We developed a wastewater process model to simulate how energy savings associated with better control of aeration could influence operations at the Calumet water reclamation plants (WRP), one of seven treatment facilities operated by the Metropolitan Water Reclamation District of Greater Chicago. The work is part of a larger effort to evaluate how cyber physical systems (CPS) could be applied at a WRP to decrease energy demand while maintaining effluent quality. CPS could hold promise for great energy savings by combining real-time process control with process simulation. Based on the analysis of historical data, model simulations, and field measurements, the Calumet WRP frequently operates with excess aeration.Our study on WRP responses to three types of scenarios suggests that all the permit requirements can be satisfied using less than half of the average hydraulic residence time, and at least 35% reduction in aeration is possible.

A water reclamation plant (WRP) should, when faced with influent perturbation, be able to rapidly return to effective operations. Perturbations such as storm events, especially long-term successive storm flows, can adversely affect operations. A better understanding of these effects can provide benefits for plant operation, especially in terms of effluent quality and energy efficiency. However, the concept of resilience for a WRP has not been widely studied. In this work we applied the concepts of resistance and recovery time to quantify resilience, and used a WRP simulation model to investigate how different storm flow characteristics and the amount of aeration influence resilience. Increasing storm flowrate and duration lead to decreasing resilience, but for a given flowrate there is a characteristic minimum resilience. Furthermore, there is an aeration threshold; higher aeration rates do not increase resilience. Results suggest that aeration costs could be reduced by as much as 50% while still maintaining the resilience needed to meet effluent quality permit requirements. 

Our study relies on the data that can be collected using a large scale sensor network. To faciliate our study, we deployed a sensor network composed of various sensors to monitor various elements related to water quality. A wireless sensor network (WSN) consists of small-sized and low-powered wireless nodes spreading over a geographical area which can collaborate with each other for control applications. In each application, there is usually a control center from which end users can query on sensory data within the network. The control center, having more computational ability than other wireless nodes, needs to gather sensory data from the network.

In the process of data gathering, data may be compressed within the network to save energy. Data aggregation is a process in which information can be gathered and expressed in a summary form according to some aggregation function such as maximum, and/or sum. Data aggregation introduces a possibility of a new energy or time efficient method to gather data, in contrast to raw data gathering. We study delay efficient data aggregation scheduling in wireless sensor networks. We construct a routing tree and propose two scheduling algorithms that can generate collision-free link schedules for data aggregation. The proposed algorithms are asymptotically optimum on delay in random wireless sensor networks. We evaluate the performances of the proposed algorithms and the simulation results corroborate our theoretical analysis. We also designed real-time data collection schemes that provide end-to-end performance guarantees for periodic queries. We carefully schedule the activities of sensor devices to satisfy multiple heterogeneous queries. For the case of overloaded networks where not all queries can be possibly satisfied, we propose an efficient approximation algorithm to select que...

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