Thermal kinetic analysis of mustard biomass with equiatomic iron–nickel catalyst and its predictive modeling. (January 2022)

• Record Type: Journal Article
• Authors: Jacob, G. Antilen; Prabhakaran, S.P. Sathiya; Swaminathan, G.; Joseyphus, R. Justin
• Is Part Of: Chemosphere. Volume 286:Part 3(2022)
• Publication Date: 2022-01
• Subjects: Polyol -- Biofuel -- Pyrolysis -- ANN -- Catalysis -- Magnetic; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2021.131901
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Mustard waste briquettes are commercially used as a fuel for power production in boilers, whereas the thermal kinetics of the biomass plays a vital role in deciding the process parameters. The pyrolysis process converts biomass to value-added products such as biochar, bio-oil, and hydrocarbon gases based on the heating rates and temperature. To enhance the pyrolytic activity of mustard biomass, magnetically separable and reusable FeNi alloy catalyst is investigated. The thermo-conversion properties are studied under variable heating rates with 2 and 10% FeNi particles prepared through a facile chemical reduction technique. Thermal kinetics is computed using Flynn-Wall-Ozawa (FOW) and Kissinger-Akahira-Sunose (KAS) methods. The activation energies calculated using FOW and KAS methods increase with FeNi addition in mustard while the calorific value decreases. The FeNi alloy particles with the spike-like morphology provide better metal-biomass binding resulting in higher activation energy and facilitates the easy decomposition of lignin. The 10% FeNi -mustard shows uniform conversion independent of heating rates, suitable for magnetically recoverable catalytic pyrolysis. Response surface methodology analysis predicts optimum conversion for 10% FeNi added mustard and less significance for the heating rates in concurrence with the experiments. Artificial neural network utilized to predict and validate mass loss for mustard biomass exhibits best fit for the three neural hidden layer and one output layered topology. Graphical abstract: Image 1 Highlights: FeNi-mustard biomass synthesized with spike-like FeNi particles. Activation energy increases while calorific value decreases with FeNi. Uniform conversion independent of heating rate is achieved with 10% FeNi. FeNi catalyst enhances the decomposition of lignin in mustard biomass. RSM and a suitable ANN model are used to validate the experimental results.