Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets

TL;DR

This paper introduces a novel method to estimate evaporator valve sizes in supermarket refrigeration systems using monitoring data, employing linear regression and ARMAX models to improve accuracy and determine optimal sampling times.

Contribution

It develops a new approach for estimating valve constants from monitoring data, incorporating thermodynamic analysis and dynamic modeling to enhance accuracy and applicability.

Findings

Linear regression requires no transient dynamics in data.

ARMAX model effectively eliminates auto-correlation in residuals.

Optimal sampling time varies per evaporator and can be determined via frequency analysis.

Abstract

In many applications, e.g. fault diagnostics and optimized control of supermarket refrigeration systems, it is important to determine the heat demand of the cabinets. This can easily be achieved by measuring the mass flow through each cabinet, however, that is expensive and not feasible in large-scale deployments. Therefore it is important to be able to estimate the valve sizes from the monitoring data, which is typically measured. The valve size is measured by an area, which can be used to calculate mass flow through the valve -- this estimated value is referred to as the valve constant. A novel method for estimating the cabinet evaporator valve constants is proposed in the present paper. It is demonstrated using monitoring data from a refrigeration system in a supermarket consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10-20 minutes is adequate for the method. Through thermodynamic analysis of a two-stage CO2 refrigeration system, a linear regression model for estimating valve constants was developed using time series data. The linear regression requires that transient dynamics are not present in the data, which depends on multiple factors e.g. the sampling time. If dynamics are not modeled it can be detected by a significant auto-correlation of the residuals. In order to include the dynamics in the model, an Auto-Regressive Moving Average model with eXogenous variables (ARMAX) was applied, and it is shown how it effectively eliminates the auto-correlation and provides more unbiased estimates, as well as improved the accuracy estimates. Furthermore, it is shown that the sample time has a huge impact on the valve estimates. Thus, a method for selecting the optimal sampling time is introduced. It works individually for each of the evaporators, by exploring their respective frequency spectrum.