Green Technologies and Sustainability · 2024 · 29 citations · 124 references
EngineeringChemical EngineeringEnvironmental EngineeringBiochemical EngineeringCatalysisReaction EngineeringBioenergy SystemsBiofuel ProductionBiomass ConversionBiomass Energy SystemsCatalytic Reaction EngineeringIndustrial Catalytic ProcessesActivation EnergyApparent Activation EnergyBiofuelsPyrolysisPyrolysis KineticsPyrolysis Of PlasticsThermochemical ConversionThermochemical Modeling
The search of sustainable route for biofuel production from renewable biomass have garnered wide interest to seek for various routes without compromising the environment. Co-pyrolysis emerges as a promising thermochemical route that can improve the pyrolysis output from simultaneously processing more than two feedstocks in an inert atmosphere. This paper focuses on the kinetic modelling and neuro-evolution optimization in the application of catalytic co-pyrolysis of microalgae and plastic waste using HZSM-5 supported on limestone (HZSM-5/LS), in which co-pyrolysis of binary mixture of microalgae and plastic wastes (i.e. High-Density Polyethylene and Low-Density Polyethylene) was investigated over different heating rates. The results have shown a positive synergistic effect between the microalgae and polyethylene in which the apparent activation energies values have reduced significantly (∼20 kJ/mol) compared to that obtained by pyrolysis of individual microalgae component. The kinetic models reflect that the mixture of microalgae and Low-Density Polyethylene for use as co-pyrolysis feedstock requires activation energy that is 23% and 13% lower compared to that required by pure microalgae and the mixture of microalgae and High-Density Polyethylene, respectively. The Progressive Depth Swarm-Evolution (PDSE) neuro-network based optimization model predicts that the optimal reaction condition at 873 K can achieve 99.6 % of degradation rate using a tri-combination of LDPE (0.13 %) + HDPE (0.77 %) + MA (0.11 %) in the presence of HZSM-5/LS catalyst.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma, Jimmy Ba · UvA-DARE (University of Amsterdam) · 2014 · 84.8K citations · Full text
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