Data-driven modeling of circulating fluidized bed boiler air emissions
Koivumäki, Sofia (2020)
2020
Ympäristö- ja energiatekniikan DI-tutkinto-ohjelma - Degree Programme in Environmental and Energy Engineering, MSc (Tech)
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2020-05-20
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202005155375
https://urn.fi/URN:NBN:fi:tuni-202005155375
Tiivistelmä
The tightened emission regulations and a continuous change towards low-grade fuel fractions make feasible air emission reduction more challenging in power plants. Therefore there is a need for data-driven advisor applications that can extract important information from process data in real-time. The objective of this thesis was to provide data-driven models of CFB boiler air emissions suitable for the use of the proposed cloud-based advisor system.
Based on the literature review models were implemented with multilayer perceptron method for SO2, NOx , CO emissions and costs. Models were trained with continuous process data gathered from a large scale reference multi-fuel CFB plant during its real operation. Process data from air, fuel and additives feed, bed and chamber conditions, process steam and flue gas stack measurements were used in modeling.
Model parameters were selected in four-fold cross-validation, in which the performance not only evaluated based on the prediction accuracy but also on the consistency between the modeled correlations and ones presented in the literature. Results show that models can predict emissions with satisfying accuracy and provide correlations between emissions and operational variables consistent with the literature in studied operation points.
It is likely, that significant improvements in the prediction accuracy cannot be achieved with this method without improving data quality and coverage, especially for fuel quality and biomass mixture. It was found out that the prediction accuracy of the models seemed to be more dependent on the process conditions than the model structure. Therefore it is suggested that models should be retrained often enough to maintain the prediction accuracy in everyday use. To conclude, this study is a solid base point for emission advisor cloud application models development. However, guarantee accurate and reliable model performance in varying process conditions, those should be developed further.
Based on the literature review models were implemented with multilayer perceptron method for SO2, NOx , CO emissions and costs. Models were trained with continuous process data gathered from a large scale reference multi-fuel CFB plant during its real operation. Process data from air, fuel and additives feed, bed and chamber conditions, process steam and flue gas stack measurements were used in modeling.
Model parameters were selected in four-fold cross-validation, in which the performance not only evaluated based on the prediction accuracy but also on the consistency between the modeled correlations and ones presented in the literature. Results show that models can predict emissions with satisfying accuracy and provide correlations between emissions and operational variables consistent with the literature in studied operation points.
It is likely, that significant improvements in the prediction accuracy cannot be achieved with this method without improving data quality and coverage, especially for fuel quality and biomass mixture. It was found out that the prediction accuracy of the models seemed to be more dependent on the process conditions than the model structure. Therefore it is suggested that models should be retrained often enough to maintain the prediction accuracy in everyday use. To conclude, this study is a solid base point for emission advisor cloud application models development. However, guarantee accurate and reliable model performance in varying process conditions, those should be developed further.