Towards application of machine learning algorithms for prediction temperature distribution within CFB boiler based on specified operating conditions

The availability of the power unit for electricity production is one of the most important issues of the power plant operator. Considering the power unit, boiler is the main source of its malfunction. Depending of the boiler construction, source of problem can be different. In case of usage of the circulating fluidized bed boiler the potential issue that can lead to interruption of the energy production is its failure caused by leakages of heating surfaces. In order to mitigate this risk different treatments or procedures are introduced. Nowadays, thanks to continuous development of mathematical tools its is possible to introduce a new solution to reduce the risk of heating surface erosion cause by the friction of solid material used in fluidization. One of possible option, that can help to resolve such a problem is application of machine learning technique. Based on real observation of the boiler operation and data analysis, it is believed that the uniform temperature distribution at the lower part of the combustion chamber should has positive impact on erosion reduction at the kick-out level where tapered walls changed to vertical one. This can be attain by careful manipulation of selected boiler operating parameters. Due to the reason that in order to find requires setup dozen of input data configuration need to be considered appropriate toll need to be developed. That is the main reason way the machine learning technique need to be applied for such purpose. Indeed, of this work is to develop artificial models that can help in adjustment boiler setup. In order to check models functionality, they were on-site tested by boiler operator. Developed model shows tremendous potential and confirm that it is worth to investigated this topic farther. [Display omitted] •An approach for reduction erosion risk in CFB units has been proposed.•Application of Deep Natural Network for development models able to predict temperature distribution in CBF unit.•AI model has been validated angst real boiler operation data.•Developed model has shown high accuracy.•Wide range of the boiler operational data for models validation is presented.