No abstract

In this study, an exergy analysis of two kinds of solar-driven cogeneration systems consisting of solar collectors and an organic Rankine cycle (ORC) is presented for series mode and parallel mode. Three kinds of solar collectors are considered: flat-plate collectors (FPC), evacuated tube collectors (ETC), and parabolic trough collectors (PTC). This study mainly compares the exergy output of the two kinds of solar cogeneration systems under different temperatures of the return heating water and different inlet temperatures of the solar collectors. This study shows that, from the perspective of Wnet or E̲n, the parallel mode is superior to the series mode. From the perspective of Ez, the parallel mode is superior to the series mode when the solar collector is FPC; however, the series mode is superior to the parallel mode when the solar collector is PTC. When the solar collector is ETC, the result depends on the temperature of the return heating water. When the temperature of the return heating water is low (below 46°C), the series mode is better, and when the temperature of the return heating water is high (above 46°C), the parallel mode is better.

In this study, the form and operation modes of a novel solar-driven cogeneration system consisted of various solar collectors (flat plat collectors (FPC), evacuated tube collectors (ETC), and parabolic trough collectors (PTC)) and ORC (organic Rankine cycle) based on building heating load are analyzed. This paper mainly obtains the fitting formula of thermal efficiency of the ORC power generation device and determines the form and operation mode of the cogeneration system. The form is the same, but the operation modes are different for PTC and FPC or ETC. There are six operating modes, respectively, based on the size relationship between the heating load of buildings and the effective heat collection of the solar collector subsystem when the solar collectors are PTC or FPC and ETC.

Artificial neural network has been widely used in air conditioning systems as an effective method for predicting parameters, and the accuracy of ANN model relies on training data and network structure. In order to increase the quality of chilled water loops model, this paper develops an optimal data processing algorithm combining Kalman filtering with particle swarm optimization to compensate for uncertain factors and disturbances of collected data from the case building and establishes the nonlinear variation trend database. Based on Elman and BP neural networks, this paper proposes the improved network structures to avoid the local optimum predicted value of chilled water loops and increase data training speed. Simulation results show that this algorithm improves the data accuracy of current percentage (CP) of chillers and chilled water temperatures 12% and 9%. Compared with Elman and BP models, mean absolute errors of CP improved models are improved 24.1% and 10.3%, and mean squared errors of water temperature improved models are improved 5.2% and 4.8%. For the purpose of energy conservation control in air conditioning systems, this work has an application value and can be used for predicting other parameters of buildings.

As an important subsystem in air-conditioning system, water system connects the chiller and terminal equipment. Therefore, it is necessary to develop an accurate thermal model for chilled water pipe to create the suitable indoor temperature and humidity environment. In this paper, the thermal model of the pipe was considered by utilizing the simplified thermal time-delay state-space model with the mass, energy balance, and heat consumption equations. Based on this improved model, the preview control as control strategy for water pipe temperature was proposed, and its robustness and stability were discussed. Subsequently, the performances of this model and control strategy were tested in a fan-coil system simulating with MATLAB and TRNSYS (Transient System Simulation Program). Explicitly, the results show that this model accurately predicts the thermal characteristic, and the average mean squared errors for water temperature were 11.14% and 12.82%, respectively. Meanwhile, the tracking effect of valve controller was better than the control strategy with no preview control.