Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst. (15th September 2020) - Bong, Jang Tyng; Loy, Adrian Chun Minh; Chin, Bridgid Lai Fui; Lam, Man Kee; Tang, Daniel Kuok Ho; Lim, Huei Yeong; Chai, Yee Ho; Yusup, Suzana
Abstract: The catalytic pyrolysis of pure microalgae (M), peanut shell wastes (PS) and their binary mixtures were analysed by introducing the microalgae ash (MA) as a catalyst. The pyrolysis processes were conducted at different heating rates from 10 K/min-100 K/min to observe their thermal degradation behaviour. Additionally, Artificial Neural Network (ANN) was applied by feeding the heating rates and temperatures to predict the weight loss of the samples. The kinetic and thermodynamic parameters were also determined through three different iso-conversional kinetic models: Friedman (FR), Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). Based on the kinetic results, FWO model achieved the lowest deviation between the activation energies ( E a ) from the experimental which aligned with the ANN predicted results. The finding also shows that the activation energy ( E a ) of the catalytic pyrolysis of binary mixtures was lower than the pure M and PS (Experimental: 142.56 kJ/mol; ANN forecast: 131.37 kJ/mol). Graphical abstract: Image 1 Highlights: The synergistic effect of co-pyrolysis of M and PS were studied using TGA. ANN modelling was applied to predict the weight loss of samples. LT transfer functions produce similar results as the experimental TG curves. The syngas composition has increased after the addition of MA as a catalyst. The E a of M/MA/PS mixture was reduced to 142.56 kJ/mol with MA as a catalyst.