Advertisement

Maximization of Combustion Efficiency: A Data Mining Approach

  • A. Kusiak
  • S. Shah
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 179)

Abstract

Maximizing combustion efficiency with minimizing emissions is of importance to electric power industry. In this research, the impact of data transformation on boiler efficiency is investigated. The study showed that the data transformed with wavelet algorithms (Haar and Daubechies) provided better cross-validation accuracy, while moving average and wavelets resulted in similar prediction accuracy. The relationship between the length of the control horizon and prediction accuracy is studied. The study shows that the control horizon of a 3-hour to a half-week long provided acceptable prediction accuracy. An ensemble predictive model of the two control horizons is proposed to increase prediction accuracy. The research findings have established foundation for maximizing combustion efficiency by introduction of meta-controllers based on data mining algorithms.

Keywords

Prediction Accuracy Move Average Haar Wavelet Decision Category Data Mining Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Authors and Affiliations

  • A. Kusiak
    • 1
  • S. Shah
    • 2
  1. 1.Intelligent Systems Laboratory Mechanical and Industrial EngineeringThe University of IowaIowaUSA
  2. 2.Intelligent Systems Laboratory Mechanical and Industrial EngineeringThe University of IowaIowaUSA

Personalised recommendations