Toward prediction of kinematic viscosity of biodiesel using a robust approach

Especially by using a renewable source of fuels such as biodiesel, a large number of high-quality researches have been performed on the reduction of pollution released from fossil fuels. Transesterification process is a common way for the production of biodiesel from vegetable oil, animal fat, and algae oil in the presence of alcohol and catalyst. Viscosity is one of the important physical fuel properties used in the selection of biodiesel. Experimental measurement of viscosity is a time-consuming task. Hence, in this contribution, applicability and performance of two artificial neural network-based models named least square support vector machine (LSSVM) and genetic algorithm-radial basis function (GA-RBF) for the prediction of kinematic viscosity of biodiesel were investigated. Root-mean-square error, coefficient of determination (R²), and average absolute relative deviation of each modeling were reported for each LSSVM and GA-RBF models. Modeling results show that the proposed LSSVM model is more accurate and robust than GA-RBF model.     

Biodiesel production from oil-rich feedstock: A neural network modeling

Biodiesel produced from oil-rich feedstocks is known as a green replacement for conventional petroleum diesel. Transesterification is the common method used for biodiesel production. Hence, in this contribution, neural network modeling and least square support vector machine (LSSVM) modeling were used to predict the transesterification of castor oil with methanol to form biodiesel. Also, genetic algorithm was used for the optimization of predictive model. Input and output parameter of predictive models for the prediction of biodiesel production yield and estimation of the efficiency of biodiesel production are catalyst weight (C), methanol-to-oil molar ratio (MOR), time (S), temperature (T), and fatty acid methyl ester (FAME) yield, respectively. Proposed LSSVM modeling predicts biodiesel production yield or FAME yield within ±2% relative deviation with a high value of coefficient of determination (0.99583) and a low value of absolute deviation (1.27) in which the mentioned statistical parameters represent the accuracy and robustness of the model.     

Combined impact of varying biogas mass flow rate and rice bran methyl esters blended with diesel on a dual-fuel engine

ABSTRACT

For fetching day-to-day energy needs, current energy requirement majorly depends on fossil fuels. But ambiguous matter like abating petroleum products and expanding air pollution has enforced the experts to strive for another fuel which can be used as an alternative or reduce the applications of fossil fuels. Considering the issues, the main objective of the present study is to find the feasibility by using blends of rice bran oil biodiesel and diesel which are used as pilot fuels by blending 10% and 20% biodiesel in fossil diesel and biogas, introduced as gaseous fuel by varying its mass flow rate in a dual-fuel engine mode. An experimentation study was carried out to find the performance and emission parameters of the engine relative to pure diesel. The results were very much similar to the majority of researchers who used biodiesel and gaseous fuels in a dual-fuel engine. Brake specific fuel consumption (BSFC) of the engine was noticed to have increased, while brake thermal efficiency was on the lower side in dual fuel mode in comparison with regular diesel. In relation with conventional diesel, it was noticed that combined effect of rice bran methyl esters and varying mass flow rate of biogas showed a decrement in NO _x and smoke emissions, whereas HC and CO exhalations were on higher side when biogas and biodiesel were utilized collectively in dual-fuel engine. Hence, it was concluded that combination of blends of biodiesel and diesel and introduction of biogas in the engine can be a promising combination which can be used as a substitute fuel for addressing future energy needs.     

Accurate modeling of biodiesel production from castor oil using ANFIS

Research for finding alternative fuel sources has been concluded that the renewable fuels such as biodiesel can be used as an alternative to fossil fuels because of the energy security reasons and environmental benefits. In this contribution, transesterification of castor oil with methanol to form biodiesel has been modeled by using artificial neural network fuzzy interference system (ANFIS) approach. Methanol to oil molar ratio, catalyst amount (C), temperature (T), and time (S) were used as input parameters and fatty acid methyl ester yield was used as output parameter for modeling the efficiency of biodiesel production from castor oil. Obtaining low value of absolute deviation (2.2391), high value of R-squared (0.98704), and other modeling results proves that ANFIS modeling is an effective approach for biodiesel production from castor oil. In conclusion, comparison between our model and other previous predictive models reported in open literature indicates the priority of our model.     

Net energy balance of small-scale on-farm biodiesel production from canola and soybean

One necessary criterion for a biofuel to be a sustainable alternative to the petroleum fuels it displaces is a positive net energy balance. This study estimated the net energy ratio (NER), net energy balance (NEB), and net energy yield (NEY) of small-scale on-farm production of canola [Brassica napus (L.)] and soybean [Glycine max (L.)] biodiesel in the upper Midwest. Direct and embodied energy inputs based on well-defined system boundaries and contemporary data were used to estimate the energy requirement of crop production, oil extraction, and biofuel processing. The NER of canola biodiesel was 1.78 compared with 2.05 for soybean biodiesel. Canola biodiesel had a NEB of 0.66 MJ MJ⁻¹ of biofuel compared with 0.81 MJ MJ⁻¹ for soybean biodiesel. The NEY of soybean biodiesel was 10,951 MJ ha⁻¹, less than canola biodiesel which had a NEY of 11,353 MJ ha⁻¹. Use of soybean as a biodiesel feedstock was more energetically efficient than canola primarily due to reduced nitrogen fertilizer requirement. In terms of energetic productivity, canola was a more productive biodiesel feedstock than soybean due to its higher oil content. A best-case scenario based on optimal feedstock yields, reduced fertilizer input, and advanced biofuel processing equipment suggested that potential gains in energetic efficiency was greater for canola than soybean. According to our results, small-scale on-farm biodiesel production using canola and soybean can be an energetically efficient way to produce energy for on-farm use.     

Emission analysis on compression ignition engine fueled with lower concentrations of Pithecellobium dulce biodiesel‐diesel blends

The present study investigates the effect of Pithecellobium dulce biodiesel (PDBD) blends with diesel fuel on compression ignition (CI) engine emissions. Initially, PDBD was prepared by using a base transesteri?cation process. The GC‐MS, ¹H NMR, and Fourier transform infrared characterization of PDBD was carried out, and fuel properties were determined. The experiments were conducted on a single cylinder, CI engine using three blended fuels: PDBD5 (5% biodiesel and 95% diesel), PDBD10 (10% biodiesel and 90% diesel), and PDBD20 (20% biodiesel and 80% diesel). The experimental outcomes revealed that 20% of PDBD reduces 19.64% carbon monoxide, 17.64% hydrocarbon, and 6.73% oxides of nitrogen emissions. Furthermore, from this study, it was inferred that the PDBD20 blend could be used as an alternative fuel for CI engines with no modi?cations in engine design.     

A comparative study of Mesua ferrea L. based hybrid fuel with diesel fuel and biodiesel

The study presents preparation of a Mesua ferrea L. seed-oil-based emulsion hybrid fuel system by blending with alcohols (ethanol and butan-2-ol) in which butan-2-ol was employed as a surfactant. The study also deals with the investigation of the effect on fuel properties of the hybrid fuel due to the variation in surfactant amount and analysis of the prepared hybrid fuel system by ¹H nuclear magnetic resonance (¹H NMR). The experimental results show that the properties of the hybrid fuels were comparable to that of diesel and biodiesel.     

ANFIS modeling of biodiesels' physical and engine characteristics: A review

 Population increase has resulted in an increase in the worldwide demand for alternative fuels due to depleting resources. There is a periodic increase in concern about the engine performance, pollutant emissions, and their predictions, from an engine using biodiesels. The use of intelligent algorithms in modeling and forecasting alternative fuels characteristics and their performance in engines are critically reviewed in this study. The paper aims at demonstrating with artificial intelligence methodologies the main conclusions of the recent research done for the above topic from 2012 to 2020. This article attempted to demonstrate an exploratory examination of the adaptive neuro-fuzzy inference system (ANFIS) soft computing technique used for the exact measurement and analysis of engine performance, emissions of exhaust engines when biodiesel is used as an alternative fuel. Additionally, the yield of biodiesel and their different characteristics predicted using ANFIS are also reviewed. Integration of particle swarm optimization (PSO), genetic algorithm (GA), and response surface methodology (RSM), either for comparison or optimization with ANFIS is presented. The summary of all studies is provided in tabular form. For the demonstration purpose, the ANFIS studies predicting different biodiesel and engine characters are provided with illustrative figures. The ANFIS prediction related to biodiesel used engine and biodiesel self-characteristics is found to be excellent. The ANFIS accuracy reported is better than the artificial neural network (ANN) accuracy. A minimum of 0.9 R² value is generally obtained which is around 5% greater than the ANN modeling results reported. However, the ANFIS predictions are much more fitter than the RSM predictions. The integration of ANFIS-PSO and ANFIS-GA provided much more optimized results.     

Biodiesel synthesis from Acacia concinna seed oil: A comprehensive study

In the present investigation, biodiesel (BD) production from Acacia concinna nonedible seed oil using physical pretreatment (turbulent agitation method) and transesterification process has been optimized and modeled using neural network Artificial neural network- Adaptive neuro-fuzzy inference system (ANN-ANFIS), Grey relational analysis? and desirability function approach? approach considering both quantity (production yield) and quality (fuel properties) responses. Different process parameters were examined for their relative significance on output responses. At optimized process variables, methanol/oil (8.3:1), catalyst KOH (0.95 wt%), and reaction temperature and time (65°C and 37.5 min), augment the yield and calorific value by 17.2 and 5.77% and reduce the viscosity and free fatty acid? valueby 18.26 and 57.30%, respectively, with global desirability of D= 0.664. The produced BD was characterized by ¹H NMR, fatty esters (GC analysis), and fuel properties. The developed model equations for output responses help in accurate prediction of results. A. concinna feedstock proved to be a viable source for biodiesel production.     

Combustion and performance evaluation of a diesel engine fueled with biodiesel produced from soybean crude oil

In this study, the biodiesel produced from soybean crude oil was prepared by a method of alkaline-catalyzed transesterification. The important properties of biodiesel were compared with those of diesel. Diesel and biodiesel were used as fuels in the compression ignition engine, and its performance, emissions and combustion characteristics of the engine were analyzed. The results showed that biodiesel exhibited the similar combustion stages to that of diesel, however, biodiesel showed an earlier start of combustion. At lower engine loads, the peak cylinder pressure, the peak rate of pressure rise and the peak of heat release rate during premixed combustion phase were higher for biodiesel than for diesel. At higher engine loads, the peak cylinder pressure of biodiesel was almost similar to that of diesel, but the peak rate of pressure rise and the peak of heat release rate were lower for biodiesel. The power output of biodiesel was almost identical with that of diesel. The brake specific fuel consumption was higher for biodiesel due to its lower heating value. Biodiesel provided significant reduction in CO, HC, NO_x and smoke under speed characteristic at full engine load. Based on this study, biodiesel can be used as a substitute for diesel in diesel engine.     

Influence of nanoadditives on ignition characteristics of Kusum (Schleichera oleosa) biodiesel

Biodiesel obtained from inedible sources emerged as a productive approach in Indian energy scenario due to the scarcity of food resources come up with extensive usage of edible crops. Kusum (Schleichera oleosa) oil is abundantly available in India and can be used as feedstock to produce biodiesel. However, issues such as higher viscosity, poor stability, and lower calorific value result in poor ignition characteristics, hence limiting its use in combustion applications. An improvement in performance and emission characteristics can be achieved by doping nanoparticles in Kusum biodiesel (KBD). The present work examines the impact of a metal compound and carbon‐primarily based nanoparticles on the evaporation time and ignition probability of the KBD. During the experimental process, different fuel samples of KBD were prepared by amalgamating nanoparticles; then, a sequence of hot plate (stainless steel) ignition test was conducted on these test fuels. The comparative assessment of neat biodiesel and the biodiesel fuel doped with 30 ppm each of alumina (Al2O3), and multiwalled carbon nanotubes (MWCNTs) nanoparticles were carried out. The Kusum oil was converted to biodiesel using two‐stage transesterification process. In the initial stage, refined oil was gone through the acid catalyst esterification process followed by the transesterification reaction. The prepared methyl ester was confirmed and characterized using GC‐MS technique. The thermophysical and spray properties of the test fuels including density, viscosity, calorific value, cloud/pour point, Sauter mean diameter (SMD), and specific surface area (SSA) were also calculated. The experimental result showed a significant increase in ignition probability and heat conduction properties due to improved surface area/volume ratio. Also, lower evaporation time was noted for metal/carbon‐based nanoparticles doped biodiesel as compared with neat biodiesel.     

Effect of ethanol addition on the properties of Jatropha ethyl ester

Fossil fuels are available in limited quantity and may extinct in future. Moreover, pollutant emission from diesel engines affects the ecological systems. Biodiesel, derived from vegetable oil, is a renewable and green source of fuel. In this study, biodiesel produced from base catalyzed transesterification was blended with different diesel volumes. The diesel–biodiesel blends showed varied flash point (168–42°C), viscosity (4.34–3.31 mm²/s), density (0.872–0.8351 g/cm³), acid value (0.3–0.4 mg KOH/g), and cetane number (51.6–49.5). The results showed that alcohol addition helped in reducing viscosity and density of biodiesel by almost half. These provide explanation on engine performance, combustion, and emission characteristics.     

Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) in modelling of waste coconut oil ethyl esters production

The attributes of renewability and environmental friendliness have made ethanol a preferable alternative to methanol in the production of biodiesel from lipid feedstocks. For the first time, this study adopted Response Surface Methodology (RSM) and Artificial Neural Network (ANN) to model coconut oil ethyl ester (CNOEE) yield. Transesterification parameters such as reaction temperature and ethanol/coconut oil molar ratio and catalyst dosage were varied. Maximum CNOEE yield of 96.70% was attained at 73 °C reaction temperature, 11.9:1 molar ratio, and catalyst dosage of 1.25 wt. %. The experimental yield was in agreement with the predicted yield. Central Composite Design was adopted to develop the RSM while feed-forward back propagation neural network algorithm was employed for the ANN model. Statistical indices were employed to compare the models. The computed coefficient of determination (R²) of 0.9564, root-mean-squarce-error (RMSE) of 0.72739, standard error of prediction (SEP) of 0.008021, mean average error (MAE) of 0.612, and average absolute deviation (AAD) of 0.674901 for RSM model compared to those of R² (0.9980), RMSE (0.68615), SEP (0.007567), MAE (0.325), and AAD (0.3877) for ANN indicated the superiority of the ANN model over the RSM model. The key fuel properties of the CNOEE met with those of biodiesel international standards.     

Cost-effectiveness analysis of algae energy production in the EU

Today’s society relies heavily on fossil fuels as a main energy source. Global energy demand increase, energy security and climate change are the main drivers of the transition towards alternative energy sources. This paper analyses algal biodiesel production for the EU road transportation and compares it to the fossil fuels and 1st generation biofuels. A cost-effectiveness analysis was used to aggregate private and external costs and derive the social cost of each fuel. The following externalities were internalized: emissions (GHG and non-GHG), food prices impact, pesticides/fertilizers use and security of supply. Currently the social cost of producing algal biodiesel at 52.3 € GJ⁻¹ is higher than rapeseed biodiesel (36.0 € GJ⁻¹) and fossil fuels (15.8 € GJ⁻¹). Biotechnology development, high crude oil prices and high carbon value are the key features of the scenario where algal biodiesel outcompetes all other fuels. A substantial investment into the biotechnology sector and comprehensive environmental research and policy are required to make that scenario a reality.     

Assessment of energy performance and air pollutant emissions in a diesel engine generator fueled with water-containing ethanol–biodiesel–diesel blend of fuels

Biomass based oxygenated fuels have been identified as possible replacement of fossil fuel due to pollutant emission reduction and decrease in over-reliance on fossil fuel energy. In this study, 4 v% water-containing ethanol was mixed with (65–90%) diesel using (5–30%) biodiesel (BD) and 1 v% butanol as stabilizer and co-solvent respectively. The fuels were tested against those of biodiesel–diesel fuel blends to investigate the effect of addition of water-containing ethanol for their energy efficiencies and pollutant emissions in a diesel-fueled engine generator. Experimental results indicated that the fuel blend mix containing 4 v% of water-containing ethanol, 1 v% butanol and 5–30 v% of biodiesel yielded stable blends after 30 days standing. BD1041 blend of fuel, which composed of 10 v% biodiesel, 4 v% of water-containing ethanol and 1 v% butanol demonstrated −0.45 to 1.6% increase in brake-specific fuel consumption (BSFC, mL kW⁻¹ h⁻¹) as compared to conventional diesel. The better engine performance of BD1041 was as a result of complete combustion, and lower reaction temperature based on the water cooling effect, which reduced emissions to 2.8–6.0% for NO_x, 12.6–23.7% particulate matter (PM), 20.4–23.8% total polycyclic aromatic hydrocarbons (PAHs), and 30.8–42.9% total BaPeq between idle mode and 3.2 kW power output of the diesel engine generator. The study indicated that blending diesel with water-containing ethanol could achieve the goal of more green sustainability.     

Environmental aspects and challenges of oilseed produced biodiesel in Southeast Asia

Research on alternative fuel for the vehemently growing number of automotivesis intensified due to environmental reasons rather than turmoil in energy price and supply. From the policy and steps to emphasis the use of biofuel by governments all around the world, this can be comprehended that biofuel have placed itself as a number one substitute for fossil fuels. These phenomena made Southeast Asia a prominent exporter of biodiesel. But thrust in biodiesel production from oilseeds of palm and Jatropha curcas in Malaysia, Indonesia and Thailand is seriously threatening environmental harmony. This paper focuses on this critical issue of biodiesels environmental impacts, policy, standardization of this region as well as on the emission of biodiesel in automotive uses. To draw a bottom line on feasibilities of different feedstock of biodiesel, a critical analysis on oilseed yield rate, land use, engine emissions and oxidation stability is reviewed. Palm oil based biodiesel is clearly ahead in all these aspects of feasibility, except in the case of NO_x where it lags from conventional petro diesel.     

Performance evaluation of adaptive neuro-fuzzy inference system and response surface methodology in modeling biodiesel synthesis from jatropha–algae oil

Biodiesel production from different feedstocks is an effective method of resolving problems related to the fuel crisis and environmental issues. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and the response surface methodology based Box–Behnken experimental design were used to model the parameters of biodiesel production for a jatropha–algae oil blend, including the molar ratio, temperature, reaction time, and catalyst concentration. A significant regression model with an R² value of 0.9867 was obtained under a molar ratio of 6–12, KOH of 0–2% w/w, time of 60–180 min, and temperature of 35–55°C using response surface methodology (RSM). The ANFIS model was used to individually correlate the output variable (biodiesel yield) with four input variables. An R² value of 0.9998 was obtained in the training. The results demonstrated that the developed models adequately represented the processes they described.     

An outcome of quaternary fuel blended Fe3O4-doped reduced graphene oxide nanocomposite on the diesel engine

The study includes the use of alcohols in conjunction with diesel as a binary fuel and biodiesel. In addition, this study was conducted on quaternary fuels (premium diesel, waste cooking biodiesel, n-butanol, and bioethanol), including Fe₃O₄ (iron(III) oxide)-doped reduced graphene oxide (rGO) nanocomposite to reduce the use of fossil fuels, their cost, and energy demand. It includes 10% bioethanol, 5%–20% n-butanol, 25 ppm Fe₃O₄-doped rGO nanocomposite, and 20% and 100% waste cooking biodiesel, all of which have been tested in a diesel engine to ensure that they are suitable for use. The findings were compared to those obtained with premium diesel, ranging from 50% to 100% at full engine load conditions. In comparison to 100% premium diesel fuel, the fuel blend (Blend G) had 37.50% brake thermal efficiency and 0.46% (brake-specific energy consumption), as well as lower rates of 316.2% carbon monoxide, 198.80% hydrocarbon, and 80.01% smoke with 28.10% higher oxides of nitrogen (NOx). Adding 20% n-butanol to premium diesel, as well as waste cooking biodiesel, bioethanol, and Fe₃O₄-doped rGO nanocomposite fuel blends, was used in this study to improve the performance of the diesel engine and reduce some of the NOx emissions. In the near future, these fuel blends may be a viable alternative combination for the diesel engine.     

Biodiesel production from Cholrella minutissima microalgae: Kinetic and mathematical modeling

In the present work, a new and pioneering hybrid technology, called hydrodynamic-cavitation reactor, was established and investigated to proof the feasibility for the biodiesel production from Chlorella minutissima microalgae. The process parameters such as inlet pressure (A), molar ratio (B), catalyst concentration (C), and reaction time (D) have been investigated over the biodiesel yield from Chlorella minutissima microalgae. Box–?Behnken design was applied to develop the second- order polynomial model. The error between experimental values and model prediction was found to be less than 10%. Interactive effects of process variables on the biodiesel yield from Chlorella minutissima microalgae was studied using contour graphs. Inlet pressure of 4 bar, molar ratio of 1: 30, catalyst concentration of 1.3%, and reaction time of 40 min produced 99% of biodiesel yield. Further, a kinetic model has also been developed and considers the transesterification reaction to be a second-order reversible, first order with respect to each of the reactants and products. Estimated values of kinetic constants are k₁ = 0.00014 L min/mol and k₂ = 0.035 L min/mol.     

Anaerobic digestion of industrial hemp-Effect of harvest time on methane energy yield per hectare

There is a worldwide emphasis to increase the share of renewable transportation fuels. When using agricultural land for production of renewable transportation fuels, the energy output per hectare for different crops and transportation fuels is a crucial factor. In this study, the gross methane energy yield per hectare from anaerobic digestion of industrial hemp (Cannabis sativa L.), was determined at four different harvest times between July and October in Southern Sweden, a cold climate region. The biomass yield was determined for three years and the methane yield was determined for two years through the biochemical methane potential test. The highest biomass yield, 16 tonnes dry matter per hectare on an average, and the highest methane energy yield per hectare was achieved when the hemp was harvested in September or October, with an average gross methane energy yield of 136 ± 24 GJ per hectare. There was no significant difference in the specific methane yield between the harvest times; the average being 234 ± 35 m³ per tonne volatile solids. Biogas from hemp turned out to be a high yielding alternative to the currently dominating renewable transportation fuels produced from crops grown in Sweden: ethanol from wheat and biodiesel from rapeseed.