Investigating the Effects of Alternative Fuels with Different Aromatic Species on Compression Ignition Engine Emissions and Performance

Harmful emissions are the major challenge for combustion systems and continuously increasing with the use of fossil-based feedstock around the globe. Compression ignition engine (CI) is one of the main emitters of harmful pollutants. As compared to spark ignition (SI) engine, CI engine produces high particulate matter (PM) and nitrogen oxides (NOx) emissions. The need for the improvement of engine performance; fuel consumption and thermal efficiency is another challenge. Investigating the effect of fuel components is one of the approaches that can be used to reduce exhaust emissions and improve performance. knowledge gaps need to be completed in how different aromatic species of the same type impact the engine performance and emissions. The contributions of the present study are in the detailed investigations reporting and analyzing the role of different alkylbenzenes and polycyclic aromatics in surrogate fuels on emissions and engine performance. This knowledge would help the future fuel industry to produce future fuels with appropriate alkylbenzenes and polycyclic aromatics for lower emissions and improved performance. In addition, the study provides more details about the influence of different aromatic concentrations in the fuel on emissions and performance. To attain the aims of the current study, different alkylbenzenes and polycyclic aromatics were blended with surrogate fuel at three different contents. The blended fuels were tested experimentally using a direct injection (DI) CI engine at two different load conditions. Appropriate sampling line, particulate and gaseous species measurement instrumentation were integrated with the engine rig in order to take measurements accurately. Impact of different properties of aromatic species on PM, NOx UHC’s, CO and engine performance has also been investigated and forms a part of contribution to knowledge. The overall results show that increasing aromatic content in fuel contributes to high levels of exhaust emissions and impacts engine performance. Comparison among alkylbenzenes surrogate blends presents that blends containing ethylbenzene produce low exhaust emissions and better performance because of its properties; high calorific value, cetane number, low density and hydrogen-to-carbon (H/C) ratio. While, indane surrogate blends have better results as compared to other polycyclic aromatics. Finally, optimum surrogate blend is formed with appropriate aromatics (ethylbenzene and indane). Operating the engine with optimum blend results in significant reduction of PM, smoke, unburned hydrocarbons (UHC) as compared to commercial diesel fuel. However, increase of brake thermal efficiency (BTE), reduction of NOx and brake specific fuel consumption (BSFC) are insignificant. NOx and PM correlations are developed as a function of significant impacted fuel properties. The prediction models developed are highly agreeing with experimental results. Overall, this work would provide basis for selection of aromatic species in future fuels, as not all aromatic species lead to higher PM, NOx or give the optimal engine performance

Energy analysis of a novel solar tri-generation system using different ORC working fluids

A novel solar tri-generation system combined with an Organic Rankin Cycle (ORC), humidification dehumidification water desalination system (HDH), and a desiccant cooling system (DCS) for electrical power generation, water desalination, and air-conditioning is investigated numerically. Such a system is intended for tiny and medium-sized structures in Gulf nations that are abundant in solar energy but deficient in fossil fuels and water supplies. Detailed parametric studies of the operating and design parameters on the system's productivities and performance are carried out using different kinds of organic fluids (n-Octane, R245fa, R113, R123, Cyclohexane, and Toluene). It is noticed that the proposed system can provide the highest electrical power, fresh water, and space cooling capacity of 152.5 kW, 77.29 kg/s kg/h, and 27.11 kW, respectively, using Cyclohexane. Moreover, the maximum energy utilization factor obtained is 0.3018 using R123 and the improvement percentage of using R123 instead of n-Octane is 20%. Finally, general numerical correlations of a system's productivities and its performance are developed and presented within reasonable errors for different organic fluids

Heat transfer and pressure drop of Al2O3/water nanofluid in conically coiled tubes: Experimental and artificial neural network prediction

A novel model of an artificial neural network (ANN) is proposed in order to predict the thermal performance of Al2O3/water nanofluid in conically coiled tubes (CCTs). Experiments were conducted at volume concentrations of 0.3 %, 0.6 %, and 0.9 % of Al2O3/water nanofluid and coil torsions ranging from 0.02 to 0.052. Feed-forward neural network (FFNN) have been modelled and trained in order to predict the experimental and non-experimental Nusselt number (Nu) and.friction factor (ƒ). Using the TRAINLM algorithm, the FFNN for predicting Nu and ƒ is well-trained, with correlation coefficients of 0.9952 and 0.9482, respectively. FFNN exhibited greater accuracy in predicting the Nu and friction factor, since the Root mean square error (RMSE) between experimental and predicted data was minimal. The average RMSE, and Mean absolute percentage error (MAPE) were 9.6166 and 3.101 for the predicted Nusselt number. The predicted results of the ANN for the average Nusselt number and friction factor at φ = 0.8 % align well with the experimental data, even though they have not yet been empirically validated. These findings demonstrate the capability of ANNs to accurately predict the Nusselt number and friction factor and yield satisfactory outcomes