Diesel engines produce a variety of particles generically classified as diesel particulate matter (PM) owing to incomplete combustion. The increasingly stringent emissions regulations require that engine manufacturers must continue to reduce the PM. The ability to predict the PM emissions is one of the key technologies that could be used in a PM reduction strategy. This paper describes a predictive technique that can be used as a virtual sensor for monitoring PM emissions in both steady and transient states for a medium- or heavy-duty diesel engine. The predictive structure is stable over a broad range of engine operation points. The input parameters are chosen on the basis of the PM formation mechanism, physical knowledge of the process, and an insight into the underlying physics. Principal-component analysis (PCA) is used to reduce the dimensionality of the inputs of a non-linear autoregressive model with exogenous inputs (NLARX) from nine inputs to five inputs. PCA not only reduces the input number but also improves the performance of the prediction model. The results show that the NLARX model could predict the particulate matter successfully with an R2 value above 0.99 with only five inputs.
Particulate matter prediction in both steady state and transient operation of diesel engines
2012-02-01
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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