Abstract：To obtain the optimal initial pressure of a steam turbine under loads,a heat rate forecasting model was established using fast symbiotic organisms search (FSOS) algorithm and extreme learning machine (ELM). The model was compared with BP neural network, symbiotic organisms search (SOS) algorithm’s optimizing extreme learning machine (ELM) and fast symbiotic organisms search (FSOS) algorithm’s optimizing support vector machine (SVM). Then, FSOS was employed to seek the optimal initial pressure and the main steam flow based on the forecasting model to make the heat rate under each load the lowest. Finally, an optimal initial pressure curve was fitted by the optimized main steam pressure and compared with the factory designed sliding pressure curve. The results showed that the average heat rate decreases about 58.51 kJ·(kW· h)-1 according to the optimal initial pressure curve, which improved the energy conversion efficiency of the unit and had a significant effect on the economic operation of the steam turbine.
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2019, Vol. 40 
