Diagnosis for monitoring system of municipal solid waste incineration plant

Authors:
Jeng-Chung Chen;Kun-Ying Lin
Affiliations:
Department of Environmental Engineering and Science, Fooyin University, 151 Chi-Hsueh Road, Ta-Liao, Kaohsiung 831, Taiwan;Department of Environmental Engineering and Science, Fooyin University, 151 Chi-Hsueh Road, Ta-Liao, Kaohsiung 831, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 3
Cited 2

An expert decision support system for monitoring and diagnosis of petroleum production and separation processes

Expert Systems with Applications: An International Journal
Comparing classification techniques for predicting essential hypertension

Expert Systems with Applications: An International Journal
Evaluation of rating systems

Expert Systems with Applications: An International Journal

Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction

Expert Systems with Applications: An International Journal
Integration of fuzzy cluster analysis and kernel density estimation for tracking typhoon trajectories in the Taiwan region

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

Monitoring system plays an important role in supervising municipal solid waste (MSW) incineration plant. But little is known about the ways to assess its efficiency. This study aimed at developing a diagnosis tool for examining whether MSW incineration plant is under a reliable monitoring. Performances on status monitor, state assessment, data complement, and risk detection were step by step examined during diagnosis. Several data mining technologies composed of cluster analysis, artificial neural networks (ANNs), and Monte-Carlo simulation were adopted to fulfill this job. In a case study of MSW incineration plant located at South Taiwan, results showed no monitoring item within its monitoring system could be verified as relative to the emissions of Dust, CO, and SOx. In addition, data deficiencies severely affected its reliability. Except Dust, data deficiencies on monitoring NOx, CO, and SOx were more than 30%. So this monitoring system can do a good status monitor on Dust emission and provide a precise state assessment on steam generation and NOx control, but it really faced a problem on handling other air pollutants like CO and SOx. Through this diagnosis, we can get more deep insights about this monitoring system. Moreover it made us easily figure out the upgrading priority of the monitoring system.