CN111242367A - Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm - Google Patents
Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm Download PDFInfo
- Publication number
- CN111242367A CN111242367A CN202010017624.XA CN202010017624A CN111242367A CN 111242367 A CN111242367 A CN 111242367A CN 202010017624 A CN202010017624 A CN 202010017624A CN 111242367 A CN111242367 A CN 111242367A
- Authority
- CN
- China
- Prior art keywords
- storage capacity
- flood
- library
- reservoir
- tailing pond
- 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
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000003860 storage Methods 0.000 title claims abstract description 38
- 239000002245 particle Substances 0.000 title claims abstract description 32
- 238000005457 optimization Methods 0.000 title claims abstract description 20
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 42
- 230000006870 function Effects 0.000 claims description 20
- 238000013178 mathematical model Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 abstract description 19
- 238000001595 flow curve Methods 0.000 abstract description 4
- 230000001105 regulatory effect Effects 0.000 abstract description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000008602 contraction Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- PASHVRUKOFIRIK-UHFFFAOYSA-L calcium sulfate dihydrate Chemical compound O.O.[Ca+2].[O-]S([O-])(=O)=O PASHVRUKOFIRIK-UHFFFAOYSA-L 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 238000004886 process control Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computing Systems (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Primary Health Care (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Sewage (AREA)
Abstract
The invention relates to a flood regulation method for improving the utilization rate of the storage capacity of a tailing pond based on a particle swarm optimization algorithm, and belongs to the field of tailing pond engineering. According to the invention, the particle swarm optimization algorithm is matched with specific parameters, so that the search range can be enlarged, the particle learning speed is accelerated, the utilization rate of the storage capacity is improved, and the flood regulation calculation precision is increased; the rechecking process given by the embodiment shows that the algorithm of the invention obtains a more accurate downward discharge flow curve, so that the design of the water discharge structure of the tailing pond is more economical and reasonable; the flood regulating capacity of the tailing pond is fully utilized, and more reliable data are provided for operation management of the tailing pond.
Description
Technical Field
The invention relates to a flood regulation method for improving the utilization rate of the storage capacity of a tailing pond based on a particle swarm optimization algorithm, and belongs to the field of tailing pond engineering.
Background
The flood regulation calculation of the tailing pond is one of important steps in the design process of the tailing pond, the accuracy and the high efficiency of the calculation result have great influence on the safety of the tailing pond and the safety production of mines, and the method has important significance on the design of a tailing dam and a tailing pond flood drainage system. The flood regulation calculation method for the tailing pond is multiple, and comprises a flood process line method which is obtained by calculating a discharge capacity relation curve according to design specification requirements, a design flood method which is obtained by calculating according to an inference formula method and the like.
The discharge capacity storage capacity relation curve method can be used for complex discharge conditions, has no limit to time segment change, has small calculated amount and does not need trial calculation. However, the process control is too complicated, and the difficulty of curve drawing is relatively high. The iterative method is the most common method for flood regulation calculation, and has short calculation time but relatively low precision. The parameters adopted by the design flood reasoning formula adopted by the reasoning formula method are determined based on national synthesis, are not necessarily suitable for local areas, and are simple and convenient to calculate but low in precision.
Disclosure of Invention
The invention provides a flood regulation method for improving the capacity utilization rate of a tailing pond based on a particle swarm optimization algorithm, which is used for realizing the flood regulation of the tailing pond by improving the capacity utilization rate of the tailing pond.
The technical scheme of the invention is as follows: a flood regulation method for improving the utilization rate of the storage capacity of a tailing pond based on a particle swarm optimization algorithm comprises the following steps:
step 1: according to the corresponding discrete data between the storage capacity and the storage water level of the tailing pond, fitting to obtain the storage capacity VLibraryWith reservoir level ZLibraryA relation function between; according to the corresponding discrete data between the reservoir level of the tailing reservoir and the downward discharge quantity, the reservoir level Z is obtained through fittingLibraryAnd the downward discharge qDrain deviceA relation function between;
step 2: determining typical flood according to the flood data of the local calendar year, namely designing a flood process line;
and step 3: according to the storage capacity VLibraryWith reservoir level ZLibraryCorresponding relation between the water level and the reservoir level ZLibraryAnd the downward discharge qDrain deviceDeducing a relation function between the lower leakage flow and the storage capacity according to the corresponding relation between the lower leakage flow and the storage capacity;
and 4, step 4: adopting a maximum peak clipping criterion as a target function, and establishing a mathematical model by taking a relation function between water balance, upper and lower limits of a reservoir capacity, a let-down flow and the reservoir capacity as a constraint condition;
and 5: and (4) solving the mathematical model in the step (4) by adopting a particle swarm optimization algorithm to obtain a lower discharge flow process line.
Parameters in the particle swarm optimization algorithm adopted in the step 5 are set as follows: the number of particles was 400 and the learning factor was 2.
The invention has the beneficial effects that: the search range can be enlarged and the particle learning speed can be accelerated by matching specific parameters according to the particle swarm optimization algorithm, so that the utilization rate of the storage capacity is improved, and the flood regulation calculation precision is increased; the rechecking process given by the embodiment shows that the algorithm of the invention obtains a more accurate downward discharge flow curve, so that the design of the water discharge structure of the tailing pond is more economical and reasonable; the flood regulating capacity of the tailing pond is fully utilized, and more reliable data are provided for operation management of the tailing pond.
Drawings
Fig. 1 is a comparison chart of flood regulation calculation results of the method of the present invention and the conventional iterative method.
Detailed Description
Example 1: as shown in fig. 1, a flood regulation method for improving the utilization rate of the storage capacity of a tailing pond based on a particle swarm optimization algorithm comprises the following steps:
step 1: according to the corresponding discrete data between the storage capacity and the storage water level of the tailing pond, fitting to obtain the storage capacity VLibraryWith reservoir level ZLibraryA relation function between; according to the corresponding discrete data between the reservoir level of the tailing reservoir and the downward discharge quantity, the reservoir level Z is obtained through fittingLibraryAnd the downward discharge qDrain deviceA relation function between;
step 2: determining typical flood according to the flood data of the local calendar year, namely designing a flood process line;
and step 3: according to the storage capacity VLibraryWith reservoir level ZLibraryCorresponding relation between the water level and the reservoir level ZLibraryAnd the downward discharge qDrain deviceDeducing a relation function between the lower leakage flow and the storage capacity according to the corresponding relation between the lower leakage flow and the storage capacity;
and 4, step 4: adopting a maximum peak clipping criterion as a target function, and establishing a mathematical model by taking a relation function between water balance, upper and lower limits of a reservoir capacity, a let-down flow and the reservoir capacity as a constraint condition;
and 5: and (4) solving the mathematical model in the step (4) by adopting a particle swarm optimization algorithm to obtain a lower discharge flow process line.
Further, parameters in the particle swarm optimization algorithm adopted in the step 5 may be set as: the number of particles was 400 and the learning factor was 2.
Further, the following experimental procedure is given in connection with the actual tailings pond:
basic conditions of a tailings pond: example a flood regulation algorithm is performed on a phosphogypsum tailing pond No. 2 of Yunnan province for 200 years (P is 0.5%), and basic hydrological calculation data are shown in tables 1 and 2:
TABLE 1 flood process for two hundred years
Table 2 water level reservoir capacity relationship
In view of the above-mentioned information, the following experimental procedures are given in conjunction with the method of the present invention:
step 1: using excel curve fitting function, and fitting to obtain a reservoir volume V according to corresponding discrete data between the reservoir volume and the reservoir water levelLibraryWith reservoir level ZLibraryA relation function between; according to the corresponding discrete data between the reservoir level of the tailing reservoir and the downward discharge quantity, the reservoir level Z is obtained through fittingLibraryAnd the downward discharge qDrain deviceFunction of the relationship between:
qdrain device＝-0.8636ZLibrary 2+3.4786ZLibrary+2.3113
Step 2: determining typical flood according to the flood data of the local calendar year, namely designing a flood process line;
and step 3: according to the storage capacity VLibraryWith reservoir level ZLibraryCorresponding relation betweenLevel Z of system and reservoirLibraryAnd the downward discharge qDrain deviceThe corresponding relation between the lower leakage flow and the storage capacity derives a relation function between the lower leakage flow and the storage capacity, namely:
and 4, step 4: the maximum peak clipping criterion is adopted as the objective function:
qmax＝min(maxqdrain device(t)),t∈[t0,tD]
In the formula: q. q.smaxRepresents the maximum flow of the downstream guard point, m3/s；qDrain device(t) represents a let-down flow rate for a period t, m3/s；t0Represents a schedule period start time; t is tDIndicating a scheduling period end time;
simultaneously, the constraint conditions are met: water quantity balance:
in the formula: vLibrary(t)、VLibrary(t +1) is the initial and final storage capacities of the initial tailing pond in the t-th time period respectively; qInto(t)、QInto(t +1) the flow rates of the initial and final tailings ponds at the t-th time period, m3/s；qDrain device(t)、qDrain device(t +1) the initial and final discharge flow rates of the initial tailing pond in the t-th time period respectively; at is the time step, here taken as 600 s.
And (4) constraint of upper and lower limits of storage capacity:
0≤Vlibrary(t)≤VLibrary max
In the formula: vLibrary(t) tailing pond capacity in flood regulation calculation, VLibrary maxThe maximum storage capacity of the tailing pond.
And 5: solving the mathematical model in the step 4 by adopting a particle swarm optimization algorithm, wherein the particle swarm optimization algorithm adopts C + + language for programming calculation, and relevant parameters comprise: the number of particles is 400, the learning factor is 2, the cycle number is 400, and the specific steps are as follows:
the method comprises the steps of initializing each particle randomly, evaluating each particle to obtain global optimum, updating the speed and the position of each particle according to results, evaluating the function fitness of each particle, updating the historical optimum position and the global optimum position of each particle, outputting the global optimum position as a result if an ending condition is met, and circulating the group from the position where the speed and the position of each particle are updated until the ending condition is met if the ending condition is not met.
Step 6: and (4) analyzing a calculation result:
obtaining the coordinates of a first intersection point of the lower discharge flow process line obtained in the step 5 and the flood process line obtained in the step 2, namely obtaining the maximum discharge flow in the flood process, and comparing the maximum discharge flow with the reservoir capacity to obtain the corresponding highest water head and the corresponding reservoir water level; determining an actual used storage capacity according to the storage water level;
and 7: and (3) checking the discharge quantity of the drainage well according to the results of the flood process line and the discharge quantity process line obtained in the steps, wherein the inlet flow capacity of the drainage well is checked according to the following formula (wherein the inlet flow capacity determines the upper limit of the discharge quantity) aiming at different discharge highest water heads:
① free run-off (in three cases)
When the water level does not submerge the frame ring beam,
when water level submerges the ring beam, Qd＝Q1+Q2(2)
When the water level submerges the well mouth,
in the formula: qcRepresenting the flow when the ring beam is not submerged; qdRepresenting the flow when the ring beam is submerged; qeRepresenting the flow rate when flooding the wellhead;
(a square hole),
(when between two ring beams); g represents the gravitational acceleration; n iscThe number of drainage openings on the same cross section is shown; m represents a weir flow coefficient; ε represents the lateral contraction coefficient; bcRepresents the width of one drain opening, m; hyRepresenting the weir discharge head, m; hiThe calculated water head m of the drainage flow of the i-th layer total flooding working window is represented; h0A drainage head, m, representing the uppermost non-submerged working window; hjRepresenting a wellhead discharge head, m; omegacDenotes the area of a drainage window, m2；ωsRepresents the area of the well head water flow contraction section m2；ζ4Representing the loss coefficient of the local water head at the inlet of the drainage well; zeta5The local head loss coefficient of the frame is represented and is the sum of the local head loss coefficients of the upright post and the cross beam; omegalRepresenting the total area of the water passing clearance between the upright columns and the ring beams of the frame, m2；
②, half pressure flow
In the formula: q represents a flow rate; g represents the gravitational acceleration; fsRepresents the area of the contracted cross section of the water flow at the inlet of the drain pipe, m2(ii) a H represents a calculated water head which is the difference m between the reservoir water level and the center elevation of the inlet section of the drain pipe; lambda [ alpha ]jRepresents the loss coefficient of the water head of the drainage well along the way, and lambda is 8g/C2(ii) a l represents the depth of water, m, above the top of the pipe in the drainage well; d represents the inside diameter of the drainage well, m; zeta2Representing a drain pipe inlet local head loss coefficient; zeta3Representing the loss coefficient of the local head of the water flow steering in the drainage well; zeta4Representing the loss coefficient of the local water head at the inlet of the drainage well;ζ5representing a frame local head loss coefficient;
③, pressure flow
In the formula: fxRepresents the cross-sectional area, m, of the downstream outlet of the drain pipe2；HzRepresenting a calculated water head which is the difference m between the reservoir water level and the center elevation of the downstream outlet section of the drain pipe; when water exists at the downstream, the water level is the height difference between the reservoir water level and the downstream water level; lambda [ alpha ]gRepresenting the coefficient of loss of the water head of the drain pipe along the way; d represents the inner diameter m of the calculated pipe section of the drainage pipe; l represents the length of the calculated pipe section of the drainage pipe, m; ζ represents the local head loss coefficient on the drain line; zeta2Representing a drain pipe inlet local head loss coefficient; zeta3Representing the loss coefficient of the local head of the water flow steering in the drainage well; zeta4Representing the loss coefficient of the local water head at the inlet of the drainage well; zeta5-frame local head loss coefficient;
the flood discharge tunnel discharge capacity rechecks the discharge capacity of the tunnel body section and is considered according to the uniform flow of the open channel, and the calculation formula is as follows:
in the formula: q represents a flow rate (m)3S); a represents the flow cross-sectional area (m)2) (ii) a C represents the thanexity coefficient, ManninggongFormula (I); r represents a hydraulic radius;
the rechecking results are shown in table 3:
table 3 drainage flow calculation table for drainage wells at different reservoir levels
Hydraulic calculation of discharge capacity of the flood discharge tunnel:
the flow section b × h is 1.8m × 2.3m, the minimum gradient i is 0.015 (according to the original design condition), the leakage flow capability of the tunnel section is rechecked firstly according to the uniform flow of the open channel, and the calculation formula is as follows:
in the formula: q represents a flow rate (m)3S); a represents the flow cross-sectional area (m)2) (ii) a C represents the metabolic coefficient, Manning formula; r represents a hydraulic radius;
the calculation table of the drainage capacity of the flood discharge tunnel in the warehouse is shown in Table 4
Table 4 flood discharging tunnel water discharging ability calculation table in storehouse
The relationship curve of the drainage flow and the time obtained according to the particle swarm optimization algorithm is shown in the attached drawing 1, and meanwhile, compared with the drainage flow curve obtained by a conventional flood regulation method, the reservoir capacity utilization is more sufficient, and the safety of a downstream protection object is better guaranteed. Flood control results are shown in figure 1: as can be seen from the figure, the algorithm adopted by the invention makes full use of the storage capacity of the tailing dam compared with an iterative method, obtains a more accurate downward discharge flow curve, and can ensure that the design of a drainage structure of the tailing dam is more economical and reasonable; the flood regulating capacity of the tailing pond is fully utilized, and more reliable data are provided for operation management of the tailing pond.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (2)
1. A flood regulation method for improving the utilization rate of the storage capacity of a tailing pond based on a particle swarm optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
step 1: according to the corresponding discrete data between the storage capacity and the storage water level of the tailing pond, fitting to obtain the storage capacity VLibraryWith reservoir level ZLibraryA relation function between; according to the corresponding discrete data between the reservoir level of the tailing reservoir and the downward discharge quantity, the reservoir level Z is obtained through fittingLibraryAnd the downward discharge qDrain deviceA relation function between;
step 2: determining typical flood according to the flood data of the local calendar year, namely designing a flood process line;
and step 3: according to the storage capacity VLibraryWith reservoir level ZLibraryCorresponding relation between the water level and the reservoir level ZLibraryAnd the downward discharge qDrain deviceDeducing a relation function between the lower leakage flow and the storage capacity according to the corresponding relation between the lower leakage flow and the storage capacity;
and 4, step 4: adopting a maximum peak clipping criterion as a target function, and establishing a mathematical model by taking a relation function between water balance, upper and lower limits of a reservoir capacity, a let-down flow and the reservoir capacity as a constraint condition;
and 5: and (4) solving the mathematical model in the step (4) by adopting a particle swarm optimization algorithm to obtain a lower discharge flow process line.
2. The flood regulation method for improving the utilization rate of the storage capacity of the tailings pond based on the particle swarm optimization algorithm according to claim 1, wherein the flood regulation method comprises the following steps: parameters in the particle swarm optimization algorithm adopted in the step 5 are set as follows: the number of particles was 400 and the learning factor was 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010017624.XA CN111242367A (en) | 2020-01-08 | 2020-01-08 | Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010017624.XA CN111242367A (en) | 2020-01-08 | 2020-01-08 | Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111242367A true CN111242367A (en) | 2020-06-05 |
Family
ID=70872472
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010017624.XA Pending CN111242367A (en) | 2020-01-08 | 2020-01-08 | Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111242367A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112116785A (en) * | 2020-08-21 | 2020-12-22 | 中国瑞林工程技术股份有限公司 | Tailing pond disaster early warning method and device based on strong rainfall weather forecast |
| CN112328952A (en) * | 2021-01-05 | 2021-02-05 | 矿冶科技集团有限公司 | Drainage well discharge flow calculation method and device and electronic equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106228242A (en) * | 2016-07-28 | 2016-12-14 | 三峡大学 | A kind of high accuracy flood routing method based on optimized algorithm |
| CN109840363A (en) * | 2019-01-16 | 2019-06-04 | 中国恩菲工程技术有限公司 | A kind of analogy method and device of Seepage Field of Tailings Dam |
-
2020
- 2020-01-08 CN CN202010017624.XA patent/CN111242367A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106228242A (en) * | 2016-07-28 | 2016-12-14 | 三峡大学 | A kind of high accuracy flood routing method based on optimized algorithm |
| CN109840363A (en) * | 2019-01-16 | 2019-06-04 | 中国恩菲工程技术有限公司 | A kind of analogy method and device of Seepage Field of Tailings Dam |
Non-Patent Citations (4)
| Title |
|---|
| 吕庭刚: "某尾矿库调洪演算及排洪系统泄流能力分析", 《工程建设》 * |
| 王国利等: "基于 PSO 算法的水库防洪优化调度模型及应用", 《水电能源可科学》 * |
| 王建群等, 河海大学出版社 * |
| 韩超: "某尾矿库复杂排洪系统水工模型试验及水力计算研究", 《中国优秀硕士学位论文全文数据库 (工程科技Ⅰ辑)》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112116785A (en) * | 2020-08-21 | 2020-12-22 | 中国瑞林工程技术股份有限公司 | Tailing pond disaster early warning method and device based on strong rainfall weather forecast |
| CN112328952A (en) * | 2021-01-05 | 2021-02-05 | 矿冶科技集团有限公司 | Drainage well discharge flow calculation method and device and electronic equipment |
| CN112328952B (en) * | 2021-01-05 | 2021-04-23 | 矿冶科技集团有限公司 | Drainage well discharge flow calculation method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111275242A (en) | Downstream protection tailing pond flood regulating method based on tabu search optimization algorithm | |
| CN104268645B (en) | Industry water supplying quantity determining method with water supplying priority level taken into consideration in water resource allocation | |
| CN111242367A (en) | Flood regulation method for improving utilization rate of storage capacity of tailing pond based on particle swarm optimization algorithm | |
| CN113050430B (en) | Drainage system control method based on robust reinforcement learning | |
| US20240328301A1 (en) | Method for predicting water invasion frontier for complex well in bottom-water gas reservoir | |
| CN108710972B (en) | Reservoir flood control scheduling method based on POA algorithm | |
| CN109271672A (en) | A kind of river-lake-pumping plant influences each other the river water surface curve calculation method under acting on | |
| CN110210109B (en) | Numerical simulation method and system for reverse water flow of weir gate engineering in river network | |
| CN112926786B (en) | Shallow lake target water level reverse prediction method and system based on association rule model and numerical simulation | |
| CN107587474A (en) | Hinge downstream runoff and trend crossing lowest design navigable water level generation method | |
| CN111210141A (en) | Reservoir capacity curve correction method based on constraint mechanism particle swarm algorithm | |
| CN110232245B (en) | Method for determining water level distribution of constant-flow pumping three-dimensional flow field of suspended curtain confined water foundation pit | |
| CN109371897B (en) | Fishway inlet water supplementing and energy dissipating model test device and test method | |
| CN116307050A (en) | Gas well hydrops prediction method based on gate-controlled circulating nerve unit | |
| CN111598757B (en) | Method for determining urban water system flow in plain river network area | |
| Benin et al. | Water flow regulator for irrigation canals | |
| CN112529247A (en) | Main flow sand discharge optimal scheduling method and system based on branch reservoir combined water replenishing | |
| CN206034391U (en) | Adaptation water level variation's fishway exit | |
| CN108614908B (en) | Method for selecting eccentric separated-injection multi-stage water nozzle | |
| CN109934506B (en) | Selecting method for judging type of channel renovation project based on quantification | |
| CN113837890B (en) | Shale gas horizontal well type optimization method based on data statistics | |
| CN116992783B (en) | Full-effective grid cell flow field simulation method under large-depth drainage of underground water | |
| CN108049849A (en) | Water-drive pool Plane Fluid Field regulates and controls design method | |
| CN116050762A (en) | Reservoir dam line selection method | |
| Chen et al. | Study and application on optimization scheme of water distribution in gravity irrigation district of large-scale plain |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200605 |
|
| RJ01 | Rejection of invention patent application after publication |