Machine learning technology in biodiesel research: A review

Aghbashlo, Mortaza; Peng, Wanxi; Tabatabaei, Meisam; Kalogirou, Soteris A.; Soltanian, Salman; Hosseinzadeh-Bandbafha, Homa; Mahian, Omid; Lam, Su Shiung

doi:10.1016/j.pecs.2021.100904

Cited by 298 publications

(64 citation statements)

References 632 publications

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“…Producing biodiesel from renewables includes the following steps: extracting oil, pretreating feedstock, transesterification reaction, separating products, recovering unreacted alcohol, neutralizing glycerin, washing, and purification of biodiesel [70,79]. In this section, we attempted to categorize and review ML technology applications in 5 crucial steps of biodiesel production, including soil, feedstock, production, consumption, and emissions [57,80].…”

Section: Methods Application In Biodiesel Life Cyclementioning

confidence: 99%

“…ere are plenty of research reviews on applications of machine learning technology in modeling biodiesel-fueled engines and combustion approaches; therefore, this study mainly focuses on the first three stages. Figure 4 shows an overview of the biodiesel production trend, inspired by Aghbashlo et al [79] and Ahmad et al [57].…”

Section: Methods Application In Biodiesel Life Cyclementioning

confidence: 99%

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A Review on Machine Learning Application in Biodiesel Production Studies

Xing

Zheng

Sun

et al. 2021

International Journal of Chemical Engineering

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The consumption of fossil fuels has exponentially increased in recent decades, despite significant air pollution, environmental deterioration challenges, health problems, and limited resources. Biofuel can be used instead of fossil fuel due to environmental benefits and availability to produce various energy sorts like electricity, power, and heating or to sustain transportation fuels. Biodiesel production is an intricate process that requires identifying unknown nonlinear relationships between the system input and output data; therefore, accurate and swift modeling instruments like machine learning (ML) or artificial intelligence (AI) are necessary to design, handle, control, optimize, and monitor the system. Among the biodiesel production modeling methods, machine learning provides better predictions with the highest accuracy, inspired by the brain’s autolearning and self-improving capability to solve the study’s complicated questions; therefore, it is beneficial for modeling (trans) esterification processes, physicochemical properties, and monitoring biodiesel systems in real-time. Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. Emissions composition and temperature estimation and motor performance analysis investigate in the consumption phase. Fatty methyl acid ester stands as the output parameter, and the input parameters include oil and catalyst type, methanol-to-oil ratio, catalyst concentration, reaction time, domain, and frequency. This paper will present a review and discuss various ML technology advantages, disadvantages, and applications in biodiesel production, mainly focused on recently published articles from 2010 to 2021, to make decisions and optimize, model, control, monitor, and forecast biodiesel production.

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Section: Methods Application In Biodiesel Life Cyclementioning

confidence: 99%

Section: Methods Application In Biodiesel Life Cyclementioning

confidence: 99%

A Review on Machine Learning Application in Biodiesel Production Studies

Xing

Zheng

Sun

et al. 2021

International Journal of Chemical Engineering

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“…The pollution caused by diesel combustion in diesel engines is one of the main contributors to global air pollution (Aghbashlo et al, 2017b;2018b). The most crucial emissions released from diesel combustion are CO 2 , NO X , sulfur oxides (SO X ), CO, and PM emissions (Aghbashlo et al, 2021b). There is evidence that these emissions play a crucial role in damage to the environment and human health (Hosseinzadeh-Bandbafha et al, 2020).…”

Section: Conversion Of Forest Biomass Into Liquid Biofuelsmentioning

confidence: 99%

An Overview on the Conversion of Forest Biomass into Bioenergy

Qian

Wang

et al. 2021

Front. Energy Res.

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Biomass plays a crucial role in mitigating the concerns associated with increasing fossil fuel combustion. Among various types of biomass, forest biomass has attracted considerable attention given its abundance and variations. In this work, an overview is presented on different pathways available to convert forest biomass into bioenergy. Direct use of forest biomass could reduce carbon dioxide emissions associated with conventional energy production systems. However, there are certain drawbacks to the direct use of forest biomass, such as low energy conversion rate and soot emissions and residues. Also, lack of continuous access to biomass is a severe concern in the long-term sustainability of direct electricity generation by forest biomass. To solve this problem, co-combustion with coal, as well as pelletizing of biomass, are recommended. The co-combustion of forest biomass and coal could reduce carbon monoxide, nitrogen oxides, and sulfide emissions of the process. Forest biomass can also be converted into various liquid and gaseous biofuels through biochemical and thermochemical processes, which are reviewed and discussed herein. Despite the favorable features of forest biomass conversion processes to bioenergy, their long-term sustainability should be more extensively scrutinized by future studies using advanced sustainability assessment tools such as life cycle assessment, exergy, etc.

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“…Biodiesel is a sustainable liquid fuel for transportation [21] and it has improved characteristics compared to petrodiesel, such as lower density and viscosity. The density of the fuel affects the engine's fuel injection system and the lower dense and viscous fuel leads to better atomization and mixing properties [22].…”

Section: Biodieselmentioning

confidence: 99%

An Overview of Biodiesel Production via Calcium Oxide Based Catalysts: Current State and Perspective

et al. 2021

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Biodiesel is a clean, renewable, liquid fuel that can be used in existing diesel engines without modification as pure or blend. Transesterification (the primary process for biodiesel generation) via heterogeneous catalysis using low-cost waste feedstocks for catalyst synthesis improves the economics of biodiesel production. Heterogeneous catalysts are preferred for the industrial generation of biodiesel due to their robustness and low costs due to the easy separation and relatively higher reusability. Calcium oxides found in abundance in nature, e.g., in seashells and eggshells, are promising candidates for the synthesis of heterogeneous catalysts. However, process improvements are required to design productive calcium oxide-based catalysts at an industrial scale. The current work presents an overview of the biodiesel production advancements using calcium oxide-based catalysts (e.g., pure, supported, and mixed with metal oxides). The review discusses different factors involved in the synthesis of calcium oxide-based catalysts, and the effect of reaction parameters on the biodiesel yield of calcium oxide-based catalysis are studied. Further, the common reactor designs used for the heterogeneous catalysis using calcium oxide-based catalysts are explained. Moreover, the catalytic activity mechanism, challenges and prospects of the application of calcium oxide-based catalysts in biodiesel generation are discussed. The study of calcium oxide-based catalyst should continue to be evaluated for the potential of their application in the commercial sector as they remain the pivotal goal of these studies.

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Machine learning technology in biodiesel research: A review

Cited by 298 publications

References 632 publications

A Review on Machine Learning Application in Biodiesel Production Studies

A Review on Machine Learning Application in Biodiesel Production Studies

An Overview on the Conversion of Forest Biomass into Bioenergy

An Overview of Biodiesel Production via Calcium Oxide Based Catalysts: Current State and Perspective

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