CN112686500A - Intelligent model selection system for water treatment engineering equipment - Google Patents
Intelligent model selection system for water treatment engineering equipment Download PDFInfo
- Publication number
- CN112686500A CN112686500A CN202011468105.1A CN202011468105A CN112686500A CN 112686500 A CN112686500 A CN 112686500A CN 202011468105 A CN202011468105 A CN 202011468105A CN 112686500 A CN112686500 A CN 112686500A
- Authority
- CN
- China
- Prior art keywords
- equipment
- professional
- library
- type selection
- rule
- 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.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an intelligent model selection system for water treatment engineering equipment, which comprises: (1) receiving project boundary conditions, intelligently recommending a sewage treatment process flow according to a preset rule, and determining a sewage plant outline; (2) reading the names of the professional devices in the professional calculation books from the outline, and determining the device types according to a preset built-in type selection first rule, wherein the built-in type selection first rule comprises the corresponding relation between the device names and the device types; (3) reading technical parameters of professional equipment in each professional calculation book from the outline, comparing each technical parameter with similar equipment in a pre-established equipment library, and searching equipment matched with the technical parameters from the equipment library; (4) and filling the matched equipment and information into a corresponding professional calculation book. The system constructs a rich equipment library and designs a strict type selection rule; under the model selection rule, a project equipment list is formed through design, and information interaction is carried out with the cost specialty and the purchasing specialty.
Description
Technical Field
The invention relates to the field of water plant equipment model selection, in particular to an intelligent model selection system for water treatment engineering equipment.
Background
In the current traditional water plant design, designers generally determine the treatment process and the treatment flow of a sewage plant according to the characteristics of the quality of sewage. After the designer completes monomer calculation and determines the basic size of the monomer, the designer communicates with an equipment manufacturer to determine the type of the selected equipment. The manufacturer carries out equipment specification and model funding on sewage plant equipment according to the information of each single body of the water plant provided by a designer. Finally, the designer completes the processing of the equipment data provided by the manufacturer and reflects the specification parameters of the equipment in the drawing.
The traditional water plant equipment model selection method has the following defects:
(1) limitation of selection
The manual type selection of the equipment is limited by the experience level of designers, and the problem of wrong equipment type selection caused by insufficient experience of the designers often occurs.
(2) Long cycle length
The sewage plant has a plurality of types and large quantity of equipment, and in the process of determining the specification parameters of the equipment by designers, the manufacturers need to communicate basic information of the water plant monomers, then the manufacturers count the specification parameters of the monomer equipment and finally feed the specification parameters back to the designers, so that the process period is long.
(3) Low degree of matching
In the design process of a water plant, the situation that construction drawing is not consistent with the parameters of supply equipment often occurs, because the design period is long, the specification parameters of the equipment cannot be updated in real time, and the matching degree of the equipment is low.
Disclosure of Invention
In order to solve the problems in the background art, the invention provides an intelligent type selection system for water treatment engineering equipment.
The invention provides an intelligent model selection system for water treatment engineering equipment, which comprises:
(1) receiving project boundary conditions, intelligently recommending a sewage treatment process flow according to a preset rule, and determining a sewage plant outline;
(2) reading the names of the professional devices in the professional calculation books from the outline, and determining the device types according to a preset built-in type selection first rule, wherein the built-in type selection first rule comprises the corresponding relation between the device names and the device types;
(3) reading technical parameters of professional equipment in each professional calculation book from the outline, comparing each technical parameter with similar equipment in a pre-established equipment library, and finding out equipment matched with the technical parameters and specification models thereof from the equipment library; the equipment library comprises the existing water treatment engineering equipment and information thereof, the information comprises specification models, technical parameters and manufacturer information, and the equipment in the equipment library is kept updated;
(4) filling the matched equipment and information searched in the step (3) into a corresponding professional calculation book.
Further, in the step (1), the boundary conditions comprise the design scale of the sewage plant and the water quality conditions of inlet and outlet water; the water quality conditions of the inlet water and the outlet water comprise chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, suspended solid, total phosphorus and total nitrogen of the inlet water and the outlet water.
Further, the built-in type selection rule maps the specialties, the equipment types and the equipment names, and the following table is shown:
when the device name is known, the device name is matched with the corresponding device type according to the built-in type selection rule shown in the table.
Further, in the step (3), comparing each technical parameter with the similar device in the pre-created device library specifically includes:
for each professional device, according to the device type obtained in the step (2), obtaining the technical parameters corresponding to the device type where the professional device is located, and searching a device set with all matched technical parameters in a device library; comprehensively considering the project level, and selecting the most suitable equipment from comprehensive consideration of price, manufacturer service quality, import or home-made.
Further, the step (4) further comprises:
and simultaneously, transmitting the matched equipment and the information thereof to a corresponding equipment model, wherein the equipment model is modeled in advance and specifically comprises the following steps: firstly, collecting the appearance and information of each device from a manufacturer; and creating an equipment model by utilizing Revit according to the collected information, and constructing an equipment model library.
The system of the invention constructs a rich equipment library and designs a strict type selection rule. Under the model selection rule, a project equipment list is formed through design, and information interaction is carried out with the cost specialty and the purchasing specialty.
The invention has the following advantages and beneficial effects:
(1) the method has the following advantages:
the equipment library in the equipment type selection system contains most of domestic and foreign known equipment manufacturers of the related sewage plant equipment, and the same type of equipment resources come from at least more than 2 manufacturers. The equipment data includes basic external dimensions, installation dimensions and unit price information of the equipment. The device types comprise grids, mud scrapers, gates, desanding devices, conveying devices, squeezing devices, mixers, pumps, fans (roots, screws, air suspension and magnetic suspension), disinfection devices, filling devices, sludge treatment devices, constant-pressure water supply systems, instruments, hoisting devices, elevators, power distribution devices, automatic control devices, deodorization devices, ventilation devices, assay devices and other auxiliary devices for water plant operation. For the same type of equipment, different types of equipment are also included, for example, the grid type equipment includes a movable rail type mechanical grid, a steel wire rope traction mechanical grid, an anti-fishing type mechanical grid, a rotary grid, a mesh plate type grid and the like. The intelligent linked design platform automatically selects and uses matched equipment according to the water quality condition of actual projects, the treatment process and the overall arrangement, and has strong applicability.
(2) The method is more scientific:
based on the standardized design basis, the rule of the common equipment of the equipment model selection system comes from the experiences of domestic and foreign known equipment manufacturers, design teams, operation teams, research and development teams and industry known experts. In the system, equipment with low efficiency, high energy consumption, high failure rate, troublesome operation and the like is eliminated, the selected equipment type has foresight, and the stable operation of the sewage plant is ensured.
(3) The method is more economical:
the equipment type selection system collects the resources of different manufacturers for the same equipment, comprises domestic high-quality and foreign imported equipment, and has the characteristics of high quality, high efficiency, stability and economy. Meanwhile, under the condition of limited investment and on the basis of ensuring the stable operation of a water plant, in the process of generating a project equipment list by each professional design, the manufacturer can be manually selected according to the purchase price of the equipment, and the economy of the selected equipment is fully embodied.
Drawings
FIG. 1 is a partial process listing of a process calculation book in an example;
FIG. 2 is a cross-sectional view of a skimmer tube apparatus model;
FIG. 3 is a perspective view of a skimmer tube apparatus model.
Detailed Description
In order to facilitate understanding of the technical principles, technical solutions and technical effects of the present invention, the following further describes the technical background related to the present invention, the related theories and the specific implementation modes of the technical solutions.
The invention aims to solve the defects caused by manual equipment type selection in the background technology, and the equipment type can be intelligently recommended according to the water quality condition, the treatment process flow and the general diagram arrangement of a project, so that the problem of type selection errors caused by insufficient experience of designers is solved from the source. The equipment model selection system collects basic information of equipment commonly used by sewage plants, so that the time for communication between designers and the plants and waiting for contribution of the plants is saved, the design work period is greatly shortened, and the defect of long design period caused by equipment model selection in the prior art is overcome. The equipment model selection system determines the model selection rule of each type of equipment on the premise of being fully communicated with technicians of various equipment manufacturers. And automatically transmitting the data in the equipment model selection system to an intelligent design platform, and importing the specification parameters of the adaptive equipment into an equipment list and a model. As a specially-assigned person maintains and updates the data of the equipment model selection system, the specification parameters of the equipment table in the drawing can be ensured to be consistent with the parameters of actual supply equipment, and the equipment matching degree is improved.
The specific use process of the intelligent type selection system of the water treatment engineering equipment is provided based on the embodiment.
Firstly, the system receives input item boundary conditions.
Inputting boundary conditions through an intelligent design platform (hereinafter referred to as "platform"), including:
1.1 input the design scale of the sewage plant, which in this example is 5 km3/d；
1.2 the quality of water condition of input business turn over water, in this embodiment, the quality of water of intaking is: the chemical oxygen demand COD is 300mg/L, the biochemical oxygen demand BOD is 150mg/L, the ammonia nitrogen is 30mg/L, the suspended solid SS is 150mg/L, the total phosphorus TP is 15mg/L, and the total nitrogen TN is 45 mg/L. The effluent quality is as follows: the Chemical Oxygen Demand (COD) is 10mg/L, the Biochemical Oxygen Demand (BOD) is 30mg/L, the ammonia nitrogen is 1.5mg/L, the Suspended Solid (SS) is 10mg/L, the Total Phosphorus (TP) is 0.3mg/L, and the Total Nitrogen (TN) is 10 mg/L.
1.3 the platform recommends sewage treatment process according to the rule of putting into in advance intelligently, and the process recommended by this embodiment is: flow-limiting well → coarse grid → lift pump house → aerated grit chamber → fine grid → biochemical tank AAO → secondary sedimentation tank → high-efficiency sedimentation tank → middle lift pump house → denitrification filter house → ultraviolet disinfection channel → water outlet metering tank → tail water lift pump house.
1.4 in the case of the determination of the respective outline of the sewage plant (i.e. the determination of the dimensions of the individual units), the plant model selection of the water plant takes place.
II, selecting equipment type
The device type is determined based on the built-in type selection rule shown in table 2 according to the device name in the calculation book under each profession (for example, a process calculation book, an electrical load table, a heating and ventilation calculation book, a building calculation book, and the like). For example, the technical book selects a movable rail type grating cleaner, and the type of the equipment belongs to the grating class. Under the built-in type selection rule (see table 2), the matching of the key technical parameters of the equipment is carried out by linking a pre-constructed equipment library according to the key technical parameters of the equipment, such as the depth of a grating channel, the depth of water in front of the grating, the width of the channel and the like. This example classifies waterworks equipment as shown in table 1 below.
TABLE 1 Water plant Equipment and classes
Thirdly, determining equipment specification parameters:
different technical parameters are selected from each professional calculation book of the intelligent design platform and enter an equipment library, the program automatically judges the specification parameters and the corresponding equipment information which accord with each professional equipment under the built-in model selection rule, and the built-in model selection rule of the main equipment of the process specialty is expressed in a table 2. For each professional device, acquiring technical parameters corresponding to the device type of the professional device according to the device type of the professional device, and searching devices with matched technical parameters in a device library; and when the technical parameters are not matched, taking the technical parameters of a higher level. Referring to table 2, if the current device is a moving rail type grid cleaner, the device category corresponding to the current device is "grid category", and a suitable device is found from the device library according to the selection rule of the grid category in table 2. The technical parameters of the grating equipment comprise equipment names (namely a movable rail type grating dirt separator), channel depth, water depth in front of the grating and channel width, an equipment set with the same equipment name is searched from an equipment library, equipment with matched channel depth, water depth in front of the grating and channel width is searched from the current equipment set, and once the equipment with the technical parameters cannot be found, the equipment with the technical parameters larger than one level is selected.
TABLE 2 built-in rules for type selection for devices
The following description will be given taking a pump-like device as an example. In a process calculation book of an intelligent design platform, referring to fig. 2, a submersible sewage pump is determined and selected in a single lifting pump room, and key technical parameters are given. At the moment, a 'process equipment list' function is clicked in an intelligent design platform to perform parameter matching, the system automatically analyzes to obtain a specification model matched with key parameters (name, equipment type, flow and lift) of a process calculation book, and finally the optimal specification model is fed back to the process equipment list. Meanwhile, relevant key parameters of the equipment in the equipment library are fed back to the process calculation book, so that the size of the equipment model is influenced.
Feeding back equipment specification parameters:
after the design user determines the final equipment specification parameters, the program returns the selected equipment specification parameters to the calculation books of the related monomers of the intelligent design platform, and finally an equipment list is formed. Meanwhile, key parameters (the shape size and the installation size) of the equipment size are transmitted to the single equipment models generated in the intelligent design platform, and the unification of the parameters of the equipment models is ensured.
The method further comprises the following steps:
4.1 the system receives the equipment name, selects the equipment type, matches the key technical parameter;
4.2 calculating the optimal solution of the equipment model selection, specifically:
according to the device types selected by a design user and corresponding parameter information, each type possibly corresponds to different parameters, a program calls device processing logic corresponding to the selected type, the parameter information is matched in sequence, and a device set meeting all the parameters is found out; and then judging whether to adopt import or domestic, high-price priority or low-price priority and how to adopt the service quality of equipment manufacturers according to the project properties (primary, secondary, tertiary and the like) of the current project, finally calculating the final score of each equipment in the equipment set, selecting the equipment with the highest score, namely the optimal solution, and if the number of the optimal solutions is more, randomly selecting one of the optimal solutions.
4.3 designing the user to finish the model selection;
4.4 the key parameters of the equipment size are transmitted to an equipment model, and the equipment model needs to be modeled in advance, and the method comprises the following steps: firstly, collecting equipment information provided by a manufacturer, wherein the equipment information comprises the appearance of equipment and parameters of equipment with different specifications; and then, establishing an equipment model base through Revit. FIGS. 2-3 illustrate the constructed skimmer tube equipment model.
4.5 returning the final equipment specification parameters, price and manufacturer information to the design user.
Fifthly, data linkage of design, cost and purchase specialties
The equipment specification parameters fed back by the platform form a final item equipment list (comprising process/electric instrument control/heating ventilation/construction professional equipment) with unit price, and the platform transmits item equipment list data to a construction professional for construction workers to perform subsequent price combination work to finally form the construction list. Meanwhile, the platform transmits the project equipment list data to the equipment purchasing specialty to form an equipment purchasing list with price, so that the later purchasing specialty can import the equipment purchasing list into other platforms to bid and tender.
The technical solution provided by the present invention is not limited by the above embodiments, and all technical solutions formed by utilizing the structure and the mode of the present invention through conversion and substitution are within the protection scope of the present invention.
Claims (5)
1. Water treatment engineering equipment intelligence lectotype system, characterized by includes:
(1) receiving project boundary conditions, intelligently recommending a sewage treatment process flow according to a preset rule, and determining a sewage plant outline;
(2) reading the names of the professional devices in the professional calculation books from the outline, and determining the device types according to a preset built-in type selection first rule, wherein the built-in type selection first rule comprises the corresponding relation between the device names and the device types;
(3) reading technical parameters of professional equipment in each professional calculation book from the outline, comparing each technical parameter with similar equipment in a pre-established equipment library, and finding out equipment matched with the technical parameters and specification models thereof from the equipment library; the equipment library comprises the existing water treatment engineering equipment and information thereof, the information comprises specification models, technical parameters and manufacturer information, and the equipment in the equipment library is kept updated;
(4) filling the matched equipment and information searched in the step (3) into a corresponding professional calculation book.
2. The intelligent type selection system of water treatment engineering equipment as claimed in claim 1, wherein:
the boundary conditions comprise the design scale of a sewage plant and the water quality conditions of inlet and outlet water; the water quality conditions of the inlet water and the outlet water comprise chemical oxygen demand, biochemical oxygen demand, ammonia nitrogen, suspended solid, total phosphorus and total nitrogen of the inlet water and the outlet water.
3. The intelligent type selection system of water treatment engineering equipment as claimed in claim 1, wherein:
the built-in type selection rule maps the specialties, the equipment types and the equipment names, and the following table is shown:
when the device name is known, the device name is matched with the corresponding device type according to the built-in type selection rule shown in the table.
4. The intelligent type selection system of water treatment engineering equipment as claimed in claim 1, wherein:
in the step (3), comparing each technical parameter with the similar device in the pre-created device library specifically includes:
for each professional device, according to the device type obtained in the step (2), obtaining the technical parameters corresponding to the device type where the professional device is located, and searching a device set with all matched technical parameters in a device library; comprehensively considering the project level, and selecting the most suitable equipment from comprehensive consideration of price, manufacturer service quality, import or home-made.
5. The intelligent type selection system of water treatment engineering equipment as claimed in claim 1, wherein:
the step (4) further comprises:
and simultaneously, transmitting the matched equipment and the information thereof to a corresponding equipment model, wherein the equipment model is modeled in advance and specifically comprises the following steps: firstly, collecting the appearance and information of each device from a manufacturer; and creating an equipment model by utilizing Revit according to the collected information, and constructing an equipment model library.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011468105.1A CN112686500A (en) | 2020-12-14 | 2020-12-14 | Intelligent model selection system for water treatment engineering equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011468105.1A CN112686500A (en) | 2020-12-14 | 2020-12-14 | Intelligent model selection system for water treatment engineering equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112686500A true CN112686500A (en) | 2021-04-20 |
Family
ID=75447631
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011468105.1A Pending CN112686500A (en) | 2020-12-14 | 2020-12-14 | Intelligent model selection system for water treatment engineering equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112686500A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101905189A (en) * | 2010-08-19 | 2010-12-08 | 刘峰 | Method for realizing underground separation of raw coal |
| JP2012208930A (en) * | 2011-03-14 | 2012-10-25 | Metawater Co Ltd | Facility maintenance support device |
| CN106611258A (en) * | 2015-10-26 | 2017-05-03 | 国网甘肃省电力公司经济技术研究院 | Establishment of distribution network engineering equipment model selection rule base |
| CN110594493A (en) * | 2019-10-18 | 2019-12-20 | 信开水环境投资有限公司 | Structure suitable for rear buried sleeve of existing pool wall and construction method |
| CN111597206A (en) * | 2020-05-27 | 2020-08-28 | 林思明 | Chip type selection method and device, electronic equipment and computer readable storage medium |
| CN111932062A (en) * | 2020-06-24 | 2020-11-13 | 内蒙古久科康瑞环保科技有限公司 | Method and device for determining sewage treatment process parameters and computer equipment |
-
2020
- 2020-12-14 CN CN202011468105.1A patent/CN112686500A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101905189A (en) * | 2010-08-19 | 2010-12-08 | 刘峰 | Method for realizing underground separation of raw coal |
| JP2012208930A (en) * | 2011-03-14 | 2012-10-25 | Metawater Co Ltd | Facility maintenance support device |
| CN106611258A (en) * | 2015-10-26 | 2017-05-03 | 国网甘肃省电力公司经济技术研究院 | Establishment of distribution network engineering equipment model selection rule base |
| CN110594493A (en) * | 2019-10-18 | 2019-12-20 | 信开水环境投资有限公司 | Structure suitable for rear buried sleeve of existing pool wall and construction method |
| CN111597206A (en) * | 2020-05-27 | 2020-08-28 | 林思明 | Chip type selection method and device, electronic equipment and computer readable storage medium |
| CN111932062A (en) * | 2020-06-24 | 2020-11-13 | 内蒙古久科康瑞环保科技有限公司 | Method and device for determining sewage treatment process parameters and computer equipment |
Non-Patent Citations (2)
| Title |
|---|
| 张洋: "环保设备选型专家系统的设计与实现", 《信息技术》, no. 2, pages 194 - 198 * |
| 郭华;: "火力发电厂锅炉补给水处理系统工艺组合及设备参数确定方法", 内蒙古电力技术, no. 05, pages 49 - 52 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Makropoulos et al. | Sewer-mining: A water reuse option supporting circular economy, public service provision and entrepreneurship | |
| Guo et al. | Towards a benchmarking tool for minimizing wastewater utility greenhouse gas footprints | |
| JP5918672B2 (en) | Water and sewage management system and method | |
| Castillo et al. | Validation of a decision support tool for wastewater treatment selection | |
| CN111080483B (en) | DMA automatic partitioning method for water supply network based on graph division | |
| CN102084394A (en) | Optimizing operations of a hydrogen pipeline system | |
| Jenkins | Aeration control system design: a practical guide to energy and process optimization | |
| CN109389251A (en) | Total amount of pollutant optimizing distribution method based on control section water quality reaching standard | |
| Zanni et al. | Modeling for sustainability: Life cycle assessment application to evaluate environmental performance of water recycling solutions at the dwelling level | |
| CN110175767A (en) | A kind of park green land equal services integrated estimation system and appraisal procedure | |
| Hochstrat et al. | Wastewater reclamation and reuse in Europe: a model-based potential estimation | |
| CN105906032A (en) | Sewage treatment plant personification experience management control system and method | |
| Joksimovic et al. | Development of an integrated simulation model for treatment and distribution of reclaimed water | |
| Zessner et al. | Estimations of municipal point source pollution in the context of river basin management | |
| Kampas et al. | Assessing the green growth trajectory through resource and impact decoupling indices: the case of Poland | |
| CN112686500A (en) | Intelligent model selection system for water treatment engineering equipment | |
| Huang et al. | An integrated model for structure optimization and technology screening of urban wastewater systems | |
| KR20170080908A (en) | Supporting method and system of sewage disposal for energy saving | |
| JP2004267865A (en) | Support system of water treatment process | |
| CN110885132B (en) | Identification method for preferential supervision set of rural domestic sewage treatment facility | |
| KR20040074626A (en) | Optimal operation control system of wide-area plant | |
| CN112966902A (en) | Water pollution load distribution method considering region fairness and pollution source treatment difference | |
| CN210030157U (en) | Sequencing batch biofilm reactor device | |
| CN109766940B (en) | Method and apparatus for evaluating similarity between multiple sewage treatment systems | |
| KR101993449B1 (en) | Energy optimization system using measuring and controling of water treatment facility |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |