Zonal control for selective catalytic reduction system using a model-based multi-objective genetic algorithm

Abstract

It is challenging for aqueous urea injection control to achieve high NO x conversion efficiency while restricting tailpipe ammonia (NH3) slip. Optimizing the selective catalytic reduction systems can reduce diesel engine emissions, potentially improve fuel economy and urea utilization efficiency, and finally reduce aftertreatment costs. In this article, a model-based multi-objective genetic algorithm is adopted to optimize selective catalytic reduction systems related to trade-off between NO x emission and NH3 slip. Selective catalytic reduction model is a one-state selective catalytic reduction model based on continuous stirred tank reactor theory, which significantly reduces the computational burden. The optimal NH3 coverage ratio map was obtained globally based on world harmonized transient cycle. The effect of temperature on optimal NH3 coverage ratio, Zonal control logics extracted from the optimal solution, and the control problems on different zones were analyzed. The zonal control logics were validated on multiple test cycle with different initial NH3 coverage ratios. Results show that the zonal control achieves high NO x conversion while restricting the tailpipe NH3 slip. With this method, NO x emission and NH3 slip of optimal solution can meet the requirements of the Euro VI emission regulation for heavy-duty diesel engines.