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This study was aimed at experimentally investigating the impact of diesel/natural gas (NG) dual-fuel retrofitting onto gaseous emissions emitted by i) legacy, model year (MY) 2005 heavy-duty engines with cooled EGR and no after-treatment system, and ii) a latest technology engine equipped with DPF and urea-SCR after-treatment systems that is compliant with 2010 US-EPA emissions standards. In particular, two different dual-fuel conversion kits were evaluated in this study with pure methane (CH4) being used as surrogate for natural gas. Experiments were conducted on an engine dynamometer over a 13-mode steady-state test cycle as well as the transient FTP required for engine certification while gaseous emissions were sampled through a CVS system. Tailpipe NOx emissions were observed at a comparable level for diesel and diesel/CH4 dual-fuel operation for the 2010 compliant engine downstream the SCR.

Emissions from 10 refuse trucks equipped with Caterpillar C-10 engines were measured on West Virginia University's (WVU) Transportable Emissions Laboratory in Riverside, California. The engines all used a commercially available Dual-Fuel™ natural gas (DFNG) system supplied by Clean Air Partners Inc. (CAP), and some were also equipped with catalyzed particulate filters (CPFs), also from CAP. The DFNG system introduces natural gas with the intake air and then ignites the gas with a small injection of diesel fuel directly into the cylinder to initiate combustion. Emissions were measured over a modified version of a test cycle (the William H. Martin cycle) previously developed by WVU. The cycle attempts to duplicate a typical curbside refuse collection truck and includes three modes: highway-to-landfill delivery, curbside collection, and compaction. Emissions were compared to similar trucks that used Caterpillar C-10 diesels equipped with Engelhard's DPX catalyzed particulate filters.

Machine learning algorithms are effective tools to reduce the number of engine dynamometer tests during internal combustion engine development and/or optimization. This paper provides a case study of using such a statistical algorithm to characterize the heat transfer from the combustion chamber to the environment during combustion and during the entire engine cycle. The data for building the machine learning model came from a single cylinder compression ignition engine (13.3 compression ratio) that was converted to natural-gas port fuel injection spark-ignition operation. Engine dynamometer tests investigated several spark timings, equivalence ratios, and engine speeds, which were also used as model inputs. While building the model it was found that adding the intake pressure as another model input improved model efficiency.

Heavy-duty diesel (HDD) engines are the primary propulsion source for most heavy-duty vehicle freight movement and have been equipped with an array of aftertreatment devices to comply with more stringent emissions regulations. In light of concerns about the transportation sector's influence on climate change, legislators are introducing requirements calling for significant reductions in fuel consumption and thereby, greenhouse gas (GHG) emission over the coming decades. Advanced engine concepts and technologies will be needed to boost engine efficiencies. However, increasing the engine's efficiency may result in a reduction in thermal energy of the exhaust gas, thus contributing to lower exhaust temperature, potentially affecting aftertreatment activity, and consequently rate of regulated pollutants. This study investigates the possible utilization of waste heat recovered from a HDD engine as a means to offset fuel penalty incurred during thermal management of SCR system.