東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Dynamic modeling, intelligent contro...

Li, Lian Zhong.

Linked to FindBook

Google Book

Amazon

博客來

Dynamic modeling, intelligent control and diagnostics of hot water heating systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Dynamic modeling, intelligent control and diagnostics of hot water heating systems./
Author:	Li, Lian Zhong.
Description:	226 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4404.
Contained By:	Dissertation Abstracts International71-07B.
Subject:	Engineering, Architectural. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR63447
ISBN:	9780494634479

Dynamic modeling, intelligent control and diagnostics of hot water heating systems.
Li, Lian Zhong.

Dynamic modeling, intelligent control and diagnostics of hot water heating systems. - 226 p.

Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4404.

Thesis (Ph.D.)--Concordia University (Canada), 2009.

Heating, ventilating and air-conditioning (HVAC) systems have been extensively used to provide desired indoor environment in buildings. It is well acknowledged that 25-35% of the total energy use is consumed by buildings, and space heating systems account for 50-60% of the building energy consumption. Furthermore, roughly half of the energy consumed goes to operation of heating systems. In the past few years the energy use has shown rapid growth. Therefore, it is necessary to design and operate HVAC systems to reduce energy consumption and improve occupant comfort. To improve energy efficiency, HVAC systems should be optimally controlled and operated.

ISBN: 9780494634479Subjects--Topical Terms:

1671790
Engineering, Architectural.

Dynamic modeling, intelligent control and diagnostics of hot water heating systems.
LDR:03016nam 2200289 4500 001 1395433
005 20110518115249.5
008 130515s2009 ||||||||||||||||| ||eng d
020 $a 9780494634479
035 $a (UMI)AAINR63447
035 $a AAINR63447
040 $a UMI $c UMI
100 1 $a Li, Lian Zhong. $3 1674121
245 1 0 $a Dynamic modeling, intelligent control and diagnostics of hot water heating systems.
300 $a 226 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-07, Section: B, page: 4404.
502 $a Thesis (Ph.D.)--Concordia University (Canada), 2009.
520 $a Heating, ventilating and air-conditioning (HVAC) systems have been extensively used to provide desired indoor environment in buildings. It is well acknowledged that 25-35% of the total energy use is consumed by buildings, and space heating systems account for 50-60% of the building energy consumption. Furthermore, roughly half of the energy consumed goes to operation of heating systems. In the past few years the energy use has shown rapid growth. Therefore, it is necessary to design and operate HVAC systems to reduce energy consumption and improve occupant comfort. To improve energy efficiency, HVAC systems should be optimally controlled and operated.
520 $a This study focuses on developing advanced control strategies and fault tolerant control (FTC) using information from fault detection and diagnosis (FDD) for hot water heating (HWH) systems. To begin with, HWH system dynamic models are developed based on mass, momentum and energy balance principles. Then, embedded intelligent control strategies: fuzzy logic control and fuzzy logic adaptive control are designed for the overall system to achieve better performance and energy efficiency. Moreover, in designing the advanced control strategies, the parameter uncertainty and noise from measurement and process are taken into account. The extended Kalman filter (EKF) technique is utilized to handle system uncertainty and measurement noise, and to improve system control performance. After that, a supervisory control strategy for the HWH system is designed and simulated to achieve optimal operation. Finally, model-based FDD methods were developed by using fuzzy logic to detect and isolate measurement and process faults occurring in HWH systems. The FDD information was employed to design model-based FTC systems for various faults and to extend the operating range under failure situations.
520 $a The contributions of this study include the development of a large scale dynamic model of a HWH system for a high-rise building; design of fuzzy logic adaptive control strategies to improve energy efficiency of heating systems and design of model-based FTC systems by using FDD information.
590 $a School code: 0228.
650 4 $a Engineering, Architectural. $3 1671790
650 4 $a Engineering, Civil. $3 783781
650 4 $a Energy. $3 876794
690 $a 0462
690 $a 0543
690 $a 0791
710 2 $a Concordia University (Canada). $3 1018569
773 0 $t Dissertation Abstracts International $g 71-07B.
790 $a 0228
791 $a Ph.D.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR63447