System and method for efficient operations of wastewater treatment plants through monitoring, modeling, prediction and optimization
Publication Date: 2014-Jun-25
The IP.com Prior Art Database
A description of a system that enables comprehensive, end-to-end operational monitoring and management of wastewater treatment plants, plant based on the following main principles: 1) Data consolidation and cleansing: Combining all of the data sources relevant to the operations of the plant onto a unified platform, and providing a unified view of the data to the user. 2) Dynamic plant model: Combining the data with a dynamic model of the plant to provide a full plant view. I.e., augmenting the available data sources with information from the model that is otherwise difficult to measure, in order to provide a true picture of the status of the plant. 3) Forecasting: Adding forecasting algorithms to predict the future load coming into the plant, and together with the dynamic plant model, provide capabilities such as: o Predicting the future behavior of the plant under the predicted load. o Providing alerts regarding the possible occurrence of future undesirable events. 4) Optimization: Adding mathematical optimization to optimize the day-to-day operations of the plant, providing recommendations on how to reduce cost of operation while mitigating/avoiding future undesirable events.
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System and method for efficient operations of wastewater treatment plants through monitoring,
Wastewater treatment plants (WWTPs) around the world are dealing with increasing pressures to reduce costs, coupled with increasingly stringent regulatory requirements regarding the treated water (effluent) and treated solid waste (sludge or biosolids. WWTPs treat wastewater through a set of complex biological, chemical and physical processes that require chemicals, large amounts of energy, and entail significant costs for the disposal of the sludge. This challenging environment leads to a very strong desire and/or need by many WWTPs to significantly increase the end-to-end efficiency and reliability of their operations. However, there are several major impediments to any effort aimed at increasing the efficiency of WWTPs and the quality of the outputs:
·The data required to understand the status of the wastewater treatment process originates from multiple sources, and is not always readily available. For example, while some sources of data, such as sources of relevant data include online sensors are available in near real time, other sources of data, such as lab tests, may take several hours or several days to process, and there are also sources, such as visual analysis through a microscope, that yield information which is more qualitative then quantitative. Finally, relevant sources may also include weather forecasts and other measurements from sources outside the plant. This means that at any given point in time, the plant operator may not even have access to all the data relevant to that time point (e.g., some information may only be provided by a lab test several hours or days in the future).
The complexity of the biological, physical and chemical processes required to treat wastewater makes them difficult to understand and control.
Some of the quantities relevant to the status of the wastewater treatment process cannot be measured, but can only be estimated based on mathematical models. However, such models are currently not incorporated into the operating centers of WWTPs, and are mostly used for design decisions.
As a result, plants are currently operated in a conservative risk averse inefficient mode, without the ability to quantify the risk, or truly minimize the costs. Moreover, such a mode of operations make it difficult to respond not only to anomalous events such as large storms, but even to daily and weekly
prediction and optimization
prediction and optimization
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standard inflow fluctuations. It is also difficult for plants to
take advantage of electricity cost changes during peak/off-peak hours.
Existing solutions only provide partial monitoring and operational control capabilities. Examples are:
· SCADA systems: These are IT system that provide consolidation of sensor data, reporting capabilities and the ability to remotely change equipment setting.
· Monitoring solutions for specif...