Disclosed embodiments relate to pump load sharing for parallel connected pumps.
Some industrial facilities operate a plurality of pumps in parallel. For example, in refining industries, tank to tank, tank to ship, and pipeline movement transfer all involve a plurality of pumps in parallel which requires some pump load share management to determine which pumps are to be running at any given time. As known in the art, pump selection is performed by grouping with respect to pump capacity and the flow demand supported.
This Summary is provided to introduce a brief selection of disclosed concepts in a simplified form that are further described below in the Detailed Description including the drawings provided. This Summary is not intended to limit the claimed subject matter's scope.
Disclosed embodiments recognize there is a large amount of pump data generally available at the process controller (e.g., Distributed Control System (DCS) or a Programmable Logic Controller (PLC)). However, known pump management systems use direct sequential pump control methods which only utilize a minimal of pump data (e.g., only pump flow capacity (PC)) for selecting the pumps to be on or off responsive to a flow demand, and thus always operate over time using the same pump sequence resulting the need for more pump maintenance of pumps and more pump downtime.
Disclosed dynamic pump selection uses a new form of pump selection which selects the pumps and balances the usage of the pumps by using a dynamic priority number (DPN) for each pump which is dynamically calculated from the PC as well as operational data regarding a plurality of other pump parameters. The DPNs are calculated for each pump with currently available pump data, and the DPNs are dynamically calculated when the pump data is changed or updated. Flow is the parameter for pump demand when the flow demand is getting changed, and the respective pumps will be started or stopped based on DPN values to balance the flow demand. Disclosed dynamic pump selection has been found to improve the pump efficiency and reduce the maintenance cost, thus improving site efficiency (see the Examples section described below).
One disclosed embodiment comprises a method of pump selection for a parallel connected plurality pumps. A DPN is calculated using pump data regarding a plurality of pump parameters for each of the pumps. The DPNs are dynamically updated when at least one of the pump parameters changes. The DPNs are used together with a current pump demand to dynamically select which pumps are to be turned on or off, and the pumps are commanded to implement the dynamic selections.
Disclosed embodiments are described with reference to the attached figures, wherein like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale and they are provided merely to illustrate certain disclosed aspects. Several disclosed aspects are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the disclosed embodiments.
One having ordinary skill in the relevant art, however, will readily recognize that the subject matter disclosed herein can be practiced without one or more of the specific details or with other methods. In other instances, well-known structures or operations are not shown in detail to avoid obscuring certain aspects. This Disclosure is not limited by the illustrated ordering of acts or events, as some acts may occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the embodiments disclosed herein.
Disclosed DPN-based pump selection control utilizes known current flow demand, but adds pump operational data as additional data inputs in generating DPN values. Pump groups are optionally used with disclosed embodiments which are generally grouped as small, medium and large pump groups with respect to the pump's flow and pumping capacity. (See
The pump operational data can comprise the following DPN parameters: Total run time (RT) is the total time the pump is used in the plant and it also includes maintenance runtime. Pump flow capacity-(PC) is the maximum flow rate support by pump for all the product used. The age of the pump (AP) is the total time from new pump installation. The last maintenance history (MH) is the number of times the pump is taken for maintenance. The last run state (IRS) is used to find pumps used in last transfer, shipment sequence. The pump idle time (PIT) is used to find the total non-run time of the pump from last run.
Optional DPN parameters include pump mode (service/out of service)-(PM) which is the current pump state. Pump state (auto/manual)-(PS) is the state used to control from remote pump logic or manual operation. The PM and PS can be set as constant values.
Pumping control system 200 is shown having a communication interface 260 that couples the process controller 220 to an asset management system 270. The communication interface 260 can be used to transfer the pump data from the asset management system 270 to the process controller 220 if DB 230 is not provided. Communication interface 260 can comprise Ethernet such as Fault Tolerant Ethernet (FTE), Modbus, Fieldbus, and the asset management system 270 can comprise control system and field assets.
In this embodiment in
The calculating of the DPNs generally comprises using a DPN equation. For example, in one particular embodiment the DPN equation can comprise:
DPN=(PEM×PC×PS×PM×PIT)/(TRT×AP×LMH):
wherein PEM is a Pump Energy Management Factor, PC is a Pump Flow Capacity, PS is a Pump state, PM is a Pump Mode, PIT is a Pump Idle Time, TRT is a Total run Time, AP is an age of the Pump, and LMH is a Last Maintenance History. Although not shown, coefficients can be added to change the weights of the respective parameters in the DPN equation. Moreover, other parameters may be added. When at least one of the pump parameters changes, then the DPNs are typically dynamically updated in real-time.
Step 403 comprises using the DPNs together with a current pump demand to dynamically select which of the pumps are to be turned on or be turned off. The current pump demand can comprise flow demand or pressure demand. Step 404 comprises commanding the pumps to implement the dynamic selections in step 403, generally by sending control signals to an actuator at each pump.
The plurality of pumps can be in an industrial facility comprising a refinery tank farm, a storage tank farm, a terminal tank farm, or can be involved in pipeline transfers. As noted above the pump data can be obtained from a database in a memory associated with an asset management system that can be cloud-based. Refining industries, tank-to-tank, tank-to-ship, and pipeline movement transfer are all examples that involve pump control that can benefit from disclosed embodiments.
Disclosed embodiments are further illustrated by the following specific Examples, which should not be construed as limiting the scope or content of this Disclosure in any way.
In a plant that fills liquid petroleum product in multiple trucks from a storage tank, when the first truck filling starts assume the flow demand is 1500 m3 then the pump with same or almost equal capacity pump will be started with DPN validation, so that the pumps selected and started will be those having the highest DPN number. Assume when subsequent truck filling starts the flow demand will increase from 1,500 m3 to a required flow of 2,500 m3 and accordingly the next pump will be started in sequence with respect to DPN validation (the pump with the next highest DPN number). When the truck filling has stopped and then flow demand decreases, the pumps will be stopped in sequence with the pumps currently on with the lower DPN values being turned off first.
A case study was performed. To demonstrate advantages of disclosed DPN-based pump selection it was considered the below data for 10 pumps and derived DPN values for each of the pumps. The following pump data was used to derive the DPNs with the DPN equation described above and shown again below.
DPN=(PEM×PC×PS×PM×PIT)/(TRT×AP×LMH)
Using current values for each of the above parameters and the DPN equation above DPN values were calculated for each pump shown as pump 1 to pump 10 in
For known pump selection the pumps are always started and stopped in same sequence so that the pump start/stop sequence is always constant from Pump 1 to Pump 10. At initial pump demand pump 1 is started and subsequent pump demand with respect to flow demand the pumps are started in same sequence.
In contrast, by using disclosed DPNs for pump control the pump start/stop sequence are controlled with currently calculated DPN numbers, and also the sequence of pump start/stop is not constant because it varies with actual pump data, which helps in improve pump usages, reduce the frequency of pump maintenance, and improves the plant efficacy. The pump parameters used to calculate the DPNs as described above can be obtained from any data interface, such as an asset management system, field inputs, database interface, or cloud data interface.
As shown by the highlight in in
With reference now to
Disclosed embodiments can be applied to generally to systems having a plurality of pumps connected in parallel which requires some pump load share management to determine which pumps to select to be running at any given time. For example, refining industries, tank to tank, tank to ship, and pipeline movement transfer.
While various disclosed embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. Numerous changes to the subject matter disclosed herein can be made in accordance with this Disclosure without departing from the spirit or scope of this Disclosure. For example, although described for pumps may be applied to multi-evaporation group air conditioning systems, and other types of systems. In addition, while a particular feature may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.
As will be appreciated by one skilled in the art, the subject matter disclosed herein may be embodied as a system, method or computer program product. Accordingly, this Disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, this Disclosure may take the form of a computer program product embodied in any tangible medium of expression having computer usable program code embodied in the medium.
Number | Name | Date | Kind |
---|---|---|---|
3453962 | Strader | Jul 1969 | A |
3744932 | Prevett | Jul 1973 | A |
4805118 | Rishel | Feb 1989 | A |
5742500 | Irvin | Apr 1998 | A |
6250894 | Dyer et al. | Jun 2001 | B1 |
7010393 | Mirsky | Mar 2006 | B2 |
7143016 | Discenzo et al. | Nov 2006 | B1 |
7195462 | Nybo | Mar 2007 | B2 |
8328523 | Kernan | Dec 2012 | B2 |
20090020173 | Lau | Jan 2009 | A1 |
20140180485 | Stavale | Jun 2014 | A1 |
20150252814 | Nakahara | Sep 2015 | A1 |
Entry |
---|
Anthony E. Stavale, et al., “Development of a Smart Pumping System”, Proceedings of the 18th International Pump Users Symposium, Houston, TX, Mar. 5-8, 2001, pp. 67-76. |
Number | Date | Country | |
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20180283390 A1 | Oct 2018 | US |