعت گاز خروجی، ولتاژ، سوخت، مواد خام و مدت زمان نمونه­برداری)، به عنوان داده­های ورودی به شبکه­های عصبی، جهت پیش­بینی میزان غلظت گردو‌غبار استفاده شد. مقادیر حاصل از اجرای مدل­ها، با نتایج اندازه­گیری­های میدانی به‌عنوان انتخاب مدل برتر، مورد مقایسه قرار گرفت.
یافته­ها: دربررسی نمودار­ها و پارامتر­های آماری، مقادیر میانگین مربعات خطا برای دو مدل شبکه­های عصبی پرسپترون و پایه شعاعی به‌ترتیب برابر 1/787 و 21/263 و مقادیر ضریب همبستگی به‌ترتیب برابر 0/99693 و 0/95811 بود که نشانگر خطای کمتر و همبستگی بیشتر مدل شبکه­های عصبی پرسپترون نسبت به مدل پایه شعاعی در پیش­بینی میزان غلظت گردو‌غبار بود.
نتیجه­گیری: به دلیل قابلیت بالای شبکه عصبی پرسپترون در پیش­بینی میزان غلظت گردو‌غبار، این مدل می­تواند یک راه‌حل مناسب و سریع در پیش­بینی میزان گردو‌غبار صنایع باشد.
نوع مقاله:مقاله پژوهشی
کلید واژهها: کارخانه سیمان، گردو‌غبار، شبکه­های عصبی مصنوعی، آلودگی هوا

کلیدواژه‌ها


عنوان مقاله [English]

Estimation of Artificial Neural Networks (MLP and RBF) Accuracy in Anticipation of the Dust of the Sabzevar Cement Factory

نویسندگان [English]

  • seyed saeed keykhosravi 1
  • Farhad Nejadkoorki 2
  • Mahmood Amintoosi 3
1 Graduate student, Department of Environmental Engineering, Yazd University of Iran
2 Faculty member of the Department of Environmental Engineering, Yazd University of Iran
3 Assistant Professor, Department of Computer Science, Faculty of Mathematics and, Hakim Sabzevari i University, Iran.
چکیده [English]

Background and Objective: Dust modeling can be considered as an appropriate tool for predicting future industrial dust and identifying pollutant emission control strategies. Perceptron (MLP) and radial base (RBF) neural networks were used as a means for predicting the outflow dust from the main cogeneration of Sabzevar cement factory located in Khorasan Razavi Province.
Method: the concentration of dust from the main cement chimney in the study area was measured through field measurements. Then, the parameters of the production line (temperature, speed of gas output, voltage, fuel, raw materials, and time of sampling) were used as input data to the nerve networks to predict the concentration of dust. The values obtained from the implementation of the models were compared with the results of field measurements as a superior model selection.
Results: The analysis of figures and statistical parameters showed that the mean squared errors for the two MLP and RBF models were as much as 1.787 and 21.263, respectively, and the correlation coefficients were as much as 0.99693 and 0.95811, respectively, which indicates a lower error and greater correlation between the MLP and RBF model in predicting the concentration of dust.
Conclusion: Because of the high ability of perceptron nervous networks to predict dust concentration, this model can be a convenient and fast solution to predict the amount of dust in the industry.

کلیدواژه‌ها [English]

  • Cement Factory
  • Dust
  • Artificial Neural Networks
  • Air pollution
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