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Chief Investigator:  Prof. Jie Bao

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.

Supported by the .Ìý

Chief Investigators: C. Menictas,ÌýJ. Bao, M. Skyllas-Kazacos, K. Meng

ARC Research Hub/Industry co-funding.

Chief Investigator: Prof. Jie 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 .ÌýIn collaboration with , University of Alberta (international partner investigator).

Chief Investigator: Prof. Jie 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 . In collaboration with , University of Alberta (international partner investigator).

Chief Investigators: Prof. Jie Bao, Prof. Barry Welch, Prof. Maria 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 Ìý²¹²Ô»åÌý.

Chief Investigators: Prof. Jie Bao, Dr. Yuchen Yao, Prof. Barry 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 .

Chief Investigators: Prof. Jie Bao and Prof. Barry 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 .

Chief Investigator: Prof. Jie 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. 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 .Ìý

Chief Investigators: Prof. Jie Bao, Prof. Maria 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 .Ìý

Chief Investigator: Prof. Jie Bao

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 . In collaboration with , University of Alberta.

Chief Investigator: Prof. Jie Bao

To achieve high economical efficiency, modern chemical plants are becoming increasingly complex, to an extent that cannot be effectively managed by existing process modelling and control techniques. By exploring the physical fundamentals in thermodynamics and their connections to control theory, this project aims to develop a new modelling and control approach that can be applied to complicated nonlinear processes. In this approach, processes over the entire plant are analysed and controlled from a network perspective using the dissipativity control theory. The outcomes of this project will form the cornerstones of a new process control paradigm that offers more robust and reliable process operation at any scale.

Supported by the . In collaboration with , Carnegie Mellon University.

Chief Investigators: Prof. Jie Bao,Ìý and Dr. Alessio Alexiadis

Fouling reduces throughput and productivity of membrane systems and as such increases operating costs and reduces profitability of water treatment industries. This work aims to reduce membrane fouling by reducing the amount of solute at the membrane surface. This is achieved by implementing destabilizing electro-osmotic flow control. The significance of this project lies in linking feedback control of electro-osmotic effects with spacer design to maximize flow instabilities. This project will advance modelling of flow in membrane channels and develop a novel feedback flow control strategy that enhances mixing. The effectiveness and operability of the new fouling reduction approach on real-world membrane systems will be evaluated.Ìý With over $9bn worth of membrane-based desalination plants either in operation, under construction or being planned in Australia, the expected outcomes of this project will lead to significant social and economical benefit and provide greater water security.

Supported by the .

Chief Investigators: Prof. Jie Bao, Prof. Barry Welch, Prof. Maria Skyllas-Kazacos

This project develops a new monitoring approach for monitoring aluminium smelting cells, including an instrumentation scheme for measuring distributed process variables and a soft sensor method for estimating the important process variables that cannot be directly measured.

Supported by Dubai Aluminium Company.

Chief Investigators: Prof. Jie Bao, Prof. Barry Welch, Prof. Maria Skyllas-Kazacos

Primary production of aluminium is highly energy intensive, with energy costs representing 22-36% of operating costs in smelters. The Australian aluminium smelting industry consumed 29,500 GWh of electricity in 2007, 13% of final electricity consumption in Australia. The long term sustainability of the aluminium smelting industry depends on energy-efficient production technologies for global competitiveness. The aim of the project is to improve auto-diagnosis of the occurrence of the root-cause for abnormal process conditions in the smelting cells that adversely impact energy and environmental efficiencies. The expected outcomes include: (1) An adaptive model for the change in control signal and control algorithms with different abnormalities and at different operating line current levels, (2) A sequence of diagnostic sub-routines based on processing signals at different, (3) A schemes for alarms and guidelines for human interface interaction when needed.

Supported by .

Prof. Jie Bao

The objective of this project is to develop an online dynamic feedback control approach to improve the operation of paste thickeners through adopting modern control strategies (in particular, model predictive control) already successfully applied in the petro-chemical industry. This would be an ideal test case for applying advanced dynamic control for complete CHPPs or other variable dynamic processes such as flotation.

Supported by .ÌýIn collaboration with Dr. Goezt Bickert,Ìý.

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 .

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.Ìý
Supported by the . 

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.Ìý
Supported by  a²Ô»å .

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

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.Ìý
Supported by the .