Wednesday, March 20th, 2019

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

Publication year: 2011brbSource:/b Energy, In Press, Corrected Proof, Available online 26 February 2011brOguz, ArslanbrRecent 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…br Research highlights: ► ANN (artificial neural network) model was developed to optimize KCS-34 for Simav. The most suitable algorithm found was LM 7 in single-hidden layer. ► A benefit ranging between US$ 56.5 and 152 million can be obtained from the plant. ► Optimum solution is get for T1b = 80 °C-X = 90% when the geothermal wells include vapor fraction of 10%. ► Optimum solution is get for T1b = 90 °C-X = 81% when the geothermal wells include vapor fraction of 30%. ► Plant is profitable for (present worth factor) PWF gt; 6.7. Most profitable conditions are obtained for X ranging between 80 and 90%.

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