Field of Research (FoR)
Professor Jie Bao is a Process Control expert, particularly in dissipativity/passivity based process control. He leads the Process Control Research Group, School of Chemical Engineering. He has been awarded more than $3.9 million competitive research grants including 9 Australian Research Council Discovery Projects/Large Grants, one CSIRO National Flagship Research Cluster project and one Australian Coal Research Association Program project....view more
Professor Jie Bao is a Process Control expert, particularly in dissipativity/passivity based process control. He leads the Process Control Research Group, School of Chemical Engineering. He has been awarded more than $3.9 million competitive research grants including 9 Australian Research Council Discovery Projects/Large Grants, one CSIRO National Flagship Research Cluster project and one Australian Coal Research Association Program project. His research interests include dissipativity theory based process control, networked and distributed control systems, decentralized control and control applications in membrane separation, flow batteries, coal preparation and Aluminium smelting. He published extensively in major process control and chemical engineering journals. He is an Associate Editor of Journal of Process Control (an International Federation of Automatic Control affiliated journal).
CURRENT/RECENT RESEARCH PROJECTS:
A Distributed Optimization-based Approach to Flexible Plantwide Control using Differential Dissipativity (ARC Discovery Project: DP180101717, 2018-2020)
In today's demand-dynamic economy, the Australian process industry needs to shift from traditional mass production to smart manufacturing for more agile, cost-effective and flexible process operation responding to the market. While governments and industries worldwide have heavy invested in this new industry paradigm, developments are largely limited to its information technology aspect. This project will investigate the process control methodologies crucial to smart manufacturing. Based on contraction and dissipativity theories, this project aims to develop a distributed optimization-based nonlinear control approach for plantwide flexible manufacturing, which can achieve time-varying operational targets including production rates and product specifications to meet dynamic market demands. This includes a contraction-based nonlinear distributed control framework that ensures plantwide stability at any feasible setpoints or references and a distributed economic model predictive control approach that coordinates autonomous controllers to achieve plantwide economic objectives in a self-organizing manner. The outcomes of this project are expected to form a process control framework for next-generation smart plants. Supported by the Australian Research Council. In collaboration with Dr. Jinfeng Liu, University of Alberta (international partner investigator).
An Integrated Approach to Distributed Fault Diagnosis and Fault-tolerant Control for Plantwide Processes (ARC Discovery Project: DP160101810, 2016-2018)
Modern industrial processes are very complex, with distributed process units via a network of material and energy streams. Their operations increasingly depend on automatic control systems, which can make the plants susceptible to faults such as sensor/actuator failures. Occurrence of faults is increased by the common practice to operate processes close to their design constraints for economic considerations. This project will develop a new approach to detect and reduce the impact of these faults, which can cause significant economic, environment and safety problems.
Based on the concept of dissipative systems, this project aims to develop a novel integrated approach to distributed fault diagnosis and fault-tolerant control for plantwide processes. The key dynamic features of normal and abnormal processes are captured by their dissipativity properties, which are used to develop an efficient online fault diagnosis approach based on process input and output trajectories, without the use of state estimators or residual generators. Using the dissipativity framework, a distributed fault diagnosis approach will be developed to identify the locations and faults in a process network. A distributed fault tolerant control approach will be developed to ensure plantwide stability and performance. Supported by the Australian Research Council.
Control of Distributed Energy Storage System using Vanadium Batteries (ARC Discovery Project DP150103100, 2015-2017)
The ever increasing integration of distributed renewable energy generation sources with the electricity grid reduces our reliance on fossil fuels and carbon emissions but also presents risks to the grid’s stable and reliable operation due to intermittent nature of such sources. This project will develop some key technologies of battery energy storage and control to address the above issues and help defer the investment for the augmentation of the transmission and distribution networks. This project aims to develop a new control approach to distributed energy storage at stack, system and microgrid levels, utilising one of the most promising flow battery technologies - Vanadium Redox batteries. This is the first attempt of a storage centric approach that includes (1) an integrated approach to design and control of Vanadium flow batteries with novel advanced power electronics technologies to achieve optimal charging/discharging conditions and (2) a scalable distributed energy storage and power management approach incorporating energy pricing for storage dispatch that allows distributed autonomous controllers to achieve optimal local techno-economic performance and microgrid-wide efficiency and reliability. Supported by the Australian Research Council. In collaboration with Prof. M. Skyllas-Kazacos.
Dissipativity based Distributed Model Predictive Control for Complex Industrial Processes (ARC Discovery Project: DP130103330, 2013-2015)
Based on the behavioural approach to systems and dissipativity theory, this project aims to integrate nonlinear control theory with distributed optimization to develop a novel distributed predictive control approach for complex industrial processes. In this approach, the global objectives (i.e., the plantwide stability and performance) are converted into the local constraints of dissipativity conditions for non-cooperative optimization performed in the distributed controllers. The outcomes will include a framework and the fundamental control theory for distributed autonomous model predictive control that achieves improved scalability, flexibility and robustness compared with existing distributed predictive control approaches. Supported by the Australian Research Council. In collaboration with Dr. Jinfeng Liu, University of Alberta (international partner investigator).
Anode Current Distribution Monitoring and Analysis (DUBAL, 2013-2015)
Supported by Dubai Aluminium Company. In collaboration with Prof. B. J. Welch and M. Skyllas-Kazacos.
Process dynamics and control, control of complex industrial processes, distributed control systems, advanced control of aluminimum smelting processes, distributed energy storage systems, monitoring and control of flow batteries, modelling and control of coal preparation processes, control of membrane systems