Monitoring, fault detection and operation prediction of MSW incinerators using multivariate statistical methods
2011; Elsevier BV; Volume: 31; Issue: 7 Linguagem: Inglês
10.1016/j.wasman.2011.02.005
ISSN1879-2456
AutoresGilberto Tavares, Zdena Zsigraiová, Viriato Semião, Maria da Graça Carvalho,
Tópico(s)Spectroscopy and Chemometric Analyses
ResumoThis work proposes the application of two multivariate statistical methods, principal component analysis (PCA) and partial least square (PLS), to a continuous process of a municipal solid waste (MSW) moving grate-type incinerator for process control – monitoring, fault detection and diagnosis – through the extraction of information from historical data. PCA model is built for process monitoring capable of detecting abnormal situations and the original 16-variable process dimension is reduced to eight, the first 4 being able to capture together 86% of the total process variation. PLS model is constructed to predict the generated superheated steam flow rate allowing for control of its set points. The model retained six of the original 13 variables, explaining together 90% of the input variation and almost 98% of the output variation. The proposed methodology is demonstrated by applying those multivariate statistical methods to process data continuously measured in an actual incinerator. Both models exhibited very good performance in fault detection and isolation. In predicting the generated superheated steam flow rate for its set point control the PLS model performed very well with low prediction errors (RMSE of 3.1 and 4.1).
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