Water Pump Scheduling via Multi-Objective Optimization
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Solution Overview
Problem
Current water supply pipe network scheduling methods fail to consider logical control rules and constraints between nodes, leading to high energy and maintenance costs due to inefficient energy consumption and water loss.
Innovation Solution
A high-dimensional multi-objective optimization model is constructed for water pump scheduling, taking energy and maintenance costs as independent objectives, using optimization algorithms to find Pareto optimal solutions and screen scheduling schemes that reduce costs through visual comparison and two-factor sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional energy-saving scheduling control is applied to secondary water supply facilities, then energy consumption of water supply pump house is reduced, but logical control rules and constraints between nodes in the water supply pipe network system are not considered, leading to certain limitations on scheduling scheme
Solution Approach 1:
The patent segments the water supply pipe network into multiple nodes and establishes logical control rules for each node. The scheduling system divides the network into controllable segments with specific constraints, allowing independent optimization at each node while maintaining overall system coordination. This segmentation enables the scheduling scheme to adapt to different local conditions and constraints throughout the network.
Solution Approach 2:
The patent introduces a new dimension of logical control rules and node constraints into the scheduling system. By adding this dimensional layer of control logic, the system transforms from simple energy optimization to a multi-dimensional optimization that simultaneously considers energy consumption, logical control rules, and node constraints, thereby resolving the limitation of traditional approaches.
2Use of energy by moving object
If water pump scheduling optimization is implemented, then energy cost is reduced, but maintenance cost is not considered
Solution Approach 1:
The patent merges energy cost optimization and maintenance cost optimization into a unified dual-objective scheduling system. By combining these two previously separate optimization goals into a single integrated framework, the system simultaneously minimizes both energy consumption and maintenance requirements, preventing the trade-off where energy savings lead to increased maintenance costs.
Solution Approach 2:
The scheduling system achieves multi-functionality by simultaneously optimizing for both energy cost and maintenance cost. The optimization algorithm is designed to handle multiple objectives concurrently, making the system universal in its ability to address different cost factors without requiring separate scheduling systems for each objective.
3Power
If scheduling focuses on reducing energy consumption through frequency conversion technology, then power consumption is reduced, but comprehensive system constraints and logical control rules are ignored
Solution Approach 1:
The patent implements dynamic scheduling that adapts to changing system conditions and constraints. The logical control rules and node constraints are dynamically integrated into the optimization process, allowing the system to adjust scheduling decisions in real-time based on current operational states, thereby managing complexity through adaptability rather than rigid predetermined rules.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor system state, constraint satisfaction, and performance metrics. This feedback loop allows the scheduling algorithm to adjust its decisions based on actual system behavior, ensuring that logical control rules and node constraints are maintained while optimizing power consumption, thereby managing system complexity through intelligent regulation.
Data Source
AI summary
A scheduling method, system and device for uniformly reducing energy and maintenance costs of a water supply pipe network system. The method comprises: acquiring a topological structure and operation data of the system, constructing a high-dimensional multi-objective optimization model for water pump scheduling of the system, and setting a decision variable, an objective function and a constraint condition of the model, the high-dimensional multi-objective optimization model taking an energy cost and a maintenance cost of each water pump in the system as independent optimization objectives; solving the high-dimensional multi-objective optimization model by applying an optimization algorithm to obtain a Pareto optimal solution set; and based on the Pareto optimal solution set, screening and outputting a scheduling scheme for cooperatively reducing the energy cost and the maintenance cost of the water pump by drawing a parallel coordinate graph for visual comparison and using a two-factor sorting method.