Method for optimizing refrigerant ratio in hybrid refrigeration process based on energy consumption estimation

By constructing a neural network model for predicting energy dissipation, the problem of predicting the impact of refrigerant composition changes on total power consumption in hybrid refrigeration processes was solved, enabling rapid and accurate ratio optimization and improving the energy efficiency and economy of hybrid refrigeration processes.

CN122157880APending Publication Date: 2026-06-05ZHIDAN LVNENG OIL & GAS TECH SERVICE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHIDAN LVNENG OIL & GAS TECH SERVICE CO LTD
Filing Date
2026-05-06
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

In existing hybrid refrigeration processes, adjustment schemes based on steady-state physical models cannot quickly and accurately predict the impact of refrigerant composition changes on total power consumption. This results in the ratio optimization results lagging behind changes in operating conditions, making it difficult to meet the collaborative optimization requirements for ultra-low energy consumption operation.

Method used

By constructing an energy dissipation prediction neural network model, using historical operating data to build an operating energy efficiency feature matrix, training an energy efficiency mapping engine, and realizing a quantitative description of the nonlinear coupling relationship between refrigerant components and key process node parameters, and determining the refrigerant ratio optimization scheme based on energy efficiency optimization gradient information.

Benefits of technology

It improves the response speed and efficiency of refrigerant ratio optimization, reduces dynamic operating energy consumption, and enhances the economic operating performance of hybrid refrigeration processes.

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Abstract

The application provides a refrigerant ratio optimization method in a hybrid refrigeration process based on energy consumption estimation. An original working condition sample space is constructed, and the original working condition sample space is converted into a running energy efficiency feature matrix representing the correlation between the device running state and energy consumption evolution; the running energy efficiency feature matrix is used to train and verify the constructed energy dissipation estimation neural network model, and the verified energy dissipation estimation neural network model is set as an energy efficiency mapping engine; the target refrigeration performance requirement parameters are determined, the target refrigeration performance requirement parameters and the mole fraction of each refrigerant component in the current hybrid refrigeration process are input into the energy efficiency mapping engine for forward reasoning to obtain an estimated comprehensive energy consumption index; the mole fraction of each refrigerant component in the current hybrid refrigeration process is mapped into candidate ratio solution particles, and based on the estimated comprehensive energy consumption index, the energy efficiency performance of each candidate ratio solution particle is inversely reasoned to determine a refrigerant ratio optimization scheme.
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