Estimation of evaporator valve sizes in supermarket refrigeration cabinets

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 in Otterup (Denmark), consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10–20 min is adequate for the method. Through thermodynamic analysis of a two stage CO2 refrigeration system, a linear regression model for estimating valve constants is 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 modelled 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) is applied, and it is shown how it effectively eliminates the auto-correlation and provides less bias and higher accuracy of the estimates. Furthermore, it is shown that the sample time has a huge impact on the 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. That way, a reliable estimate of the actual valve constant can be achieved for each evaporator in the system and it is shown that sampling times above 10 min are optimal for the analysed systems.