Membrane Bioreactor-based Wastewater Treatment Plant Energy Consumption: Environmental Data Science Modeling and Analysis

Abstract

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.