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Peter Adewale

Researcher at McGill University

Publications -  21
Citations -  625

Peter Adewale is an academic researcher from McGill University. The author has contributed to research in topics: Biodiesel production & Transesterification. The author has an hindex of 9, co-authored 15 publications receiving 448 citations. Previous affiliations of Peter Adewale include Lakehead University & University of Ilorin.

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Recent trends of biodiesel production from animal fat wastes and associated production techniques

TL;DR: In this paper, a comprehensive review of trends and techniques in biodiesel production from animal fat wastes (AFWs) is presented, and a critical overview of homogeneous and heterogeneous (one- or two-step) catalytic transesterification is presented.
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Optimization of enzyme-catalyzed biodiesel production from crude tall oil using Taguchi method

TL;DR: In this article, the optimal conversion of CTO to crude biodiesel was obtained at 16h reaction time, 40°C reaction temperature, 1:1.5 cTO to methanol molar ratio, and 1.0wt% enzyme dose.
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Enzyme-catalyzed synthesis and kinetics of ultrasonic assisted methanolysis of waste lard for biodiesel production.

TL;DR: In this article, the effects of ultrasonic parameters (amplitude, cycle and pulse) and major reaction factors (molar ratio and enzyme concentration) on the reaction kinetics of biodiesel generation from waste lard bio-catalyzed by immobilized lipase [Candida antarctica Lipase B (CALB)] were investigated.
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Enzyme-catalyzed synthesis and kinetics of ultrasonic-assisted biodiesel production from waste tallow.

TL;DR: The kinetic model results revealed interesting features of ultrasound assisted enzyme-catalyzed transesterification (as compared to conventional system): at ultrasonic amplitude 40%, the reaction activities within the system seemed to be steady after 20 min which means the reaction could proceed with or without ultrasonic mixing.
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Determination of the iodine value and the free fatty acid content of waste animal fat blends using FT-NIR

TL;DR: In this article, the effects of four spectra pre-processing (first derivative (FD), second derivative (SD), multiplicative scatter correction (MSC) and vector normalization (VN) methods were investigated to predict the reproducibility and robustness of the PLS-NIR model developed.