In this paper, investigations were performed on a dual-fueled constant-speed engine. Initially, the emissions and performance of a diesel engine were investigated, and after moving to the dual-fuel engine, experimental tests were carried out under different loads (10, 25, 50, 75, and 100% of the full load) and pilot to gaseous fuel (PGF) ratios (30, 40, and 50%). The results showed that under different loads and PGF ratios, the emissions of nitrous oxides and particle materials in the dual mode were lower than those for the diesel engine, and that the difference was significant. The emission of carbon dioxide in the dual-fueled mode varied little compared to that of the diesel mode, although it was lower than that for the diesel engine. In loads lower than 75% of the full load, the emission of carbon monoxide in the diesel engine was lower than that for the dual-fueled engine. However, in full load, this result was reversed and had significant difference. The dual mode had lower hydrocarbon emission compared to that of the diesel mode in all PGF ratios and loads. A computational fluid dynamics-based numerical simulation was performed by KIVA3V, and its results showed good agreements with the experimental results under cylinder pressure. ARTICLE HISTORY

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced through a process that dissociates it from its compounds. Several methods are used for hydrogen production, which first of all differ in the energy used in this process. Investigating the viability and exact applicability of a method in a specific context requires accurate knowledge of the parameters involved in the method and the interaction between these parameters. This can be done using top-down models relying on complex mathematically driven equations. However, with the raise of computational intelligence (CI) and machine learning techniques, researchers in hydrology have increasingly been using these methods for this complex task and report promising results. The contribution of this study is to investigate the state of the art CI methods employed in hydrogen production, and to identify the CI method(s) that perform better in the prediction, assessment and optimization tasks related to different types of Hydrogen production methods. The resulting analysis provides in-depth insight into the different hydrogen production methods, modeling technique and the obtained results from various scenarios, integrating them within the framework of a common discussion and evaluation paper. The identified methods were benchmarked by a qualitative analysis of the accuracy of CI in modeling hydrogen production, providing extensive overview of its usage to empower renewable energy utilization.

Biodiesel can easily be used as an alternative fuel in diesel engines. It is environmentally friendly and can be produced from low-cost feedstocks such as waste cooking oil (WCO). WCO contains a significant amount of free fatty acid, which is extracted by a two-step process of converting the free fatty acid by acid catalysis (H 2 S0 4) and converting the triglycerides using an NaOH catalyst. Currently, the major challenge for the industrial production of biodiesel is optimizing the yield while meeting American Society for Testing and Materials (ASTM) standards. In this study, experiments were performed to optimize the reaction conditions. The studied experimental parameters were the alcohol types (methanol, ethanol), the alcohol-to-oil molar ratio (AOMR; 3:1, 6:1, 9:1), the amount of catalyst (0.5, 1.0, 1.5 wt% of the oil), the temperature of the reaction (50, 60, 70, 80°C), the mixing intensity (300, 600, 900 rpm), and the reaction time (30, 60, 90 min). The biodiesel production yield (BPY) was optimized based on the experimental data. The optimum value of the BPY based on methanol is 95.92%, which is obtained at 73.80°C, with a reaction time of 74.02 min, an AOMR of 6.58:1, a catalyst concentration of 1.13 and a mixing intensity of 824.45 rpm. In the case of ethanol, the optimum BPY is 95.53%. which is obtained at 64.96°C, with a reaction time of 88.02 min, an AOMR of 7.005:1, a catalyst concentration of 1.25 and a mixing intensity of 592.18 rpm. These results of biodiesel production were confirmed by the experimental data.

Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25-30% of full load, which is the same as the calculated range obtained from mathematical modeling.

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