Showing papers on "Diesel engine published in 2022"

Experimental Investigation to Utilize Adsorption and Absorption Technique to Reduce CO2 Emissions in Diesel Engine Exhaust Using Amine Solutions

Abstract: Due to the increased demand for global transportation needs, a long-range diesel engine is considered an important prime mover to fulfill the transportation demand. The major problem addressed by the diesel engines is it liberates harmful emissions and it also increases global warming. CO2 is considered an important greenhouse gas and it has to be controlled by diesel engines. In this research, extensive experimental work is done to identify a suitable solution to control CO2 emissions. For five different mole concentrations (0.1 to 0.5), three types of amino solutions (L-alanine, L-aspartic acid, and L-arginine) were prepared. By passing diesel exhaust through each solution, CO2 absorption is investigated. For CO2 absorption, an appropriate CO2 control system is built and tested. The tests were carried out in a diesel engine which is a naturally aspirated single-cylinder engine with a water-cooled system. It was possible to obtain an effective CO2 reduction of up to 90%, as well as a simultaneous reduction in HC and CO.

Comparison of Random Forest and Neural Network in Modeling the Performance and Emissions of a Natural Gas Spark Ignition Engine

Abstract: Abstract Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods toward predicting the performance of a diesel engine modified to natural gas (NG) spark ignition (SI), based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest (RF) and the artificial neural network (ANN), in forecasting engine responses related to in-cylinder combustion phenomena was compared. The results indicated that both algorithms with spark timing (ST), mixture equivalence ratio, and engine speed as model inputs produced acceptable results with respect to predicting engine performance, combustion phasing, and engine-out emissions. Despite requiring more effort in hyperparameter optimization, the ANN model performed better than the RF model, especially for engine emissions, as evidenced by the larger R-squared, smaller root-mean-square errors (RMSEs), and more realistic predictions of the effects of key engine control variables on the engine performance. However, in applications where the combustion behavior knowledge is limited, it is recommended to use a RF model to quickly determine the appropriate number of model inputs. Consequently, using the RF model to define the model structure and then using the ANN model to improve the model’s predictive capability can help to rapidly build data-driven engine combustion models.