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![Schematic diagram of a typical diesel engine fuel system [12].](publication/277503095/figure/fig6/AS:398015232790533@1471905634926/Schematic-diagram-of-a-typical-diesel-engine-fuel-system-12.png)
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![Figure 2. Transeterification reaction [60].](publication/277503095/figure/fig2/AS:398015232790529@1471905634452/Transeterification-reaction-60_Q320.jpg)
![Figure 3. Soap formation [60].](publication/277503095/figure/fig3/AS:398015232790530@1471905634612/Soap-formation-60_Q320.jpg)
![Figure 4. Acid pre-esterification [60].](publication/277503095/figure/fig4/AS:398015232790531@1471905634759/Acid-pre-esterification-60_Q320.jpg)
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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... These sources can be categorized as renewable, ancient, or nuclear. Ancient sources formed billions of years ago are non-renewable on a short-term scale (Jahirul et al., 2013). They include tar sands, coal, liquefied crude oil, and natural gas. ...
... In this context, deep learning approaches offer promising solutions to address the biofuel industry's challenges. By leveraging large datasets and advanced neural network architectures, deep learning models can analyze complex relationships between feedstock characteristics, production parameters, and environmental factors (Jahirul et al., 2013). These models enable rapid and accurate prediction of biodiesel yield, quality, and ecological sustainability, facilitating informed decision-making in feedstock selection and production process optimization . ...
Biodiesel as a renewable alternative to conventional diesel is a growing topic of interest due to its potential environmental benefits. It is typically produced from oilseed crops such as soybean, rapeseed, palm oil, or animal fats. However, its sustainability is debated, primarily because of the reliance on edible oil feedstocks and associated economic and environmental concerns. This study explores alternative, non-edible feedstocks, such as algae and jatropha, that do not compete with food production, offering increased sustainability. Despite their potential, these feedstocks are hindered by high production costs. To address these challenges, innovative approaches in feedstock assessment are imperative for ensuring the long-term viability of biodiesel as an alternative fuel. This review examines explicitly the application of deep learning techniques in selecting and evaluating biodiesel feedstocks. It focuses on their production processes and the chemical and physical properties that impact biodiesel quality. Our comprehensive analysis demonstrates that ANNs provide significant insights into the feedstock assessment process, emerging as a potent tool for identifying new correlations within complex datasets. By leveraging this capability, ANNs can significantly advance biodiesel research, producing more sustainable and efficient feedstock production. The study concludes by highlighting the substantial potential of ANN modeling in contributing to renewable energy strategies and expanding biodiesel research, underscoring its vital role in accelerating the development of biodiesel as a sustainable fuel alternative.
... In contrast, biofuels derived from vegetable oils or animal fats could are the best option to replace fossil fuels. Among the biofuels, biodiesels (BD) are the most widespread compared to biogas and bio-alcohols as they are, renewable, low cost, non-toxic, environmentally friendly, biodegradable, and possess good emission profile upon combustion [2,3]. ...
... The extraction efficiency of soap from BD was calculated by using Eq. (2). ...
... It is an ecosystem-friendly transportation fuel containing methyl and ethyl esters of fatty acids (i.e. straight fatty acid chain) [22,375]. Biodiesel production from oil extracted from the marine algal biomass is feasible by simultaneous transesterification which considerably reduces Fig. 8. ...
Brown algae adsorb a considerable amount of CO2 and store carbon in their biomass more than many other algae species. Nowadays, depletion of fossil fuel resources, greenhouse gas emissions, climate change, and the future of human food security have encouraged scientists and policymakers to develop safer and more sustainable alternatives of energy sources. Brown algae have attracted most of this attention in North American countries to generate a wide range of products and biomass for biorefinery. In this review paper, various aspects of brown algae cultivation and its biorefinery process are discussed. Main drivers of the sectors, opportunities, challenges, and future prospects of brown algae-based biofuels in North America have been taken into account; the most appropriate processes and pathways are compared to maximize the outputs and environmental benefits, and minimize costs and drawbacks of the industry. Analysis and predictions of renewable energy future in North American countries revealed that biomass-derived fuels, including brown algae biofuels, will play an important role especially in the transportation sector. These biofuels, including bioethanol and biodiesel are accompanied by various co-products and by-products in an optimized biorefinery system to convert every available component of brown algae. However, cultivation of brown algae to produce biomass at a commercial scale with minimum costs and market demands are the main challenges of algal biofuels. Finally, expanding the value chain of the brown algae biorefinery by producing more advanced biofuels such as aviation fuels and value-added co- and by-products would significantly impact North American countries' gross domestic product.
... 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. ...
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.
... Similarly, Barradas Filho and Viegas 34 also reviewed trends in the utilization of ANNs in biofuel production. Jahirul et al. 35 reviewed the use of ANNs in identifying sustainable biodiesel feedstocks. Thus, this article aims to provide a comprehensive review of the application of ANNs, as well as ANFIS, in predicting various biomass properties (elemental composition, HHV, gross heating value), modeling several LCB conversion processes such as biomass pre-treatment, thermal processing (pyrolysis and gasification), enzymatic processing, and production of certain major value-added products (bioethanol, biogas, organic acids, enzymes, and lignin). ...
Value‐added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value‐added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre‐treatment processes (microwave‐assisted, organosolv‐, ultrasound‐assisted pre‐treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids. © 2022 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.
... 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. ...
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.
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... 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). ...
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.
... 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. ...
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.
... 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]. ...
... Potential of biodiesel feedstocks worldwide[26]. ...
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.
... Various machine learning techniques have been used to investigate the effects of temperature, biomass load, inoculum size, enzyme concentration, and time [80]. Their wide aspects have been discussed through artificial neural networks [81]. Algal biomass productivity and lipid content were explored, and machine learning techniques to predict optimum levels of biomass growth were discussed [82]. ...
Global population growth is driving daily increases in demand for conventional fuel (i.e., petroleum). Moreover, the regular, widespread use of petroleum is not good, as it results in carbon dioxide emissions that pollute the atmosphere. In addition, the reserves of these conventional fuels are limited and will soon be exhausted. Therefore, there is an urgent requirement to find renewable biofuels to fulfill future energy demand. Within this context, the production of renewable biofuels using microalgae could be a better option for fulfilling future energy demand along with environmental sustainability. Because microalgae have the potential to produce biofuels and could replace conventional sources of energy—coal, petroleum, etc. Isolating microalgae is the primary step in biofuel extraction. This chapter discusses recent advances in biofuel production using microalgae and the challenges and future scope of this area. Machine learning/artificial intelligence techniques to enhance energy efficiency/savings are also explored.
