The objective of the paper is to assess the feasibility of the neural network (NN) approach in industrial process facilities. The energy consumption of the plant can be improved by defining suitable operating levels of the various parallel components connected to the plant facility using computerized system. The concept of using a computerized procedure capable of recognizing the status of the equipment from monitoring systems and using that data to automatically optimize the plant operation could lead to significant economic and energy consumption improvements. To demonstrate this goal a “Feed Forward Neural Network” technique with a back propagation algorithm was applied to an existing facility equipped with a cogeneration system based on natural gas engines, hot water boilers, standby boilers and other heat sources. In this paper, the heat capacity of a typical installation is presented and a procedure to optimize energy utilization based on a computational model is developed, the plant existing condition is taken as a reference condition, a general block diagram of the system is presented and discussed and the installation heat load allocation is analyzed. Then the data from the physical model of the facility was used to train such a NN model. Results obtained using a conventional computing technique are compared with those of the direct method based on a NN approach. The NN simulator was capable of performing calculations in a very short computing time with a high degree of accuracy. The optimizations of neural network parameters such as the number of hidden neurons; training sample size and learning rate are discussed in the paper. Trained neural network outputs are compared with those of the computational method and discussed.
Application of Neural Network in Monitoring an Industrial Plant for a Complex
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Seyedan, B, & Hynes, R. "Application of Neural Network in Monitoring an Industrial Plant for a Complex." Proceedings of the ASME 2004 Power Conference. ASME 2004 Power Conference. Baltimore, Maryland, USA. March 30–April 1, 2004. pp. 723-730. ASME. https://doi.org/10.1115/POWER2004-52073
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