The circulating fluidized bed boiler is an advanced clean energy technology that has received much attention in the power industry due to its fuel flexibility. In this study, a deep neural network with a modified early stopping algorithm and least square support vector machine were developed to predict SOx and NOx emissions associated with coal conversion in energy production. The models were trained on commercial plant data and the effect of dynamic coal and limestone properties (which is assumed constant in the literature) such as proximate analysis, ultimate analysis and particle size distribution on prediction accuracy were investigated. The results revealed that training the models without the assumptions improved the accuracy of the testing phase by at least 10% and 40% with a coefficient of efficiency of 0.8925 and 0.9904 for SOx and NOx respectively. In addition, interactive and pairwise correlation featuring were implemented which gave a maximum computational time reduction of 46.67% for NOx emission prediction. The developed models and findings can be applied not only for online operation and optimization of a coal-fired CFB boiler with high accuracy but also in the scale-up of power production at a low computational cost.
Bibliographical noteFunding Information:
This research was supported by the Agency for Defense Development, South Korea .
© 2020 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering