Saliza Ramli

Work place: Department of Electrical Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia



Research Interests: Bioinformatics, Computational Biology


Saliza Ramli holds a B. Eng in Electrical and Electronic from Akita University, Japan and M.Sc in Information and Telecommunication Engineering from Universiti Teknologi MARA, Malaysia. She is currently a fellow at Universiti Teknikal Malaysia Melaka, Malaysia. Her research interest is signal processing in biomedical application.

Author Articles
Physical and Soft Sensor Technologies for Wastewater Quality Management

By Nor Hana Mamat Saliza Ramli Nor Arymaswati Abdullah Samia Khan Chandima Gomes

DOI:, Pub. Date: 8 Nov. 2018

Physical sensors are used mostly to detect sludge and odour in wastewater. Black box modelling or data-derived model using the correlation of input-output parameters is the preferred method as we have assessed. This is due to the non-complex approach of such models as opposed to model-driven, mechanistic models. The latter is hard to be adopted for soft-sensor development due to the inherent complexities and uncertainties. The commonest methods for soft sensor model development are ANN and ANFIS. Many other improvements of these methods are achieved by combining with other techniques to enhance the prediction performance of the soft sensors. Accuracy and precision of data collected for soft sensor modelling has become a vital concern at present to ensure the reliability of wastewater quality indices predicted by the soft sensors. Reduction of the level of reliability of the sensor system in monitoring and controlling of WWTPs would lead to serious lapses in the wastewater quality management. In this backdrop we recommend SEVA soft sensor as one of the best potential solutions which could be offered by the existing technologies.

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