Catalytic conversion of Australia's natural gas to value added products. While natural gas (of which methane is the primary component) is an abundant source of energy, it is normally found in remote areas and for its successful exploitation it needs to be processed. The processing usually requires significant energy and resources input. In this project we will develop a fundamental understanding to a single step catalytic process that can utilise natural gas and nitrous oxide (both potent greenh ....Catalytic conversion of Australia's natural gas to value added products. While natural gas (of which methane is the primary component) is an abundant source of energy, it is normally found in remote areas and for its successful exploitation it needs to be processed. The processing usually requires significant energy and resources input. In this project we will develop a fundamental understanding to a single step catalytic process that can utilise natural gas and nitrous oxide (both potent greenhouse gases) and oxygen to produce selectively methanol and hydrocarbons from a natural gas feedstream in a controlled manner. A single step process for natural gas conversion utilising waste green-house gases is expected to be of great benefit to the Australian economy, environment and energy securityRead moreRead less
Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achiev ....Data-based Control of Process Feature Dynamics through Latent Behaviours. This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achieve desired dynamic features. This project aims to develop such a data-based approach by controlling latent variable dynamics, using the behavioural systems framework integrated with big data analytics and artificial neural networks. The outcomes are expected to help build a cornerstone for future smart manufacturing.Read moreRead less