CN102930019A - Type selection method and sample database of fans - Google Patents
Type selection method and sample database of fans Download PDFInfo
- Publication number
- CN102930019A CN102930019A CN2012104316131A CN201210431613A CN102930019A CN 102930019 A CN102930019 A CN 102930019A CN 2012104316131 A CN2012104316131 A CN 2012104316131A CN 201210431613 A CN201210431613 A CN 201210431613A CN 102930019 A CN102930019 A CN 102930019A
- Authority
- CN
- China
- Prior art keywords
- fan blower
- fan
- type selecting
- units
- fans
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention relates to an optimization type selection method, a sample database and a system of fans in sewage treatment plant design. The method includes step1 calculating air supply quantity and required pressure in the largest flow time section, the average flow time section and the smaller flow time section of an activated sludge system, step2 selecting type selection accuracy of the fans and maximum number of working platforms; step3 preferably selecting fan types meeting design requirements of the three flow time sections and the number and the running state of the working platforms in each time section; step4 returning to execute the step2 and reducing the type selection accuracy of the fans or determining the maximum number of the working platforms again if fans meeting the type accuracy requirement cannot be selected; and step5 queuing the fans according to energy consumption if the types of the selected fans are larger than 1 and providing the most energy-saving fan and a running combination of the fan for a user to achieve the aims of saving energy and improving working efficiency.
Description
Technical field
The present invention relates to fan blower optimized Selection method in a kind of Designing Sewage Treatment Plant, definitely
Say a kind of fan blower selection method and sample database.
Background technology
In current technical field of sewage, activated sludge process is one of most widely used technology.Adopt most blast aeration techniques that adopt in the sewage treatment plant of activated sludge process at home and abroad.The fan blower energy consumption accounts for the over half of whole Sewage Plant total energy consumption, is the higher main cause of activated sludge process energy consumption.At present, when carrying out the fan blower type selecting, the designer is by consulting design manual and the fan blower sample carries out artificial type selecting.Because selected data is not necessarily perfect, and manually looks into the chart type selecting and certainly exist the reasons such as error, cause the model of selected fan blower not necessarily accurately rationally, do not reach best energy-saving effect, this also is to cause one of higher reason of fan blower energy consumption.In order to improve the type selecting precision of fan blower, set up complete fan blower technical information database and a kind of Intelligent Model Selection software of exploitation, be extremely important for improving fan blower type selecting precision and design efficiency, reduction energy consumption.
Summary of the invention
The invention provides a kind of fan blower selection method and sample database, adopting computing machine to carry out the fan blower optimized Selection must be according to comparatively comprehensive fan blower sample database, this systematic collection fan blower model sample commonly used in more than 1,000 kind of sewage treatment process, institute's building database has comprised the main technical parameters of fan blower, such as flow, pressure, shaft power, bore, rotating speed, power of motor etc.The needs of New blower being selected for satisfying the user, this database is provided with the functions such as interpolation, modification, deletion, inquiry, makes things convenient for the user that database is in time safeguarded and upgrades.
Set up fan blower optimized Selection model according to " Code for design of outdoor sewerage engineering ", " water supply and drainage design manual " and the actual operating position of engineering, satisfy following three standards according to fan blower model and the combination of operation number of units that this model is selected from database:
1. satisfy the requirement of required air demand of maximum flow period and required pressure;
2. by adjusting fan blower operation number of units and other flow regulation modes, satisfy average and than the requirement of required air demand of low discharge period and required pressure;
3. maximum, on average, less three flow period fan blowers are all in efficient district's work.
The flow of fan blower and pressure are difficult to just coincide with required air demand and blast, and is general all greater than required numerical value.The flow of the fan blower of selecting and pressure and desirable value more approach, and just more reduce investment outlay and operating cost.In order to improve the energy-saving effect of fan blower type selecting, the parameter of type selecting precision has been founded by this system.Improve the type selecting precision, both can improve energy-saving effect; Reduce the type selecting precision, relax exactly the type selecting standard, can select more fan blower model and select voluntarily for the user.
The basic procedure of fan blower type selecting is: step 1. calculated activity sludge system aeration tank is maximum, on average, air demand and the required pressure of less three flow periods; Step 2. is selected fan blower type selecting precision and the number of units of working at most; Step 3. optimizes the fan blower model of simultaneously satisfied three flow period designing requirements and work number of units and the running status of day part from database; If step 4. can not selected the fan blower that meets the type selecting accuracy requirement, return execution in step 2, reduce fan blower type selecting precision or redefine maximum work number of units, repeating step 2-3 is until select till the satisfactory fan blower execution in step, 5; If the fan blower model that step 5. is selected is ranked according to the energy consumption size more than a kind, for the user recommends the most energy-conservation fan blower and operation combination thereof.
Description of drawings
Fig. 1 fan blower Intelligent Model Selection method implementation and operation process flow diagram.
Fig. 2 fan blower Intelligent Model Selection modular structure figure.
Embodiment
Generally understand technician's fan blower model sample commonly used in the multiple sewage treatment process of grasp fan blower of certain Computer Database method for building up, but building database all, database has comprised the main technical parameters of fan blower, such as flow, pressure, shaft power, bore, rotating speed, power of motor etc.The needs of New blower being selected for satisfying the user, this database is provided with the functions such as interpolation, modification, deletion, inquiry, makes things convenient for the user that database is in time safeguarded and upgrades.
Referring to Fig. 1 fan blower Intelligent Model Selection method implementation and operation process flow diagram, fan blower optimized Selection embodiment comprises:
Step 1. working procedure is by user's In-put design source book, design parameter;
Step 2. calculated activity sludge system aeration tank is maximum, on average, air demand and the required pressure of less three flow periods;
Step 3. is by the selected maximum flow period fan blower of user work at most number of units and type selecting precision;
Step 4. is selected fan blower by maximum flow period air demand and required pressure requirement from database;
Step 5. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 6,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-5;
Step 6. is selected fan blower by average discharge period air demand and required pressure requirement from the fan blower of having selected;
Step 7. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 8,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-7;
Step 8. is selected from step 7 by than low discharge period air demand and required pressure requirement
Select fan blower in the fan blower that goes out;
Step 9. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 10,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-9;
If the fan blower model that step 10. is finally selected is ranked according to the energy consumption size more than a kind, recommend fan blower model and the combination of day part operation number of units thereof of energy consumption minimum for the user;
Step 11. output drum Fan Selection result finishes.
Referring to Fig. 2 fan blower Intelligent Model Selection modular structure figure, fan blower database maintenance embodiment comprises:
Step 12. is opened fan blower database maintenance forms, clicks " increase fan blower " button, and input needs the fan blower number of increase, opens the fan blower database;
The parameters such as the fan blower model that step 13. input increases newly, flow, pressure, shaft power, bore, rotating speed, power of motor are finished newly-increased fan blower operation;
Step 14. is clicked " Update Table storehouse " button, opens the fan blower database, needs is revised the technical parameter that upgrades make amendment, and finishes the database update operation;
Step 15. is clicked " deletion fan blower " button, opens the fan blower database, and deletion energy consumption height or the fan blower data that has not re-used are finished the database deletion action;
Step 16. is clicked " save data storehouse " button after step 13,14,15 is finished, finish database maintenance and upgrade operation, to adapt to the ever-increasing needs of New blower.
Claims (2)
1. fan blower Intelligent Model Selection method said method comprising the steps of:
Set up the fan blower database, calculate the aeration tank maximum, on average, air demand and the required pressure of less three flow periods, select fan blower work at most number of units and type selecting precision, from database, optimize the fan blower model that satisfies simultaneously three flow time slot requests and work number of units and the running status of day part; Selected fan blower by adjustment work number of units and running status can satisfy simultaneously maximum, on average, the requirement of less three flow period air feed, and fan blower is all in efficient section work; Concrete steps are as follows:
Step 1. working procedure is by user's In-put design source book, design parameter;
Step 2. calculated activity sludge system aeration tank is maximum, on average, air demand and the required pressure of less three flow periods;
Step 3. is by the selected maximum flow period fan blower of user work at most number of units and type selecting precision;
Step 4. is selected fan blower by maximum flow period air demand and required pressure requirement from database;
Step 5. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 6,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-5;
Step 6. is selected fan blower by average discharge period air demand and required pressure requirement from the fan blower of having selected;
Step 7. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 8,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-7;
Step 8. is selected from step 7 by than low discharge period air demand and required pressure requirement
Select fan blower in the fan blower that goes out;
Step 9. judges whether selected fan blower meets the type selecting accuracy requirement, is execution in step 10,
Otherwise return execution in step 3, reduce fan blower type selecting precision or redefine maximum work number of units, repeat the step of 3-9;
If the fan blower model that step 10. is finally selected is ranked according to the energy consumption size more than a kind, recommend fan blower model and the combination of day part operation number of units thereof of energy consumption minimum for the user;
Step 11. output drum Fan Selection result finishes.
2. sample database such as the described a kind of fan blower Intelligent Model Selection method of claim one, it is characterized in that: system made comprises the sample database of fan blower model commonly used in more than the 1000 kind of sewage treatment process, intelligent selecting type is selected, can add at any time, revise, upgrade, delete according to user's needs fan blower sample database content, the requirement that continue to bring out to adapt to New blower, the database needs upgrades in time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210431613.1A CN102930019B (en) | 2012-11-02 | 2012-11-02 | A kind of aerator selection method and sample database |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210431613.1A CN102930019B (en) | 2012-11-02 | 2012-11-02 | A kind of aerator selection method and sample database |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102930019A true CN102930019A (en) | 2013-02-13 |
| CN102930019B CN102930019B (en) | 2016-08-03 |
Family
ID=47644817
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210431613.1A Expired - Fee Related CN102930019B (en) | 2012-11-02 | 2012-11-02 | A kind of aerator selection method and sample database |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102930019B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105868874A (en) * | 2015-01-19 | 2016-08-17 | 恺亿讯捷（北京）科技有限公司 | Method and device for selecting models of pump and fan based on energy efficiency evaluation |
| CN106288220A (en) * | 2016-08-26 | 2017-01-04 | 蚌埠依爱电子科技有限责任公司 | The control method that in a kind of breeding house, blower fan circulation industrial is made |
| CN109436834A (en) * | 2018-09-25 | 2019-03-08 | 北京金茂绿建科技有限公司 | A kind of method and device for choosing funnel |
| CN109544276A (en) * | 2018-10-30 | 2019-03-29 | 珠海格力电器股份有限公司 | A kind of product type selection method and processor |
| CN115879192A (en) * | 2022-11-03 | 2023-03-31 | 中交机电工程局有限公司 | BIM-based rail transit machine room design method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102681498A (en) * | 2011-03-15 | 2012-09-19 | 中国科学院沈阳自动化研究所 | Sewage treatment process optimizing operation method |
-
2012
- 2012-11-02 CN CN201210431613.1A patent/CN102930019B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102681498A (en) * | 2011-03-15 | 2012-09-19 | 中国科学院沈阳自动化研究所 | Sewage treatment process optimizing operation method |
Non-Patent Citations (3)
| Title |
|---|
| 朱文强等: ""罗茨鼓风机快速选型软件的应用"", 《计算机应用》 * |
| 李玉华: ""通风机计算机选型"", 《万方数据》 * |
| 袁泉: ""污水厂离心鼓风机选型中风量和风压的计算"", 《工业用水与废水》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105868874A (en) * | 2015-01-19 | 2016-08-17 | 恺亿讯捷（北京）科技有限公司 | Method and device for selecting models of pump and fan based on energy efficiency evaluation |
| CN106288220A (en) * | 2016-08-26 | 2017-01-04 | 蚌埠依爱电子科技有限责任公司 | The control method that in a kind of breeding house, blower fan circulation industrial is made |
| CN109436834A (en) * | 2018-09-25 | 2019-03-08 | 北京金茂绿建科技有限公司 | A kind of method and device for choosing funnel |
| CN109544276A (en) * | 2018-10-30 | 2019-03-29 | 珠海格力电器股份有限公司 | A kind of product type selection method and processor |
| CN109544276B (en) * | 2018-10-30 | 2022-07-15 | 珠海格力电器股份有限公司 | Product model selection method and processor |
| CN115879192A (en) * | 2022-11-03 | 2023-03-31 | 中交机电工程局有限公司 | BIM-based rail transit machine room design method |
| CN115879192B (en) * | 2022-11-03 | 2023-08-01 | 中交机电工程局有限公司 | BIM-based rail transit machine room design method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102930019B (en) | 2016-08-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102930019A (en) | Type selection method and sample database of fans | |
| CN1293346C (en) | Energy-saving central air conditioning system | |
| CN103809506B (en) | The method of part processing optimal scheduling scheme is obtained based on a dimension particle cluster algorithm | |
| US9350562B2 (en) | Energy management control system and method based on cloud computing | |
| CN1206485C (en) | Central air-conditioning energy conserving fuzzy controlling method and fuzzy controller thereof | |
| CN100416463C (en) | Low power consumption real time task parameter model dispatching method facing embedded system | |
| CN101929721B (en) | Predicting method of central air conditioner energy-conservation control autoregressive (AR) model load predicting system | |
| CN103206364A (en) | System and method for single-model water pump energy-saving operation dispatching | |
| CN201812187U (en) | Energy management control system for electronic information system machine rooms based on cloud computing | |
| CN1186572C (en) | Variable pressure difference and variable flow rate control method and system for air conditioning water system | |
| CN103093017A (en) | Distributed energy system design method | |
| CN102193527A (en) | System and method for managing and controlling energy sources of electronic information system machine room based on cloud computing | |
| CN103699941A (en) | Method for making annual dispatching operation plan for power system | |
| CN103994553A (en) | Refrigerating system cooling water energy-saving control method, system and device | |
| CN106196234B (en) | A kind of reclaimed water resource heat pump heating system collaboration optimizing operation method | |
| CN113468645A (en) | Building energy consumption optimization method based on green building standard | |
| CN106016605A (en) | Parallel cooling pump control method and device thereof based on genetic algorithm | |
| Yang et al. | A GUI-based simulation platform for energy and comfort management in Zero-Energy Buildings | |
| CN105937823A (en) | Control method and system for ground source heat pump | |
| CN111968008B (en) | Comprehensive energy monitoring system and method based on multi-energy cooperation | |
| CN105299846A (en) | Machine room group control device and method based on overall relevance optimization | |
| CN115392045A (en) | Temporary building air conditioner heating operation optimization method, device, equipment and medium | |
| CN102359732B (en) | Heat supply system and control method thereof | |
| CN205330943U (en) | Energy -saving control cabinet of pump house | |
| CN111753262B (en) | Air conditioner cooling water system design method based on probability analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information |
Inventor after: Ban Fuchen Inventor after: Yin Shijun Inventor after: Jing Yaping Inventor after: Lv Guifeng Inventor after: Zhang Yongjian Inventor before: Yin Shijun Inventor before: Ban Fuchen Inventor before: Jing Yaping Inventor before: Lv Guifeng Inventor before: Zhang Yongjian |
|
| COR | Change of bibliographic data | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160803 Termination date: 20161102 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |