Modeling Viscosity and Density of Ethanol-Diesel-Biodiesel Ternary Blends for Sustainable Environment

Rapid depletion in fossil fuels, inflation in petroleum prices, and rising energy demand have forced towards alternative transport fuels. Among these alternative fuels, diesel-ethanol and diesel-biodiesel blends gain the most attention due to their quality characteristics and environmentally friendly nature. The viscosity and density of these biodiesel blends are slightly higher than diesel, which is a significant barrier to the commercialization of biodiesel. In this study, the density and viscosity of 30 different ternary biodiesel blends was investigated at 15 °С and 40 °С, respectively. Different density and viscosity models were developed and tested on biodiesel blends soured from different feedstock’s including palm, coconut, soybean, mustard, and calophyllum oils. The prognostic ability and precisions of these developed models was assessed statistically using Absolute Percentage Error (APE) and Mean Absolute Percentage Error (MAPE). The MAPE of 0.045% and 0.085% for density model and 1.85%, 1.41%, 3.48% and 2.27%, 1.85%, 3.50% for viscosity models were obtained on % volume and % mass basis. These developed correlations are useful for ternary biodiesel blends where alcohols are the part of biodiesel blends. The modeled values of densities and viscosities of ternary blends were significantly comparable with the measured densities and viscosities, which are feasible to avoid the harm of vehicles’ operability

Two-phase frictional pressure drop with pure refrigerants in vertical mini/micro-channels

Environmental concerns have urged a search for eco-friendly refrigerants in the refrigeration industry to overcome ozone depletion and global warming problems. Therefore, current research emphasizes frictional pressure drop during flow boiling of environment-friendly refrigerants (GWP\u3c150), isobutane, HFC-152a, HFO-1234yf were tested against commonly reported HFC-134a. The data presented here was collected under heat flux-controlled conditions; the test piece was a round tube (1.60 mm diameter). The data collection was performed at 27 and 32 °C with mass velocities in 50-500 kg/m2s range. Effects of critical controlling parameters, like heat flux, mass velocity, exit vapor quality, operating pressure and medium, were studied in detail. It was observed that pressure drop increases along with mass velocity increment in the test piece and increases with exit vapor quality increment. The same was noticed to decrease with saturation temperature increment. Parametric effects and prediction of assessment methods are reported

Comparative study of nanoparticles and alcoholic fuel additives-biodiesel-diesel blend for performance and emission improvements

This study aims to investigate a CI diesel engine characteristic of diesel-biodiesel blend with oxygenated alcohols and nanoparticle fuel additives. Biodiesel was synthesized from a complementary palm-sesame oil blend using an ultrasound-assisted transesterification process. B30 was mixed with fuel additives as the base fuel to form ternary blends in different proportions before engine testing. The oxygenated alcohols (DMC and DEE) and nanoparticles (CNT and TiO2) were used to improve both the fuel characteristics and engine emission and performance parameters. B30 fuel was mixed with 5% (DEE) and 10% (DMC) by volume and 100 ppm concentration of CNT and TiO2 nanoparticles, respectively, which are kept constant during this study. Engine performance and emissions characteristics were studied using a CI diesel engine with variable engine rpm at full load condition. The results were compared with B30 fuel and B10 (commercial diesel). The main findings indicated that the B30 + TiO2 ternary blend shows an overall decrease in brake specific fuel consumption up to 4.1% among all tested fuels. B30 + DMC produced a higher 9.88% brake thermal efficiency, among other fuels. B30 + DMC ternary blend showed a maximum decrease in CO and HC emissions by 29.9% and 21.4%, respectively, collated to B30. B30 + CNT ternary blend showed a maximum reduction of 3.92% in NOx emissions compared to B30

Effect of additivized biodiesel blends on diesel engine performance, emission, tribological characteristics, and lubricant tribology

Abstract: This research work focuses on investigating the lubricity and analyzing the engine characteristics of diesel–biodiesel blends with fuel additives (titanium dioxide (TiO2) and dimethyl carbonate (DMC)) and their effect on the tribological properties of a mineral lubricant. A blend of palm–sesame oil was used to produce biodiesel using ultrasound-assisted transesterification. B30 (30% biodiesel + 70% diesel) fuel was selected as the base fuel. The additives used in the current study to prepare ternary fuel blends were TiO2 and DMC. B30 + TiO2 showed a significant reduction of 6.72% in the coefficient of friction (COF) compared to B30. B10 (Malaysian commercial diesel) exhibited very poor lubricity and COF among all tested fuels. Both ternary fuel blends showed a promising reduction in wear rate. All contaminated lubricant samples showed an increment in COF due to the dilution of combustible fuels. Lub + B10 (lubricant + B10) showed the highest increment of 42.29% in COF among all contaminated lubricant samples. B30 + TiO2 showed the maximum reduction (6.76%) in brake-specific fuel consumption (BSFC). B30 + DMC showed the maximum increment (8.01%) in brake thermal efficiency (BTE). B30 + DMC exhibited a considerable decline of 32.09% and 25.4% in CO and HC emissions, respectively. The B30 + TiO2 fuel blend showed better lubricity and a significant improvement in engine characteristics

Effect of palm-sesame biodiesel fuels with alcoholic and nanoparticle additives on tribological characteristics of lubricating oil by four ball tribo-tester

Dilution of engine oil with unburned fuels alters its lubricity and tribological properties. In this research paper, SAE-40 lubricating oil samples were contaminated with known percentages (5%) of fuels (diesel, palm-sesame biodiesel blend (B30), B30 + ethanol, B30 + dimethyl carbonate, B30 + carbon nanotubes and, B30 + titanium oxide). The effect of all these fuels on wear and frictional characteristics of lubricating oil was determined by using a 4-ball tribo tester and wear types on worn surfaces were analyzed by using SEM. Lubricating oil diluted with B10 (commercial diesel) showed highest COF (42.95%) with severe abrasive and adhesive wear than mineral lubricant among other fuels. Lubricating oil diluted with palm-sesame biodiesel (B30 blend) with alcoholic additives showed comparatively less COF, less wear scar diameter and polishing wear due to presence of ester molecules. Lub + B30 + Eth exhibited increment in COF value (35.81%) compared to SAE-40 mineral lubricant. While lubricating oil contaminated with B30 with nanoparticles showed least frictional characteristics with abrasive wear. Lub + B30 + TiO2 showed least increment in COF value (13.78%) among all other contaminated fuels compared to SAE-40 mineral lubricant. It is concluded that nanoparticles in biodiesel blends (B30) helps in reducing degradation of lubricants than alcoholic fuel additives and commercial diesel. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University

Wastewater driven trace element transfer up the food chain in peri-urban agricultural lands of Lahore, Pakistan

In peri-urban agricultural lands of Lahore city, untreated wastewater from trans-boundary Hudiara drain is widely used for agriculture. The irrigated water may pose a hazard of trace element (TE) contamination in agricultural produce and consequently threat to human health. This study was designed to investigate the quality and transferability of TE contamination in water, soil, fodder and buffalo milk. Samples from the 4 assets and products were collected at upstream, midstream and downstream sites along Hudiara drain. Potentially toxic elements (Cd, Cu, Ni and Zn) were analyzed using standard methods. Physicochemical analysis of water, bioaccumulation factor (BAF) from soil to fodder and Pearson correlation of metal contamination in water, soil, fodder and milk were determined. TE contamination increased from upstream to downstream site. Highest Cd concentration in water, soil, fodder and milk was 0.29 mg/L (downstream), 1.10 mg/kg (midstream), 2.12 mg/kg (downstream) and 0.29 mg/L (downstream), respectively, which surpassed permissible limits for all the 4 mediums. Similar results were found with Cu, Ni and Zn which increased downstream and the concentrations higher than permissible limits in all mediums except soil. Significantly high Hazard Index (HI) values were recorded for irrigation wastewater (23.25–31.75), fodder (7.41–11.13), and milk (11.12–17.85), which were increasing down the stream of drain. All 4 metals have shown strong positive correlation among water, soil, fodder and milk, showing transferability risk up to food chain. Transfer of TE from soil to fodder was highest for Ni (BAF 8.44) and lowest for Zn (BAF 0.41) with the following ascending trend: Zn < Cd < Cu < Ni. The physicochemical parameters of drain water also did not meet the permissible limits for wastewater irrigation. Use of untreated water of Hudiara drain for agriculture in peri-urban areas of Lahore needs to be stopped until appropriate treatment and reuse

Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics&mdash;including density, viscosity, calorific value, acid value, and oxidation stability&mdash;were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm&ndash;cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst concentration (1 wt %), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively

Response Surface Methodology and Artificial Neural Networks-Based Yield Optimization of Biodiesel Sourced from Mixture of Palm and Cotton Seed Oil

In this present study, cold flow properties of biodiesel produced from palm oil were improved by adding cotton seed oil into palm oil. Three different mixtures of palm and cotton oil were prepared as P50C50, P60C40, and P70C30. Among three oil mixtures, P60C40 was selected for biodiesel production via ultrasound assisted transesterification process. Physiochemical characteristics—including density, viscosity, calorific value, acid value, and oxidation stability—were measured and the free fatty acid composition was determined via GCMS. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized for the sake of relation development among operating parameters (reaction time, methanol-to-oil ratio, and catalyst concentration) ultimately optimizing yield of palm–cotton oil sourced biodiesel. Maximum yield of P60C40 biodiesel estimated via RSM and ANN was 96.41% and 96.67% respectively, under operating parameters of reaction time (35 min), M:O molar ratio (47.5 v/v %), and catalyst concentration (1 wt %), but the actual biodiesel yield obtained experimentally was observed 96.32%. The quality of the RSM model was examined by analysis of variance (ANOVA). ANN model statistics exhibit contented values of mean square error (MSE) of 0.0001, mean absolute error (MAE) of 2.1374, and mean absolute deviation (MAD) of 2.5088. RSM and ANN models provided a coefficient of determination (R2) of 0.9560 and a correlation coefficient (R) of 0.9777 respectively

Maximising yield and engine efficiency using Optimised waste cooking oil biodiesel

In this study, waste cooking oil (WCO) was used as a feedstock for biodiesel production, where the pretreatment of WCO was performed using mineral acids to reduce the acid value. The response surface methodology (RSM) was used to create an interaction for different operating parameters that affect biodiesel yield. The optimised biodiesel yield was 93% at a reaction temperature of 57.50 ◦C, catalyst concentration 0.25 w/w, methanol to oil ratio 8.50:1, reaction stirring speed 600 rpm, and a reaction time of 3 h. Physicochemical properties, including lower heating value, density, viscosity, cloud point, and flash point of biodiesel blends, were determined using American Society for Testing and Materials (ASTM) standards. Biodiesel blends B10, B20, B30, B40, and B50 were tested on a compression ignition engine. Engine performance parameters, including brake torque (BT), brake power (BP), brake thermal efficiency (BTE), and brake specific fuel consumption (BSFC) were determined using biodiesel blends and compared to that of high-speed diesel. The average BT reduction for biodiesel blends compared to HSD at 3000 rpm were found to be 1.45%, 2%, 2.2%, 3.09%, and 3.5% for B10, B20, B30, B40, and B50, respectively. The average increase in BSFC for biodiesel blends compared to HSD at 3500 rpm were found to be 1.61%, 5.73%, 8.8%, 12.76%, and 18% for B10, B20, B30, B40, and B50, respectively.</p