A direct-fired absorption chiller systes more heat nenrgy than a vapour-comlpression chiller system of slmilar capacity. The combined tise of fuel energy and electricity consumption an important criterion is its performance evaluation. Many current research efforts on absorption chiller system are related to the search of an optimal supervisory control stategy to minimize the energy costs System simulation has been a contventional approach for the investigation. However. the task of developing an accurater system model through the component-modelling approach can be tedious Oversimplification in the process, and the nonlinear structure of the equation set as result. are often the limitations for raching reliable conveging solutions. The system-based artificial neural network (ANN) modelling approach appears to be an atternative. This article describes the process of deriving an ANN model of a commercial direct-fired double-effect absorption chiller system. The techniques and the ways to overcome the diffculties ln the training orocess are discussed.

The optimal use of fuel and electricity in a direct-fired absorption chiller system is important in achieving economical operation. Previous work on the control schemes mainly focused on the component local feedbck control. A system-based control approach, which allows an overall consideration of the interactive nature of the plant, the building and their associated variables is seen to be the right direction. This paper introduces a new concept of integrating neural network (NN) and genetic algorithm (GA) in the optimal control of absorption chiller system. Based on a commercial absorption unit, neural network was used to model the system charcteristics and genetic algorithm as a global optimization. The results appear promising.

A convection transport visualization technique is suggested to analyze the indoor air environment (IAE). A two-dimensional and laminar displacement ventilated room with discrete heat and contaminant sources is numerically investigated. Based on the governing equations, three convection transport functions, i.e. streamfunction, heatfunction, and massfunction, are derived to describe the fluid, heat, and contaminant transport processes, respectively. Main attentions are focused on the effects of the strength of heat source (Gr), the strength of contaminant source (Br), the strength of external ventilation (Re), and the positions of inlet/outlet openings on IAE. Numerical results illustrated that the abstract transport behaviors of the fluid, heat, and contaminant indoors are clearly exhibited by the convection transport functions which provides a simple but practical means of assessing IAE.

It is necessary that a reliable and optimal control system has the ability of fault tolerance, which recovers the faulty and/or missed data in time. Principal component analysis (PCA) is presented to model HVAC monitored systems by using the measured data under normal operation condition (NOC). PCA splits the measurement space into two subspaces, one principal component subspace (PCS) and the other residual subspace (RS). When the faulty or missed data is observed, it will be projected into PCS and RS. It is then recovered by sliding the faulty or missed data to PCS via iteration. Examples of HVAC monitoring system have demonstrated that the approach has good performance to recover faulty or missed data and thus can be embedded into the system to achieve fault-tolerant control.