Probing plasma methanation via artificial intelligence
Supervisory team: Prof Rose Amal, A/Prof Jason Scott and Dr Hassan Masood
Description: Heterogeneous catalysis is inherently a complex phenomenon and is particularly challenging from an informatics perspective.1 Catalyst design can be aided by process modelling, but is often hampered by the limited understanding of intricate interaction between catalyst surface, physicochemical properties and reaction environment. Fortunately, the modern advancements in artificial intelligence techniques have heavily facilitated the modelling and prediction of complex high-dimensional data. Artificial neural networks, in particular, exhibit exceptional ability to ‘learn’ from a given set of experimental data, and predict the process response without knowledge of physical and chemical laws governing the system.2 Such technique has been successfully utilised for designing various heterogeneous catalysts, including ammoxidation of propylene catalyst, methane oxidative decoupling catalyst, propane ammoxidation catalyst, as well as analysis and prediction of nitric oxide over zeolites.3 In addition, the approach is frequently employed for the modelling of photocatalytic, photooxidative and electrochemical processes. However, the potential of this technique in terms of plasma-assisted catalytic applications is still unexplored.
In a broad perspective, the project aims to elucidate the catalyst structure-property-function relationship, coupled with process conditions, for plasma methanation. The specific objective is to formulate artificial neural network-based machine learning model using experimental data from in-house plasma system. The empirical model will be validated to test generalisation abilities and will provide insights on the reaction mechanism under external stimuli.
The students selected for this project will be given opportunity to work in Particles and Catalysis Research Group (PartCat) at School of Chemical Engineering, under the supervision of Scientia Professor Rose Amal, Dr Hassan Massood and A/Prof Jason Scott. The students will have access to state-of-the-art experimentation facilities and computational tools, and will gain necessary expertise facilitating their career in industry or academic research.
1. Medford AJ, Kunz MR, Ewing SM, Borders T and Fushimi RR, Extracting knowledge from data through catalysis informatics. ACS Catalysis (2018).
2. Khataee A and Kasiri M, Artificial neural networks modeling of contaminated water treatment processes by homogeneous and heterogeneous nanocatalysis. Journal of Molecular Catalysis A: Chemical 331:86-100 (2010).
3. Serra JM, Corma A, Chica A, Argente E and Botti V, Can artificial neural networks help the experimentation in catalysis? Catalysis Today 81:393-403 (2003).
Suitable for: Chemical Engineers and Industrial Chemistry student. Project maybe available to do during summer semester.
Level of difficulty: Very Challenging
Further information can be obtained by contacting Professor Rose Amal (email@example.com).