The critical component of water and waste treatment processing is the common architecture of electric motors and controls. In the United States, the treatment process is driven by electric motor-driven devices, including energy-intensive pumps.
Studies and publications from the past ten years demonstrate that the energy efficiency of Wastewater Treatment Plants is unsatisfactory.
As much as 40 percent of operating costs for drinking water systems are tied to energy. For many municipal governments managing these processes, drinking water and wastewater plants typically are the largest energy consumers, accounting for 30 to 40 percent of total energy consumed and significant annual cost.
In addition, the operation of wastewater treatment plants results in direct emissions from the biological processes of greenhouse gases (GHG) such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), as well as indirect emissions resulting from energy generation. How can the process become predictively more efficient, effectively lower cost, and evolve into an eco-friendlier enterprise? This puzzle can be solved with data analytics.
Using Predictive Analytics to Streamline Cost and Efficiency
The overall performance of the water treatment process depends on the performance of its pumps. All pumps have four common performance characteristics: capacity, head, power, and overall efficiency that provide numerous data points to gather and model. Head is the energy supplied to the wastewater per unit weight, typically expressed as feet of water. Power is the energy consumed by a pump per unit time, typically measured as kilowatt-hours. Efficiency reflects the pump’s relative power losses. Well-designed pump systems operate at 75 to 85 percent efficiency most of the time. (“Collection System Study Time (Discussion) – Pumps”)
The overall pump efficiency is highly dependent on the type of pumps, their control system, and the fluctuation of the influent wastewater flow. The need for a comprehensive energy-water optimization tool to understand how fundamental water parameters influence the energy demand and identify the best energy-saving solutions on a single platform is obvious.
Of note, engineers and operators have been essential onsite.
Performance anomalies in water and waste processes can become burdensome when they increase costs and decrease the quality of a finished product. It was believed that operators needed to be onsite to remediate anomalies as quickly as possible. Then, the COVID-19 pandemic shifted the world’s workforce to off-site or hybrid work. While essential employees remained, the loss of employees in a plant and lack of advanced analytical tools led to more digitalization efforts. Machine Learning Techniques represent the most innovative approach to reducing the energy demand of wastewater treatment plants.
IOT and Automation Driving Cost Savings
Industrial analytics, IoT, and automation have become more critical to the water and wastewater industries. Pumps used at treatment plants are the most energy-consuming equipment, whose optimization can save 5-30% of the total energy demand. Further, these pumps are very expensive, typically costing more than $5,000 per pump. The loss of a pump is not only costly, but it also puts the facility at risk of violating regulations. Even though permit violation costs are often budgeted, facilities prefer to avoid such fines. Protecting pumps from damage and optimizing performance saves money and protects the environment. Those water authorities with vision are looking at how digital solutions enable data strategies, leading to modeling and predictive, decision-dashboard tools for optimizing pump switching and maintenance schedules to achieve energy efficiency and predictive cleaning and maintenance schedules.
Another area of opportunity in some wastewater facilities is in the second stage of treatment, a biological process that is required to remove any suspended and dissolved solids. The method uses microorganisms that are oxygen-dependent. If oxygen is too low, the microorganisms will perish; however, regulating a stable amount of oxygen is complex. Conversely, if the dissolved oxygen level gets too high, energy is wasted, and expensive aeration equipment undergoes unnecessary usage. Improving the energy efficiency of the aeration process can reduce carbon emissions between 10-30%*and up to 40%* with machine learning control strategies.
Leveraging your Data for Operational Reliability and Affordability
The use of analytics, automation, IoT, and self-analyzation trends can help control water and wastewater issues. By harnessing and organizing the data, custom decision tools can be developed to monitor power, pressure metrics, and flow switches to prevent dry-run conditions. In addition, the same tools can be used to extend the life of the pumps, reveal cost savings from energy efficiencies, and avoid non-compliance with regulatory limits. Most importantly, operators can better protect our sensitive environment.