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With the depletion of coal in the world, coal quality fluctuates and deviates greatly from the designed coal in many large scale coal-fired power plants. This increases the coal consumption while reduces the boiler combustion efficiency and stability. Thus, it is very important to conduct real-time measurement to the quality of the coal for optimizing the operation. The calorific value analysis is a significant part of the coal quality analysis, and regular proximate analysis method can’t meet real-time control requirements. In this chapter, an artificial neural network (ANN) model using real plant data for prediction of net calorific value of coal in a China power plant is reported. A three-layer BP neural network has been adopted. The input parameters selection was optimized with a compromise between smaller number of parameters and higher level of accuracy through sensitivity analysis. The activation function selection was also discussed in details. The results indicate that when the pureline was selected as the activation function for hidden layer and logsig was selected as the activation function for output layer, the prediction is most accurate. The results have shown good potential for predicting the net calorific value of coal using the real time data. This information will enhance the performance of the combustion control system for power utilities.
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- Prediction of Calorific Value of Coal Using Real Power Plant Data
- Springer Berlin Heidelberg