Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Mechanical measurement system
Reexamination Certificate
2002-03-08
2004-10-19
Nghiem, Michael (Department: 2863)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Mechanical measurement system
Reexamination Certificate
active
06807494
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to sewage flow monitoring systems. More particularly, the present invention relates to a method and system of monitoring the flow of a fluid substance to detect flow loss based on a predicted flow volume.
2. Description of the Related Art
Fluid flows in pipes and open channels are common in numerous industrial, commercial, municipal, and residential systems. Proper and efficient operation of these systems, and meaningful planning for future expansion and maintenance of such systems, depends upon accurate measurement of the flow thatpasses through such systems. Sewer systems, such as municipal sanitary sewer systems, are an example of one system in which accurate flow measurement is critical.
Many sewer flow measuring devices operate by detecting both the depth of flow in a channel or pipe and the velocity of the flow in the same location of channel of pipe. The data is collected at periodic sampling times and is used to calculate a flow rate. Examples of such flow measurement devices are disclosed in U.S. Pat. No. 4,397,191, to Forden; U.S. Pat. No. 4,630,474, to Petroff; and U.S. Pat. No. 5,198,989, to Petroff, each of which is incorporated herein by reference in its entirety. In the wastewater industry, real-time detection of problem events and accurate prediction of future system operation have become increasingly important. Real-time detection of system problems, such as leaks or system breaks, sanitary sewer overflows, and system blockages, allows system managers to quickly respond to such problems. With a rapid response, system managers can prevent or minimize unwanted incidents such as basement back-ups or sewage in waterways that may result from system overflows or breaks. For example, with early detection of a system blockage, managers could respond to and clear the blockage or repair the pipe before it causes an overflow or a buildup of pressure within the system resulting in a break or leak. Further, if an overflow occurs, such as may happen during a storm event, system managers can take action to redirect the flow to other channels within the system in order to reduce or eliminate the overflow condition.
Further, a system with predictive capabilities could allow managers to stop overflows before they occur, to more effectively use existing system features, and identify and plan for required system expansions.
Conventional monitoring systems have exhibited several problems. The conventional systems are limited to reporting of data and basic alarming. Such systems do not reliably validate, in real time, monitored data. Further, alarm conditions are typically triggered based on predetermined levels, and the monitoring systems are susceptible to false alarms during storm conditions, holidays, and other unusual events that are not necessarily reflective of a sewer system problem. Further, the conventional monitoring systems lack reliable predictive capabilities for predicting flow at various points in a sewer system.
Accordingly, it is desirable to provide an improved method and system for monitoring flow in a sewer system.
SUMMARY OF THE INVENTION
It is therefore a feature and advantage of the present invention to provide an improved flow monitoring method and system.
In accordance with one embodiment of the present invention, a method of monitoring and analyzing flow in a sewer system includes the steps of using a monitoring assembly to collect data representative of actual flow volume of a fluid substance in a first location such as a sewer pipe, storing the actual flow volume data in a memory, maintaining previously stored data in the memory, determining a predicted flow volume and comparing the actual flow volume wit the predicted flow volume to yield a difference value. The predicted flow volume is dependent on the data selected from the previously stored data and a day and time that corresponds to both the actual flow volume data and the data selected from the previously stored data. Optionally, the predicted flow volume may also be dependent upon additional data corresponding to a rain event.
In situations where the difference value exceeds a predetermined variance value, the method may further include the step of issuing a flow loss notification. If the difference value does not exceed a predetermined variance value, the method may also include storing the actual flow volume in the memory as stored calibration data. As additional options, the method may include the additional step of transmitting the flow velocity data, depth data, and/or the actual flow volume over a data network such as the Internet to a computing device. Also optionally, the actual flow volume may be a rolling average flow volume.
As additional options, at least one of the determining step and the comparing step may be performed by the monitoring assembly. In the alternative, the determining step and/or the comparing step may be performed by the computing device. As a further option, the method may include the additional step of validating the data representative of flow velocity and depth. In such a case, the validating step may optionally be performed by the monitoring assembly. In addition, the data representative of actual flow volume may include at least one of flow velocity data and depth data, and the method may include calculating the flow volume based upon such data.
In accordance with an additional embodiment of the present invention, a flow monitoring system includes a first monitoring assembly having at least one sensor. The sensor operates to collect data representative of actual flow volume at a first location. The system also includes a processor and a memory. The memory operates to store the data representative of flow volume as well as a detection time associated with the data. The system also includes a central computing device in communications with the first monitoring assembly. The processor is trained to compare the actual flow volume with a predicted flow volume to yield a difference value. The predicted flow volume is dependent on the data stored in the memory and the detection time associated with such data.
Optionally, the processor is further trained to issue a notification if the difference value exceeds a predetermined variance value. Also, the data representative of actual flow volume may include depth data and/or velocity data, and the processor would be further trained to calculate the actual flow volume corresponding to such data.
As an additional option, the processor may be integral with the first monitoring assembly. As an alternative option, the processor may be integral with the central computing device.
Also in accordance with this embodiment, a first monitoring assembly may optionally be capable of validating the flow velocity in depth. As an additional option, the system may include a second monitoring assembly that has a means for detecting the quantity of rain at a location during a period of time, such as a rain gauge, a weather service, or even a weather web site. Further, the central computing device may be trained to predict an anticipated flow velocity, depth, and/or flow volume of the fluid substance at a second location.
REFERENCES:
patent: 4211111 (1980-07-01), Petroff
patent: 4397191 (1983-08-01), Forden
patent: 4630474 (1986-12-01), Petroff
patent: 5198989 (1993-03-01), Petroff
patent: 5821427 (1998-10-01), Byrd
patent: 5942698 (1999-08-01), Stevens
patent: 2002/0156590 (2002-10-01), Schutzbach et al.
patent: 2002/0170350 (2002-11-01), Schutbach
patent: 3720345 (1988-12-01), None
patent: 4333095 (1995-03-01), None
patent: 0 105 229 (1984-04-01), None
English Abstract for EP 0105229 for Heidi Gerhard, Apr. 11, 1984, p. 1, esp@cenet database-12.
Schutzbach James S.
Stevens Patrick L.
ADS Corporation
Gromada James A.
Katten Muchin Zavis & Rosenman
Nghiem Michael
Villacorta Gilberto M.
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