Superheat control for air conditioning and refrigeration systems: Simulation and experiments

Ever since the invention of air conditioning and refrigeration in the late nineteenth century, there has been tremendous interest in increasing system efficiency to reduce the impact these systems have on global energy consumption. Efficiency improvements have been accomplished through component design, refrigerant design, and most recently control system design. The emergence of the electronic expansion valve and variable speed drives has made immense impacts on the ability to regulate system parameters, resulting in important strides towards efficiency improvement. This research presents tools and methodologies for model development and controller design for air conditioning and refrigeration systems. In this thesis, control-oriented nonlinear dynamic models are developed and validated with test data collected from a fully instrumented experimental system. These models enable the design of advanced control configurations which supplement the performance of the commonly used proportional-integral-derivative (PID) controller. Evaporator superheat is a key parameter considered in this research since precise control optimizes evaporator efficiency while protecting the system from component damage. The controllers developed in this thesis ultimately provide better transient and steady state performance which increases system efficiency through low superheat set point design. The developed controllers also address the classical performance versus robustness tradeoff through design which preserves transients while prolonging the lifetime of the electronic expansion valve. Another notable contribution of this thesis is the development of hardware-in-the-loop load emulation which provides a method to test component and software control loop performance. This method alleviates the costs associated with the current method of testing using environmental test chambers

Operation of Compressor and Electronic Expansion Valve via Different Controllers

WOS: 00039941330001

Improving the Accuracy and Scope of Control-Oriented Vapor Compression Cycle System Models

The benefits of applying advanced control techniques to vapor compression cycle systems are well know. The main advantages are improved performance and efficiency, the achievement of which brings both economic and environmental gains. One of the most significant hurdles to the practical application of advanced control techniques is the development of a dynamic system level model that is both accurate and mathematically tractable. Previous efforts in control-oriented modeling have produced a class of heat exchanger models known as moving-boundary models. When combined with mass flow device models, these moving-boundary models provide an excellent framework for both dynamic analysis and control design. This thesis contains the results of research carried out to increase both the accuracy and scope of these system level models. The improvements to the existing vapor compression cycle models are carried out through the application of various modeling techniques, some static and some dynamic, some data-based and some physics-based. Semiempirical static modeling techniques are used to increase the accuracy of both heat exchangers and mass flow devices over a wide range of operating conditions. Dynamic modeling techniques are used both to derive new component models that are essential to the simulation of very common vapor compression cycle systems and to improve the accuracy of the existing compressor model. A new heat exchanger model that accounts for the effects of moisture in the air is presented. All of these model improvements and additions are unified to create a simple but accurate system level model with a wide range of application. Extensive model validation results are presented, providing both qualitative and quantitative evaluation of the new models and model improvements.Air Conditioning and Refrigeration Project 17

Realtime Optimization of MPC Setpoints using Time-Varying Extremum Seeking Control for Vapor Compression Machines

Recently, model predictive control (MPC) has received increased attention in the HVAC community, largely due to its ability to systematically manage constraints while optimally regulating signals of interest to setpoints. For example, in a common formulation of an MPC control problem for variable compressor speed vapor compression machines, the setpoints often include the zone temperature and the evaporator superheat temperature. However, the energy consumption of vapor compression systems has been shown to be sensitive to these setpoints. Further, while superheat temperature is often preferred because it can be easily correlated to heat exchanger efficiency (and therefore cycle efficiency), direct measurement of superheat is not always available. Therefore, identifying alternate signals in the control of vapor compression machines that correlate to efficiency is desired. Conventionally, methods for maximizing the energy efficiency rely on the use of mathematical models of the physics of vapor compression systems. These model-based approaches attempt to describe the influence of commanded inputs on the thermodynamic behavior of the system and the consumed electrical energy, and they are used to predict the combination of inputs that both meet the heat load requirements and minimize energy consumption. However, these models of vapor compression systems rely on simplifying assumptions in order to produce a mathematically tractable representation. Further, they are difficult to derive and calibrate, and often do not describe variations over long time scales, such as those due to refrigerant losses or accumulation of debris on the heat exchangers. In this paper, we consider a model-free extremum seeking algorithm that adjusts setpoints provided to a model predictive controller. While perturbation-based extremum seeking methods have been known for some time, they suffer from slow convergence rates---a problem emphasized by the long time constants associated with thermal systems. Our method uses a new algorithm (time-varying extremum seeking), which has dramatically faster and more reliable convergence properties. In particular, we regulate the compressor discharge temperature using an MPC controller with setpoints selected from a model-free time-varying extremum seeking algorithm. We show that the relationship between compressor discharge temperature and power consumption is convex (a requirement for this class of realtime optimization), and use time-varying extremum seeking to drive these setpoints to values that minimize power. The results are compared to the traditional perturbation-based extremum seeking approach. Further, because the required cooling capacity (and therefore compressor speed) is a function of measured and unmeasured disturbances, the optimal compressor discharge temperature setpoint must vary according to these conditions. We show that the energy optimal discharge temperature is tracked with the time-varying extremum seeking algorithm in the presence of disturbances