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Register a new userModelling the drinking Water Turbidity of Al-Sin Purification Plant using Dynamic Neural Networks
نمذجة عكارة مياه شرب محطة السن باستخدام شبكات عصبونية ديناميكية
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Tishreen University ورقة بحثية
Publication date
2018
fields
Environmental Engineering
and research's language is
العربية
Authors
عزيز عادل عسيكريه( باحث )
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Shamra Editor
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إن نمذجة العلاقة بين عكارة مياه الشرب في محطة تنقية مياه الشرب في السن، و بقية بارامترات جودة المياه باستخدام أسلوب الشبكات العصبونية الصنعية الديناميكية يساعد على تحقيق الاستقرار في أداء محطة تنقية مياه الشرب، حيث توفر هذه الشبكات أداةً فعالة للتعامل مع الطبيعة المعقدة، و الديناميكية، و غير الخطية لعمليات التنقية، و لديها القدرة على الاستجابة للتغيرات الآنية المختلفة للبارامترات المؤثرة في تنقية المياه. صمم في هذا البحث أربعة نماذج للشبكات العصبونية الديناميكية ذات التغذية الأمامية و الانتشار العكسي للخطأ للتنبؤ بعكارة المياه المرشحة الخارجة من محطة تنقية مياه الشرب في السن، بالاعتماد على بارامترات عكارة و ناقلية و pH المياه الخام الداخلة إلى المحطة، بينما استخدمت بيانات عكارة المياه الخارجة من المحطة للتحقق من دقة أداء الشبكة العصبونية الصنعية، حيث أثبتت نتائج الدراسة قدرة الشبكات العصبونية الصنعية الديناميكية في نمذجة و محاكاة السلوك غير الخطي للعكارة و التنبؤ بقيمها، و هو ما يدعم استخدامها في محطة تنقية مياه الشرب في السن للمساهمة في تحقيق الاستقرار في عمل المحطة.
Modelling the relationship between drinking water turbidity and other indicators of water quality in Al-Sin drinking water purification plant using Dynamic Artificial neural networks could help in the implementation of the stabilization for the performance of the plant because these neural networks provide efficient tool to deal with the complex, dynamic and non-linear nature of purification processes. They have the ability to response to various instant changes in parameters influencing water purification. In this research, four models of feed-forward back-propagation dynamic neural network were designed to predict the effluent turbidity from Al-Sin drinking water purification plant. The models were built based on turbidity, pH and conductivity of raw water data while the effluent turbidity data were used for verify the performance accuracy of each network. The results of this research confirm the ability of dynamic neural networks in modeling and simulating the non-linearity behavior of water turbidity as well as to predict its values. They can be used in Al-Sin drinking water purification plant in order to achieve the stabilization of its performance.
References used
World Health Organization. Guidelines for Drinking-Water Quality. Vol. 1, Recommendations, 3rd Ed., 2006, 515
MUÑIZ, C. D; NIETO, P. J. G; FERNÁNDEZ, J. R. A; NIETO, P. J. G; FERNÁNDEZ, J. R. A. Detection of outliers in water quality monitoring samples using functional data analysis in San Esteban estuary (Northern Spain). Science of the Total Environment, Vol. 435, 2012, 54-61
OGWUELEKA, T. C; OGWUELEKA, F. N. Optimization Of Drinking Water Treatment Processes Using Artificial Neural Network. Nigerian Journal of Technology, Nigeria, Vol. 28, No. 1, 2009, 16-25
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