Keynote Speaker 3

Assoc. Prof. Ir. Dr. Norhaliza Abd Wahab

Universiti Teknologi Malaysia

"Process Control"

Biography: Ir. Dr. Norhaliza Abdul Wahab is an Associate Professor in the Faculty of Engineering, Universiti Teknologi Malaysia. She is currently the Director for Control and Mechatronic Engineering at the School of Electrical Engineering. Dr. Norhaliza received her Ph.D in Electrical Engineering majoring in Control System from the University of Strathclyde, United Kingdom in July 2009. She is actively involved in researching and teaching in the field of industrial process and predictive control.

Her expertise is in modelling and control of industrial process including water and wastewater treatment plants. Dr. Norhaliza's primary research area is on process automation for industrial water and wastewater treatment plants using different membrane filtration technologies. Revolution in water and wastewater treatment process automation has been emerging recently. One of her recent works is on the application of artificial intelligent and optimization algorithms for improving the real time control and monitoring of the treatment plants towards energy saving systems.

Topic: "Digitalization in Process Automation and Controls"

Abstract: Today, most of the industries are moving toward a digitalization strategy for better improvement in process unit management. The digitalization in process automation and control give high impact on the role of process control. As seen from digitalization, more improvements in throughput, product quality and reduced processing costs that increase overall profitability. The evolution of process control skills are changing and require more information technology (IT)-related skills. Of course, these changes demand industrial corporations to enhance the capability of their operational technologies as well as IT skills. Process automation and control of water and wastewater can also benefit from this digital technology to improve their performance. Advanced process automation of the treatment system using membrane technology is thus utilized in this work. As technology capabilities advance towards digital water more available data can be easily achieved and this help us leverage augmented intelligence to better interpret larger data scale. Several challenges are present in the process automation related to computational speed due to big data processing and structuring through which artificial intelligent would have great potential once those issues are addressed. The artificial neural networks as a regression model, combined with the gravitational search algorithm, as one of the global optimization techniques are applied. Advances in computers speed along with the significant reduction in their cost have favoured the application of model predictive controllers for processes of commercial interests, enable real time water quality monitoring towards more efficient membrane water automation technologies.