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Article
Effect of Operation Conditions on Exit Water Temperature of Condenser (Atmospheric) by Using Neural Network

Authors: Hayder Mohammad Jaffal حيدر محمد جفال --- Hisham Hassan Jasim هشام حسن جاسم
Journal: Journal of Engineering and Sustainable Development مجلة الهندسة والتنمية المستدامة ISSN: 25200917 Year: 2011 Volume: 15 Issue: 3 Pages: 61-74
Publisher: Al-Mustansyriah University الجامعة المستنصرية

Abstract

The goal of this research is the determination exit water temperature of a condenser (atmospheric) use in steam power plant by artificial neural network with various operation conditions. Input of neural network include surface area, inlet water temperature, water flow rate, steam temperature, enthalpy difference and steam flow rate. Output of the neural network consists of the exit water temperature. For the subject of the neural network, training or learning algorithm are applied the most famous of which is back propagation algorithm. This algorithm is a systematic method for training multi layer artificial neural network. The real exit water temperature first using experimental work and is defined as a goal function for neural network (NN) , so that all outputs of the network can be compared to this function and the error can be calculated. Then another a set of input from experimental work was used to test the NN, the performance of the NN is optimum. Compared with a validated first model, the standard deviations of neural network models are less than 0.12%, and all errors fall into .

فائدة هذا البحث هو حساب درجة حرارة خروج ماء مكثف يعمل بالضغط الجوي في محطة توليد قدرة بواسطة الشبكة العصبية ولظروف عمل مختلفة. قيم الإدخال للشبكة العصبية هي المساحة السطحية لانتقال الحرارة، درجة حرارة دخول الماء، معدل تدفق ماء التبريد، درجة حرارة البخار ، فرق المحتوى الحراري ومعدل تدفق البخار. مخرجات الشبكة العصبية هي درجة حرارة خروج الماء . اعتمدنا اشهر طريقة للتدريب وتعليم الخوارزمية وهي Back propagation algorithm .هذه الخوارزمية لها طريقة منظمة لتدريب الشبكة العصبية الاصطناعية متعددة الطبقات.القيم الحقيقة لدرجة حرارة خروج الماء يتم الحصول عليها من الجانب العملي للبحث وتعرف كأنها هدف الشبكة العصبية لذلك كل نواتج الشبكة يمكن مقارنتها وحساب الخطأ. تم استخدام قيم عملية أخرى لاختبار الشبكة العصبية وطبقا للنتائج آن أداء الشبكة العصبية متكامل. ومن مقارنة نتائج الشبكة مع قيم الجانب العملي وجدنا انحراف معياري اقل من 0.12% وكل نسب الخطأ اقل من .

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Article
STEAM CONDENER PERFOMANCE EVALIATION BYUSING NEURAL NETWORK
تقييم اداء مكثف بخاري بأستخدام الشبكة العصبية

Author: Hisham Hassan Jasim
Journal: Iraqi journal of mechanical and material engineering المجلة العراقية للهندسة الميكانيكية وهندسة المواد ISSN: 20761819 Year: 2012 Volume: 12 Issue: 2 Pages: 319-333
Publisher: Babylon University جامعة بابل

Abstract

This work applied Artificial Neural Network (ANN) for performance evaluation of steam condensers which are widely used in power plants and refineries. Twocondensers were experimentally investigated. Experimental data was obtained by useunit steam power from G.U.N.T Company and industrial condenser operates in Durarefinery. The commonly used Back Propagation (BP) algorithm was used to train andtest network. Input of neural network include inlet water temperature, water flow rate,steam temperature and enthalpy difference. The exit water temperature representedoutput of the neural network. The maximum deviation between the predicted resultsand experimental data was less than 1%. It is recommended the (ANN) can be used topredicate the performance of thermal system in engineering applications, such asmodeling condenser for heat transfer analysis. Afterwards, ANN resulted used to findthermal parameters (convection heat transfer coefficient of water side and steamflow rate ) based on software program built by Matlab language. Comparing the resulted from modeling with experimental data reveals a good agreement (-3% to3%). smhw

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