Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization
Abstract
:1. Introduction
2. Scheduling Reuse System Model
2.1. Hierarchical Reuse Strategy
2.2. Reuse Strategy Model
2.3. Reuse System Constraints
3. Design and Optimization of Mine Water Dispatching Method Based on Particle Swarm Optimization
4. Inertia Weight Strategy Analysis
4.1. Inertia Weight Decreasing Strategy
4.2. Improvement Strategy of Inertia Weight
- (1)
- The state, population size, spatial dimension, iteration times, and parameters of each water supply node are initialized.
- (2)
- The fitness value pi, individual extreme value pibest and global extreme value Gbest of each particle are calculated. If pi < pibest is satisfied, then pibest = pi; if pi < Gbest, then Gbest = pi.
- (3)
- The inertia weight of the improved algorithm is updated.
- (4)
- The position and velocity of each iteration particle is updated.
- (5)
- Judge whether the particle reaches the termination condition. If it meets the condition, terminate the search. If not, return to the second step and continue.
4.3. Test Results and Analysis
5. Simulation Examples and Experiments
5.1. Example of Results Validation
5.2. Discussion
- (1)
- Mine water reuse environment sensing. Based on the perception of big data multi-data fusion, big data causality, and data mining and other advanced analysis technologies, this sensor continuously receives information related to water quality and water quantity, the status of open and closed valves, and the water demand of the mine area during the mine water treatment process. The sensor detects the underground–surface mine water treatment environment and the system operation status. The specific sensor is shown in Figure 19.
- (2)
- Sensor data fusion analysis. Advanced analysis techniques, such as multiple data fusion [35,36], big data causality, and data mining, are applied to the sensed data to scientifically analyze various heterogeneous datasets based on their attributes and categories, providing information that can be utilized as a basis for intelligent and accurate judgment.
- (3)
- Construction of mine water dispatch and reuse model. This is a mine water reuse model in the control system. Based on the fusion analysis of sensor data, this mathematical model can reflect actual information relating to the quantity and quality of mine water. In addition, the state of underground–surface water use is determined to intelligently allocate water resources in the mining area to achieve efficient and coordinated dispatch of underground water resources.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable Hierarchy | Water Point | Monthly Water Consumption/m3 | Concentration/(mg/L) | Preset Variation Range (%) | |||||
---|---|---|---|---|---|---|---|---|---|
Heating Season | Non-Heating Season | SS | COD | Hardness | Oils | Turbidity | |||
Underground—clean water tank | Underground fire water | 2880 | 2880 | ≤30 | - | - | - | ≤5 | ±10 |
Grouting water | 1840 | 576 | ≤30 | - | - | - | ≤5 | ±10 | |
Downhole dust removal water | 144 | 288 | ≤30 | - | - | - | - | ±10 | |
Cooling water | 720 | 720 | ≤30 | ≤60 | ≤450 | ≤1 | ≤5 | ±10 | |
Hydraulic support water | 105.2 | 105.2 | ≤20 | ≤60 | ≤450 | ≤1 | ≤5 | ±10 | |
Pretreatment—intermediate tank | Ground dust removal water | 2304 | 2592 | ≤150 | - | - | - | - | ±20 |
Fire water | 96 | 2880 | ≤30 | ≤50 | ≤450 | 10 | ±10 | ||
Secondary treatment—high tank | Coal preparation water | 1728 | 1296 | ≤400 | ≤500 | ±20 | |||
Heat exchange station water | 864 | 864 | - | soft water | ±25 | ||||
Cooling water | 720 | 720 | ≤30 | ≤80 | ≤450 | ≤1 | ≤5 | ±20 | |
Greening water | 288 | 288 | - | ≤50 | ≤450 | ≤10 | ±40 | ||
Other domestic water | 1440 | 1728 | - | ≤50 | ≤450 | ≤5 | ±40 | ||
Advanced processing—reuse tank | Boiler water | 1728 | 288 | ≤30 | ≤60 | ≤450 | ≤1 | ≤5 | ±40 |
Drinking water | 57.6 | 57.6 | Nothing visible | ≤20 | ≤450 | ≤0.05 | ≤1 | ±40 |
Mine Water Reuse Sites | Processing Speed (h/m3) | |
---|---|---|
Heating Season | Non-Heating Season | |
Downhole—Clear water pool | 4.48 × 10−3 | 4.48 × 10−3 |
Pretreatment—Intermediate pool | 1.36 × 10−3 | 6.77 × 10−4 |
Secondary treatment—High-level tank | 1.36 × 10−3 | 6.77 × 10−4 |
Deep processing—Multiplexed pools | 1.36 × 10−3 | 6.77 × 10−4 |
Test Function | Function Expression | Dimension | Search Scope | Meaning |
---|---|---|---|---|
Sphere | 30 | [–10, 10] | Global search capability | |
Rastrigin | 30 | [−10, 10] | Practicality | |
Rosenbrock | 30 | [−10, 10] | Local search capability | |
Griewank | 30 | [−10, 10] | Beyond local restrictions |
Function | Pattern | LDIW-PSO | EDIW-PSO | ADIW-PSO |
---|---|---|---|---|
Sphere | Optimal value | 6.665 × 10−3 | 9.88 × 10−2 | 4.336 × 10−12 |
Average value | 3.048 × 10−1 | 8.27 × 10−1 | 5.211 × 10−9 | |
Rastrigin | Optimal value | 3.095 × 10−1 | 2.668 × 10−1 | 5.819 × 10+0 |
Average value | 6.720 × 10−1 | 5.466 × 10−1 | 2.608 × 10−1 | |
Rosenbrock | Optimal value | 1.918 × 10+0 | 3.342 × 10+0 | 1.897 × 10+0 |
Average value | 7.933 × 10+0 | 7.745 × 10+0 | 6.934 × 10+0 | |
Griewank | Optimal value | 2.252 × 10−7 | 9.299 × 10−5 | 1.623 × 10−5 |
Average value | 4.798 × 10−4 | 4.064 × 10−4 | 2.797 × 10−4 |
Mine Water Reuse Grade | Mine Water Reuse Point | Heating Season | Non-Heating Season |
---|---|---|---|
Underground treatment clear water tank | Underground fire fighting | 65,735.22073 | 85,125.85209 |
Grouting water | 39,833.105 | 17,084.23175 | |
Underground watering and dust removal | 3916.148483 | 8607.025781 | |
Cooling water | 16,007.81491 | 20,629.249 | |
Hydraulic support | 2321.444076 | 3084.392126 | |
Pretreatment middle tank | Ground dust removal | 85,951.97709 | 77,688.64651 |
Fire water | 106,881.3539 | 77,298.12662 | |
Secondary treatment—high level tank | Coal treatment water | 48,634.14471 | 36,839.152 |
Heat exchange station water | 14,866.30572 | 21,469.65259 | |
Cooling water | 19,027.62374 | 20,063.64865 | |
Greening water | 11,815.05183 | 8218.430315 | |
Other water use | 34,813.89783 | 47,655.63901 | |
Deep treatment reuse tank | Boiler water | 80,165.00478 | 8252.076366 |
Life Drinking | 2076.096765 | 1727.431289 |
Water Reuse Points at All Levels of the Mine (N) | Recycling Volume in Traditional Mode m3/Month | Optimized Scheduling System Reuse m3/Month | ||
---|---|---|---|---|
Heating Season | Non-Heating Season | Heating Season | Non-Heating Season | |
Clear water tank (1) | 160,770 | 160,770 | 12,7813.73 | 134,530.75 |
Middle tank (2) | 0 | 0 | 192,833.33 | 154,986.77 |
High level tank (3) | 0 | 0 | 129,157.02 | 134,246.52 |
Reuse tank (4) | 51,840 | 58,875 | 82,241.10 | 9979.51 |
Mode | Mine Water Reuse Rate (Month) | Mine Water Reuse Time (Month) | Reuse Speed (m3/h) |
---|---|---|---|
Traditional Scheduling | 30.75% | 720 h | 295.29 |
ADIW-PSO | 76.95% | 572.41 h | 929.48 |
LDIW-PSO | 76.95% | 620.18 h | 857.89 |
EWIW-PSO | 76.95% | 615.21 h | 864.82 |
Mode | Mine Water Reuse Rate (Month) | Mine Water Reuse Time (Month) | Reuse Speed (m3/h) |
---|---|---|---|
Traditional Scheduling | 17.95% | 720 h | 305.06 |
ADIW-PSO | 35.45% | 602.49 h | 719.92 |
LDIW-PSO | 35.45% | 666.49 h | 650.79 |
EDIW-PSO | 35.45% | 658.82 h | 658.38 |
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Liu, Y.; Zhang, Z.; Bo, L.; Zhu, D. Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization. Sensors 2021, 21, 4114. https://doi.org/10.3390/s21124114
Liu Y, Zhang Z, Bo L, Zhu D. Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization. Sensors. 2021; 21(12):4114. https://doi.org/10.3390/s21124114
Chicago/Turabian StyleLiu, Yang, Zihang Zhang, Lei Bo, and Dongxu Zhu. 2021. "Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization" Sensors 21, no. 12: 4114. https://doi.org/10.3390/s21124114
APA StyleLiu, Y., Zhang, Z., Bo, L., & Zhu, D. (2021). Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization. Sensors, 21(12), 4114. https://doi.org/10.3390/s21124114