Data based digital twin for operational performance optimization in CFB boilers

The benchmarks of controllable variables for circulating fluidized bed (CFB) boilers with small capacity dependencies on operator experience result in boiler efficiency penalties, increased energy consumption, and performance degradation. This study proposes a digital twin (DT) for a CFB boiler based on data-driven methods to determine the benchmarks of controllable variables by mining historical operating data to save energy, with a coefficient, Cm, proposed for the online evaluation of boiler performance. A hybrid data-driven model integrating kernel density estimation and the K-means++ algorithm was proposed to optimize boiler performance for different coal types, with Cm set as the clustering objective to determine the benchmarks of controllable variables from an operator's perspective. In addition, a classification model adopting an extreme gradient boosting algorithm was presented to identify the coal type online. The proposed DT was applied to an on-duty 150 t/h CFB boiler. The results showed that the proposed DT could identify coal types online with an accuracy of no lower than 94.8 %. Moreover, after the industrial commission of DT, the boiler efficiency increased from 84.41 % to 85.96 % on average, the energy savings reached 151.01 GJ, while steam production increased 59,998 kg over three days, thus highlighting that the proposed benchmarks for controllable variables help enhance the performance of CFB boilers.