A review on temperature and humidity control methods focusing on air-conditioning equipment and control algorithms applied in small-to-medium-sized buildings

Accepted Manuscript Title: A review on temperature and humidity control methods focusing on air-conditioning equipment and control algorithms applied in small-to-medium-sized buildings Authors: Xiangguo Xu, Ziwen Zhong, Shiming Deng, Xiaobo Zhang PII: DOI: Reference:

S0378-7788(17)31504-9 https://doi.org/10.1016/j.enbuild.2017.12.038 ENB 8231

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Received date: Revised date: Accepted date:

25-4-2017 8-12-2017 17-12-2017

Please cite this article as: Xiangguo Xu, Ziwen Zhong, Shiming Deng, Xiaobo Zhang, A review on temperature and humidity control methods focusing on air-conditioning equipment and control algorithms applied in small-to-medium-sized buildings, Energy and Buildings https://doi.org/10.1016/j.enbuild.2017.12.038 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

A review on temperature and humidity control methods focusing on air-conditioning equipment and control algorithms applied in small-to-medium-sized buildings Xiangguo Xu1,*, Ziwen Zhong1, Shiming Deng2, Xiaobo Zhang1 Institute of Refrigeration and Cryogenics, Key Laboratory of Refrigeration and Cryogenic Technology of Zhejiang Province, Zhejiang University Hangzhou, Zhejiang, China, 310027 Department of Building Services Engineering

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2

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The Hong Kong Polytechnic University, Hong Kong SAR, China

Highlights

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 A comprehensive review on temperature and humidity control methods applied in

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small-to-medium-sized buildings was provided

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 Control methods were summarized into two kinds, i.e., hardware-based decoupled (HWBD) control and software-based decoupled (SWBD) control

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 Principles, merits, and obstacles of these two methods are presented

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 Potential benefits of energy saving and better control performance brought by

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combining the two methods are discussed

Abstract

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Humidity is an important factor that influences both thermal comfort and indoor air quality. Air-conditioning (A/C) systems in large-scale buildings can employ different equipment to control temperature and humidity independently. However, A/C systems commonly seen in small- and medium-sized buildings have no specific device to deal with moisture due to space limitations, which may leave humidity in these buildings 1

without regulation. This paper summarizes two methods applied to control temperature and humidity in these buildings using A/C systems. One is to modify the structure of A/C systems or to introduce an additional dehumidifying equipment that is adaptive in small- and medium-sized buildings to decouple temperature and humidity control

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loops. The other is to develop advanced control algorithms to realize simultaneous temperature and humidity control in a variable speed direct expansion air-conditioning

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unit. Principles, merits, and obstacles of these two methods are presented. Since the paths for development of the two methods were totally separated before, potential

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benefits of energy saving and better control performance brought by combining the two

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methods are discussed. It is expected that this paper could promote relevant studies and

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applications.

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1. Introduction

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conditioning system; decouple

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Keyword: Temperature and humidity control; small- and medium-sized buildings; air-

The control objects of modern heating, ventilation, and air-conditioning (HVAC) systems can be summarized into three aspects: indoor thermal comfort, indoor air

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quality (IAQ) and indoor air pressurization. Taking indoor thermal comfort as an example, there are six parameters that could affect it: temperature, humidity, air velocity, mean radiant temperature, activity level and clothing [1]. Modern HVAC systems focus on controlling three of them: indoor air temperature, humidity, and 2

velocity (motion). Appropriately controlling indoor humidity level is as important as controlling indoor temperature, since humidity directly affects human comfort, indoor air quality, and operating efficiency of air-conditioning (A/C) systems. However, adding humidity as a control target in addition to temperature would significantly

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increase the difficulty of developing suitable control method since heat and mass transfer are highly coupled, leading to two highly coupled control loops for controlling

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temperature and humidity respectively.

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For conventional all-air central A/C systems, temperature and humidity are controlled

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by cooling, reheating and humidifying equipment. A typical central A/C system with

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cooling coil and reheating coil is shown in Fig. 1 [2]. The cooling/dehumidifying and

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reheating process eliminates the coupling between temperature and humidity control

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loops, but it is costly and inefficient. In order to reduce the energy consumption, strategies like heat pipe heat exchanger (HPHE) [3], pre-conditioning outdoor air [2],

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evaporator bypass [4] were developed. In recent years, studies concerning the

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temperature and humidity independent control (THIC) systems [5] have kept burgeoning. Since sensible heating loads are dealt by high-temperature cooling terminals [6, 7], and moisture is processed by latent load units [8, 9], the THIC system

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has no need of reheating and humidifying and so is considered to have good energy performance.

Extra equipment is necessary for the above A/C systems to control temperature and 3

humidity separately, which makes them more applicable to large-scale buildings. However, A/C systems commonly seen in small- and medium-sized buildings have no specific dehumidifying equipment to deal with moisture due to space limitations, leaving humidity in these buildings out of regulation. The problematic humidity control

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exists in different regions. In the United States, most residential buildings are equipped with mechanical ventilation systems to meet the requirements of fresh air [10]. As a

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consequence, moisture from the outdoor air is also brought into conditioned space. In warm and humid areas, A/C systems which only control indoor temperature may leave

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relative humidity (RH) at a high level [11]. In Europe, applications of radiant heating

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and cooling (RHC) terminals in residential buildings are increasing steadily [12]. The

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RHC system has merits of high thermal comfort level, quiet operation, energy saving

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potential, integration with building design, and so on, but it also needs to be

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supplemented with a ventilation system and has to face the problem of condensation [13]. In most areas in Europe, the outdoor air is dry enough in summer so that concern

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on condensation can be alleviated [5]. But in humid areas, the condensation is the

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biggest obstacle to apply RHC systems, making dehumidification ventilation systems necessary[6]. In East Asia, most residential buildings are equipped with split airconditioners or window air-conditioners. Variable refrigerant flow (VRF) systems

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whose markets are growing strongly in China [14] can be seen in many medium-sized office buildings. These systems are classified as direct expansion (DX) A/C systems as air is directly cooled in evaporators (in summer) or heated in condensers (in winter) by the refrigerant. The structure of the DX A/C system is so simple and compact, making 4

it difficult to equip extra dehumidifying equipment. Therefore, most DX A/C systems merely control temperature.

Efforts have been put into the temperature and humidity control in small- and medium-

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sized buildings in recent years. The relevant studies are based on A/C equipment or control algorithms. Hugh I. Henderson [15] classified fourteen humidity control

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options into two types that applied extra devices to process the moisture or that implemented simultaneous temperature and humidity control in a single air-conditioner.

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Similarly, we summary the studies of this field into two methods. The first method that

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uses extra equipment or systems to control temperature and humidity separately, is

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defined as hardware-based decoupled (HWBD) temperature and humidity control. An

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HWBD control method that could be applied in residential buildings is the integration

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of an RHC system and a dedicated outdoor air system. The second method, that employs control algorithms to control indoor temperature and humidity simultaneously,

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is defined as software-based decoupled (SWBD) temperature and humidity control.

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The SWBD control method is basically developed for DX A/C systems which were shown to have the ability to output a wide range of sensible and latent cooling capacity

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by varying evaporative temperature and air flow rate in the evaporator [16].

No dedicated review concerning HWBD control methods in small- and medium-sized buildings was previously proposed to the best of the authors’ knowledge. Furthermore, no review of SWBD control methods has ever been presented. To outline the 5

development of the temperature and humidity control in small- and medium-sized buildings, a state of art literature review is presented. We will put more focus on SWBD control methods in this paper as little attention was paid to it before. The contents is as follows. The First section introduces a basic concept of HWBD and SWBD control

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methods. The second section presents recent developments of HWBD control methods. This section is divided into three parts: multi heat exchangers systems, RHC systems

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with dehumidification ventilation systems (DVS), joint solid desiccant heat pump and VRF systems. The third section provides a systematic introduction to SWBD control

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methods. The coupled mechanism of temperature and humidity control loops in the DX

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A/C system and two kinds of SWBD control methods are discussed in detail in this

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section. The fourth section discusses further developments of the HWBD and SWBD

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control methods. Possibility and benefits of combining the two methods are suggested.

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Finally, a brief summarization is provided and a prospect is concluded.

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2. HWBD temperature and humidity control methods

The HWBD control method is to use different equipment to control coupled parameters separately. For compact A/C systems used in small- and medium-sized buildings,

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strategies like using multi-heat exchangers, integrating dehumidifying ventilation system to RHC systems, are more applicable. The relevant studies are listed in Table 1.

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2.1. Multi-heat exchangers

A way to realize reheating in a DX A/C system is to bypass some refrigerant from the condenser to an additional heat exchanger which works as a reheating coil. Air is

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cooled and dehumidified in the evaporator at first and then is reheated in the heat exchanger. A typical system with multi-heat exchangers is shown in Fig. 2. [17]. If

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valve 1 and valve 4 are open while valve 2 and valve 3 are closed, the dry and cool air from coil 1 is reheated in coil 2 and the refrigerant is subcooled in coil 2 at the same

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time. If valve 1 and valve 4 are closed while valve 2 and valve 3 are open, coil 1 and

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coil 2 both work as an evaporator. The system is very suitable for transition season

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when the sensible load is small but the latent load is huge. Fan et al. [18] presented a

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heat pump with multi-indoor units. The system could work at four modes: (1) Cooling

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mode at summer season as a common DX air-conditioner; (2) Dehumidification mode with full heat recovery as a common dehumidifier; (3) Dehumidification mode with a

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part of heat recovery; (4) Heating mode at winter season as a heat pump. Fye et al. [19]

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presented a two-evaporator DX A/C system for humidity control. The above systems were tested by experiments. Results showed that adding reheating coil could attain better dehumidification capability, but Fye’s test [19] showed that the coefficient of

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performance (COP) of the system with reheating coil was lower than the COP of the system without reheating coil. The cooling capacity of the system with reheating coil also decreased [19].

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2.2. Integration of RHC and DVS

For the RHC system, Moisture should be processed carefully to prevent condensation on the cooling surface. The temperature of the supply water must be higher than the

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dew-point temperature [13]. Dehumidification ventilation systems (DVS) need to be integrated with RHC systems in hot and humid areas since the limitation of the water

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temperature would lower the cooling capacity [13]. An integration of RHC and DVS is shown in Fig. 3 [20]. The system has two evaporators. One is to generate medium-

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temperature water to deal with the sensible load, and the other is to dehumidify the

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mixed air to deal with the latent load. The system could save cooling energy by about

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15.6% against traditional residential air-conditioners. Song et al. [21] proposed a

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radiant floor cooling system combined with DVS. The system was able to meet the

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minimum requirements for indoor air quality and maintain a low dew-point temperature to prevent condensation on the radiant cooling floor surface. Jung-Min

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Seo et al. [22] analyzed a system which coupled outdoor air cooling with radiant floor

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cooling by simulation. The simulation showed that the energy efficiency of the system is 20% higher than that of existing radiant floor cooling system in hot and humid areas in Korea. Ge et al. [23] presented a hybrid heat pump system which combined a solid

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desiccant wheel and a radiant panel for a residential house.

2.3. Integration of DX A/C systems and HPD

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Another HWBD temperature and humidity control method suitable for small- and medium-sized buildings is to integrate desiccant dehumidification units into DX A/C systems. Aynur et al. [24, 25] firstly combined the VRF system with the heat pump desiccant (HPD) system. Their system could work under three modes: VRF system

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only mode; VRF system plus HPD system (ventilation only) mode; VRF system plus HPD system (ventilation-humidification in heating season or ventilation-

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dehumidification in cooling season) mode. The field test showed an increase of energy consumption when HPD system operated, but the HPD ventilation-humidification and

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HPD ventilation-dehumidification assisted VRF systems also provided the best indoor

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thermal comfort [25]. The configuration of a typical HPD system is shown in Fig. 4

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[26]. Solid desiccant is coated on the surface of the heat exchanger. By switching the

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air pipe and the four-way valve, the solid desiccant can dehumidify outdoor air

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periodically. Meanwhile, the solid desiccant can be self-regenerated by condensation heat. Water vapor generated from the regeneration process is took out by the exhaust

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air. More details of HPD systems can be found in following studies [27-32]. Further

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developments of integration of VRF system and HPD system were promoted by Jiang et al. [26, 33, 34]. The experiment showed that the joint solid desiccant heat pump and VRF system (JDVS) was able to maintain the relative humidity at about 51% and the

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temperature at about 28℃ under the cooling condition.

3. SWBD temperature and humidity control methods

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Though HWBD control methods could be a good way to improve indoor humidity control performance, there are few commercial products that could be applied in households currently. Most residential buildings are still equipped with conventional DX A/C systems. Many DX A/C systems are equipped with single-speed compressor

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and fan. These systems rely on On-Off cycling as a low-cost approach to maintain only indoor dry-bulb temperature, whereas the indoor air humidity is not controlled directly.

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Dehumidification appears a by-product of the cooling process. When the temperature set point is reached, the compressor in an On-Off controlled DX A/C system will be

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stopped and so will be the dehumidification. In hot and humid climate, the requirement

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for removing moisture from air can be often more demanding than that for removing

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the sensible load. Therefore, indoor humidity may remain at a high level in the space

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served by an On-Off controlled DX A/C System [35]. The situation may become worse

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when the supply fan in a DX A/C system runs continuously while its compressor is On-Off operated. As pointed out by Khattar [36] and Henderson [37], the air passing

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through the system’s cooling coil may lead to the re-evaporation of the residual

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moisture on coil’s finned surface (in the form of tiny water droplets) during an Offperiod, causing indoor humidity to rise.

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The introduction of variable-frequency inverters has made the speed control of compressor and fan more achievable, and paved a new way to meet the challenge for DX A/C systems to simultaneously control indoor air temperature and humidity. The variable-speed control enables a DX A/C system to output various and continuous 10

combinations of the sensible cooling capacity (SCC) and the latent cooling capacity (LCC) within its operating range, providing the foundation for the match between outputted SCC and LCC of a DX A/C system and the sensible cooling load (SCL) and the latent cooling load (LCL) of a conditioned space, consequently keeping indoor

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temperature and humidity both steady at their respective set point. Researchers prefer to use the total cooling capacity (TCC), the total cooling load (TCL) and the sensible

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heat ratio (SHR) to illustrate this association. The TCC is the sum of SCC and LCC. The SHR can be divided into two aspects: Equipment SHR and Application SHR. The

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Equipment SHR is the ratio of the SCC to the TCC of a DX A/C system. It reflects the

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output cooling capacity distribution for cooling and dehumidification. The smaller the

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Equipment SHR, the higher the dehumidifying capacity. The Application SHR is the

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ratio of the LCL to the TCL. It reflects the proportion of requirements for space cooling

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and dehumidification in a conditioned space. Therefore, to simultaneously control temperature and humidity could also be regarded as to match the TCC and the TCL as

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well as to match the Equipment SHR and the Application SHR.

The association mentioned above is the prerequisite of the SWBD control method. Moreover, to realize the SWBD control method using DX A/C systems still needs to

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conquer the following four obstacles: 1) Two control loops are strongly coupled due to the coupling of cooling and dehumidification inside a DX evaporator; 2) It is difficult to simultaneously evaluate both cooling and dehumidification abilities 11

of a DX A/C system; 3) The variation of two controlled parameters, i.e., temperature and humidity, will conversely affect the output cooling capacity of a DX A/C system; 4) There are no expert experiences available for developing the strategies of

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simultaneous control of temperature and humidity using DX A/C systems.

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In spite of these challenges, efforts have been put into developing various control algorithms to realize SWBD control in past two decades. Krakow et al. [16, 38] firstly

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pointed out that temperature and humidity can be simultaneously controlled by

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controlling surface temperature and air flow rate in the evaporator. Afterwards, several

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control algorithms have been proposed, which are listed in Table 2. These algorithms

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can be divided into two types: model-based control, and black-box control which is

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mainly developed from the fuzzy logic control algorithm.

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3.1. The coupling mechanism and the inherent correlation of cooling and

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dehumidification in a DX evaporator

There are two reasons for coupling between cooling and dehumidification inside a DX

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evaporator. The first reason is that the mechanism of mass transfer is similar to that of heat transfer, and the mass transfer coefficient can be derived from the heat transfer coefficient, as pointed out by Chilton and Colburn [39] in as early as 1934. If the air velocity passing through the evaporator changes, it would simultaneously affect the 12

heat and mass transfer coefficients between the air and the evaporator surface, thus further affect the cooling and dehumidifying abilities of the evaporator. Therefore, varying the supply fan speed in a DX A/C system would impact simultaneously both its cooling and dehumidifying abilities. This is the fundamental reason for the coupling.

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The second reason is due to the same driving force for both cooling and dehumidification: temperature of the evaporator surface. The temperature determines

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the temperature difference for the heat transfer between air and the evaporator surface as well as the saturated vapor pressure corresponding to the surface temperature. The

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saturated vapor pressure determines the moisture content difference for the mass

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transfer. Therefore, the variation of the temperature would simultaneously affect the

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heat and mass transfer rates between air and the evaporator surface. When the thermal

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parameters on the air side remain constant, the temperature of the evaporator surface

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would be mainly influenced by the compressor speed, suggesting it is impossible to

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decouple the cooling and dehumidification by only changing the compressor speed.

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According to the two reasons, the coupled SCC and LCC of an A/C system is restrained within a specific range, which means they cannot be regulated separately by a single equipment. If this coupling was so strong that the variations of cooling and

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dehumidifying abilities are straightly proportional, Equipment SHR would stay unchanged, and the simultaneous control of temperature and humidity can only be realized for a specific Application SHR. Fortunately, the degrees of influence that changing air velocity would make on heat and mass transfer are different, which means 13

the Lewis Number would vary with the changing of air Reynolds Number. Consequently, varying the fan speed can change both the TCC and Equipment SHR of a DX A/C system. On the other hand, since the relationship between the temperature of the evaporator surface and the corresponding saturated vapor pressure is nonlinear,

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the variation of compressor speed can also change both the TCC and the Equipment SHR. This provides the possibility for a DX A/C system to simultaneously control

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indoor air temperature and humidity under a wide range of space cooling loads.

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However, there still exists a fundamental difference between the simultaneous

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temperature and humidity control and the mere temperature control. To control

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temperature simply, as long as the maximum output SCC from an A/C system is greater

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than the maximum SCL, theoretically, the objective could certainly be achieved. But

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to simultaneously control temperature and humidity, not only the TCC has to be greater than the TCL, but also the variation ranges of both the TCC and the Equipment SHR

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of a DX A/C system have to cover those of the TCL and the Application SHR. The

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inherent correlation between the TCC and the Equipment SHR needs to be carefully studied to reveal a DX A/C system’s potential of realizing SWBD control [40-42]. Xu et al. [43] presented a study on the inherent correlation to illustrate this issue. As shown

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in Fig. 5, the two parameters are mutually coupled and constrained within the quadrangle A-B-C-D. When the DX A/C system outputs a specific SCC, the possible LCC will then be constrained within a small range, which means the LCC is unable to vary from its minimum to maximum. Therefore, even for a conditioned space where 14

the TCL is smaller than the TCC, it is possible that the DX A/C system still can’t simultaneous control temperature and humidity to their respective set point since the A/C system is unable to produce the required combination of the TCC and the Equipment SHR. Take point H (which represents a certain situation of TCL and

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Application SHR) shown in Fig. 5 as an example. Point H locates within the rectangle W-X-Y-Z but outside the quadrangle A-B-C-D. If the TCC meets its TCL, then the

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Equipment SHR will always be lower than its Application SHR, which will make the conditioned space over-dehumidified. On the contrary, if the Equipment SHR meets its

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Application SHR, the TCC will always be higher than its TCL, which will make the

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can’t be realized under this condition.

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conditioned space over-cooled. So simultaneous temperature and humidity control

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Although dehumidification ability of a DX A/C system is restrained within in a certain range, the current trend of designing an evaporator is to lower its moisture removal

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capacity. The trend partly originates from an attempt to boost energy-efficiency ratings

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(EER) and COP of DX A/C systems [44]. Increasing frontal area and decreasing depth of an evaporator are employed to improve efficiency. This allows a DX A/C system to operate at a higher refrigerant temperature in the evaporator, which can lead to a better

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energy performance in an experimental test. However, the method would deteriorate the dehumidification ability of a DX A/C system in practical applications. Users would lower temperature set point to compensate the adverse effects caused by a high humidity level, which may lead to a larger cooling load and higher energy consumption. 15

Hence, it is necessary to consider the inherent correlation between the TCC and the Equipment SHR of a DX A/C system in the stage of design not only for realizing better thermal comfort but also for energy performance in small- and medium-size buildings

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[43].

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3.2. Model-based controllers

A straightforward way to decouple the two control loops of temperature and humidity

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is using an A/C system model to evaluate its cooling and dehumidifying capacities

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under variable speed operation. A steady-state A/C systems model can be used to get

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an in-depth understanding of decoupling mechanism, and a dynamic DX A/C system

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model can be applied to develop a model-based controller [45-57]. These controllers

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are divided into two types according to the models they are based upon: physical

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model-based controller, and empirical model-based controller.

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A physical model is built on the precise mathematical description of a physical phenomenon. A physical A/C system model can maintain high accuracy under various working conditions, which means it is applicable under wide operating ranges.

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However, to simulate heat and mass transfer and to obtain operating characteristics of all equipment of an A/C system have to face a lot of obstacles. One obstacle is that it is hard to determine an accuracy value for Lewis number. To simplify a modeling process, Lewis Number is normally assumed to be unit [58-61]. But studies which 16

focused on the heat and mass transfer phenomenon on wet surfaces have demonstrated that Lewis Number could vary from 0.6 to 1.2[62-64]. An analytical evaluation of the thermal performances of wet air cooling coils under both unit and non-unit Lewis Number concluded that adopting different values for Lewis Number would impact

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much more on evaluating mass transfer than on evaluating heat transfer [65]. Another obstacle is that a large number of iterations are required to solve the physical A/C

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system model, consuming huge computational efforts. For example, the heat transfer coefficient between the air and the evaporator surface is different under wet and dry

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condition [66-68], thus a physical model should evaluate the surface condition by

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iteration first and then choose appropriate heat transfer correlations. Furthermore,

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tremendous iterations are required to work out states of air and refrigerant at the

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condenser outlet and the evaporator outlet. So the use of physical model-based control

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method in DX A/C systems stick in a dilemma: the heat and mass transfer has to be analyzed in detail to improve the control accuracy; on the other hand, a model needs to

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be simplified to reduce the computational duration and to improve the control

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sensitivity. To strike a reasonable balance between the control accuracy and control sensitivity has to be cogitated when developing model-based SWBD controllers.

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Li and Deng [45] developed a direct digital control (DDC) based capacity controller for a DX A/C system. The controller was based on a physical model whose core element is numerical calculation algorithm (NCA). The controller could work out compressor speed and fan speed based on the current SCL and LCL. The use of NCA 17

and real-time measured parameters vastly reduced iteration times, though it also increased the response time of the controller. To improve the control sensitivity and diminish the steady errors, periodic operating and PI control were introduced into the

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controller [46].

An empirical model is built based on data training. It contains a set of mathematical

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correlations extracted from the operational characteristics of a controlled system rather than the detailed description of the system dynamics, thus it has the advantages of

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simplicity and fast response. The artificial neural network (ANN) is a normal tool to

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build empirical models. An ANN model can simulate the operating characters of a DX

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A/C system [51, 69, 70], and establish the input-output relationships between speeds

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of compressor/supply fan and output sensible/latent cooling capacities. A model-based

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controller can be developed based on a dynamic ANN model [50, 52].

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Qi proposed a multi-input-multi-output (MIMO) controller [48] based on a dynamic

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model of a DX A/C system [47]. The dynamic model was written in state-space representations and was linearized around a certain operating condition. Parameters of the linearized matrices were trained from the experimental data, which means the

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model was an empirical model. The MIMO controller was evaluated by two experiments. One was disturbance rejection test in which cooling load disturbances were introduced. The other was command following test in which temperature and humidity set points changed. Though results showed good performance of the MIMO 18

controller, the controller could only work well around the operating condition where the dynamic model was linearized. If operating conditions deviated, the performance of the controller became worse. Li et al. [51] developed a steady-state ANN model for a DX A/C system. The inputs of the model were the compressor speed and the fan

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speed, while outputs were the SCC and the LCC. This model had two neurons in the input layer, two neurons in the output layer and six neurons in each of the two hidden

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layers, i.e. 2-6-6-2 configuration, as shown in Fig. 6. Since the model is a steady-state model, it could not be applied to build a controller. It could predict the operating

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performances of the A/C system with an accuracy of ±1% within the training data. A

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dynamic ANN model was designed for an SWBD controller by Li et al. [50]. Its

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configuration is shown in Fig. 7(a). An inverse model which is also shown in Fig. 7(a)

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was combined with the ANN model to output the needed compressor speed and fan

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speed. The flowchart of the controller based on the model is shown in Fig. 7(b). The

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results showed a good performance of the controller, as shown in Fig. 7(c).

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The MIMO model and the dynamic ANN model were trained based on limited experimental data. As pointed out by Swider, an empirical model does not allow to accurately extrapolate beyond the range of the data used for training the model [71].

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Hence, empirical model-based controllers could only realize simultaneous temperature and humidity control when the A/C systems worked near the operating points that the training was based on. The strong coupling between the temperature and humidity control loops would significantly increase control errors once the operating condition 19

moves away from the training point. Since indoor air temperature and humidity would both influence the outputs of a DX A/C system, an empirical model must be trained at every possible combination of indoor air temperature and humidity to ensure that it can be used to develop a controller covering all possible indoor air states. At each

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combination of temperature and humidity, the model must also be trained under various combinations of compressor and fan speeds, which means tremendous training

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experiments need to be carried out. Furthermore, if any components of the DX A/C system have a change, all experiments will have to be repeated to establish a new

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empirical model, which is extremely time-consuming.

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The use of online adaptive training is a possible way to avoid a huge training database.

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In an online adaptive controller, the ANN-based dynamic model can be re-trained

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online once the operating condition changed. The updated model can reflect dynamics of the DX A/C system working under the new operating points. Li et al. [52] introduced

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a controller based on an online adaptive training model for simultaneous temperature

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and humidity control for an experimental DX A/C system. Though the controller performed well under different operating points, it should be noted that sufficient time was required to collect training data and re-train the model online. If a strong

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disturbance is introduced to the system during this time, a large deviation of the system operating status may arise after the online training. This may lead to the updated ANN model’s failure to promptly reflect the current operating status of the system. From this perspective, the online adaptive training model-based controller may not be applicable 20

to situations when there is a strong load disturbance or the thermal inertia of indoor air is small. A possible solution is to employ an empirical model that demands fewer training data and less training time. Flavio Munoz et al. [56] presented an empirical model that was built based on the recurrent high order neural network (RHONN). The

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model could be trained by the Extended Kalman Filter (EKF) in real time. Inverse optimal control was employed to work out compressor speed and fan speed to minimize

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a cost function error.

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Since the physical model and empirical model both has advantages and disadvantages,

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Xu [54, 55] presented a hybrid model of a DX A/C system by combining the two to

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avoid their drawbacks. Its structure is illustrated in Fig. 8. For a hybrid model, the

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evaporator is modeled by the physical modeling approach to accurately simulate the

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cooling and dehumidification process under various working conditions, while other components of the DX A/C system is modeled by the ANN to reduce computation load

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[57]. Based on a steady-state hybrid model, a controller was developed by Xu et al. [53]

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to provide insights of the control mechanism of the hybrid modeling methods. Though the simulation performance of the controller was better than On-Off control, the controller was still unable to process time delay as it could not reflect dynamics of the

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system. A dynamic hybrid model is necessary when developing model-based controllers.

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3.3. Fuzzy logic control algorithms

Fuzzy logic control is a kind of black-box control algorithms. Lee [72, 73] systematically demonstrated the principles and the applications of the fuzzy logic

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control. The core of a fuzzy logic controller (FLC) is a set of fuzzy rules. These rules are derived from users’ common sense and experience, rather than characteristics of

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the controlled system. The fuzzy logic control algorithm proves useful if the system is too complex to be analyzed by conventional quantitative techniques or the available

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information of the system is interpreted qualitatively, inexactly, or uncertainly.

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Although the fuzzy logic control algorithm has amounts of merits, there are no

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human experience in a simple format.

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standards or methods for designing an FLC, since it is difficult to exactly express

When being applied to a DX A/C system, an FLC is usually used to control uncoupled

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parameters as it is easier to propose and optimize fuzzy rules for uncoupled parameters

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than that for coupled parameters. For example, it is easy to propose control rules for an A/C system where temperature and humidity are controlled by a heater and a humidifier respectively: if the temperature is too low, then increase the power supply

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to the heater; if the humidity is too low, then increase the power supply to the humidifier. But things become harder for simultaneous temperature and humidity control. If the temperature is too high while the humidity is too low, it is hard to determine whether the speed of compressor should be increased or decreased. 22

To avoid the huge workload brought by setting fuzzy rules for a fuzzy matrix, Xu et al. [74] presented a weights-based fuzzy logic control algorithm (WBFLCA) and designed a PD-type fuzzy logic controller (PFC) for a load generator. The control algorithm

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assigned a weight to each linguistic variable of the PFC, rather than assigned a specific value to each combination of certain two linguistic variables, which vastly reduces the

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number of fuzzy rules. The algorithm also omitted the anti-fuzzy module, which further simplified the PFC. The test result showed that the PFC had better control performance

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compared with a PID controller. Li et al. [75] built a controller based on WBFLCA for

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a DX A/C system. The controller regulated temperature by varying fan speed, and

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controlled humidity by varying compressor speed. Its flowchart is shown in Fig. 9. The

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result showed that the controller had a good decoupling control performance. Further

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developments of similar controllers can be found in following studies [76, 77].

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Though these controllers simplify the fuzzy reasoning unit by introducing weights to

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replace a common fuzzy matrix, the weights are still obtained from large quantities of trial tests. A Systematic procedure for setting the weights of WBFLCA has not been studied yet. Efforts can be put into an exploration of the weights optimization. Zhong

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et al. [78] proposed a weights distribution mode to classify two simple and typical weights setting rules. Sixteen combinations of weights distribution modes were simulated, and four of them were shown to have better control performance than others, which means weights should be set within these four combinations. But the result still 23

needed to be verified by experiments to prove its feasibility in real systems.

4. Discussions on further development of HWBD and SWBD control

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4.1. Combination of HWBD and SWBD control methods

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methods

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Although extra equipment is added to A/C systems to implement the HWBD control

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method, the decoupling ability of each HWBD system locates at different levels. For

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an RHC system that combines with a DVS, since the sensible cooling load is processed

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by the radiant cooling panel and moisture is processed by the dehumidification system,

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the temperature and humidity control loops are totally decoupled and a simple control algorithm can be used to achieve a good control performance. But for the system

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presented by Han et al. [17], though it is able to dehumidify and reheat air in different

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heat exchangers, the temperature and humidity control loops are still coupled for two reasons: the same airflow is shared by cooling coil and reheating coil; the enthalpy of the refrigerant at the inlet of cooling coil is influenced by the reheating coil. Hence, it

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is reasonable for this systems to employ SWBD control algorithms to realize a better control performance.

Readers may see a gap lying between developments of HWBD control methods and 24

SWBD control methods. Researchers focusing on HWBD control methods care more about the system capability and energy performance but cast rare consideration on improved control algorithm. Likewise, those focusing on SWBD control methods restrained the control performance of their advanced control algorithms within a fixed

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operating range of a certain DX A/C system. For those HWBD systems without enough decoupling abilities, a combination with SWBD control methods can help achieve a

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better control performance. On the other hand, the SWBD control method should extend its application from the DX A/C system to other systems that have wider

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operating ranges to reach its potential of energy saving and thermal comfort in practice.

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A

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Efforts can be put into the combination of the two methods in the future.

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4.2. Optimization of reheating and humidifying by using SWBD control methods

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This part will introduce a possible application of SWBD control methods in a central

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A/C system with reheating coil and humidifier. The quadrangle in Fig. 10 shows the inherent correlation of the SCC and the LCC of a DX A/C system. If a combination of the SCL and the LCL locates within the quadrangle, simultaneous temperature and

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humidity control can be realized. If the combination is beyond the quadrangle (such as point A, B, C, and D), then reheating or humidifying are required. The simplest way to deal with the sensible and latent cooling loads is to operate the cooling coil under the maximum opening (i.e. point M) at first, and then reheat or humidify air along the red 25

dash line to compensate the over cooling and the over dehumidification. Undoubtedly the process will cost the most energy. To obtain higher energy efficiency, the cooling coil should be operated at points which are closer to point A, B, C, and D, like point 1, 2, 3, and 4 located on the edge of the quadrangle. Subsequently, air can be reheated or

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humidified along the blue solid line. Since the process from the operating points to respective load points is reheating or humidifying, the path direction must be the minus.

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For example, the process from point 2’ to point B cannot to be realized, because it requires additional latent cooling capacity, which is impossible to be provided by the

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reheating coil or the humidifier. Point A can represent a typical climate in hot and dry

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areas, while point 1 means the cooling coil is able to cover SCC but also over

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dehumidifies air. The process from point 1 to point A means air is humidified using

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least energy. Likewise, point C represents a typical climate in cold and wet areas, and

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the process from point 2 to point C means air is reheated using least energy. However, it is hard to evaluate whether cooling loads are within or beyond the quadrangle in

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practical applications. Furthermore, the range of the quadrangle is not so clear because

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the operating condition of an A/C system is changeable. Hence, it is hard to directly find appropriate point 1, 2, 3 and 4 in real applications.

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SWBD control methods can actuate an A/C system to approach its temperature and humidity set point. If one of the set points can never be approached, it can be judged that the load point is beyond the quadrangle, then the steady operating condition of the system will locate on the edge near the load point. Since the operating point would 26

locate randomly on the edge, the SWBD control method still needs to be developed to position the operating point at the place that requires the least reheating or humidification. Better energy performance can be obtained by introducing SWBD control methods into central A/C systems. Here follows an example. Zhang et al. [79]

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developed a PID control algorithm for a museum storeroom A/C system which had a cooling coil, a reheating coil, and a humidifier to ensure precise temperature and

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humidity control. The system employed two three-way valves to control the flow rate and temperature of chilled water flowing into the cooling coil. The algorithm can

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appoint two of the three equipment (cooling coil, heater, and humidifier) to work

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together. In other words, the algorithm can ensure only cooling or dehumidification to

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be excessive, so that the air merely need to be reheated or humidified. So the over-

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cooling capacity of the cooling coil, as well as energy needed to reheat and to humidify

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can be significantly reduced. Comparing to conventional systems, the system can save energy consumption by 20%-30%. Control of air flow rate could be considered to

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further optimize energy saving performance in this case.

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4.3. Half-decoupled temperature and humidity control methods

Previously mentioned HWBD and SWBD control methods aim to regulate indoor air temperature and humidity precisely. This part will introduce some control methods that may not be able to implement precise temperature and humidity control, but still able 27

to improve indoor thermal comfort level by modifying humidity control performance. Such strategies are defined as half-decoupled temperature and humidity control methods.

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The compressor of a DX A/C system would be completely shut down to avoid overcooling when the indoor air dry-bulb temperature set point is reached under On-

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Off control. No cooling and dehumidification are provided during the off-period, so indoor humidity may fluctuate significantly. The DX A/C system under On-Off control

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is alternatively operated at point C (high speed or full speed mode) and base point (off

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mode) in Fig. 5.

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Xu et al. [35] proposed a high-low (H-L) control for a DX A/C system to improve the

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indoor humidity during the off-period. When the temperature got to the set point, the compressor would run at a low-speed mode (point B in Fig. 5) under the H-L control,

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rather than completely stop as it works under On-Off control. At the same time, the

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TCC of low-speed mode was significantly reduced compared with that of high-speed mode. Nevertheless, the Equipment SHR could still be maintained at a similar level. This would help maintain the dehumidifying capacity of the DX A/C system. On the

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other hand, the conditioned space would not be over cooled.

The H-L control algorithm only takes temperature as the control parameter instead of controlling temperature and humidity simultaneously. Although the indoor humidity 28

may still fluctuate under the H-L control compared with SWBD control methods, the experimental result showed that the H-L control was able to constrain the indoor humidity within the thermal comfort zone by appropriately choosing operation points

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for both high speed and low-speed modes [35].

Half-decoupled temperature and humidity control methods could lower the

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requirements for both hardware and software of a DX A/C system, which makes it possible to be applied in multi evaporator air-conditioning (MEAC) systems. In

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addition to the coupling between the heat and mass transfer inside a single evaporator,

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the inherent coupling among evaporators also exist in MEAC systems, since all

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evaporators share a same evaporating pressure. It is fundamentally unlikely to realize

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simultaneous temperature and humidity control in every indoor unit unless additional

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controlling valves are introduced. Because the controlling parameters with the number of 1 + 𝑛 (one compressor speed plus n speeds of n supply fans) are less than the

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controlled parameters with the number of 2n (temperature and humidity of n

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conditioned spaces). However, the half-decoupled control methods could be applied in the MEAC system. Yan et al. [80] proposed a capacity controller for a three-evaporator air-conditioning (TEAC) system to improve indoor humidity level. The H-L control is

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applied to each indoor units. The required compressor speed is calculated by the total required refrigerant flow rate of all evaporators. Indoor temperature is controlled in the dead-band around its set point, while indoor RH fluctuation is constrained but not controlled. According to the experimental results, fluctuation of indoor temperature 29

and RH in each conditioned space are both smaller than on-off control.

5. Conclusion

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Air-conditioning systems commonly applied in small- and medium-sized buildings have to face many challenges in temperature and humidity control. These A/C systems

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usually have a compact structure, making it hard to install reheating coil or independent dehumidifying device. On the other hand, temperature and humidity control loops are

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highly coupled and nonlinear, resulting in difficulties to control both temperature and

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humidity in a single unit. Hardware-based decoupled (HWBD) control methods

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decouple temperature and humidity control loops based on modified system structures

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or extra equipment. Relevant studies mainly focused on simulating or testing the

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system capacity and energy performance. Software-based (SWBD) control methods take advantages of control algorithms to realize simultaneous temperature and

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humidity control in a single direct expansion (DX) air-conditioning (A/C) unit. The

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coupling mechanism in the evaporator is complex. The model-based controllers and black-box control algorithms were developed and validated by experiments.

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It can be seen that development paths of HWBD and SWBD control methods are totally separated, thus more attention can be paid to combining the two methods. On the one hand, SWBD control methods need to expand its application scenarios from DX A/C systems to other types of A/C systems. On the other hand, it is possible for HWBD 30

control methods to apply SWBD control methods to improve control performance and realize further energy saving. For relevant studies, future research can be implemented but not restricted in following areas.

Optimal humidity control of RHC systems.



Optimal control of channel switch periods of HPD.



Inherent correlations of cooling and dehumidifying in HPD.

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Mechanism of coupled heat and mass transfer in a wet cooling coil

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Dynamic inherent correlations of cooling and dehumidifying in DX A/C systems.

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Control-oriented dynamic A/C system models for simultaneous T&H control.



SWBD control in A/C systems with multi-units.

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

Acknowledgement

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 Optimal designing standards for WBFLCA

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The authors thank Zhejiang Provincial Natural Science Foundation of China (Grant No.

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Y17E060002) and the National Key Research and Development Program of China (Grand No. 2016YFB0901404) for financially supporting the work reported in this

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paper.

31

Nomenclature Abbreviation Air-conditioning

ANN

Artificial neural network

COP

Coefficient of performance

DDC

Direct digital control

DVS

Dehumidification ventilation system

DX

Direct expansion

EER

Energy-efficiency ratings

EKF

Extended Kalman Filter

FLC

Fuzzy logic controller

H-L

High-low

HPD

Heat pump desiccant

HPHE

Heat pipe heat exchanger

HVAC

Heating, ventilation, and air conditioning

HWBD

Hardware-based decoupled

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ED

M

A

N

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A/C

Indoor air quality

JDVS

Joint solid desiccant heat pump and VRF system

JHVS

Joint heating recovery ventilation and VRF system

LCC

Latent cooling capacity

LCL

Latent cooling load

MEAC

Multi-evaporator air-conditioning

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IAQ

32

MIMO

Multi-input-multi-output

NCA

Numerical calculation algorithm

PFC

PD-type fuzzy logic controller

RHC

Radiant heating and cooling

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RHONN Recurrent high order neural network Sensible cooling capacity

SCL

Sensible cooling load

SHR

Sensible heat ratio

SWBD

Software-based decoupled

TCC

Total cooling capacity

TCL

Total cooling load

TEAC

Three-evaporator air-conditioning

THIC

Temperature and humidity independent control

VRF

Variable refrigerant flow

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M

A

N

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SCC

A

CC E

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WBFLCA Weights based fuzzy logic control algorithm

33

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A

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PT

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M

A

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http://dx.doi.org/10.1016/j.applthermaleng.2016.01.052

39

List of table captions Table 1. List of HWBD control methods for small- and medium-sized buildings.

A

CC E

PT

ED

M

A

N

U

SC R

IP T

Table 2. List of SWBD control methods for DX A/C systems.

40

Table 1. List of HWBD control methods for small- and medium-sized buildings.

Fye et al. [19]

2012

Experiment

additional indoor units; reheating;

Experiment

two indoor units; switch between cooling mode and isothermal dehumidification mode by switching valves

Han et al. [20]

CC E A

VRF integrated with dehumidif ication systems

2008

2011

PT

Ge et al. [23]

2011

SC R

DX evaporator

Experiment

U

2014

DX evaporator & reheating coil

two indoor units, single expansion valve and fourway valve; 4 operating modes; condenser heat recovery

Dehumidif ied ventilation system

radiant floor cooling system; outside air reset control to prevent condensation; indoor temperature feedback control to meet internal load

DX evaporator

consists an air cooling evaporator and a water cooling evaporator; cooling capacity controlled by corresponding EEV

Simulation

Desiccant wheel

regenerative heat supplied by condenser dissipated heat;

Controlled outdoor cooling system

switch operating modes of outdoor air ventilation system according to outdoor air temperature

Experiment & Simulation

N

Song et al. [21]

2013

Radiant floor

A

Han et al. [17]

Key strategy and result

IP T

Approach

Fan et al. [18]

RHC integrated with dehumidif ication systems

Humidity control

Year

M

Multi-heat exchanger s

Temperature control

Literatures

Experiment

ED

Type

Radiant panel

Seo et al. [22]

2014

Simulation

Aynur et al. [24, 25, 30]

2010

Experiment

better thermal comfort with higher energy consumption

Simulation

HPD module built up for Energy Plus; JDVS consume 18.7% less energy than JHVS through whole year

Jiang et al. [26]

Jiang et al. [33]

2013

2014

Radiant floor

VRF system

HPD system

able to maintain RH at 52%, temperature at 26.2℃ in summer; 17.2% energy

Experiment 41

saving than JHVS

Table 2. List of SWBD control methods for DX A/C systems.

Flavio Munoz et al.[56]

Krakow et al. [16]

Li Zhao et al.[75, 76]

CC E

Blackbox

DDC model-

IP T

State space representations; Linearized at control set points; Limited operating range

MIMO model-

2012

ANN modelExperiment

2013

2017

Online adaptive ANN model

RHONN model

1995

2012

Experiment & Simulation

PID

2018

State space representation; Real-time EKF training neural network model; Optimal inverse control; Parameters from trial tests;; PID for each control loop; Parameters from both trial tests and analysis Implemented on decoupled control loops; The first WBFLCA;

WBFLCA

Experiment

WBFLCA

Weights based PD type fuzzy logic controller; Good decoupling performance; Weights from trial tests; Weights based PD type fuzzy logic controller; Partly adaptive weights;

WBFLCA

A

Yan et al.[77]

2015

Dynamic ANN model based; Quick response; Limited operating range

Online training ANN model based; Adaptable to various operating conditions; Unsuitable for strong disturbance;

PT

Xu et al. [74]

Physical model based; Stable; Poor sensitivity;

SC R

Li Ning et al. [52]

2009

Key strategy

U

Model based

Li Ning et al. [50]

2007

Approach

N

Qi et al. [48]

Model/Controller

A

Li Zheng et al. [45, 46]

Year

M

Literatures

ED

Type

42

List of figure captions Fig. 1.The A/C system with cooling/dehumidification coil and re-heating coil.

IP T

Fig. 2. The reheating coil in a DX A/C system. Fig. 3. The combination of high-temperature radiation panel and dehumidification coil.

SC R

Fig. 4. The schematic diagram of solid desiccant heat pump working on two cycles.

Fig. 5. The inherent correlation between the TCC and Equipment SHR under variable

U

speed operation.

N

Fig. 6. The structure of the selected 2-6-6-2 ANN model.

A

Fig. 7(a). The structure of the ANN-based dynamic model (left) and inverse model

M

(right) with respective inputs and outputs.

ED

Fig. 7(b). The ANN-based controller under direct inverse control strategy for the experimental DX A/C system.

PT

Fig. 7(c). The variations of the indoor air dry-bulb and wet-bulb temperatures in

CC E

command following test.

Fig. 8. The basic principle of the hybrid modelling method for a DX A/C system. Fig. 9. The schematic diagram of the complete novel PD law based fuzzy logic

A

controller for a DX A/C system. Fig. 10. Optimization of reheating and dehumidification by using SWBD control methods.

43

o

m

Supply air

A

r

-

+

Cooling/ Dehumidification coil

Re-heating coil

SC R

IP T

Outside air

Return air

Valve4

N

Expansion Device 2

U

Fig. 1. The A/C system with cooling/dehumidification coil and re-heating coil. [2]

Coil 2

ED

M

Coil 1

A

Valve3

CC E

PT

Expansion Device 1

Valve2 Valve1

Outdoor Condensor

A

Fig. 2. The reheating coil in a DX A/C system. [17]

44

IP T SC R U N A M ED PT CC E

A

Fig. 3. The combination of high-temperature radiation panel and dehumidification coil. [20]

45

IP T SC R U N A M ED PT

A

CC E

Fig. 4. The schematic diagram of solid desiccant heat pump working on two cycles. [26].

46

IP T SC R U N A

CC E

Qs

A

PC

PT

W

ED

M

Fig. 5. The inherent correlation between the TCC and the Equipment SHR under variable speed operations. [43]

Ql

PF

Fig. 6. The structure of the selected 2-6-6-2 ANN model. [51] 47

IP T SC R

A

CC E

PT

ED

M

A

N

U

Fig. 7(a). The structure of the ANN-based dynamic model (left) and inverse model (right) with respective inputs and outputs. [50]

Fig. 7(b). The ANN-based controller under direct inverse control strategy for the experimental DX A/C system. [50]

48

IP T SC R

N

U

Fig. 7(c). The variations of the indoor air dry-bulb and wet-bulb temperatures in command following test. [50]

ED

M

A

Other components (ANN-based model)

Indoor air thermal states

PT

System operating conditions

CC E

T,H

qm and Hin

A

mass flow rate of refrigerant, the enthalpy of refrigerant at evaporator inlet

C

F,C F

Evaporator (physical-based model)

Tout when superheat degree is constant Pout, Hout

QS, QL and QT

Fig. 8. The basic principle of the hybrid modelling method for a DX A/C system.

49

IP T

SC R

Fig. 9. The schematic diagram of the complete novel PD law based fuzzy logic controller for a DX A/C system. [75]

U B

3.5

A

N

4.0

M

1

C

2.5

3

ED

3.0

2.0

M

2

2'

PT

Latent cooling capacity (kW)

4.5

A

4

CC E

D

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

Sensible cooling capacity (kW)

A

1.5 2.5

Fig. 10. Optimization of reheating and dehumidification by using SWBD control methods.

50