Robustness and Accuracy Test of Particular Matter Prediction Based on Neural Networks

Jiamei  Deng; Shaohua  Zhong; Andrew  Ordys

doi:10.4236/cn.2013.52B010

Jiamei Deng^*, Shaohua Zhong, Andrew Ordys

Abstract: The increasingly stringent emissions regulations require that engine manufacturers must reduce emissions of particulate matter (PM). PM is made up mainly of carbon in the form of PM and complex compound that are absorbed into the PM particles. Significant quantities PM poses a threat to health. Technologies available for PM reduction are heavily dependent on after-treatment systems, which are respectively active and passive. An active trap system requires a control unit to trigger and control the regeneration process (clean the trap system). The measurement of PM is crucial for the trap system to enable the fine judgment as to when to initiate the process. If the regeneration is too infrequent, the filter will block. The frequency of the regeneration and the life of the filter are compromised. Diesel engine particular matter prediction has always been a major challenge to the industry. A simple way to handle the PM estimation is to use black-box modelling as described in [J. Deng, B. Maass, R. Stobart, PM prediction in both steady state and transient operation of diesel engines, Proc IMechE, Part D: Journal of Automobile Engineering, 2011, 225, in press, DOI: 10.1177/0954407011418029]. This method is used to estimate the PM successfully in both steady and transient engine operation condition. The main question is how robust and accurate the neural networks are for a regeneration trigger signal of diesel particular filters. In order to answer such a question, the robust test of PM estimation is carried out based on different composition of bio-diesel. In this paper, regular diesel fuel will be blended with up to 20% bio-diesel to test the effect of different fuel resources on particulate matter. Bio-diesel is often added to regular diesel fuel to improve the burning properties and reduce carbon emissions, also is alternative source of fuels. The aim of this paper with these tests is to ensure that with a realistic change in the fuel composition the estimation of PM is still accurate. Therefore, neural networks could be used to produce a regeneration signal for diesel particular filter. In this paper a virtual sensor is proposed to measure the PM. The purpose of the proposed virtual sensor is to estimate the accumulation of PM and to trigger a regeneration cycle. The virtual sensor based on neural network is used to estimate the PM. In this paper, the performance, robustness and accuracy of simulated the sensor are evaluated in measuring the particulate amount for non-road transient cycle tests.

Keywords: Neural Networks; Particulate Matter; Smoke; Prediction; Robustness

Cite this paper: J. Deng, S. Zhong and A. Ordys, "Robustness and Accuracy Test of Particular Matter Prediction Based on Neural Networks," Communications and Network, Vol. 5 No. 2, 2013, pp. 53-59. doi: 10.4236/cn.2013.52B010.

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