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Modern solutions used in compression-ignition internal combustion engines are quite similar to each other. The use of high-pressure, direct fuel injection results in high combustion rates with controlled exhaust emissions. One of the combustion system quality criteria is to obtain adequately high thermodynamic indicators of the combustion process, which are obtained through, among others, the right combustion chamber geometry. Its shape influences the fuel atomization process, turbulence of fuel dose, evaporation and the combustion process. Optimizing the combustion chamber shape is one of the decisive factors proving the correct execution of the combustion process. This article presents the methodology of choosing the combustion chamber shape (changes of three selected combustion chamber dimensions) by using the optimization methods. Generating multidimensional data while maintaining the correlation structure was performed by using the Latin hypercube method. Chamber optimization was carried out by using the Nelder-Mead method. The combustion chamber shape was optimized for three engine load values (determined by the average indicated pressure) at selected engine operating conditions. The presented method of engine combustion chamber optimization can be used in low and high speed diesel propulsion engines (especially in maritime transport applications).
The article presents the results of emission test Sport Utility Vehicle (SUV) with diesel engine, equipped with start-stop system. Measurements was carried out in on-road conditions in urban traffic. Article presents a method of work implemented start-stop system, and the characteristics of the implemented battery Absorbent Glass Mat (AGM). Analysis of the results allowed to indicate the effectiveness of the system and its impact on emission per second. In addition was determined road emission and fuel consumption from performed test. For measurement was used mobile gas analyser SEMTECH DS included in Portable Emissions Measurement Systems (PEMS)
promising biofuels for CIengines – A review. In Renewable and Sustainable Energy Reviews, vol. 78, pp. 1068–1088. BELOEV, I. – GABROVSKA-EVSTATIEVA, K. – EVSTATIEV, B. 2017. Compensation of CO2 emissions from petrol stations with photovoltaic parks: Cost-benefit and risk analysis. In Acta Technologica Agriculturae, vol. 20, no. 4, pp. 85–90. BIRZIETIS, G. – PIRS, V. – DUKULIS, I. – GAILIS, M. 2017. Effect of commercial diesel fuel and hydrotreated vegetable oil blend on automobile performance. In Agronomy Research, vol. 15, no. S1, pp. 964–970. CISEK, J. – SZLACHTA, Z
Vegetable oil based fuels significantly enable reducing the costs of fuel purchased. CI engine vehicles with rotary and inline injection pump can be fuelled by vegetable oil based fuels instead of being fuelled by diesel. This is very common, since their price is lower in comparison with diesel. The article focuses on the impact of using fuels made from vegetable oil on selected vehicle characteristics in particular conditions. It includes the measurements of the impact of using fuels such as FAME, fresh oil and used oil on the engine smoke opacity, content of selected emissions in the exhaust gases as well as on the engine power and torque’s course. The measurement results are mutually compared with the results measured when using diesel. In order to secure the measurements to be repeatable, they were performed in laboratory at the cylinder test station MAHA MSR 1050. The vehicle tested during its last 100,000 kilometres driven by vegetable oil based fuel has been selected for these measurements. Therefore, by these measurements, it was also possible to assume partially the impact of long-term using aforesaid fuels on selected vehicle characteristics.
The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a self-learning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.