Process parameter assessment of biodiesel production from a Jatropha–algae oil blend by response surface methodology and artificial neural network

Kumar, Sunil; Jain, Siddharth; Kumar, Harish

doi:10.1080/15567036.2017.1403514

Cited by 55 publications

(12 citation statements)

References 16 publications

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“… 1 Biodiesel was generated from the jatropha–alga oil with a transesterification process along with the response surface methodology (RSM). 2 Biodiesel was also generated from inedible food materials, like Mahua and Karanja, with 50:50 by v/v mixing of two. A double-pass reaction with acid esterification that decreased the quantity of free fatty acid (FFA) to a required limit followed by an alkaline transesterification method were carried out to convert the oils to fatty acids of methyl esters.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Biodiesel Production Parameter Optimization of Jatropha–Algae Oil Mixtures and Performance and Emission Analysis of a Diesel Engine Coupled with a Generator Fueled with Diesel/Biodiesel Blends

Kumar¹,

Jain

Kumar

2020

ACS Omega

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Methyl ester production from jatropha–algae oil is conducted through a transesterification process. Consequences of four parameters, the molar ratio (oil:methanol), the reaction temperature, the amount of catalyst, and the reaction time for obtaining a higher yield of biodiesel, are derived, and the process was optimized using the response surface methodology based on the Box–Behnken Design. An optimized biodiesel yield of 96% is achieved at a molar ratio of 1:10, a reaction temperature of 53° C, a 0.3 wt% catalyst, and a reaction time of 172 min. The predicted optimal conditions were experimentally validated with a relative error of 4% of the experimental result (96%). The P value of ANOVA is <0.0001, which shows that the model is significant. Finally, the performance and emissions in a diesel engine coupled with an electricity generator powered by biodiesel blends (B0, B5, B10, and B20% vol.) were investigated, concluding a significant reduction of exhaust gases. The engine was run with numerous blends of biodiesel by changing the brake power from 0 load to 0.5, 1, 1.5, and 2 KW.

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Section: Introductionmentioning

confidence: 99%

Experimental Study on Biodiesel Production Parameter Optimization of Jatropha–Algae Oil Mixtures and Performance and Emission Analysis of a Diesel Engine Coupled with a Generator Fueled with Diesel/Biodiesel Blends

Kumar¹,

Jain

Kumar

2020

ACS Omega

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“…Trained and tested experimental data were evaluated in both steady conditions and abnormal conditions; a high correlation coefficient showed ANN optimistic results for online monitoring of the thermophilic reactors. In a jatropha-algae oil blend study, ANN performed better than RSM [95].…”

Section: Yield Estimationmentioning

confidence: 92%

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%

“…In several studies, ANN was used together with Response Surface Methodology (RSM) for prediction of the yield of biodiesel from Jatropha-algae oil [34], goat tallow [35], and enterobacter species [36]. In [34], ANN outperformed the RSM. [35] used RSM and ANN for identifying optimal parametric values that resulted in maximum FA conversion while maintaining FAME yield that met the American Society for Testing and Materials (ASTM) biodiesel specifications.…”

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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Process parameter assessment of biodiesel production from a Jatropha–algae oil blend by response surface methodology and artificial neural network

Cited by 55 publications

References 16 publications

Experimental Study on Biodiesel Production Parameter Optimization of Jatropha–Algae Oil Mixtures and Performance and Emission Analysis of a Diesel Engine Coupled with a Generator Fueled with Diesel/Biodiesel Blends

Experimental Study on Biodiesel Production Parameter Optimization of Jatropha–Algae Oil Mixtures and Performance and Emission Analysis of a Diesel Engine Coupled with a Generator Fueled with Diesel/Biodiesel Blends

A Review on Machine Learning Application in Biodiesel Production Studies

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

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