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Developing a soft sensor to monitor water quality

Project

Project Details

Program
Applied Mathematics and Computer Science
Field of Study
Applied mathematics
Division
All Divisions
Faculty Lab Link
https://www.kaust.edu.sa/en/study/faculty/peiying-hong

Project Description

Ensuring the performance of wastewater treatment processes is important to guarantee that the final treated wastewater quality is safe for reuse. However, bacterial concentration present along the different stages of treatment process is not easily measured routinely for the plant operators. A moving horizon sensing approach based on neural networks has been proposed in [1] for measuring the bacteria concentration from easy to measure variables. The obtained model has been successfully tested on KAUST wastewater plant. In this project, the student will test and extend the model to other sets of data. A particular interest will be on incorporating self-calibration or self-training strategies to guarantee good performance of the proposed algorithm. [1] Mohammed Alharbi, Pei-Ying Hong, Taous-Meriem Laleg-Kirati. Sliding window neural network based sensing of bacteria in wastewater treatment plants, Journal of process control, Volume 110, February 2022, Pages 35-44

About the Researcher

Peiying Hong
Professor of Environmental Science and Engineering
Biological and Environmental Science and Engineering Division

Affiliations

Education Profile

  • Postdoctoral Fellow, University of Illinois, Urbana-Champaign, 2012
  • Ph.D., National University of Singapore, 2009
  • B.Eng., National University of Singapore, 2004

Research Interests

Professor Honga's research interests include molecular microbiology and microbial aspects in water and wastewater ecosystems. Her research aims to understand the roles and interactions of microorganisms in these ecosystems, and to utilize the insights to solve issues related to water quality and water reuse. Professor Honga's research also looks at the biotic contaminants (e.g. antibiotic resistance genes, mobile genetic elements, pathogens) that are present in the natural and engineered environments.

Selected Publications

  • Hong, P.-Y., X. Li, X. Yang, T. Shinkai, Y., Zhang, X. Wang, and R.I. Mackie (2012) Monitoring airborne biotic contaminants in the indoor environment of pig and poultry confinement buildings. Environmental Microbiology 14: 1420-31
  • Hong, P.-Y., C. Hwang, F. Ling, G.L. Andersen, M.W. LeChevallier, W.-T. Liu (2010) Pyrosequencing analysis of bacterial biofilm communities in water meters of a drinking water system. Applied and Environmental Microbiology 76: 5631-5635
  • Hong, P.-Y., J.-H. Wu, and W.-T. Liu (2009) A high-throughput and quantitative hierarchical oligonucleotide primer extension (HOPE)-based approach to identify sources of fecal contamination in water bodies. Environmental Microbiology 11: 1672-1681
  • Hong, P.-Y., J.-H. Wu, and W.-T. Liu (2008) Relative abundance of Bacteroides spp. in stools and wastewaters as determined by hierarchical oligonucleotide primer extension. Applied and Environmental Microbiology 74: 2882-2892
  • Pang, C.M., P.-Y. Hong, H.-L. Guo, and W.-T. Liu (2005) Biofilm formation characteristics of bacterial isolates retrieved from a reverse osmosis membrane. Environmental Science and Technology 39: 7541-7550

Desired Project Deliverables

We expect the student to test the existing model on a new dataset. Various neural network configurations will be also tested and compared.
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