Monitoring and analysis of air emissions based on condition models derived from process history

Abstract

Evaluation of online information on operating conditions is necessary when reducing air emissions in energy plants. In this respect, automated monitoring and control are of primary concern, particularly in biomass combustion. As monitoring of emissions in power plants is ever more challenging because of lowgrade fuels and fuel mixtures, new monitoring applications are needed to extract essential information from the large amount of measurement data. The management of emissions in energy boilers lacks economically efficient, fast, and competent computational systems that could support decision-making regarding the improvement of emission efficiency. In this paper, a novel emission monitoring platform based on the self-organizing map method is presented. The system is capable, not only of visualizing the prevailing status of the process and detecting problem situations (i.e. increased emission release rates), but also of analyzing these situations automatically and presenting factors potentially affecting them. The system is demonstrated using measurement data from an industrial circulating fluidized bed boiler fired by forest residue as the primary fuel and coal as the supporting fuel.

Predicting CO and NOxemissions from gas turbines: novel data and abenchmark PEMS

“…Idzwan et al used support vector machines for predicting NO x emission of a power generation plant in Malaysia [14]. Liukkonen and Hiltunen presented a novel emission monitoring platform based on the self-organizing maps [15].…”