Hvac Control Systems Modelling Analysis And Design Pdf
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Readers of this book will be shown how, with the adoption of ubiquituous sensing, extensive data-gathering and forecasting, and building-embedded advanced actuation, intelligent building systems with the ability to respond to occupant preferences in a safe and energy-efficient manner are becoming a reality. The articles collected present a holistic perspective on the state of the art and current research directions in building automation, advanced sensing and control, including:.
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Next, the performance of MPC is compared with that of other control Accepted 18 November approaches. All rights reserved. Applications Performance Comparison 1. The categories of building HVAC systems. Therefore, the development and implementa- storage, computing, and communication devices, it is now feasible tion of effective control techniques for HVAC systems is of primary to adopt and implement a proper control approach to overcome the importance.
In particular, with the decreased costs of data pro- inherent issues in HVAC control. The focus of this paper is on a cessing, storage, and communication over recent years, the design survey of control methods for HVAC systems, and emphasis is and implementation of more complex control techniques have placed on the model predictive control MPC approach because become feasible. Afram, F. In particular, selected trends and issues process within the given bounds.
First, a review of accurate control of the process. Section 2 includes a brief review of previous sur- cooling coil units [16,17], room temperature control [18e22], veys related to HVAC control.
Section 4 discusses the comparison of MPC with other control , and heater control . Most of the research is methods as well as the factors that affect its performance.
Previous surveys time delays. Re- Brief reviews of hard and soft control techniques were reported tuning or auto-tuning approaches for the PID controller  can in Refs. The hard control techniques reviewed in be time-consuming.
In certain applications, auto-tuning might be Ref. The soft or intelligent operation . Hard control algorithm GA. Intelligent control techniques such as neuro- and genetic-fuzzy approaches were also reviewed in Ref. A review Hard controllers are based on a theory for control systems of hybrid controllers resulting from the fusion of hard and soft composed of gain scheduling control, nonlinear control, robust control techniques was also provided in Ref.
A review of hybrid and soft piecewise linear regions. For each of the linear regions, a linear PI or techniques i. Self-tuning neural controllers and multi-agent control systems MACs for PI or PID controllers are also proposed in the literature to vary energy management was provided in Ref. A review of fuzzy the controller gains based on the state of the process.
A in Ref. In Ref. An overview of supervisory and optimal control For nonlinear controller design, the control law can be derived of HVAC systems was given in Ref. The control law is used to drive the search, gradient-based search, sequential quadratic programming, nonlinear system toward a stable state while achieving the control evolutionary programming and GA were also reviewed in Ref.
Automatic controls for HVAC systems i. Additionally, a survey of the theory and applications of rameters. Examples of robust control include supply air tempera- adaptive control for HVAC systems was given in Ref. The control methods are divided into classical tion and control effort and maximization of thermal comfort.
Soft control Soft control techniques such as those based on FL [42e45] and ANN [6,26,46e48] are comparatively new techniques made possible by the advent of digital controllers.
In an FL controller [42e45], control actions are implemented in the form of if-then-else statements. The FL also can be incorporated for the auto-tuning of PID controller gains in which PID control represents the local scope of control, and the FL supervisor is often used to optimize the response of the system on the global scale. Alter- natively, the FL can be implemented on both the local and super- visory levels of control.
Examples of FL design include predicted mean vote PMV -based thermal comfort control , which controls temperature, humidity, and air velocity in an AHU. Another example of FL is the design of a three-level hierarchical supervisory-FL controller to generate the operating modes of the water and air subsystems and the set-points for the lower level controllers .
The algorithm is a black box modeling technique that does not require an under- standing of the underlying physics of the process. The ANN is commonly used in feed-forward control, and ANN can be trained on the controller inputeoutput in an attempt to replace a conventional controller in that application. Examples of ANN design include a PMV-based thermal comfort controller for zone temperature con- trol , optimization of air conditioning setback time based on outdoor temperature , and fan control of an air cooled chiller .
The implementation of FL control requires comprehensive knowledge of the plant operation and its different states, whereas ANN-based control design requires training data on a wide range of operating conditions, which may not be available for many systems.
Additionally, industry is usually reluctant to adopt and use a black Fig. Hybrid control Examples of optimal control design include active thermal storage Hybrid controllers are produced by the fusion of hard and soft control , passive thermal storage control , energy optimi- control techniques.
Several controllers, including quasi-adaptive zation of HVAC system [37,38], VAV system control , and fuzzy control , adaptive-neuro control  and fuzzy-PID building heating and cooling control [40,41]. For gain scheduling adaptive controllers at the lower levels of the control structure. Both hard and soft control techniques complement each tuning of multiple PID controllers in these regions can be quite other, and a combination of both can solve problems that may cumbersome.
Optimal control and robust control are promising not be solved by each technique separately. Examples of hybrid techniques for HVAC process control because they are capable of control include a fuzzy self-tuning PI controller for supply air rejecting disturbances and time-varying parameters.
Many of these ap- control. Among the hard control approaches, MPC is one of the with those techniques. Other control techniques range limit constraints. In many areas, energy has a variable price structure. In the presence of all of these challenges, an ideal Other control techniques, such as direct feedback linear DFL controller should be able to handle time-varying disturbances, control , pulse modulation adaptive controller PMAC , wide operating conditions, actuator constraints, and variable price pattern recognition adaptive controller PRAC , preview con- structures.
Apparently, many control systems display several trol , two parameter switching control TPSC , and rein- shortcomings in their application to HVAC control. For instance, the forcement learning control [59,60] have also been proposed for the classical controllers require manual tuning and perform sluggishly control of HVAC systems. Soft control of the system. By applying inputeoutput linearization, the coupled requires massive amounts of data for training and reinforcement, equations of the system are converted to linear uncoupled equa- and learning techniques require extensive time, rendering them tions to which conventional linear control techniques can be impractical for industrial implementations.
Alternatively, MPC applied. The DFL has been applied for control of zone temperature provides a solution to many of the aforementioned problems and in Ref. The MPC uses a system model to predict the future states of the The PID controller measures the error of the system from its set system and generates a control vector that minimizes a certain cost point and generates an analog signal as its output.
The entire process is repeated in the discrete input of the system instead of an analog signal. For next time instant. The cost function can take the form of tracking example, in Ref.
Constraints can be placed on the compressor. The PRAC automatically tunes the gain and integral rate and range limits of the actuators and the manipulated and time of the PI controller based on the closed loop response patterns controlled variables e. The process output is measured and are also modeled, and their predicted effects on the system are used fed to a digital PRAC, which estimates the process noise and tunes during control vector computation.
This effort results in a controller the PI controller parameters to tightly regulate the process. Comparison of MPC with other control approaches measurements of slab temperature and air temperature . The system undergoes a Most researchers use one or two of the above performance wide range of operating conditions.
In fact, MPC for HVAC systems is shown to outper- However, the exact solution method was unable to maintain the form most control techniques using the aforementioned perfor- room temperature at the set point at all times because it did not use mance metrics.
By simulation and experimental categories. For the charging and discharging control of an ice storage sys- 4. Simulation results tem, MPC outperformed conventional control strategies i. Compared with a PI control , as reported in Ref. Compared with conventional chiller sure disturbances. When a supervisory MPC-based optimal the PI controller responded too aggressively, which resulted in sequence of tank water set points was used in Ref.
In contrast, the MPC- consumption of heat pump was reduced. When zone 4. The MPC was also applied to regulate the process within a feasible range close to the set point. However, the evaporator temperature and pressure by controlling the electronic control effort put forth by the PI controller was much larger than expansion valve EEV and compressor speed. For comparison that of the MPC controller.
From inspecting the control signals purposes, local level PI controllers were also implemented on the generated by the PI and MPC controllers, it was observed that the PI aforementioned processes. Adding supervisory MPC to the need for re-tuning. In the simulations, MPC produced less over- centralized and distributed MPC in the presence of coupling effects shoot and a faster settling time compared with the PID controller.
Each PI controller regulated the zone temperature superior tracking performance compared with the PID controller. The multi-zone was controlled using controllers designed using prescribed error decentralized MPC controllers also behaved in a fashion similar to dynamics and MPC techniques in conjunction with feedback line- that of the PI controller. However, the centralized and distributed arization .
Download HVAC Control Systems: Modelling, Analysis and Design PDF
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Start reading HVAC Control Systems for free online and get access to an unlimited library of Modelling, Analysis and Design neural networks and fuzzy logic systems, all of which are given a thorough analytical treatment in the book.
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A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable PV being controlled with the desired value or setpoint SP , and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint.
Cooling towers or recoolers are one of the major consumers of electricity in a HVAC plant. The implementation and analysis of advanced control methods in a practical application and its comparison with conventional controllers is necessary to establish a framework for their feasibility especially in the field of decentralised energy systems.
Overall value is simply the sum of all the value computations for each design concept. All Chapter 2 - Units and Measurement Exercises Questions with Solutions to help you to revise complete Syllabus and boost your score more in examinations. NET, this is usually done as follows: 1. They include information on the design of bridge spans, mechanical systems, electrical systems, and bridge protection systems, as well as information on seismic analysis and vessel impact analysis. Digital Design 4th Edition - Morris Mano. Lam, and J.
The building has four floors in total, with separate air-handling units AHUs on each floor. The factors of wind direction and velocity are also applied as disturbances. By comparing usage data on simulated power consumption versus measured data for the three months of October, November, and December , good agreement was achieved with simulated data. The main aim is to develop a state feedback controller and then apply it toward optimal functionality of a control system. However, heating and cooling loads typically change according to the exterior environment, as well as with the specific needs of the users. HVAC systems require a control system to keep the comfort level and air quality relatively constant with variable conditions.