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Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34

Arslan, Oguz
Energy 2011 v.36 no.5 pp. 2528-2534
algorithms, electricity, enthalpy, neural networks, neurons, power generation, temperature, working conditions
Recent technical developments have made it possible to generate electricity from geothermal resources of low and medium enthalpy. One of these technologies is the Kalina Cycle System. In this study, electricity generation from Simav geothermal field was investigated using the Kalina cycle system-34 (KCS-34). However, the design of these technologies requires more proficiency and longer times within complex calculations. An artificial neural network (ANN) is a new tool used to make a decision for the optimum working conditions of the processes within the expertise. In this study, the back-propagation learning algorithm with three different variants, namely Levenberg–Marguardt (LM), Pola–Ribiere Conjugate Gradient (CGP), and Scaled Conjugate Gradient (SCG), were used in the network so that the best approach could be found. The most suitable algorithm found was LM with 7 neurons in a single hidden layer. The obtained weights were used in optimization process by coupling the life-cycle-cost concepts.