AI-Driven Modeling of Catalytic Pyrolysis for Sustainable Fuel Production: A Neural Network Approach

Idres M.  Khder; Saba A. Gheni; Zainab F. Hassan; Marwan I.  Hamd; Nalan Turkoz  Karakullukcu; Ataallah K.  Tahah

doi:10.52716/jprs.v15i4.1090

Authors

Idres M. Khder Chemical Engineering Department, College of Engineering, Tikrit University, Iraq
Saba A. Gheni Chemical Engineering Department, College of Engineering, Tikrit University, Iraq
Zainab F. Hassan Petroleum Research and Development Center, Ministry of Oil, Baghdad, Iraq
Marwan I. Hamd North Oil Company, Ministry of Oil, Iraq
Nalan Turkoz Karakullukcu Advanced Technology Research and Application Center, Ondokuz Mayis University, Samson, Turkey
Ataallah K. Tahah North Refineries Company (NRC), Baiji, Salahuldeen, Ministry of Oil, Iraq.

DOI:

https://doi.org/10.52716/jprs.v15i4.1090

Keywords:

Catalytic pyrolysis, Waste Plastics, artificial intelligence, Green fuel

Abstract

The growing demand for solutions to plastic waste and sustainable fuel options around the world has inspired research into catalytic pyrolysis as a potential method to convert waste plastic into profitable biofuels. The intensity of pyrolysis processes affected by many process factors makes traditional modeling methods difficult. This research uses artificial nervous network (ANNS) to create a prediction model aimed at increasing biofuel conversion in catalyst pyrolysis. The range of variables considered in the study, including temperature, residence time, catalyst type, conversion, density, particular gravity, API, viscosity, and higher heating value were used to train the ANN model, giving accurate predictions of biofuels production under various conditions. The Levenberg-Marquard method was employed for network training, guaranteed better accuracy and low error. The comparative comparison of traditional modeling functioning and AI-operated approaches reflect the advantage of the artificial nervous network (ANN) model in real-time managing non-linear interactions and optimizing processes. Conclusions suggest that the AI-operated approaches clearly promote process efficiency, reduce waste, and improve decision making in industrial contexts.in this study a perfect match was achieved between the predicted data and experimental data, with R2 value of 1, indicating a perfect alignment between the predictions and experimental results. This research highlights the ability of artificial intelligence to increase permanent chemical engineering functioning and improve biofuel production from waste plastic.

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