Smart repair and replacement decision support model for cathodic protection

About this project

This project aims at designing and delivering an innovative industry automation solution for the SA Water (SAW) passive cathodic protection (CP) system, which is currently inspected and maintained manually by the SAW cathodic protection field technicians. 

Current Challenge and issues: 

• Labour intensive operation: Unlike the network connected PLCs on the impressed current CP systems, the inspector is required to drive to the location, moisturise the ground soil, connect to the anode and voltage measurement devices, read the value and record data in the spreadsheet. Since approximately 4000+ inspection points need to be manually checked, the overall process results in significant maintenance cost. 
• Knowledge retention: Each reading value has to be manually interpreted by the experienced CP technician onsite due to the different types of sacrificial metal (eg. magnesium) and measurement (eg. copper) anodes as well as the actual pipeline materials (eg. welded steel). Such CP interpretation knowledge are not available from general trades (e.g. electricians). 
• Data usage: With no system integration, reading data is kept within spreadsheet documents and not available in the corporate analytics systems. 
• Lack of advance commercial solutions: Although the above ground and underground communication technology has advanced over time, CP detection still relies heavily on the voltage measurement. Soil moisture level is critical to the voltage measurement loop. 

As a result, the permanent measurement devices eventually dry out and fail to provide accurate readings. This project aims at exploring a new innovative approach to perform cathodic protection detection.

As a technology adoption study, this project aims at establishing an innovative CP protection system measurement/ detection method complementing the existing technology, designing and prototyping an IoT solution, which can be potentially integrated to the existing SAW IT infrastructure to deliver business benefits and potential commercialization opportunities for the industry at large. 

This project does not only consider the hardware sensor design but also places a specific focus on the complete solution design including the hardware/software technology, background infrastructure integration and adoption process change management. 

Key innovations 

• EMF­based indirect measurement: This project will investigate and design an EMF­based CP detection method complementing the voltage measurement method. The EMF­based detection method has not been introduced to CP measurement before. However, there are existing studies, discussing the CP impact on EMF­based electronic flowmeters. Indeed, EMF detection has a number of advantages over the voltage measurement (e.g. no reliance on soil moisture levels, no direct contact requirement, etc.). 
• Field Device Power and Communication capability: This project will investigate both power and data communication methods from both underground and above ground sensors and relay modules. The total cost of ownership is a critical consideration when designing this automation solution (e.g. multiple Bluetooth devices communicate with one ground LTE­M relay station to reduce the power requirement and reduce the communication cost). Such findings also contribute to future smart sensors / IoT project in SAW. 
• Seamless Integration: This project will use industry standards (e.g MQTT communication protocols) to enable seamless integration with SAW standard IT infrastructure. This also ensures speedy deployment and early ROI.

What you’ll do 

The applicant is expected to have engineering and computer science background. As a research project, the applicant will perform literature review, identify the research questions and collect empirical evidences under the guidance of the supervisor team. During the project, the applicant is expected to perform interviews, field observation, develop experiments and use computer software to collect empirical evidence. Coding is required.

Where you’ll be based

The applicant will work with the supervisor team located in the Mawson Lakes Campus, University of South Australia. SA Water will also offer spaces for the applicant to work with the project team. 

Jing Gao will be the principal supervisor of the HDR student. He has extensive research experience in technology adoption studies for smart asset management through the CIEAM CRC. He has worked closely with SA Water previously on similar technology adoption studies. He has a strong track record in the field of asset management. 

Chris Chow is a former SA Water Manager of Sensor, Technology and Assets Research and currently has several PhD students in the water infrastructure / asset management space. 

Rameez Rameezdeen also has plenty of experience in asset management. His research interests include topics such as: sustainable materials, sustainable operation of built assets, smart maintenance, demolition waste management and reverse logistics. His experience will ensure that the project will take a holistic view on the recommended asset management process instead of focusing only on the technology selection.

Nima Gorjian, Senior Manager Maintenance (SA Water) and Adjunct Assoc. Prof. (UniSA), has strong track record of developing asset management research project. He is the SA Water internal project sponsor of this project. Financial Support 

This project is funded for reasonable research expenses. Additionally, a living allowance scholarship of $32,500 per annum is available to eligible applicants. Australian Aboriginal and/or Torres Strait Islander applicants will be eligible to receive an increased stipend rate of $45,076 per annum. A fee-offset or waiver for the standard term of the program is also included. For full terms and benefits of the scholarship please refer to our scholarship information for domestic students or international students.

Eligibility and Selection 

This project is open to application from both domestic and international applicants.

Applicants must meet the eligibility criteria for entrance into a PhD. Additionally applicants must meet the projects selection criteria:

• Essential tertiary degree in Electrical Engineering, Material Engineering, Statistics, Applied mathematics, or Computer Science.
• Essential to process and analyse a large amount of data.
• Essential to understand artificial intelligence techniques.
• Essential Python, MATLAB, R or SQL computer programming.
• Desirable industrial work experience or equivalent

All applications that meet the eligibility and selection criteria will be considered for this project. A merit selection process will be used to determine the successful candidate.

The successful applicant is expected to study full-time and to be based at our Mawson Lakes Campus in the north of Adelaide. Note that international students on a student visa will need to study full-time.

Essential Dates 

Applicants are expected to start in a timely fashion upon receipt of an offer.  Extended deferral periods are not available. Applications close on Tuesday, 7th of May.