Prediction of Boiler Drum Pressure and Steam Flow Rate Using Artificial Neural Network
Prediction of Boiler Drum Pressure and Steam Flow Rate Using Artificial Neural Network作者机构：Department of Mechanical Engineering Government College of Engineering Karad Maharashtra 415124 India Department of Mechanical Engineering Government College of Engineering Chandrapur Maharashtra 442401 India Department of Mechanical Engineering Army Institute of Technology Pune Maharashtra 411015 India
出 版 物：《Journal of Energy and Power Engineering》 (能源与动力工程（美国大卫英文）)
年 卷 期：2010年第4卷第8期
页 面：9-15页
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Boiler artificial neural network steam flow rate drum pressure.
摘 要：Numerical simulation of complex systems and components by computers is a fundamental phase of any modern engineering activity. The traditional methods of simulation typically entail long, iterative processes which lead to large simulation times, often exceeding transient real time. Artificial neural networks （ANNs） may be advantageous in this context, the main advantage being the speed of computation, the capability of generalizing from the few examples, robustness to noisy and partially incomplete data and the capability of performing empirical input-output mapping without complete knowledge of underlying physics. In this paper, the simulation of steam generator is considered as an example to show the potentialities of this tool. The data required for training and testing the ANN is taken from the steam generator at Abott Power Plant, Champaign （USA）. The total number of samples is 9600 which are taken at a sampling time of three seconds. The performance of boiler （drum pressure, steam flow rate） has been verified and tested using ANN, under the changes in fuel flow rate, air flow rate and load disturbance. Using ANN, input-output mapping is done and it is observed that ANN allows a good reproduction of non-linear behaviors of inputs and outputs.
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