Projects per year
Measurements and Analytics for Petroleum and Chemical Industries
Oil and gas and other chemical process industries are constantly under pressure to minimise costs and environmental footprint. To achieve these objectives effective control and optimisation of the processes are required. Two key enabling technologies are needed to achieve effective control and optimisation. One is online measurement systems that provide continuous information on key physical and chemical attributes of the process streams. The second enabling technology is for the extraction of critical information from process measurements through the application of big data analytics and process modelling. These technologies are essential to develop reliable predictive models that effectively utilise the large amount of data that are typically collected in petroleum and chemical plants. Combining this databased approach with first-principles-based knowledge of process systems can potentially provide significantly improved predictive models.
The technologies we are developing have broad applications across the chemical and agricultural sectors. We welcome enquiries from prospective industry and academic collaborators as well as from researchers interested in pursuing a Ph.D. within the following project areas.
Data Driven Process Analysis – Big Data Analytics
In many areas of engineering, the commercial availability of variety sensors based on different measurement techniques has increased due to them becoming progressively cheap and thus affordable. This has led to the collection of vast amounts of data. Most of these data are of disparate types. For example, in a chemical process data collected consists of a large number of qualitative and quantitative measurements taken at different time intervals.
We are investigating cutting edge machine learning techniques for building process models to provide information on the current status of the process in terms of critical product attributes and also to predict the end point conditions so that timely corrective actions can be taken. The handling of disparate data types is also an active area of investigation in our group. We are investigating approaches based on Bayesian principles for combining data from different sources. The goal is to develop data and model fusion techniques that can be applied to upstream and downstream oil and gas processes.
Online monitoring of critical attributes of a process stream
We are engaged in the development of a measurement platform that can measure multiple chemical and physical properties of a process stream. Optical spectroscopy has been our main focus. We are developing a novel fibre-optic probe system that utilises Ultraviolet-Visible-Near Infrared spectroscopy. We are investigating a number of measurement configurations based on spatially and angularly resolved measurements that will enable the simultaneous determination of particle/droplet size and composition of process streams that are slurries or emulsions. The challenge of extracting the required information from these measurements is being investigated through a combination of machine learning techniques and fundamental light propagation models.
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4/11/19 → 1/12/21
21/09/21 → 30/05/24
A study of bacteria adhesion and microbial corrosion on different stainless steels in environment containing Desulfovibrio vulgarisTran, T. T. T., Kannoorpatti, K., Padovan, A. & Thennadil, S., 13 Jan 2021, In: Royal Society Open Science. 8, 1, p. 1-22 22 p., 201577.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile18 Downloads (Pure)
Crase, S., Hall, B. & Thennadil, S., 21 Jul 2021, In: Computers, Materials and Continua. 69, 2, p. 1945-1965 21 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile30 Downloads (Pure)
Effect of pH regulation by sulfate-reducing bacteria on corrosion behaviour of duplex stainless steel 2205 in acidic artificial seawaterTran, T. T. T., Kannoorpatti, K., Padovan, A. & Thennadil, S., 27 Jan 2021, In: Royal Society Open Science. 8, 1, p. 1-13 13 p., 200639.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile11 Downloads (Pure)
Kannoorpatti, K., Padovan, A., Thennadil, S. & Nguyen, K., May 2021, In: PLoS One. 16, 5, p. 1-17 17 p., e0251524.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile15 Downloads (Pure)