Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

The work to be performed at Northwestern University (NU) is divided into two sections as part of application (task) 1: 1) Data analytics and development of data-driven control concept for high-rate CS systems; and 2) molecular microbial population monitoring. All work will be performed in close collaboration with DC Water, where pilot- and full-scale testing will be performed, and with Dr. Kris Villez at ONL for support of data-driven control concepts. 1) Data analytics and development of data-driven control setup In coordination with DC Water, NU will compile data for pilot-scale high rate CS pilot scenario evaluations with different MLSS setpoints, including COD, N and P mass balances and settling behavior. Allied datasets will be collected based on historical data collection from full-scale CS and non-CS operation. In coordination with ONL, the datasets will be employed to build a data driven model of settleability, effluent quality, and carbon/ nutrient redirection. The model will then be used to develop a data-driven control system that will be implemented and tested on the pilot-scale high rate CS system. 2) Molecular microbial population monitoring NU will use a molecular fingerprinting approach to evaluate predictability of settling behavior based on amplicon sequencing. Biomass samples from pilot- and full-scale systems will be subject to 16S rRNA gene amplicon sequencing using Illumina MiSeq, and in select samples with alternate rapid sequencing technologies. Molecular signatures will be characterized as a fingerprint of settling regime and potential future signal for settling behavior detection.