Achieving cost savings in wastewater treatment using a comprehensive decision support tool for sensor technology selection
Water/Energy Production Nexus
Corina Carpentier, Benten Water
Judith Herschell Cole, Herschell Environmental, LLC
INTRODUCTION:
Increasingly, municipalities are implementing smart systems that collect actionable data and are building the interconnectivity of the IoT into their treatment systems. Sensors are collecting data and incorporating it into a framework that integrates multi-dimensional analysis. A platform that allows the selection of the best sensor technology for the application and that meets the goals of management was used to optimize operations and cost savings at the facilities analyzed in this study.
PROJECT DESCRIPTION:
This project provides a comparative analysis of municipal wastewater treatment facilities with optimized sensor technologies. In this analysis, the optimization was achieved through the use of an interactive platform that innovatively incorporates a database that contains thousands of sensors. This abstract highlights two of the facilities that have achieved cost savings from implementing a sensor selection tool that guides management in their decision-making process to the sensor technology that offers the most benefit to the system cost and operation
System 1:
Management at the first facility implemented dissolved oxygen sensors to enable sensor-controlled aeration of the active sludge treatment step instead of following a time-controlled regime. As a result, the number of blower hours necessary to adequately aerate the treatment process could be reduced significantly, leading to a savings of more than 20 percent in energy costs. The process by which this savings was accomplished will be detailed.
Data from the Wijer wastewater treatment plant in Belgium is shown in Figure 1. Management at this facility implemented dissolved oxygen sensors to enable sensor-controlled aeration of the active sludge treatment step instead of following a time-controlled regime. Whenever dissolved oxygen levels dropped below a certain threshold value, the blowers were started to increase the amount of oxygen for the bacterial treatment process. As a result, the number of blower hours necessary to adequately aerate the treatment process could be reduced significantly, leading to a savings of more than 20 percent in energy costs. The aeration in the activated sludge process can be responsible for up to 60 percent of the total plant energy consumption (Source: EPA.gov). The energy savings that can be achieved by improved DO control depends on the plant loading characteristics, plant configuration, and the level of instrumentation.
Figure 1: Wijer Wastewater Treatment Plant in Belgium (Image credit: Optimedar EU eco-innovation project, Adasa, Spain)
Conventional fixed set-points for dissolved oxygen control provide effective aeration of activated sludge units, but more sophisticated advanced process control which uses inputs from other sensor measurements offers additional benefits such as reduced energy and chemical consumption and hence carbon emission as well as improved plant stability and a more constant effluent quality. In this application, an ammonia monitor is used as part of the feed-back control to adjust the dissolved oxygen set-point and hence the rate of aeration.
Reliable and robust dissolved oxygen measurement is fundamental to aeration control and optimization of the energy demanding activated sludge process used to treat wastewater. Poor dissolved oxygen measurement results in poor aeration control leading to variable treatment and excessive energy use. When applied over a whole site, even relatively small errors in instrumentation readings, resulting from poor calibration, instrument drift or fouling, can add tens of thousands of euros per year to treatment costs through excess energy use for aeration.
Dissolved oxygen measurement has benefited from a genuine advance in technology in the past years, and there is now a generation of sensors using optical techniques which potentially offer better performance and lower maintenance than the traditional electrochemical technology.
The system uses advanced process control (APC) to evaluate energy usage, chemical consumption, carbon emissions, stability of plant operation, and the quality of the treated water or wastewater. The three common APC approaches are combined: conventional feedforward and feedback control incorporating a process model based on an established IWAPRC model, control which utilizes a predictive model of the plant built from actual observation of the plant behavior over a representative period, and an empirical rule-based system of control, which adjusts the dissolved oxygen set-point according to the ammonia load using a look-up table.
Figure 2: Utrecht Wastewater Treatment Plant in The Netherlands (Image credit: HDSR, NL)
System 2:
The second system analyzed is the Utrecht wastewater treatment plant. Data from this system is shown in Figure 2. Sensor technology was implemented to better control alum and iron dosing, in order to achieve a more stable effluent quality from which valuable resources such as phosphorus can be recovered. Figure 2 shows that the implementation of P-sensors and a sensor controlled dosing regimen of metal salts significantly reduced the effluent variability.
Due to the improved chemical dosing, a return on the investment was realized in less than two (2) years, as a cost savings of US$220,000 was realized over this time period. This savings was from a combination of using less chemicals, an annual savings of US$75,000, and reduced cost of waste disposal, an annual savings of US$36,600. The reduction of chemicals added in the process allowed more efficient dewatering of the sludge. Thereby, resulting in a significant reduction in sludge disposal costs.
CONCLUSIONS:
These facilities benefitted, financially and operationally, from the use of information provided by the decision support tool for sensor selection due to its insight into the procurement, implementation, and operational costs, information on actual user-experience and case studies which demonstrate process optimization. The searchable database considers approximately 70 water quality parameters and offers integrated information on over 10,000 instruments and nearly 300 manufacturers.