Energy-saving optimization control method and system for chemical equipment

By constructing the initial feasible region of control variables and multivariate factor evaluation for chemical equipment, and combining neural networks and genetic algorithms, the energy-saving control problem of chemical equipment under environmental fluctuations was solved, and the energy efficiency and stability of the entire system were maximized.

CN122362867APending Publication Date: 2026-07-10TIANJIN NAVISTAR FLUID EQUIP CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN NAVISTAR FLUID EQUIP CO LTD
Filing Date
2026-04-30
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing chemical equipment control schemes suffer from decreased model prediction accuracy when faced with fluctuations in raw material properties, load, or ambient temperature, leading to control strategy failure. Furthermore, they fail to effectively consider the thermal coupling relationship between multiple devices, resulting in localized energy savings but overall energy waste.

Method used

By collecting historical operating data and performing operating condition clustering, an initial feasible domain for control variables is constructed. Combining multiple factors such as energy consumption cost, efficiency, temperature margin, pressure drop loss, and control stability, a heat transfer evaluation channel is trained using a neural network. Multi-objective optimization and genetic algorithms are used to screen out the control parameters with the highest comprehensive energy efficiency, ensuring the rational allocation of energy in the entire system, and verifying the equipment status online.

Benefits of technology

It achieves efficient and energy-saving control of chemical equipment in complex environments, avoids energy efficiency decline caused by equipment deterioration, and ensures the overall energy efficiency and stability of the system.

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Abstract

This invention provides an energy-saving optimization control method and system for chemical equipment. The method collects historical operating data and performs operating condition clustering to construct an initial feasible domain for control variables. It constructs a multivariate heat transfer evaluation factor including energy cost, efficiency, temperature margin, pressure drop loss, and control stability, and uses a neural network to train and generate heat transfer evaluation channels, integrating multiple conflicting energy-saving indicators into a unified comprehensive evaluation value. Based on this, it employs multi-objective optimization and a genetic algorithm for progressive optimization, selecting the control parameter vector with the highest comprehensive energy efficiency from a global perspective, ensuring the rational allocation of energy throughout the system. Finally, it verifies the scaling status of equipment online, eliminating control strategies that may fail due to equipment deterioration before execution, thus solving the problem of energy efficiency decline caused by limiting control of chemical equipment to local equipment and ignoring equipment deterioration.
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