Gas separation membrane device control method based on multi-target particle swarm optimization algorithm
Optimizing the control parameters of the gas separation membrane device through the multi-objective particle swarm optimization algorithm, solving the problem of the inability to optimize parameters in industrial applications, improving separation efficiency and reducing energy consumption, and maintaining high purity of product gas.
CN120437798AInactive Publication Date: 2025-08-08XILINGOL VOCATIONAL COLLEGE
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Patent Information
- Application Number
- CN202510531707.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-08-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Technical Problem
In industrial applications, the empirical parameters cannot reach the optimal solution, resulting in difficult improvement in production efficiency and cost-effectiveness.
Method used
The multi-objective particle swarm optimization algorithm is used to optimize the control parameters of the gas separation membrane device. By defining the optimization target, particle swarm initialization, fitness function, iterative optimization and control of the closed loop, the optimal control of pressure, temperature, partial pressure difference and gas flow field are achieved.
Benefits of technology
The gas separation efficiency is improved by 15%, energy consumption is reduced by 10%, product gas purity is improved by 5%, and the optimal effect of industrial production is achieved.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a gas separation membrane device control method based on a multi-objective particle swarm optimization algorithm, and relates to the technical field of gas membrane separation, and the method comprises the following steps: S1, defining an optimization objective; s2, initializing a particle swarm; s3, fitness function definition: defining four fitness functions corresponding to the four optimization targets respectively; s4, performing an iterative optimization process; s5, control parameter optimization: selecting a solution from the Pareto optimal solution set as a final control parameter; s6, closed-loop control implementation: applying the optimized control parameters to the gas separation membrane device to realize closed-loop control; and S7, result verification. By adopting the gas separation membrane device control method based on the multi-target particle swarm optimization algorithm, the problem that the empirical parameters cannot reach the optimal solution when the membrane separation process is industrially applied is solved, the optimal solution of the target parameters in the production process is realized, and the efficiency and cost performance of industrial production are improved.
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