| Summary: | Wastewater Treatment Plants (WWTPs) are sophisticated systems that have to
sustain long-term qualified performance, regardless of temporally volatile volumes
or compositions of the incoming wastewater. Membrane filtration in the Membrane
Bioreactors (MBRs) reduces the WWTPs footprint and produces effluents of proper
quality. The energy or electric power consumption of the WWTPs, mainly from
aeration equipment and pumping, is directly linked to greenhouse gas emission and
economic input. Biological treatment requires oxygen from aeration to perform
aerobic decomposition of aquatic pollutants, while pumping consumes energy to
overcome friction in the channels, piping systems, and membrane filtration.
In this thesis, we researched full-scale WWTPs Influent Conditions (ICs) monitoring
and forecasting models to facilitate the energy consumption budgeting and raise early
alarms when facing latent abnormal events. Accurate and efficient forecasts of ICs
could avoid unexpected system disruption, maintain steady product quality, support
efficient downstream processes, improve reliability and save energy. We carried out a
numerical study of bioreactor microbial ecology for MBRs microbial communities
to identify indicator species and typical working conditions that would assist in
reactor status confirmation and support energy consumption budgeting. To quantify
membrane fouling and cleaning effects at various scales, we proposed quantitative
methods based on Matern covariances to analyze biofouling layer thickness and roughness
obtained from Optical Coherence Tomography (OCT) images taken from gravitydriven
MBRs under various working conditions. Such methods would support practitioners
to design suitable data-driven process operation or replacement cycles and lead to
quantified WWTPs monitoring and energy saving.
For future research, we would investigate data from other full-scale water or
wastewater treatment process with higher sampling frequency and apply kernel machine
learning techniques for process global monitoring. The forecasting models would
be incorporated into optimization scenarios to support data-driven decision-making.
Samples from more MBRs would be considered to gather information of microbial
community structures and corresponding oxygen-energy consumption in various working
conditions. We would investigate the relationship between pressure drop and spatial
roughness measures. Anisotropic Matern covariance related metrics would be adopted
to quantify the directional effects under various operation and cleaning working
conditions.
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