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This study tests the use of thoroughly-evaluated waste plastic pyrolytic oils (WPOs) as substitute fuels in a modern, single-cylinder, diesel research engine. Emissions results are supported by FTIR analysis of exhaust gases, identifying 20 species. The results show that contemporary Tier 4-compliant combustion systems with split injection can handle high polypropylene-based WPO content in diesel fuel without re-calibration. Combustion phasing is delayed only at near-idle loads. Hydrocarbon and CO emissions are elevated, but to an acceptable extent. Engine fueling with high admixtures of polystyrene- based WPO results in unstable combustion at low loads and emission issues across the whole load range.

Ammonia, which is one of the most produced inorganic chemicals worldwide, has gained significant attention in recent years as a carbon-free fuel due to its significant energy density in maritime and power plant applications. This fuel offers several advantages including low production costs and being safe for storage and transport. Reactivity controlled compression ignition (RCCI) combustion mode is considered as a promising strategy reducing the level of nitrogen oxides (NOx) emissions and particulate matters (PM) in internal combustion engines (ICEs) due to the lower combustion temperatures and charge homogeneity. Ammonia-based RCCI combustion strategy can offer a simultaneous reduction of CO2 and NOx. In this study, a RCCI engine fuelled by ammonia and diesel is numerically simulated considering chemical reactions kinetics mechanism of the combustion.

In this work neural network models are used to reconstruct in-cylinder pressure from a vibration signal measured from the engine surface by a low-cost accelerometer. Using accelerometers to capture engine combustion is a cost-effective approach due to their low price and flexibility. The paper describes a virtual sensor that re-constructs the in-cylinder pressure and some of its key parameters by using the engine vibration data as input. The vibration and cylinder pressure data have been processed before the neural network model training. Additionally, the correlation between the vibration and in-cylinder pressure data is analyzed to show that the vibration signal is a good input to model the cylinder pressure.The approach is validated on a RON95 single cylinder research engine realizing homogeneous charge compression ignition (HCCI). The experimental matrix covers multiple load/rpm steady-state operating points with different start of injection and lambda setpoints.

To comply with the ambitious CO2 targets of the European Union, greenhouse gas emissions from the transport sector should be eliminated by 2050. Incremental powertrain improvement and electrification are only a part of the solution and need to be supplemented by carbon-neutral fuels. Due to the high technology readiness level, biofuels offer a short-term decarbonization measure. The high process energy demand for transesterification or hydrotreating however, hinders the well-to-wheel CO2 reduction potential of current market biodiesels. An often-raised, economically and energetically feasible alternative is to use unprocessed oils with viscosity and cold-properties improvers instead. The present work investigates the suitability of one such biofuel (PlantanolTM) for advanced common rail engines operating in a partially premixed compression ignition mode. Preliminary investigations are carried out on a Euro VIb light-duty car engine.

A single cylinder research engine with negative valve overlap (NVO) and direct gasoline injection was run in a homogeneous charge compression ignition (HCCI) mode. The split fuel injection technique was used, where the first injection was applied during exhaust re-compression, while the second injection was applied at the beginning of main compression. The quantities of the fuel injected at the two timings were varied from the whole fuel injection during NVO to the whole fuel injection during the main compression event. These split fuel ratio sweeps were repeated both for a stoichiometric mixture and for a slightly lean mixture. In the study, NVO reactions were assessed via analysis of the exhaust-fuel mixture composition after the NVO period and referred to the main event combustion. The results showed that fuel injection during NVO resulted in the production of substantial quantities of auto-ignition promoting species, such as acetylene and formaldehyde.

This paper presents an analysis of combustion phasing in a controlled auto-ignition (CAI) engine fuelled with gasoline. Auto-ignition was achieved using an exhaust gas trapping method via negative valve overlap (NVO). Under slightly lean mixture conditions variable intake and exhaust valves timings were applied in order to analyze influence of amount of retained exhaust on auto-ignition timing and combustion duration. Combustion on-set was independent of exhaust valve closing event, which was responsible for amount of trapped residuals. However, it was found that auto-ignition timing was determined by intake valve timing. Combustion duration was affected by both exhaust and intake valve timings. Direct injection allowed for application of different mixture formation strategies including in-cylinder fuel reforming during the NVO phase. When fuel was injected in the late stage of NVO increase of air-fuel ratio (AFR) caused a retard of auto-ignition and reduction of heat release rate.

The paper presents a comparative analysis of the operational parameters, smoke emission and the content of toxic components in exhaust gases of a compression ignition engine fuelled with fossil diesel, commercially available biodiesel (fatty acid methyl ester) and their 50%/50% blend. The study was carried out using a diesel engine equipped with an in-line injection pump used in delivery vans. An indirect injection, considerably worn engine was chosen because such engines are very often fuelled with the less expensive biodiesel. Measurements were made on an engine operated at different rotational speeds under full load as well as using the European Stationary Cycle (ESC) test. Emissions of regulated and non-regulated compounds were measured with the use of the Fourier-Transform Infrared (FTIR) analytical system, which provided concentrations of 23 exhaust gas components.

The paper presents results of research of a gasoline engine capable of controlled auto-ignition (CAI) mode of combustion. The experiments were conducted on a single cylinder research engine equipped with direct fuel injection and a fully variable valvetrain. CAI operation was obtained with the use of the negative valve overlap (NVO) technique. The engine was operated at variable rotational speed and load. Comparative analysis of performance, fuel consumption and exhaust emissions was made for both SI and CAI combustion modes. Combination of variable valvetrain settings and variable excess air ratio allowed loads between 0.2 and 0.42 MPa IMEP in CAI combustion mode. Direct fuel injection provided the possibility to control the heat release rate via injection angle.

The paper presents results of spectroscopic research of the air excess ratio in a combustion chamber of a spark- ignited engine fuelled with gasoline and LPG. For the evaluation of air excess ratio a correlation with relative concentration of HC and C2 radicals existing in combustion flame was used. Concentration of radicals was measured on the basis of chemiluminescence forming as a result of transition from electron-excited states. Light emission from the combustion chamber was recorded using measurement system composed of optical combustion sensor, transmission optical waveguides, optical filters, monochromators and electronic devices for signal conversion. A strong correlation of relative optical emission with air excess ratio in combustion chamber was confirmed. However, for different engine loads and rotational speeds, some scattering of measurement points was noticed. Therefore it was necessary to use artificial neural networks to analyze courses of optical signal.

Evaluation of the air excess ratio (lambda) in the combustion chamber can be done on the base of simplified flame spectrum analysis. Although the obtained results are valuable for the better understanding of the process, they do not let to determine the air excess ratio in different operation conditions. The research limitations have appeared due to incomprehension of chemical and physical aspects of combustion process. Light intensity during the combustion is influenced by several parameters such as engine load, rotational speed, ignition timing, temperature etc. Thus, it is extremely difficult to develop an accurate measurement method based on evaluation of radicals' emission ratio. Artificial neural networks (ANN) can be used in order to derive air excess ratio from time-domain courses of light intensity signals measured in chosen wavelengths.

The paper describes investigations on the nature of the misfire phenomenon in a SI engine. Authors performed a series of experiments on a single-point gasoline injection engine in a wide range of loads and rotational speeds, including different air-fuel ratios. The data was gathered using optical combustion sensor integrated with a spark plug. The main aim of the experiments was to develop fast and reliable misfire detection algorithm basing on the optical signal, which would be simple and easy to use within the engine control system. Misfire identification algorithm proved to be reliable, with neglectable detection error. Analysis of the results included not only quantitative measurements, but also those regarding distribution and probability of occurrence. Results made it possible to establish conditions in which the investigated engine is more likely to misfire. The influence of misfire occurrence on the further engine performance was also analyzed.