Co-pyrolysis of microalgae residue and sewage sludge: An in-depth characterization of kinetics, drivers, and gas-oil-char behaviors

Journal of Analytical and Applied Pyrolysis(2024)

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摘要
Environmental concerns linked to fossil fuels, including but not limited to global warming, climate change, water and soil acidification, are compelling countries to investigate and advance biomass energy sources. In this study, lipid-isolated Chlorella vulgaris residue (CVR) and sewage sludge (SS) were characterized for their pyrolytic drivers, behaviors, in situ gasses, kinetics, bio-chars, and bio-oils using a hyphenated thermogravimetric–Fourier infrared spectroscopy/gas chromatography− mass spectrometry (TG-FTIR/GC-MS), two-dimensional correlation spectroscopy (2D-COS), and fixed bed reactor techniques. Also, an integrated response surface methodology and artificial neural network (RSM-ANN) modelling technique were utilized to optimize the pyrolytic products. Based on the TG/DTG analyses of the co-pyrolysis, two decomposition stages can be distinguished: the first stage involved degradation of CVR (150–350 °C), whereas the second stage (350–550 °C) was due to degradation of CVR-SS blends. The average activation energy (Eavg) was 250kJ/mol and 235kJ/mol for CVR and SS degradation, respectively. The integrated RSM-ANN modelling led to the maximum bio-oil and minimum of gas yield. As revealed by TG-FT/IR-GC/MS, 2D-COS, and bio-oils’ GC-MS analyses, co-pyrolysis synergistically improves the hydrocarbon production, while inhibited most of the nitrogenous and oxygenous compounds. However, some upgrading processes are necessary in order to be used as a drop-in fuel. In addition, the SEM and elemental analysis was used to evaluate the char product, which revealed its potency as a fuel in a variety of contexts. The findings presented herein offer both a practical and theoretical foundation for augmenting and optimizing the comprehensive circularity of CVR and SS co-pyrolysis.
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关键词
Microalgal residue,Sewage sludge,Response surface methodology,Artificial neural network,Thermal kinetics,Gas-oil-char
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