Experimental study on the effects of feedstock on the properties of biodiesel using multiple linear regressions

Mairizal, Aulia Qisthi; Awad, Sary; Priadi, Cindy Rianti; Hartono, Djoko Mulyo; Moersidik, Setyo Sarwanto; Tazerout, Mohand; Andrès, Yves

doi:10.1016/j.renene.2019.06.067

Cited by 50 publications

(15 citation statements)

References 23 publications

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“…Gleason et al [83] compared the Linear Mixed-effects Regression (LME), Cubist, Support Vector Regression (SVR), and Random Forest (RF) methods to predict biomass in a moderately dense forest with 40 2 provides a summary of feedstock phase studies [86][87][88] to classify the efficient method and study purposes.…”

Section: Applications In Soil Stagementioning

confidence: 99%

“…3 Blend composition, temperature, mixing speed, and mixing time are typical input variables, and the output variables are viscosity, flash point, oxidation stability, density, methane fraction, higher heating values, and cetane number. Mairizal et al[86] examined biodiesels generated from various resources such as walnut oil, sunflower oil, peanut oil, rapeseed oil, hydrogenated coconut oil, hydrogenated copra oil, and beef tallow to predict higher heating value, viscosity, flashpoint, biodiesel's oxidative stability, and density by using multiple linear regressions. Results showed that prediction performance increases by adding PU/MU (mono-and polyunsaturated fatty acids balance) as an independent parameter.…”

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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: Applications In Soil Stagementioning

confidence: 99%

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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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“…It was revealed that sludges from the chemical industry have a relatively higher impact on methane in the produced biogas. Mairizal et al [25] used multiple linear regressions to predict viscosity, Flash Point (FP), density, higher heating value, and oxidative stability of biodiesel produced from sunflower oil, peanut oil, hydrogenated coconut oil, hydrogenated copra oil, beef tallow, rapeseed oil, and walnut oil. It was inferred from the results that the addition of PU/MU as an independent parameter increase prediction performance.…”

Section: Feedstockmentioning

confidence: 99%

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

et al. 2021

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Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models, and the frequently used input and output variables for each stage. The findings of this article would be beneficial for academia and industry-related professionals involved in model development in different stages of biofuel’s life cycle.

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“…It was revealed that sludges from the chemical industry have a relatively higher impact on methane in the produced biogas. Mairizal et al [22] used multiple linear regressions to predict viscosity, flash point, density, higher heating value, and oxidative stability of biodiesel produced from sunflower oil, peanut oil, hydrogenated coconut oil, hydrogenated copra oil, beef tallow, rapeseed oil, and walnut oil. Saponification value, iodine value, and the polyunsaturated fatty acids content of feedstock were used as inputs of the model.…”

Section: Feedstockmentioning

confidence: 99%

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Ahmad¹,

Kano²,

Menezes³

et al. 2020

Preprint

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Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

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Experimental study on the effects of feedstock on the properties of biodiesel using multiple linear regressions

Cited by 50 publications

References 23 publications

A Review on Machine Learning Application in Biodiesel Production Studies

A Review on Machine Learning Application in Biodiesel Production Studies

Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

Machine Learning Applications in Biofuels' Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions

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