Olatunji Fapojuwo, AbrahamZarabian, Maryam2024-01-242024-01-242024-01-17Zarabian, M. (2024). An integrated deep learning model with genetic algorithm (GA) for optimal syngas production using dry reforming of methane (DRM) (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.https://hdl.handle.net/1880/118065https://doi.org/10.11575/PRISM/42909The dry reforming of methane is a chemical process transforming two primary sources of greenhouse gases, i.e., carbon dioxide (CO2) and methane (CH4), into syngas, a versatile precursor in the industry, which has gained significant attention over the past decades. Nonetheless, commercial development of this eco-friendly process faces barriers such as catalyst deactivation and high energy demand. Artificial intelligence (AI), specifically deep learning, accelerates the development of this process by providing advanced analytics. However, deep learning requires substantial training samples and collecting data on a bench scale encounters cost and physical constraints. This study fills this research gap by employing a pretraining approach, which is invaluable for small datasets. It introduces a software sensor for regression (SSR) powered by deep learning to estimate the quality parameters of the process. Moreover, combining the SSR with a genetic algorithm offers a prescriptive analysis, suggesting optimal thermodynamic parameters to improve the process efficiency.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Deep LearningGenetic AlgorithmMulti-Objective OptimizationPre-training approachDry reforming of methaneInformation Sciencemaster thesis