Hybrid Modeling Of Refrigeration System Based On Neural Network Component Models

Computational simulation on refrigeration and air-conditioning appliances has been fairly developed for several decades, and has made a huge contribution to the design of products, optimization and energy saving."Component-oriented"system modeling, which bases on physics-based component models, has good generality to be applied to different systems and can give reasonable trends prediction. Computational simulation should meet different needs under different phase and conditions. Currently, the simulation speed and robustness are new and clear requirements besides accuracy.In order to meet these requirements, a new approach of refrigeration system simulation has been presented in the present work, namely, hybrid modeling of refrigeration system based on Neural Network (NN) component model. The hybrid modeling integrates the merits and avoids the shortcomings of"component-oriented"system modeling and NN modeling. The hybrid modeling is the product of new modeling requirement, and can be regarded as an extension of system model based on physics-based component model. In detail, the main contents of present work include:1. Propose the hybrid modeling of refrigeration system based on NN component model clearly. What is hybrid modeling, why propose and how to realize hybrid modeling are stated in detail. In system modeling level, the hybrid modeling keeps the generality of"component-oriented"system model; in component modeling level, NN model simplifies component model and remarkably increases simulation speed. The hybrid modeling shows many merits including the genrality, quick simulation and good robustness. Therefore, the hybrid modeling is a new alternative solution of refrigeration system modeling.2. Improvement of five steps in development of NN model. Deep analysis was carried out at first, and then corresponding improvement methods to overcome shortcomings and mitigate risks in each step were proposed respectively. NN model property is improved, including reduction of over-fitting risk, improvement of accuracy in a large range, prediction of tendency and so on. The main improvement methods include:a) Reasonable processing of data sample to improve model's accuracy and trends, such as adding theoritical points, even distribution of training data along with outputs other than inputs, and so on.b) Choose input and output parameters based on analysis of physics-based model to avoid redundancy or omission parameters.c) Specify polynomial transfer function which is proved identical to polynomial correlation. Over-fitting risk is avoided by this way in theory.d) Optimize NN structure according to the property of the object.Through this way, both flexibility of neural network and training efficiency are improved.3. Application of the hybrid modeling. Develop refrigeration system modeling based on developed component models, and then do experimental validation and analysis:a) Performance simulation of two economized water-cooled screw chillers. Due to the accurate and continuous NN model of screw compressor, the chiller model can simulate from full load to unload continuously, and the prediction accuracy under part load conditions are improved obviously.b) Performance simulation of a light commercial air-conditioner. Since NN component model avoids iteration, its simulation speed is much quicker than physics-based model. In turns, the hybrid model of the light commercial air-conditioner based on four NN component models can save simulation time dramatically. Compared to physics-based system model, simulation speed is improved around 12 times, and system model runs more robustly under working conditions.Finally, the author summarizes the present work and proposes the further research ideas in the field.