Artificial neural networking model for the prediction of high efficiency boiler steam generation and distribution

• The boilers highest efficiency model is developed.
• The real data of the generated steam in the existing system boilers will be compared.
• The steam production costs are reduced in the highest efficiency regime operate.
• Lower fuel consumption and lower greenhouse gas emissions.
• Savings in the purchase of CO2 emission credits.

Development of artificial neural network (ANN) models using real plant data for the prediction of fresh steam properties from a brown coal-fired boiler of a Slovenian power plant. The power plant generates electrical and thermal energy used for the city-wide district heating. The energy is produced in three blocks. Each block consists of a coal-fired boiler and an extraction condensing steam turbine. The electricity production is planned, while the generation of heat for heating purposes depends on the ambient temperature. A model will be presented which, using an ANN, predicts the power production of the power plant and distributes the production between the boilers so that the latter operate at their highest efficiency. The real data on the amount of the generated steam in the existing system boilers will be compared to the results of the model and the findings will be indicated regarding the coal consumption savings and their impact on the environment. However, the final set of input parameters was optimised with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. Data for training were carefully selected from the available real plant data.