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.

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