Synergistic effects of catalytic co-pyrolysis Chlorella vulgaris and polyethylene mixtures using artificial neuron network: Thermodynamic and empirical kinetic analyses

• Main Authors: Yap, T.L., Loy, A.C.M., Chin, B.L.F., Lim, J.Y., Alhamzi, H., Chai, Y.H., Yiin, C.L., Cheah, K.W., Wee, M.X.J., Lam, M.K., Jawad, Z.A., Yusup, S., Lock, S.S.M.
• Format: Article
• Published: Elsevier Ltd 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125755055&doi=10.1016%2fj.jece.2022.107391&partnerID=40&md5=a80dbff94aba18ebee678cde67296944; http://eprints.utp.edu.my/33061/

The catalytic pyrolysis of Chlorella vulgaris, high-density polyethylene (Pure HDPE) and, their binary mixtures were conducted to analyse the kinetic and thermodynamic performances from 10 to 100 K/min. The kinetic parameters were computed by substituting the experimental and ANN predicted data into these iso-conversional equations and plotting linear plots. Among all the iso-conversional models, Flynn-Wall-Ozawa (FWO) model gave the best prediction for kinetic parameters with the lowest deviation error (2.28-12.76). The bifunctional HZSM-5/LS catalysts were found out to be the best catalysts among HZSM-5 zeolite, natural limestone (LS), and bifunctional HZSM-5/LS catalyst in co-pyrolysis of binary mixture of Chlorella vulgaris and HDPE, in which the Ea of the whole system was reduced from range 144.93-225.84 kJ/mol (without catalysts) to 75.37-76.90 kJ/mol. With the aid of artificial neuron network and genetic algorithm, an empirical model with a mean absolute percentage error (MAPE) of 51.59 was developed for tri-solid state degradation system. The developed empirical model is comparable to the thermogravimetry analysis (TGA) experimental values alongside the other empirical model proposed in literature © 2022 Elsevier Ltd.