Valorization of Biodiesel Into Bio‐Based Plasticizers: Optimization of Epoxidized Fatty Acid Isobutyl Ester Production Using Response Surface Methodology and Artificial Neural Network DOI Open Access
Xiaojiang Liang, Haotian Fei,

Fengjiao Wu

и другие.

European Journal of Lipid Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 9, 2025

ABSTRACT Biodiesel is a promising green chemical feedstock due to its renewability and sustainability. In this study, bio‐based plasticizers, epoxidized fatty acid isobutyl esters (Ep‐FABEs), were prepared using biodiesel as through combination of transesterification the formic autocatalytic method. The effects reaction temperature, time, FA/C═C molar ratio, H 2 O /C═C ratio on conversion oxirane (RCO) during epoxidation process investigated central composite design. Both response surface methodology (RSM) artificial neural network (ANN) models developed model optimize process. Comparative analysis revealed that ANN demonstrated superior predictive capabilities, with lower mean squared error (MSE), absolute (MAE), higher coefficient determination ( R ) compared RSM model. predicted an RCO 92%, which was closely aligned experimental value 91% under optimized conditions (reaction temperature 66°C, time 6.7 h, 0.35, 2.70). Additionally, physico‐chemical properties Ep‐FABEs further analyzed. These findings provide valuable insights into production plasticizers feedstock.

Язык: Английский

A comprehensive review of the evolution of biodiesel production technologies DOI

Mehedi Hassan Pranta,

Haeng Muk Cho

Energy Conversion and Management, Год журнала: 2025, Номер 328, С. 119623 - 119623

Опубликована: Фев. 8, 2025

Язык: Английский

Процитировано

9
Study on Biodiesel Production: Feedstock Evolution, Catalyst Selection, and Influencing Factors Analysis DOI Creative Commons
Fangyuan Zheng, Haeng Muk Cho

Energies, Год журнала: 2025, Номер 18(10), С. 2533 - 2533

Опубликована: Май 14, 2025

As fossil fuel depletion and environmental pollution become increasingly severe, biodiesel has emerged as a promising renewable alternative to conventional diesel due its biodegradability, low sulfur emissions, high combustion efficiency. This paper provides comprehensive review of the evolution feedstocks, major production technologies, key factors influencing efficiency quality. It traces development feedstocks from first-generation edible oils, second-generation non-edible oils waste fats, third-generation microalgal fourth-generation biofuels based on synthetic biology, with comparative analysis their respective advantages limitations. Various technologies such transesterification, direct esterification, supercritical alcohol methods, enzyme-catalyzed transesterification are examined in terms reaction mechanisms, process conditions, applicability. The effects critical parameters including alcohol-to-oil molar ratio, time, temperature yield quality discussed detail. Particular attention is given role catalysts, both homogeneous heterogeneous types, enhancing conversion In addition, life cycle assessment (LCA) briefly considered evaluate impact sustainability production. serves valuable reference for improving advancing sustainable feedstock development, promoting commercial application biodiesel.

Язык: Английский

Процитировано

1
Valorization of Biodiesel Into Bio‐Based Plasticizers: Optimization of Epoxidized Fatty Acid Isobutyl Ester Production Using Response Surface Methodology and Artificial Neural Network DOI Open Access
Xiaojiang Liang, Haotian Fei,

Fengjiao Wu

и другие.

European Journal of Lipid Science and Technology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 9, 2025

ABSTRACT Biodiesel is a promising green chemical feedstock due to its renewability and sustainability. In this study, bio‐based plasticizers, epoxidized fatty acid isobutyl esters (Ep‐FABEs), were prepared using biodiesel as through combination of transesterification the formic autocatalytic method. The effects reaction temperature, time, FA/C═C molar ratio, H 2 O /C═C ratio on conversion oxirane (RCO) during epoxidation process investigated central composite design. Both response surface methodology (RSM) artificial neural network (ANN) models developed model optimize process. Comparative analysis revealed that ANN demonstrated superior predictive capabilities, with lower mean squared error (MSE), absolute (MAE), higher coefficient determination ( R ) compared RSM model. predicted an RCO 92%, which was closely aligned experimental value 91% under optimized conditions (reaction temperature 66°C, time 6.7 h, 0.35, 2.70). Additionally, physico‐chemical properties Ep‐FABEs further analyzed. These findings provide valuable insights into production plasticizers feedstock.

Язык: Английский

Процитировано

0