Computational modeling of biodiesel production using supercritical methanol

Renewable fuels such as biodiesel are introduced as promising environmental friendly fuels and they can be applied as alternative fuels instead of fossil fuels. In the present study, a modeling study based on statistical learning theory was investigated by the least square support vector machine (LSSVM) approach for non-catalytic biodiesel production in supercritical methanol. This model can estimate the biodiesel yield as a function of temperature, pressure, reaction time, and Methanol/oil ratio. The results indicated that the suggested LSSVM model was a satisfactory model to predict biodiesel yield that was confirmed by a high value of R² (0.9961) and low value of absolute deviation (1.17%). In addition, our model has been compared with another previous Artificial neural network (ANN)-based model and great estimations of both models were proved.     

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.     

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 in a serpentine minireactor—Effect of flow distribution

The present work aims to find out the influence of flow pattern on pressure drop and fatty acid methyl ester (FAME) yield in a reactive system. Experiments are carried out with Jatropha oil and methanol by using potassium hydroxide (KOH) as catalyst for biodiesel production in two serpentine minireactors made of glass capillary of 2‐mm internal diameter. One is having a circular cross section, and the other is annulus. Slug flow, slug with droplet flow, and dispersed flow are observed in both the reactors. Effects of flow distribution on pressure drop and FAME yield have been studied. FAME yield of 98.5% is observed in both reactors for a molar ratio 20 (methanol to Jatropha oil), and the time for this yield in the first reactor is 16.6 minutes and that for the second reactor is 7.7 minutes. Higher yield also resulted in lower pressure drop due to lower viscosity of biodiesel in comparison with oil.     

Pretreatment of yellow grease for efficient production of fatty acid methyl esters

Biodiesel is a renewable fuel comprised of fatty acid methyl esters (FAME) derived from vegetable oils or animal fats. Comparisons between biodiesel and petroleum-based diesel have shown biodiesel to be effective in reducing exhaust emissions of carbon monoxide, hydrocarbons, particulate matter, and sulfur dioxide. While there are advantages of biodiesel over the traditional petroleum based diesel, biodiesel commercialization is limited by production cost that is dominated by the price of the feedstock (soybean oil). Yellow grease has the potential to be an effective feedstock with lower cost, but the chemical composition of these oils is variable depending on the source of collection and differs from that of virgin oil due to the presence of free fatty acids (FFA). Esterification has been previously demonstrated to reduce the FFA levels of YG; however, large quantities of methanol were required to drive the reaction to high yield. Methanol usage for processing and FFA content are the main factors affecting the economics of FAME production from YG. In this study, the relationship between composition and process variables was systematically studied. The effect of FFA ranging from 2% to 32% (w/w) was studied at three different molar ratios of methanol to FFA (4.5:1, 9:1, 18:1) and was found to have a non-linear relationship. Data obtained from this full factorial screening was used to develop a predictive statistical model to forecast the conversion based on initial FFA level and proportion of alcohol applied for esterification.     

Biodiesel production by transesterification of duck tallow with methanol on alkali catalysts

Duck tallow was employed as a feedstock for the production of biodiesel by transesterification with methanol. The content of fatty acid methyl ester (FAME) was evaluated on various alkali catalysts during transesterification. The composition and chemical properties of the FAME were investigated in the raw duck tallow and the biodiesel products. The major constituent in the biodiesel product was oleic acid. The FAME content was 97% on KOH catalyst in the reaction. It was acceptable for the limit of European biodiesel qualities for BD100. Acid value, density, and kinematic viscosity of the biodiesel products also came up to the biodiesel qualities.     

Synthesis and characterization of monk fruit seed (Siraitia grosvenorii)-based heterogeneous acid catalyst for biodiesel production through esterification process

This research investigated for the first time the synthesis of monk fruit seed (Siraitia grosvenorii)-based solid acid catalyst for biodiesel production. The catalyst was synthesized using a two-step surface functionalization method with trimethoxy phenyl silane and chlorosulfonic acid. The as-synthesized catalyst was characterized to ascertain its catalytic characteristics through surface morphology, chemical bonding, and thermal stability. The effects of activating agent impregnation ratio, carbonization temperature, and sulfonation temperature towards fatty acid methyl ester (FAME) yield were elucidated. The esterification reaction with palmitic acid was found to produce FAME yield up to 98.5% with 4 wt.% catalyst loading, 6-h reaction duration and 120°C reaction temperature. The catalyst also demonstrated high reusability with 84.4% FAME yield being successfully maintained after four successive cycles without reactivation. These proved that the as-synthesized catalyst had high prospect to become a suitable low-cost alternative for biodiesel production through catalytic esterification process in the future.     

A facile noncatalytic methyl ester production from waste chicken tallow using single step subcritical methanol: Optimization study

In this modern era, an increase in urbanization causes the escalating trend of fuel demand as well as environmental pollution problems. Various biofuels research with the respect of climate change and emission reduction recently intensifies, particularly in biodiesel. In Indonesia, diesel oil currently in use contains 20% of biodiesel. Utilizing waste‐based resources such as rendered chicken tallow as the feedstock could be the solution to both energy and environmental challenges. However, chicken tallow contains a significant amount of free fatty acid (FFA) which will obstruct the production yield of biodiesel. In this study, catalyst‐free subcritical methanol has been employed to convert waste chicken tallow (WCT) with high FFA into biodiesel. Design of experiment was conducted to study the effect of temperature, time, and the molar ratio of methanol to fats on the purity and recovery of fatty acid methyl esters (FAMEs). Based on the optimization study performed by response surface methodology (RSM), all three independent variables gave a significant effect on the recovery of FAME. From the experimental results, the maximum FAME yield obtained was 98.43 ± 0.22% with the optimum condition as follows: 167°C, 36.8 minutes, and 42.7:1 (methanol/WCT, mol/mol), while the predicted FAME yield obtained using RSM was 97.76%. The methyl ester composition of WCT‐based biodiesel ranges from C13 to C24.     

Optimization of biodiesel production from camelina oil using orthogonal experiment

Camelina oil is a low-cost feedstock for biodiesel production that has received a great deal of attention in recent years. This paper describes an optimization study on the production of biodiesel from camelina seed oil using alkaline transesterification. The optimization was based on sixteen well-planned orthogonal experiments (OA₁₆ matrix). Four main process conditions in the transesterification reaction for obtaining the maximum biodiesel production yield (i.e. methanol quantity, reaction time, reaction temperature and catalyst concentration) were investigated. It was found that the order of significant factors for biodiesel production is catalyst concentration > reaction time > reaction temperature > methanol to oil ratio. Based on the results of the range analysis and analysis of variance (ANOVA), the maximum biodiesel yield was found at a molar ratio of methanol to oil of 8:1, a reaction time of 70 min, a reaction temperature of 50 °C, and a catalyst concentration of 1 wt.%. The product and FAME yields of biodiesel under optimal conditions reached 95.8% and 98.4%, respectively. The properties of the optimized biodiesel, including density, kinematic viscosity, acid value, etc., were determined and compared with those produced from other oil feedstocks. The optimized biodiesel from camelina oil meets the relevant ASTM D6571 and EN 14214 biodiesel standards and can be used as a qualified fuel for diesel engines.     

Free lipase-catalyzed biodiesel production from phospholipids-containing oils

Free lipase-catalyzed biodiesel has drawn more and more attentions in recent years because of its advantages of lower cost and faster reaction rate. Utilizing free lipase to convert low quality oils such as crude vegetable oils and microbial oils is beneficial to further reduce the cost of biodiesel production. However, these oils typically contain some amount of phospholipids. Phospholipids were found to affect the lipase-catalyzed process and further influence the enzyme's thermal stability in biodiesel production process. In this work, free lipase NS81006-mediated biodiesel production from oils containing phospholipids at varied temperature was investigated systematically. It was found that the presence of phospholipids at high temperature led to a decreased fatty acid methyl esters (FAME) yield and poor reuse stability of the lipase during NS81006-catalyzed biodiesel production process. The higher the temperature was, the greater negative effect was observed. This inhibitory effect was found to be mainly caused by the coexistence of phospholipids and methanol in the system. Based on this finding, a novel two-step enzyme-mediated process was further developed, with which the above-mentioned inhibitory effect was eliminated, and a FAME yield of 95.1% could be obtained with oils containing 10% phospholipids even at high temperature of 55 °C.     

Toward estimation of biodiesel production from castor oil using ANN

Nowadays, a green replacement for the conventional petrodiesel introduced as biodiesel in which its economical production way is using feedstock. Also, environmentally friendly fuels attracted more attention due to the serious global warming problem. In the present study, two different artificial intelligencebased modeling was utilized to predict the production of biodiesel from castor oil. Also, a comparison between the two methods was carried out, and the more applicable method for the prediction of biodiesel production was introduced. To this end, biodiesel production yield from castor oil assumed to be the target of the model and various parameters such as temperature (T), time (S), methanol to oil molar ratio, and catalyst weight (C) expected as input parameters. ANN modeling shows high accuracy and robustness for the prediction of biodiesel production, and statistical parameters such as coefficient of determination and root-mean-square error are 0.9984 and 1.13, respectively.     

The effect of fatty acid profiles of biodiesel on key fuel properties of some biodiesels and blends

In the present investigation, some key fuel properties such as acid value (AV), iodine value (I), peroxide value (PV), water content (WC), and flash point (FP) of cottonseed oil methyl esters (CSOME) and palm stearin methyl esters (PSME) biodiesels, and their blends with petroleum diesel are estimated as per American Society for Testing Materials (ASTM) and American Oil Chemists Society (AOCS) methods. The variations of all the properties with blends have been analyzed as a function of fatty acid profile of biodiesels. The fatty acid profile of the biodiesels is determined using gas chromatography–mass spectrometer (GC-MS) method. CSOME biodiesel is rich in unsaturated fatty acid methyl esters (FAME) and PSME in saturated FAME. Both the biodiesel blends have shown different types of variations for different properties.     

Review of biodiesel composition, properties, and specifications

Biodiesel is a renewable transportation fuel consisting of fatty acid methyl esters (FAME), generally produced by transesterification of vegetable oils and animal fats. In this review, the fatty acid (FA) profiles of 12 common biodiesel feedstocks were summarized. Considerable compositional variability exists across the range of feedstocks. For example, coconut, palm and tallow contain high amounts of saturated FA; while corn, rapeseed, safflower, soy, and sunflower are dominated by unsaturated FA. Much less information is available regarding the FA profiles of algal lipids that could serve as biodiesel feedstocks. However, some algal species contain considerably higher levels of poly-unsaturated FA than is typically found in vegetable oils.Differences in chemical and physical properties among biodiesel fuels can be explained largely by the fuels’ FA profiles. Two features that are especially influential are the size distribution and the degree of unsaturation within the FA structures. For the 12 biodiesel types reviewed here, it was shown that several fuel properties - including viscosity, specific gravity, cetane number, iodine value, and low temperature performance metrics - are highly correlated with the average unsaturation of the FAME profiles. Due to opposing effects of certain FAME structural features, it is not possible to define a single composition that is optimum with respect to all important fuel properties. However, to ensure satisfactory in-use performance with respect to low temperature operability and oxidative stability, biodiesel should contain relatively low concentrations of both long-chain saturated FAME and poly-unsaturated FAME.     

Machine learning technology in biodiesel research: A review

Biodiesel has the potential to significantly contribute to making transportation fuels more sustainable. Due to the complexity and nonlinearity of processes for biodiesel production and use, fast and accurate modeling tools are required for their design, optimization, monitoring, and control. Data-driven machine learning (ML) techniques have demonstrated superior predictive capability compared to conventional methods for modeling such highly complex processes. Among the available ML techniques, the artificial neural network (ANN) technology is the most widely used approach in biodiesel research. The ANN approach is a computational learning method that mimics the human brain's neurological processing ability to map input-output relationships of ill-defined systems. Given its high generalization capacity, ANN has gained popularity in dealing with complex nonlinear real-world engineering and scientific problems. This paper is devoted to thoroughly reviewing and critically discussing various ML technology applications, with a particular focus on ANN, to solve function approximation, optimization, monitoring, and control problems in biodiesel research. Moreover, the advantages and disadvantages of using ML technology in biodiesel research are highlighted to direct future R&D efforts in this domain. ML technology has generally been used in biodiesel research for modeling (trans)esterification processes, physico-chemical characteristics of biodiesel, and biodiesel-fueled internal combustion engines. The primary purpose of introducing ML technology to the biodiesel industry has been to monitor and control biodiesel systems in real-time; however, these issues have rarely been explored in the literature. Therefore, future studies appear to be directed towards the use of ML techniques for real-time process monitoring and control of biodiesel systems to enhance production efficiency, economic viability, and environmental sustainability.     

Optimization of biodiesel production from waste cooking oil using waste bone as a catalyst

This work determined the association between several parameters of biodiesel production from waste cooking oil (WCO) using waste bovine bone (WBB) as catalyst to achieve a high conversion to fatty acid methyl ester (%FAME). The effect of three independent variables was used as the optimum condition using response surface methodology (RSM) for maximizing the %FAME. The RSM analysis showed that the ratio of MeOH to oil (mol/mol), catalyst amount (%wt), and time of reaction have the maximum effects on the transform to FAME. Moreover, the coefficient of determination (R²) for regression equations was 99.19%. Probability value (P < 0.05) demonstrated a very good significance for the regression model. The optimal values of variables were MeOH/WCO ratio of 15.49:1 mol/mol, weight of catalyst as 6.42 wt%, and reaction time of 128.67 min. Under the optimum conditions, %FAME reached 97.59%. RSM was confirmed to sufficiently describe the range of the transesterification parameters studied and provide a statistically accurate estimate of the best transform to FAME using WBB as the catalyst.     

Optimization of wet microalgal FAME production from Nannochloropsis sp. under the synergistic microwave and ultrasound effect

The synergistic effect of microwave and ultrasound irradiations was evaluated for biodiesel production from microalgae biomass (Nannochloropsis sp.) as raw material. A response surface methodology technique based on central composite design was used to understand the process parametric interdependence and optimize the process reaction variables. Reaction kinetics of algal fatty acid methyl ester (FAME) production was also studied. The optimum reaction conditions were determined as wet algal biomass to methanol ratio of 20 g to 30 mL, 1 wt% catalyst concentration, and 7‐minute reaction time at 140 W of microwave power and 140 W of ultrasound power. The estimated activation energy was 17,298 J/mol^?1 K^?1 for a first‐order reaction kinetics. This study revealed that microwave energy dissipation at a low rate of 140 W combined with 140 W of ultrasound intensity is adequate to produce FAMEs at a maximum yield of 48.2%. Results from this optimization study suggest that a more detailed and mechanistic energy optimization study is critical to increase the FAME yield and maximize energy benefits.     

Improved biodiesel manufacture at low temperature and short reaction time

Biodiesel, which is derived from oil/fat by transesterification with alcohol, has attracted considerable attention over the past decades due to its ability to subsidise fossil fuel derived energy as a renewable and carbon neutral fuel. Several approaches for biodiesel fuel production have been developed, among which transesterification using a catalyst gives high yields of methyl ester. This method has therefore been widely utilized for biodiesel production in a number of countries. In this study, a Downflow Liquid Contactor Reactor (DLCR) has been used for the liquid–liquid transesterification reaction of sunflower oil with alcohol with extraordinary results. The reactor provides great potential for chemical reactions, which are normally limited by mass transfer and possesses a number of distinctive advantages over conventional multiphase reactors. Inside the reactor a high velocity liquid jet stream is produced which generates powerful shear and energy, causing vigorous agitation in the upper part of the reactor. The high mixing intensity in the DLCR enabled the manufacture of biodiesel to European Standard EN14214 (ester content 96.5%) in 2.5 min at 40 °C with 0.43 wt.% alkali catalyst and alcohol to oil molar ratio of 4.5 to 1.0. The separation of FAME from glycerol is done by gravity settling only without water washing. The effect of the alcohol type (methanol, ethanol) on biodiesel yield was also investigated. The process offers the advantage of continuous large scale production with limited reactor volume.     

Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm

This paper described the production of karanja biodiesel using response surface methodology (RSM) and genetic algorithm (GA). The optimum combination of reaction variables were analyzed for maximizing the biodiesel yield. The yield obtained by the RSM was 65% whereas the predicted value was 70%. The mathematical regression model proposed from the RSM was coupled with the GA. By using this technique, 90% of the yield was obtained at a molar ratio of 38, a reaction time of 8 hours, a reaction temperature of 40 oC, a catalyst concentration of 2% oil, and a mixing speed of 707 r/min. The yield produced was closer to the predicted value of 94.2093%. Hence, 25% of the improvement in the biodiesel yield was reported. Moreover the different properties of karanja biodiesel were found closer to the American Society for Testing & Materials (ASTM) standard of biodiesel.     

Optimization and oxidative stability of biodiesel production from rice bran oil

Biorefinery approach is introduced for the biodiesel production by utilizing low cost raw material, such as rice bran oil (RBO). The valorization of RBO was carried out by homogeneous transesterification process using response surface methodology (RSM) based on a two-variable central composition design (CCD). The process variables, temperature and catalyst concentration were found to have significant influence on biodiesel yield. The optimum combination derived via RSM for high ester yield (99.4%) was found to be 0.75% wt catalyst concentration at a reaction temperature of 45 °C. As biodiesel chemically is a long-chain alkyl methyl esters, its long-term fuel properties have become of great concern to the fuel industry. In order to determine the effects of long storage on oxidation stability, RBO biodiesel sample was stored for 24 months and the different physical–chemical properties were checked with respect to time. The results show that the acid value (AV), peroxide value (PV), and viscosity (ν) increased while the iodine value (IV) decreased. Based on results, correlations were obtained in terms of AV, IV, PV and ν as a function of time. Those correlations can be used to predict how long time biodiesel can safely be stored. AV, IV and PV of the biodiesel sample which was stored were within the limits in European biodiesel specifications (EN 14214).     

Study on the use of MgAl hydrotalcites as solid heterogeneous catalysts for biodiesel production

This paper, reports experimental work on the use of new heterogeneous solid basic catalysts for biodiesel production: double oxides of Mg and Al, produced by calcination, at high temperature, of MgAl lamellar structures, the hydrotalcites (HT). The most suitable catalyst system studied are hydrotalcite Mg:Al 2:1 calcinated at 507 °C and 700 °C, leading to higher values of FAME also in the second reaction stage. One of the prepared catalysts resulted in 97.1% Fatty acids methyl esters (FAME) in the 1st reaction step, 92.2% FAME in the 2nd reaction step and 34% FAME in the 3rd reaction step. The biodiesel obtained in the transesterification reaction showed composition and quality parameters within the limits specified by the European Standard EN 14214. 2.5% wt catalyst/oil and a molar ratio methanol:oil of 9:1 or 12:1 at 60–65 °C and 4 h of reaction time are the best operating conditions achieved in this study. This study showed the potential of Mg/Al hydrotalcites as heterogeneous catalysts for biodiesel production.