Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique

Kumar, Sunil; Jain, Siddharth; Kumar, Harish

doi:10.1080/15567036.2018.1548507

Cited by 35 publications

(8 citation statements)

References 25 publications

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“…Kumar et al [92] trained an ANN model with Levenberg-Marquardt (LM) algorithm and backpropagation learning algorithm to predict biodiesel yield in the transesterification process, using jatropha-algae oil blends as inputs.…”

Section: Yield Estimationmentioning

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 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: Yield Estimationmentioning

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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“…Several studies based on biodiesel yield prediction through ML methods have been reported in the literature where the biodiesel was formed from jatropha-algae, castor oil, and anaerobic sludge, e.g., [41][42][43]. Kumar et al [41] developed an ANN model to predict biodiesel yield using various jatropha-algae oil blends as inputs. Banerjee et al [42] used an ANN model for predicting the fractional formation of FAME.…”

Section: Yield Estimationmentioning

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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“…Several studies based on biodiesel yield prediction through ML methods have been reported in the literature where the biodiesel was formed from jatropha-algae, castor oil, and anaerobic sludge [31][32][33]. [31] developed an ANN model to predict biodiesel yield using various jatropha-algae oil blends as inputs. [32] used ANN model for predicting the fractional formation of FAME.…”

Section: Yield Estimationmentioning

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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Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique

Cited by 35 publications

References 25 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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