CURRENT/RECENT RESEARCH PROJECTS:

• A Probabilistic Approach to Big Data-Based Industrial Process Control (DP250101137, 2025-2027, $578K)
  Chief Investigator: Prof. J. Bao
  Based on the behavioural systems theory for stochastic systems, this project aims to develop a novel probabilistic behavioural process control approach by utilizing big industrial process operation data. Unlike many existing data-driven control methods for deterministic systems, the proposed approach deals with the uncertain operation conditions encountered in daily industry operations by using the statistical information from big process data and controlling the probability distribution of process variables (e.g., leading to products with more consistent specifications). The research outcomes are expected to help the Australian process industries leverage the power of Industry 4.0 to improve the efficiency and economy of their operations.
  Supported by the Australian Research Council. 

• Big Data-based Distributed Control using a Behavioural Systems Framework (DP240100300, 2024-2026, $432K)

Chief Investigator: Prof. J. Bao

With Industry 4.0 turning into reality, industrial processes are becoming distributed cyber-physical systems which generate, process, store and communicate large amounts of data. Using the behavioural systems framework, this project aims to develop a novel distributed control approach for complex processes directly based on big process data. A new model-free framework will be developed to represent and analyse the process/controller networks and interaction effects, and determine the feasibility of desired control performance under distributed control. Novel big data-based distributed control designs will be developed by extending the dissipativity, contraction and differential dissipativity conditions for behavioural systems. 
Supported by the Australian Research Council. 

• ARC Research Hub for Smart Process Design and Control (IH230100010, 2023-2027, $5,035K)

Key chief investigators: Prof. A.B Yu, Prof. V. Strezov, Prof. J. Bao, Prof. G.X. Wang, Prof. Y.S. Shen

The Research Hub aims to develop and apply advanced computational technologies to model and optimise complex multiphase processes by integrating the novel multiscale and AI modelling approaches. The outcomes include theories, computer models and simulation techniques, advanced knowledge about process modelling and optimisation, innovative technologies and processes for low carbon operations, and tens of postdoc and PhD students through academic, industrial and international collaboration. Their application will significantly improve energy/process efficiency and reduce CO2 emission. The Hub will generate a significant impact on the mineral and metallurgical industries which are important to Australia.
Supported by the Australian Research Council. 

• Long Duration Energy Storage Solutions by Using Vanadium Flow Batteries (2023-2026, $1,815K)
  Chief Investigators: Prof. J. Bao (co-lead), A/Prof. C. Menictas (co-lead), Prof. M. Skyllas-Kazacos

This project will develop technologies to optimize the design and operations of Vanadium Flow Batteries to improve their technical and economic viability for applications to remote grid mine sites. This includes technoeconomic modelling and analysis in a range of applications including operations for commercialisation pathways. Advanced VFB online monitoring and control approaches will be developed to improve the battery efficiency and longevity.
Supported by Australian Government Trailblazer program and Rio Tinto.

• Control of Feature Dynamics Distilled from Big Process Data through Latent Variable Behaviours (ARC Discovery Projects DP220100355, 2022-2024, $405K)

Chief Investigator: Prof. J. Bao

This project aims to develop a novel big 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 product quality can be distilled from high dimensional process operation data. However, little effort has been made to achieve these dynamic features using data-based control. This project aims to develop such an approach based on the behavioural systems and dissipativity theories, integrated with big data analytic and machine learning techniques. The outcomes are expected to benefit the Australian process industries, where many processes are controlled by inadequate logic controllers.
Supported by the Australian Research Council.

• Improved Redox Flow Batteries and Integration into the Grid (ARC Industry Research Hub/Industry, 2022-2024, $996K)
  Chief Investigators: A/Prof C. Menictas, Prof. J. Bao, Prof. M. Skyllas-Kazacos, Dr. K. Meng

Project description: in commercial confidence.
Supported by the Australian Research Council and North Harbour Clean Energy.

• A System Behavioral Approach to Big Data-driven Nonlinear Process Control (ARC Discovery Projects DP210101978, 2021-2023, $449K)

Chief Investigator: Prof. J. Bao

This project aims to develop a novel process control approach that utilises big process data to improve the cost-effectiveness of industrial processes. Existing monitoring systems in the industry have been collecting a tremendous amount of process operation data but little effort has been made to use the big process data to enhance process operations. Based on the system behavioural approach and dissipativity theory, integrated with machine learning techniques, this project expects to develop a novel framework for data-driven control using big process data. The outcomes are expected to benefit the Australian process industry, where many processes are controlled by inadequate logic controllers, by improving their operational efficiency.
Supported by the Australian Research Council.  In collaboration with Dr. Biao Huang, University of Alberta (international partner investigator).

• Power Modulation of Aluminium Smelting Cells for Power Demand–Supply Balancing (ARC Industry Research Hub/Emirate Global Aluminium, 2020-2024, $1000K)

Chief Investigators: Prof. J. Bao, Prof. B.J. Welch, Prof. M. Skyllas-Kazacos

The aluminium smelting process is very energy-intensive, with Australia’s smelting industry consuming 29.5 TWh of electricity in 2007, representing 13% of total electricity generated in Australia. Existing aluminium smelting operations typically operate at constant current levels to reduce the variability of the smelting process and simplify process operation. However, this approach results in little flexibility in power modulation of smelting cells. New smelting process operation strategies, and cell monitoring and control approaches will be developed to allow flexible power modulation. This will enable the production rate of aluminium to be reduced or increased to match the supply of power and/or electricity prices. Such virtual storage can provide significant benefits to the stability and efficiency of the electricity network while reducing operating costs for aluminium producers. There are major challenges in power modulation of aluminium smelting cells. Variable amperage may lead to significant problems in heat balance of the cells and current efficiency, and abnormal conditions may occur if the smelting cells are not tightly controlled. The research will focus on (1) studying the feasible operation ranges that minimise irreversible damage to smelting cells based on coupled thermal and mass balance of the smelting cells; (2) cell monitoring approaches that can detect and thereby avoid any abnormal operation conditions caused by power modulation, including using individual anode current measurements, and (3) advanced process control approaches for tightly controlling operations of smelting cells with varying current, based on multivariable nonlinear control theory.
Supported by the Australian Research Council and Emirates Global Aluminium. In collaboration with Nadia Ahli and Amal Aljasmi.

• ARC Research Hub for Integrated Energy Storage Solutions (IH180100020, 2019-2025, $3,189K)

Key chief investigators: Prof.  J. Bao (Hub Director), Prof G.X. Wang, Prof R. Amal, A/Prof C. Menictas, Prof. J. Fletcher

ARC Research Hub for Integrated Energy Storage Solutions. The ARC Research Hub for Integrated Energy Storage Solutions aims to develop advanced energy storage technologies, including printed batteries, structural supercapacitors, innovative fuel cells and power-to-gas systems. It plans to integrate these storage solutions with existing energy networks and applications using novel storage monitoring, control and optimisation technologies. The Hub is expected to generate new knowledge in storage technology manufacturing, control and management. Expected outcomes include cheaper and more effective storage devices and better storage integration solutions, supporting renewables, reducing carbon emissions, and improving efficiency in the energy sector. Resulting benefits include a more sustainable, secure, reliable and economically efficient energy supply. This Hub will contribute to improving the economic efficiency of Australia’s energy sector.
Supported by the Australian Research Council. 

• A Distributed Optimization-based Approach to Flexible Plantwide Control using Differential Dissipativity (ARC Discovery Project: DP180101717, 2018-2020, $383K)

Chief Investigator: Prof. J. Bao

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 heavily 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).

• Advanced Distributed Cell Control for Aluminium Smelting Cells (Industrial Project sponsored by Emirate Global Aluminium, 2018-2024, $867K) 

Chief Investigators: Prof. J. Bao and Prof. B.J. Welch

This project aims to develop a novel alumina feeder design and an advanced real-time cell control strategy to achieve more uniform and smooth alumina concentration spatially and temporally, more uniform anode current distribution, and better-distributed heat management, resulting in a more balanced and stable cell with reduced background perfluorocarbon emission and sludge formation.
Supported by Emirate Global Aluminium.

• Advanced Anode Current Monitoring System for Aluminium Reduction Cells (Industrial Project sponsored by Emirate Global Aluminium, 2020-2024, $209K)

Chief Investigators: Prof. J. Bao, Prof. B.J. Welch and Dr. Y.C. Yao

This project aims to develop a prototype of the smart sensing system for monitoring aluminium reduction cells, which requires low maintenance. Soft-sensor techniques based on a multi-level extended Kalman filter is developed to estimate the important process variables in real time.
Supported by Emirate Global Aluminium.

• An Integrated Approach to Distributed Fault Diagnosis and Fault-tolerant Control for Plantwide Processes (ARC Discovery Project: DP160101810, 2016-2018, $285K)

Chief Investigator: Prof. J. Bao

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. The 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, $341K)

Chief Investigators: Prof. J. Bao, Prof. M. Skyllas-Kazacos

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.