Soft Sensing for On-Line Fault Detection of Ammonium Sensors in Water Resource Recovery Facilities.

The increasing demand for online sensors applied to advanced control strategies in water resource recovery facilities has resulted in the increasing investigation of fault-detection methods to improve the reliability of sensors installed in harsh environments. The study herein focuses on the fault detection of ammonium sensors, especially for effluent monitoring, given their potential in ammonium-based aeration control applications. An artificial neural network model was built to predict the ammonium content in the effluent by employing the information from five other sensors installed in the activated sludge tank: NH4+, pH, ORP, DO, and TSS. The residual between the model prediction and the effluent ammonium sensor signal was utilized in a fault-detection mechanism based on principal component analysis and Shewhart monitoring charts. In contrast to previous studies, the present work utilizes typical faults collected from a 1 year historic dataset of an actual sensor setup. Treatment process anomalies, calibration bias faults, and fouling drifts were the most common issues identified from the historic dataset, and they were promptly identified by the proposed fault-detection methodology. Once a fault is detected, the model prediction can be actively used in place of the sensor for process control without affecting the treatment process by utilizing faulty datasets.

Dynamic load shifting for the abatement of GHG emissions, power demand, energy use, and costs in metropolitan hybrid wastewater treatment systems.

The installation of satellite water resource recovery facilities (WRRFs) has strengthened the ability to provide cheap and reliable recycled water to meet the increasing water demand of expanding cities. As a result, recent studies have attempted to address the problem of how to optimally integrate satellite systems with other sectors of the urban sphere, such as the local economy, the power supply, and the regional carbon footprint. However, such studies are merely based on the spatial domain, thus neglecting potential time-dependent strategies that could further improve the sustainability of metropolitan water systems. Therefore, in this study a new conceptual framework is proposed for the dynamic management of hybrid systems comprised of both centralized and satellite WRRFs. Furthermore, a novel set of integrated real-time control (RTC) strategies are considered to analyze three different scenarios: 1) demand response, 2) flow equalization of the centralized WRRF and 3) reduction of greenhouse gas emissions. Data from a case study in California is used to develop an integrated dynamic model of a system of 8 facilities. Our results show that by dynamically shifting the dry-weather influent wastewater flows between hydraulically connected WRRFs, a reduction in power demand (up to 25%), energy use (4%), operating costs (8.5%) and indirect carbon emissions (4.5%) can be achieved. Therefore, this study suggests that a certain degree of hydraulic interconnection coupled with dynamic load-shifting strategies, can broaden the operational flexibility and overall sustainability of hybrid WRRF systems.

Relationship between manual air valve positioning, water quality and energy usage in activated sludge processes.

Diffused aeration is the most implemented method for oxygen transfer in municipal activated sludge systems and governs the economics of the entire treatment process. Empirical observations are typically used to regulate airflow distribution through the adjustment of manual valves. However, due to the associated degrees of freedom, the identification of a combination of manual valves that optimizes all performance criteria is a complex task. For the first time a multi-criteria optimization algorithm was used to minimize effluent constituents and energy use by parametrizing manual valves positions. Data from a full-scale facility in conjunction with specific model assumptions were used to develop a base-case facility consisting of a detailed air supply model, a bio-kinetic model and a clarification model. Compared to the base-case condition, trade-offs analysis showed potential energy savings of up to 13.6% and improvement of effluent quality for NH4+ (up to 68.5%) and NOx (up to 81.6%). Based on two different tariff structures of a local power utility, maximum costs savings of 12800 USD mo-1 to 19000 USD mo-1 were estimated compared to baseline condition.