Soy-based biodiesel blend estimation and accommodation in a modern diesel engine
Abstract
Biodiesel is an alternative fuel for diesel engines that has the potential to reduce dependence on foreign sources of petroleum, reduce harmful particulate matter (soot), carbon monoxide, and unburned hydrocarbon emissions, as well as reduce net carbon dioxide emissions. Biodiesel can be produced from a number of domestically-available feedstocks such as soybeans, sunflower seed, waste frying oils, and many others. Despite these benefits, biodiesel utilization has a number of combustion-related challenges, namely increased fuel consumption due to lower energy density and increases in emission of nitrogen oxides relative to diesel combustion. This work presents a practical closed-loop control-based estimation and accommodation strategy to mitigate these combustion-related disadvantages. This strategy estimates the biodiesel blend fraction of the fuel being supplied to the engine and then automatically accommodates the fuel so that the resulting engine performance is near-optimal for all blends of diesel and biodiesel. The estimation strategy utilizes existing, production-grade sensors to estimate the blend fraction. The accommodation strategy utilizes rule-based methods, therefore the accommodation process is very practical and not calibration intensive. The combination of the estimation and accommodation strategies has resulted in practical, “fuel-flexible” biodiesel combustion in modern diesel engines without additional engine hardware and without the need for extensive additional calibration.
Degree
Ph.D.
Advisors
Shaver, Purdue University.
Subject Area
Mechanical engineering
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