Abstract
A unique data-based and physically meaningful nonlinear continuous-time model of heating element is presented. The model is considered to be of low complexity yet achieving high simulation performance. The physical meaningfulness of the model provides enhanced insight into the performance and functionality of the system. In return, this information can be used during the system simulation and improved model based control designs for tight temperature regulation. The second contribution presented in this work is the parameter estimation of the derived nonlinear model in continuous-time domain itself. For this purpose the application of refined instrumental variable methods has been found to be particularly suitable.
Highlights
The paper reports on modelling and data-based identification of a heating element, which preheats the inlet air used by a testbed for testing and modelling of phase change materials (PCM) used in passive air conditioning for sustainable housing applications, for more details see [1]
The main motivation for the work is the actual modelling of the phase change materials itself where a clear parallel between the air being conditioned by a heating element or the PCM based heat exchanger can be found
The identified nonlinear model reduces to a so called bilinear model class for constant inlet air temperatures, where such models have been found useful in modelling of heating, ventilation and air conditioning (HVAC) systems by the authors
Summary
The paper reports on modelling and data-based identification of a heating element, which preheats the inlet air used by a testbed for testing and modelling of phase change materials (PCM) used in passive air conditioning for sustainable housing applications, for more details see [1]. 3. Data-based model identification Having obtained the measured input and output data, being the inlet and outlet air temperatures together with the air velocity, a continuous-time model is identified in a data-based mechanistic manner [3, 4, 5]. The main outcome of applying the physical laws to prime the data-based model structure has been the selection of two model inputs being the product between the inlet air temperature and air velocity and the product between the outlet air temperature and air velocity, i.e. Ti(tk)v(tk) and To(tk)v(tk), respectively This makes the suggested model nonlinear in structure, while still being linear in parameters so that least-squares based model parameter estimation methods can be adopted.
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