Prediction of Optimal Conditions in the Methanolysis of Mustard Oil for Biodiesel Production Using Cost-Effective Mg-Solid Catalysts

Chakraborty, Rajat; Das, Sukamal; Pradhan, Piasy; Mukhopadhyay, P.

doi:10.1021/ie501084z

Cited by 13 publications

(11 citation statements)

References 36 publications

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“…However, research elsewhere regarding alternative feedstocks has been broadening the range of options available for the production of biodiesel. Recent initiatives in this area suggest that products such as mustard oil which accounts for 12 per cent of the world’s vegetable oil production (third largest source after soy and palm oil) could represent a new feedstock for biodiesel (Chakraborty et al , 2014, 2015). Nevertheless, the current and probable future focus of government mediated research interest is likely to remain on other forms of renewable energy and on the development of primary feedstocks with regard to biofuels (SEAI/Dept.…”

Section: Resultsmentioning

confidence: 99%

Resolving the policy paradox: the case of biofuel production in Ireland

Gilmer¹,

McGarrity²,

Byers

2016

IJESM

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Purpose The purpose of this paper is to determine the status of policy design and policy implementation in the biofuel sector in Ireland. The focus of the work addresses the overarching operational context of the biofuel sector in Ireland and the role of different actors in shaping and resolving inconsistencies in policy outlook and practice. Design/methodology/approach This study used a qualitative research approach involving a series of semi-structured interviews with members of the relevant sub-groups concerned. This study sought to address two questions – whether current or proposed policy is likely to affect consumption of indigenous biofuel feedstocks in the biofuel sector and what are the controlling factors in the demand for indigenous feedstocks for biofuel. Findings Outcomes suggest that while Irish government policy recognises the need to support the development of renewable energy, it also operates under a number of parallel and potentially inconsistent paradigms in relation to biofuels as a renewable energy commodity. It is contended that the outcome of this position is a lack of coherent and coordinated policy in the area of biofuel production, including second generation biofuel using indigenous feedstocks. Originality/value This paper provides a new cross sectoral perspective on the status of biofuel policy in Ireland with particular reference to second generation biofuel feedstocks. It focuses analysis on the nature of policy-operational inconsistencies and the need for a deeper ecological perspective in governance.

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Section: Resultsmentioning

confidence: 99%

Resolving the policy paradox: the case of biofuel production in Ireland

Gilmer¹,

McGarrity²,

Byers

2016

IJESM

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“…Titration method was used for the acidity analysis of Ni―Fe/FA catalyst samples, i.e. NI‐5, NI‐10 and NI‐15.…”

Section: Resultsmentioning

confidence: 99%

Fly ash supported Ni―Fe solid acid catalyst for efficient production of diesel additive: intensification through far‐infrared radiation

Pradhan

Chakraborty

2015

Asia-Pacific J Chem Eng

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Novel Ni-Fe solid acid catalyst was prepared through co-wet impregnation of Fe 2 (SO 4 ) 3 ·H 2 O and Ni (NO 3 ) 2 ·6H 2 O on fly ash (FA) support. XRD, BET-BJH, SEM-EDX, XPS and FTIR methods were employed to characterize the catalyst. The excellent catalyst activity was assessed and optimized in the production of methyl oleate (biodiesel/dieseladditive). The response surface methodology (RSM) computed optimal parametric values pertaining to maximum oleic acid (OA) conversion (98.37 ± 0.01%) were 15:1 methanol/OA molar ratio, 1:15:2 nickel nitrate: ferric sulphate:FA (wt. ratio) and 4 wt.% (of OA) catalyst concentration in 1 h under conventional heating at mild reaction temperature (60°C). The optimal catalyst possessed 27.64 m 2 /g specific surface area and 0.0233 cc/g pore volume with predominantly mesoporous framework. XPS bands confirmed the presence of Fe 3+ and mixed Ni 2+ /Ni 3+ species rendering an acidity of 5.5 mmol KOH/g. The catalyst comprised of crystalline Fe 2 O 3 , Ni 2 O 3 and NiS phases as revealed from XRD analysis. This study also reports the remarkable intensification of esterification reaction through an energy-efficient application of far-infrared radiation protocol (1/3 rd of conventional power requirement) resulting 99.20% OA conversion in only 10 min at the derived optimal conditions.

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“…A biodiesel synthesis process from waste goat tallow containing remarkable free fatty acids (FFAs) has been modeled by RSM and ANN to identify optimum parametric values that resulted in maximum FA conversion. Under optimal conditions, response surface methodology (RSM) and ANN presented similar predictability performance [96].…”

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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Prediction of Optimal Conditions in the Methanolysis of Mustard Oil for Biodiesel Production Using Cost-Effective Mg-Solid Catalysts

Cited by 13 publications

References 36 publications

Resolving the policy paradox: the case of biofuel production in Ireland

Resolving the policy paradox: the case of biofuel production in Ireland

Fly ash supported Ni―Fe solid acid catalyst for efficient production of diesel additive: intensification through far‐infrared radiation

A Review on Machine Learning Application in Biodiesel Production Studies

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