4.7 Article

Thermogravimetric analysis of hazardous waste: Pet-coke, by kinetic models and Artificial neural network modeling

Journal

FUEL
Volume 287, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2020.119470

Keywords

Pet coke; Artificial neural network; Combustion indices; Thermal degradation kinetics; Solid state reaction models

Funding

  1. Ministry of Human Resource and Development, Government of India

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Pet coke is a hazardous waste obtained from petroleum refinery, with low reactivity and predominantly inorganic composition. The study provides reliable kinetic parameters for scale up and utilization of pet coke.
Pet coke is a hazardous waste obtained from petroleum refinery on an enormous scale. Understanding pet coke's material characteristics and its decomposition kinetics is a prerequisite for its highly stable combustion behavior. Proximate analysis revealed that high fixed carbon and low volatile matter makes pet coke a low reactive fuel. Advanced analytical techniques like FTIR, XRD, ICP-OES revealed that pet coke is predominantly inorganic, constituting graphitic carbon and hydrogenation materials like nickel & vanadium, respectively. Thermogravimetric studies were conducted, and average activation energy calculated by model-free methods was in the range of 152.70 to 206.18 kJ/mol, respectively. The study of solid state reaction mechanism by Coats-Redfern, Kennedy-Clark and Master plot methods suggested contracting volume (g(alpha) = (1-(1-alpha))(1/3)) and two dimensional diffusion reaction mechanism (g(alpha) = (1-alpha) ln(1-alpha) + alpha) models which falls under decelerating type reaction mechanism. Combustion indices of pet coke like CCI, DI, DB were evaluated at different rates of heating. Pet coke's thermal degradation behavior were modelled using an artificial neural network (NN7). The research carried out permits to identify the characteristics of one of the most dominant hazardous waste, along with the futuristic potential usage and the findings provide reliable kinetic parameters for scale up, utilization, optimization, limitation for the system.

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