Schematic diagram of a typical diesel engine fuel system [12].  

Schematic diagram of a typical diesel engine fuel system [12].  

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Over the past few decades, biodiesel produced from oilseed crops and animal fat is receiving much attention as a renewable and sustainable alternative for automobile engine fuels, and particularly petroleum diesel. However, current biodiesel production is heavily dependent on edible oil feedstocks which are unlikely to be sustainable in the longer...

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... the advent of the diesel-powered engine, compression ignition engine technology has been under continuous development. However, the basic components of the engine (Figure 7) have been unchanged, with the main difference between a modern day engine and its predecessor being its combustion performance [129]. . Schematic diagram of a typical diesel engine fuel system [12]. ...

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... Viscosity is another major physical flow behaviour quality of fluid, measured by its flow quantity, in which less viscous fluid imply the presence of hydrocarbon branches such as alcohol and acids groups [16,28]. However, low viscosity of fuel may result to poor lubrication of fuel injection pump and lead to fuel leakage [29]. Meanwhile, high viscosity fuel which made up of straight-chain hydrocarbon resulting to poor combustion [30], high energy requirement for fuel pumping [31] and release of carbon monoxide (CO) with simultaneous risk of engine deposits to occurs. ...
... CN is one of the main factors to improve performance of liquid fuels or biocrudes production. Ristovski et al. [29] and Dorn [37] reported that CN could influence fuel ignition delay time, ignition quality, cold start properties and formation of white smoke in the exhaust that simultaneously reduce the emission of CO and unburned hydrocarbons [38,39]. Higher CN can be achieved through addition or increase of alkanes including normal alkenes, branched alkenes, cycloalkanes and aromatics. ...
Article
Liquid fuels derived from fossil source took thousands of years to be converted to crude oil and gas. Alternatively, liquid fuel converted from biomass or generally known as biocrudes is an alternative and feasible source that worth to be explored. Nevertheless, converting biomass from its natural solid form to liquid fuels requires proper pre-treatment and upgrading processes to meet standard properties for commercial use. Besides, chemical properties such as oxygen, nitrogen, sulfur, kinetic value, cetane number, vapor pressure, oxidation stability are other crucial indicators to indicate the quality of biocrudes. This review article highlights the current available technologies to convert biomass to biocrudes, biocrudes upgrading approaches, limitations, and challenges to meet fuel standards, as well as related local and international policies for upgrading biocrudes to fuels. It is expected that this review could pave a positive momentum to drive biocrudes production from agricultural biomass for the sustainability of energy resources.
... Moreover, higher fuel viscosity has been reported to increase carbon monoxide (CO) and unburnt hydrocarbon (UHC) [47]. On the other hand, very low fuel viscosity results in poor lubrication of fuel injection pumps which causes leaks and wears [48]. Therefore, viscosity of microalgae bio-crude oil obtained in the present study was better than the other bio-oil viscosity reported in the literature, albeit exhibited an inferior viscosity with respect to diesel or biodiesel. ...
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The present study demonstrates a promising approach for production of bio-crude oil via hydrothermal liquefaction of microbial biomass grown in large-scale open raceway pond. Three key attributes to achieve process feasibility are co-cultivation of microalgae and bacteria resulting in high biomass titre, utilization of paper industry wastewater as cheap source of nutrients and water, and one-step direct conversion of biomass into bio-crude oil. High biomass titre of 4 g L−1 with 90% of COD removal efficiency was achieved, depicting robust performance of the microalgae-bacteria consortium in industrial wastewater and under fluctuating environmental condition. Statistical optimization resulted in highest bio-crude oil yield of 21.7 (%, w/w) under optimal temperature, biomass loading and reaction time of 299.7 °C, 16.1 (%, w/v) and 65 min, respectively. Bio-crude oil with energy recovery of 43% and heating value of 33.1 MJ kg−1 reflects 81.7% and 73.4% heating value of biodiesel and diesel, respectively. While high percentage of hydrocarbon content in bio-crude oil indicates good oil quality, the presence of significant esters fraction might offer resemblance to biodiesel. Lower H/C ratio and higher O/C ratio in comparison to diesel indicate requirement of upgradation of bio-crude oil before it can be realized at commercial scale. Graphical abstract
... Palm oil is used as a major source of biodiesel in Malaysia and Indonesia. However, current biodiesel industries mostly depend on feedstocks made from food "feedstuffs", and these are known as first-generation biodiesel [21]. Although the available biodiesel spectrum shows the versatility and popularity of the biodiesel industry, this capability has not been fully adopted by first-generation biodiesel systems due to some social and environmental concerns. ...
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This study investigated the suitability of stone fruit seed as a source of biodiesel for transport. Stone fruit oil (SFO) was extracted from the seed and converted into biodiesel. The biodiesel yield of 95.75% was produced using the alkaline catalysed transesterification process with a methanol-to-oil molar ratio of 6:1, KOH catalyst concentration of 0.5 wt% (weight %), and a reaction temperature of 55 °C for 60 min. The physicochemical properties of the produced biodiesel were determined and found to be the closest match of standard diesel. The engine performance, emissions and combustion behaviour of a four-cylinder diesel engine fuelled with SFO biodiesel blends of 5%, 10% and 20% with diesel, v/v basis, were tested. The testing was performed at 100% engine load with speed ranging from 200 to 2400 rpm. The average brake specific fuel consumption and brake thermal efficiency of SFO blends were found to be 4.7% to 15.4% higher and 3.9% to 11.4% lower than those of diesel, respectively. The results also revealed that SFO biodiesel blends have marginally lower in-cylinder pressure and a higher heat release rate compared to diesel. The mass fraction burned results of SFO biodiesel blends were found to be slightly faster than those of diesel. The SFO biodiesel 5% blend produced about 1.9% higher NOx emissions and 17.4% lower unburnt HC with 23.4% lower particulate matter (PM) compared to diesel fuel. To summarise, SFO biodiesel blends are recommended as a suitable transport fuel for addressing engine emissions problems and improving combustion performance with a marginal sacrifice of engine efficiency.
... Not only that, testing the capability of several metaheuristic algorithms on feedstocks of locally prepared coconut oil, caustic soda (NaOH), and fermented palm wine contaminated by microorganism biodiesel framework is relatively scarce. The most commonly used methods for biodiesel prediction are conventional modeling approaches of linear and non-linear statistical techniques and response surface methodology (Agarwal et al. 2010;Balabin et al. 2011;Jahirul et al. 2013;Mohamad et al. 2017;Nayak and Vyas 2019;Behera et al. 2019). However, the modeling process of an ill-defined system of the biodiesel production process is intricate and the biological nature of the biodiesel together with its mathematical and statistical techniques is complex (Faizollahzadeh Ardabili et al. 2017). ...
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This paper for the first time synthesizes novel biodiesel experimentally using low-cost feedstocks of coconut oil, caustic soda, and fermented palm wine contaminated by microorganisms. The alkaline catalyzed transesterification method was used for biodiesel production with minimal glycerol. The produced biodiesel was biodegradable and effective in cleaning a shoreline oil spill experiment verified by our developed oil spill radial numerical simulator. For the first time, an adaptive neuro-fuzzy inference system (ANFIS) was hybridized with invasive weed optimization (IWO), imperialist competitive algorithm (ICA), and shuffled complex evolution (SCE-UA) to predict biodiesel yield (BY) using obtained Monte Carlo simulation datasets from the biodiesel experimental seed data. The test results indicated ANFIS-IWO (MSE = 0.0628) as the best model and also when compared to the benchmarked ANFIS genetic algorithm (MSE = 0.0639). Additionally, ANFIS-IWO (RMSE = 0.54705) was tested on another coconut biodiesel data in the literature and it outperformed both response surface methodology (RMSE = 0.72739) and artificial neural network (RMSE =0.68615) models used. The hybridized models proved to be robust for biodiesel yield modeling in addition to the produced biodiesel serving as an environmentally acceptable and cost-effective alternative for shoreline bioremediation.
... Due to its comparable properties to diesel fuel, biodiesel is often added in diesel engine [21][22][23][24][25]. It can be produced from a variety of sources that are grown regionally as depicted in Fig. 1 [26]. One of promising biodiesel sources is produced from waste cooking oil (WCO) owing to its affordability and accessibility worldwide [27][28][29]. ...
... Cascade forward network is comparable to the feed-forward network, but it differs by including a connection from the input with each previous layer being connected to the subsequent layer. It was found that the ANN model could predict the target response with 95,82%, Fig. 1 Potential of biodiesel feedstocks worldwide [26]. ...
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In automotive applications, artificial neural network (ANN) is now considered as a favorable prediction tool. Since it does not need an understanding of the system or its underlying physics, an ANN model can be beneficial especially when the system is too complicated, and it is too costly to model it using a simulation program. Therefore, using ANN to model an internal combustion engine has been a growing research area in the last decade. Despite its promising capabilities, the use of ANN for engine applications needs deeper examination and further improvement. Research in ANN may reach its maturity and be saturated if the same approach is applied repeatedly with the same network type, training algorithm and input–output parameters. This review article critically discusses recent application of ANN in ICE. The discussion does not only include its use in the conventional engine (gasoline and diesel engine), but it also covers the ANN application in advanced combustion technology i.e., homogeneous charge compression ignition (HCCI) engine. Overall, ANN has been successfully applied and it now becomes an indispensable tool to rapidly predict engine performance, combustion and emission characteristics. Practical implications and recommendations for future studies are presented at the end of this review.
... Finally, the use of sustainable raw materials to produce biodiesel has been considered, looking for local crops that are adapted to local climates and offer good oil yields, with suitable properties like high oxidative stability. That is the case for rapeseed, a popular crop to produce biodiesel, especially in Europe, where its production is considerable and relatively cheap [56], which can contribute to sustainability as it is resistant to extreme climates and its production is relatively easy in many different areas (presenting resistance to extreme weather conditions and to different kind of soils). Indeed, it has been widely studied, normally under homogeneous catalysis, and the subsequent biodiesel showed good results, complying with most of the requirements of standards [57][58][59]. ...
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The use of heterogeneous catalysts to produce fatty acid methyl esters (FAME) through transesterification with methanol might contribute to both green chemistry and a circular economy, as the process can be simplified, not requiring additional stages to recover the catalyst once the reaction takes place. For this purpose, different catalysts are used, including a wide range of possibilities. In this research the use of NaNO3/SiAl as a heterogeneous catalyst for FAME production through transesterification of rapeseed oil with methanol is considered. A thorough characterization of the catalyst (including XDR and XPS analysis, SEM microscopy, lixiviation and reusability tests, among others), specific optimization of transesterification by using the final catalyst (considering catalyst amount, stirring rate, methanol/oil ratio, and temperature), and quality determination of the final biodiesel (following the UNE-EN 14214 standard) were carried out. In conclusion, 20 mmolNa·gsupport−1 (that is, NaNO3/SiAl 20/1) offered the best results, with a high activity (exceeding 99% w/w of FAMEs) without requiring higher impregnation amounts. The best chemical conditions for this heterogeneous catalyst were 5% w/w catalyst, 700 rpm, 9:1 methanol/oil ratio, and 65 °C, obtaining Ea = 73.3 kJ·mol−1 and a high-quality biodiesel, similar to those obtained through homogeneous catalysis. Consequently, this catalyst could be a suitable precursor for FAME production.
... Specifically, artificial neural networks (ANN) have recently emerged as effective, prognostic modelling systems that could have enormous potential within biorefineries to better understand and cope with the inconsistent and unreliable feedstocks. A few literature reviews have been published that highlight the application of ANN models for sustainable development [20], biodiesel production [21,22], microbial biofuel production [23], pyrolysis [24], and prediction of biomass thermal properties and HHV [25][26][27], although they are all limited by only focusing on one specific aspect within a biorefinery concept. Therefore, this novel work aims to review and critically assess studies that have applied ANN systems for a wider range of biomass conversion processes relevant to many different potential biorefinery schemes. ...
Article
Machine learning through artificial neural networks have emerged as vital tools to predict chemical behavior for many of the most recognized biomass valorization processes relevant to biorefineries for the purpose of optimization of desired products and reaction conditions. Until recently, these neural network methodologies have successfully been utilized in the petroleum industry where much more extensive databases are available for effective algorithm training. These systems provide compelling advantages for pattern recognition when interpreting the influence of ever-changing feedstock compositions for complex biomass conversion processes as they do not require any a prior knowledge of reaction mechanisms or thermodynamic phenomena. This has been revealed to be tremendously beneficial for real-time, dynamic control applications of biochemical processes for rapid parameter monitoring and regulation such as during fermentation or anaerobic digestion. This review aims to present and evaluate studies that have attempted to apply these neural network strategies to various aspects of biorefining and how these models address the common challenges that occur when relying on conventional mechanistic modelling approaches to estimate sophisticated, non-linear systems. Comparisons are then identified when implementing these artificial intelligence computing practices in traditional petroleum refineries where feedstock inconsistencies are not as paramount compared to biorefineries. Subsequently, the practicality of these neural networks is critically assessed and recommendations are presented on how to strengthen its applicability and predictability towards future bio-based chemical production. Mathematical models such as artificial neural networks will be an integral technology in the future bioeconomy for the realization of innovative biorefinery concepts as computational power continues to advance.
... At the same time, oil consumption in the transportation sector is fast increasing, resulting in a rapid depletion of non-renewable petroleum-based fuel [1][2][3]. Alternative renewable and environmentally friendly sources of car fuel, such as biodiesel [4][5][6][7][8], oxygenated fuel [6,9,10], and blends with petroleum-based fuels [11,12], have received increased attention in recent decades. However, due to economic and environmental concerns, waste-to-fuel technology has received increased attention from researchers around the world in recent years [13]. ...
Article
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Utilising pyrolysis as a waste tyre processing technology has various economic and social advantages, along with the fact that it is an effective conversion method. Despite extensive research and a notable likelihood of success, this technology has not yet seen implementation in industrial and commercial settings. In this review, over 100 recent publications are reviewed and summarised to give attention to the current state of global tyre waste management, pyrolysis technology, and plastic waste conversion into liquid fuel. The study also investigated the suitability of pyrolysis oil for use in diesel engines and provided the results on diesel engine performance and emission characteristics. Most studies show that discarded tyres can yield 40–60% liquid oil with a calorific value of more than 40 MJ/kg, indicating that they are appropriate for direct use as boiler and furnace fuel. It has a low cetane index, as well as high viscosity, density, and aromatic content. According to diesel engine performance and emission studies, the power output and combustion efficiency of tyre pyrolysis oil are equivalent to diesel fuel, but engine emissions (NOX, CO, CO, SOX, and HC) are significantly greater in most circumstances. These findings indicate that tyre pyrolysis oil is not suitable for direct use in commercial automobile engines, but it can be utilised as a fuel additive or combined with other fuels.
... This can result in the formation of engine deposits and lead to incomplete fuel combustion [313] . The higher density of biodiesel fuel results in the formation of large fuel droplets during the atomization process [354] , which predominantly affects engine performance and emission characteristics. It is noteworthy that some important biodiesel properties like CN, HV, and DV are directly associated with its density [331] . ...
... However, the higher FP of biodiesel leads to carbon deposit formation in the combustion chamber due to inadequate cold engine startup, excessive misfiring, and longer ID [13] . It is worth quoting that increasing the fatty acid saturation degree of biodiesel fuels increases its FP [354] . The FP of biodiesel fuel can be reliably measured using various experimental apparatus, but the process is lengthy and costly. ...
Article
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.
... It is generally accepted that biodiesel from all sources has about 10% less energy content compared with petroleum diesel [6]. Similarly, other important fuel properties like density, oxidation stability (OS), cold filter plugging point temperature (CFPP), flash point temperature (FP) and iodine value (IV) also affect the combustion performance of diesel engines and have been discussed by Jahirul et al. [7]. ...
Article
Biodiesel will provide a significant renewable energy source for transportation in the near future. In the present study, principal component analysis (PCA) has been used to understand the relationship between important properties of biodiesel and its chemical composition. Finally, several artificial intelligence-based models were developed to predict specific biodiesel properties based on their chemical composition. The experimental study was conducted in order to generate training data for the aritificial neural network (ANN). Available (experimental) data from the literature was also employed for this modelling strategy. The analytical part of this study found a complex multi-dimensional correlation between chemical composition and biodiesel properties. Average numbers of double bonds in the chemical structure (representing the unsaturated component in biodiesel) and the poly-unsaturated component in biodiesel had a great impact on biodiesel properties. The simulation result in this study demonstrated that ANN is a useful tool for investigating the fuel properties from its chemical composition which eventually can replace the time consuming and costly experimental test.