In the present work a procedure for optimum design of waste heat recovery boiler of a combined cycle power plant has been developed. This method enables the optimization of waste heat recovery boiler independent of the rest of the system and the design thus obtained can directly be employed in an existing plant.
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.
The objective of this paper is to assess the optimum heat load capacity of a real CHP plant and provide recommendations to improve heat utilization and reduce costs using a computerized system. A simulation model based on component actual behaviour has been developed. The simulation model is capable of plant optimization that could lead to significant economic and energy consumption improvements. The general modular structural of the plant component is described together with a discussion of the results and cost analysis. In the second part, feasibility of the Artificial Neural Network (ANN) approach is evaluated. The data from the simulation model of the plant is used to train such an ANN model. Results from the conventional computer technique are compared with that of the direct method based ANN approach. The results indicate it is feasible to use ANN to predict plant-operating conditions. The ANN gives a good time response and performance prediction capability with change of boundary conditions. Significantly shorter computation time is obtained with the ANN compared to the physical model. The accuracy of the ANN output and its suitability for on-line monitoring of a CHP plant are discussed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.