• Energy Research

The aim of present study is to develop an accessible accurate estimation of CN based on fatty acid methyl esters and to provide a proper solution for presenting a user‐friendly method. In fact, this study calculates the density, viscosity, and HHV based on FAMEs and predicts the CN by employing fuzzy method within a set. This is an interesting approach that has not been used in similar articles. Gaussian membership functions with 81 roles were employed to develop the fuzzy model. The model was developed based on Carbon number, Double bond, Saponification number, and Iodine value to predict the CN. Performance factors of r, RMSE, MAE, and R2 were calculated as 0.9912, 1.0723, 0.63427, and 0.9828, respectively to predict the CN. The results of FAMEs effect on properties of biodiesel showed, increasing Carbon number of FAMEs increases the CN, viscosity, and HHV, but increasing the number of Double bonds decreases CN, viscosity, and HHV. While the effect of increasing Carbon number of FAMEs on density was vice versa. Based on results, C16:00, C18:00, C18:1, and C18:02 FAMEs are approximately in components of all conventional oils; therefore, they can be effective on physical properties of biodiesels. © 2019 American Institute of Chemical Engineers Environ Prog, 38: 584–599, 2019

Biodiesel is among the biofuels. In this study, biodiesel was produced from Iranian bitter almond (BAO) oil through the transesterification process by using ethanol (BAO ethyl ester). BAO can be considered a non-edible oil, so this parameter can add to the benefits of biodiesel production. Biodiesel was prepared in two samples, containing 5 and 20 volumetric percent of biodiesel in diesel fuel respectively (B5 and B20). The tests were done based on the ECE R-85 standard. According to this standard, the engine test is performed at four loads (25%, 50%, 75%, and 100%) and two rotational speeds, 1400 rpm and 2000 rpm. These correspond to the maximum torque and power respectively. Experimental tests were run on engine performance and emissions of biodiesel fuels. The results were studied and all the parameters were compared with diesel fuel. According to the results, the maximum brake power increased (about 9.5% compared with diesel fuel) in B20, at a load of 20% and a rotational speed of 2000 rpm. Also the maximum reduction of BSFC (about 8.6% lower than diesel fuel) happened for B20 at a load of 25% and a rotational speed of 2000 rpm. Although, the brake-specific fuel consumption (BSFC) decreased at a rotational speed of 2000 rpm and all loads for both B5 and B20 compared with diesel. Regarding the effect of biodiesel on engine emissions, the emission of nitrogen oxide (NOx) at all loads for both B5 and B20 was greater than that for diesel fuel. The carbon dioxide (CO2) emission of the engine was lower for B5 at a rotational speed of 2000 rpm and at all loads compared with diesel fuel.

The presence of vast forests is the largest source of abundant biomass in Iran. Wood waste can be used as the primary source of energy generation. In the present research, sawdust was employed to produce the syngas. For this purpose, a downdraft gasifier was designed and the produced syngas was evaluated in a spark ignition (SI) engine. Based on results, the production yield of biohydrogen and its heating value at 960 ℃ were about 786 mL/g biomass and 3.75 MJ/m3, respectively. The synthesized syngas compositions were Hydrogen (13.82%), CH4 (1.52%), CO2 (2.75%), CO (13.6%), N2 (65.31%) and water vapor (3%). Increasing the temperature of the combustion zone inside the gasification reactor from 650 to 960 ℃, increased the H2, CH4, and CO contents of syngas. Moreover, the results indicated that, using syngas in engine reduces CO2 and UHC emissions about 58.6% and 16.6%, respectively compared to those for gasoline fuel.

Diesel fuel (DF) is a significant power supply in agricultural, industrial, and transportation applications. Establishing sustainable and renewable fuel substitutes for diesel has become increasingly common due to the rising expense of petroleum resources and the pollution rate crises. A biodiesel-DF mixture in a dual-fuel (DuF) diesel engine (DE) can bring favorable environmental results. In the present study, three rates of ethanol (0, 2, and 4%), two rates of biodiesel (0 and 5%), and four rates of water (0, 0.3, 0.6, and 0.9%) were blended with DF. All these samples were considered pilot fuel (PF) in the DuF combustion process with an 80% natural gas (NG) replacement percentage. The combustion process was investigated from engine emissions and performance, power cost, and life cycle assessment (LCA) to obtain a sustainable fuel formulation. As a result, water, ethanol, and the combination of water-ethanol and NG can enhance the DE’s performance by rising the inside pressure of the cylinder. The presence of oxygen content in ethanol can improve the combustion process by pushing the combustion towards complete combustion. The optimum engine performance point at full load was obtained with a fuel sample containing 1.57% biodiesel, 4.38% ethanol, 1.1% water, and 80% NG. In optimum condition, the brake power (BP) was 24.16 kW, and the brake-specific fuel consumption (BSFC) was 60.64 g/kWh. This fuel sample produces 0.46, 364.08, 1.66, and 1088.29 g/kWh of BSCO, BSCO2, BSNOx, and BSO2, respectively. At this point, the energy production cost was $0.783/kWh. The environmental impacts of the combustion process at optimal fuel formulation were 0.34249, 1.00 E + 02 , 1.53 E + 00 , and 1.94 E − 06 , respectively, for ecosystem quality (EQ) (PDF ∗ m2 ∗ yr), resources (R) (MJ primary), climate change (CCh) (kg CO2 eq), and human health (HH) (DALY). Accordingly, the best fuel combination was selected to be NG+B1.5E4.3W1.1.

Abstract A combined cooling, heating, and power generation system is proposed for the recovery of exhaust waste heat of the diesel engine. The engine rejected heat recovery system consisted of a domestic water heater (DWH), an organic Rankine cycle (ORC) and an Ejector Refrigeration Cycle (ERC) for heating aims, power generation, and cooling aims, respectively. The Diesel engine is fed by different fuels and biofuels to drive the bottoming cycles. The Transesterification method has been used for biodiesel extraction from different oil-basis materials. The produced biodiesel supplied to the Diesel engine in different engine speeds and loads to evaluate the system performance from viewpoints of the energy and exergy. The highest generated power for the total system is obtained in the case of sunflower biodiesel blend and B10 sample as the fuel. The maximum overall system energy efficiency obtained when the canola B30 blend is applied in 100 % load and 2400 r p m . Based on the obtained results, the exergy efficiency considerably increases in the case of biodiesel, and diesel blends as the fuel compared to the pure diesel. Exergy efficiency was optimized for the proposed combined heating, cooling and power generation system at 100% load, 1700 rpm and B10 blend obtained from canola oil.

The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for 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.