Control of flue gas emissions based on models derived from historical plant operation data

Abstract

Although the last decade there is a remarkable trend towards green energy, thermal power plants still cover most of the energy production and will continue to do so in the near future. Consequently, it is important to apply methods reducing the gas emissions. Towards this goal, an innovative software application that improves the regulation of combustion parameters is presented in this paper. Data mining techniques are used to process historical data and extract models on which regulation rules are based, rather on the thermodynamic theory. Multi Agent Systems are adopted to represent the various tasks that constitute the control operation procedure. The statistical analysis suggest that a reduction of NOx and CO emissions should be expected.

A Data-Driven Approach for the Ultra-Supercritical Boiler Combustion Optimization Considering Ambient Temperature Variation: A Case Study in China

“…We extract the historical operating data (nearly 600,000 records) of the unit for the past year from the DCS historical database of the target unit. The historical data contain rich and valuable unit status information [33], including unit equipment characteristics, operator operating habits, and experience. The steady-state operation data of the unit are screened through the sliding window method, and the steady-state data are used to establish an approximate linear partition of the main steam flow (MSFlow), ambient temperature (AT), the lower heating value of coal (LHVC), and the combination mode of coal mills (COM-Mill).…”