Abstract
A direct-fired absorption chiller system rejects more heat energy than a vapour-compression chiller system of similar capacity. The combined use of fuel energy and electricity consumption is an important criterion in its performance evaluation. Many current research efforts on absorption chiller system are related to the search of an optimal supervisory control strategy to minimize the energy costs. System simulation has been a conventional approach for the investigation. However, the task of developing an accurate system model through the component-modelling approach can be tedious. Oversimplification in the process, and the nonlinear structure of the equation set as a result, are often the limitations for reaching reliable converging solutions. The system-based artificial neural network (ANN) modelling approach appears to be an attractive alternative. This article describes the process of deriving an ANN model of a commercial direct-fired double-effect absorption chiller system. The techniques and the ways to overcome the difficulties in the training process are discussed.
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