Toward estimation of biodiesel production from castor oil using ANN

Zhang, Yongsheng; Chong-huai, Niu

doi:10.1080/15567036.2018.1477873

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Optimization of transesterification of pretreated NO using KOH as catalyst resulted in 99% yield of biodiesel. Zhang and Niu [65] used LS-SVM with GA to estimate and optimize biodiesel yield from castor oil. Based on high accuracy in prediction, the use of the LS-SVM model was recommended for efficient prediction in the process.…”

Section: Quality and Yield Optimizationmentioning

confidence: 99%

“…ANN-GA(9), genetic algorithm-based support vector machines (3), ANFIS-GA (2), multi-objective genetic algorithm(2), multivariate regression analysis, ELM-RSM, GA-LSSVM, HGAPSO-LSSVM,multi-objective optimization with Orthogonal collocation on finite elements (OCFE) method Methanol-to-oil molar ratio(15), reaction temperature(14), reaction time(13), stirring speed (6), catalyst concentration (6), catalyst weight (5), dosage of NaOH catalyst (3), humidity (3), impurities (3), mixing time (3), Free Fatty Acid (FFA) content, sulfuric acid-to-rice bran ratio, duty-cycle, methanol-to-rice bran ratio, FAME concentration, overall heat duty, initial acid value of vegetable oil, calcination temperature, reactor's residence time, and pressure 0.8690 ≤ R 2 ≤ 0.9999[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67] …”

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confidence: 99%

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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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Section: Quality and Yield Optimizationmentioning

confidence: 99%

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confidence: 99%

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

et al. 2021

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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. This study reviews 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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Prospects and Potential of Calophyllum Inophyllum as a Renewable Feedstock for Biodiesel Production

Niju

Balajii

Vishnupriya

et al. 2019

Horizons in Bioprocess Engineering

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Toward estimation of biodiesel production from castor oil using ANN

Cited by 5 publications

References 13 publications

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

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

Prospects and Potential of Calophyllum Inophyllum as a Renewable Feedstock for Biodiesel Production

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