Dynamic characteristic model building method of chilling system, chilling system monitoring method and chilling method monitoring device

Abstract

The invention relates to a dynamic characteristic model building method of a chilling system, a chilling system monitoring method and a chilling method monitoring device. The dynamic characteristic model building method of the chilling system comprises the following steps of: computing an actual running efficiency value; obtaining a plurality of control points, wherein the control points determine a representative resultant curve dynamic characteristics model; computing one most appropriate moving direction vector of the control points, wherein after each control point is separately adjusted according to one corresponding element value in the most appropriate moving direction vector, the obtained calculative resultant cure dynamic characteristic model is closer to the actual running efficiency value than a resultant curve dynamic characteristic model; providing a movement variable, wherein the control points are adjusted according to a product value of the movement variable and the most appropriate moving direction vector to accordingly obtain the resultant curve dynamic characteristic model which is close to the actual running efficiency value; and during iterative computations, determining the value of the movement variable according to a univariable minimum searching method (a technique for finding the extremum of a unimodal function).

Description

technical field [0001] The invention relates to a method for establishing a dynamic characteristic model of a chilling system. The invention also relates to a water cooler monitoring method and a water cooler monitoring device. Background technique [0002] The air-conditioning ice water system used in large commercial buildings and factories is mainly composed of water chillers, ice water pumps, cooling water pumps, and cooling water towers. Among them, water chillers have the highest energy demand. Taking a 12-inch wafer factory with a total refrigerated tonnage (RT) of 21,000 as an example, the total power consumption of air-conditioning ice water accounts for about 26% of the total power consumption of the whole factory, and the water chiller accounts for 50% of the total power consumption. . Therefore, if the water chillers can be guaranteed to operate in the highest efficiency range, considerable energy loss can be reduced. [0003] The characteristic curves of wate...

AI Technical Summary

Problems solved by technology

However, these two models have the following disadvantages: (1) A certain amount of operating data needs to be prepared, and the model cannot be established if there is too little operating data

On the other hand, the water chiller needs maintenance after running for a long time.

However, after the water chiller has been repaired and maintained, its actual host operating characteristics will be different from the modeled characteristics

In this situation, the original characteristic curve needs to be adjusted, and the characteristic curve established by using the regression model and the artificial intelligence model will be trained for a long time again, and it is impossible to directly adjust the characteristic curve locally and dynamically fix

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