Nonlinear Parameter Estimation in a Typical Industrial Air Handler Unit

Author(s):  
Lujia Feng ◽  
Pierluigi Pisu ◽  
Laine Mears ◽  
Jörg Schulte

The energy usage inside of a manufacturing plant is mainly from two sources: energy demand from the production lines to support manufacturing processes, and the plant building temperature control to maintain a comfortable working environment. It is reported that in the US, 14% of the primary energy and 32% of electricity is used by the industry and commercial building heating, ventilation and air conditioning (HVAC) system. As an important part of the HVAC system, the air handler unit (AHU) is a comprehensive air control system consisting of multiple sub-units. Accurate modeling of the supply air temperature of AHU is important for later controller design and fault detection, but it is also challenging because of the application of variable frequency drive (VFD) systems, overall degradation, and limited sensor information and meter data. Parameter estimation of the industry AHU is therefore worth studying. In this study, the authors intend to establish a deterministic physical model of AHU system, identify the unknown parameters based on the limited meter inputs, and compare the nonlinear parameter estimation results with the design parameters, in order to achieve the goal of improving the modeling accuracy without installing expensive metering systems.

2006 ◽  
Vol 10 (3) ◽  
pp. 395-412 ◽  
Author(s):  
H. Kunstmann ◽  
J. Krause ◽  
S. Mayr

Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2 in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.


2015 ◽  
Vol 63 (23) ◽  
pp. 6423-6428 ◽  
Author(s):  
Pooria Pakrooh ◽  
Ali Pezeshki ◽  
Louis L. Scharf ◽  
Douglas Cochran ◽  
Stephen D. Howard

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