119 research outputs found

    Model Identification for Optimal Diesel Emissions Control

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    Abstract In this paper we develop a model based controller for diesel emission reduction using system identification methods. Specifically, our method minimizes the downstream readings from a production NO x sensor while injecting a minimal amount of urea upstream. Based on the linear quadratic estimator, we derive the closed form solution to a cost function that accounts for the case that some of the system inputs are not controllable. Our cost function can be tuned to emphasize optimization of either the use of inputs or the output. Our approach performs better than a production controller in simulation. Our NO x conversion efficiency was 92.7% and the production controller achieved 92.4%. For NH 3 conversion, our efficiency was 98.7% compared to 88.5% for the production controller

    Opportunistic experiments to constrain aerosol effective radiative forcing

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    Aerosol–cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide “opportunistic experiments” (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change
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