Use of Neural Networks for modeling and predicting boiler's operating performance

Abstract

The need for high boiler operating performance requires the application of improved techniques for the rational use of energy. The analysis presented is guided by an effort to find possibilities for ways energy resources can be used wisely to secure a more efficient final energy supply. However, the biggest challenges are related to the variety and stochastic nature of influencing factors. The paper presents a method for modeling, assessing, and predicting the efficiency of boilers based on measured operating performance. The method utilizes a neural network approach to analyze and predict boiler efficiency and also to discover possibilities for enhancing efficiency. The analysis is based on energy surveys of 65 randomly selected boilers located at over 50 sites in the northern province of Serbia. These surveys included a representative range of industrial, public and commercial users of steam and hot water. The sample covered approximately 25% of all boilers in the province and yielded reliable and relevant results. By creating a database combined with soft computing assistance a wide range of possibilities are created for identifying and assessing factors of influence and making a critical evaluation of practices used on the supply side as a source of identified inefficiency. © 2012 Elsevier Ltd.