\r\n\t \r\n\tContamination with biomedical waste and its impact on the environment are global concerns. Biomedical waste that has not been collected and disposed in accordance with the regulations can become a total environmental hazard and cause negative impact on human health and the environment. Medical centers including hospitals, clinics, and places where diagnosis and treatment are conducted generate waste that is highly hazardous and put people under risk of fatal diseases. On the other hand, food waste is commonly produced in all the steps of food life cycle, such as during agricultural production, industrial manufacturing, processing and distribution, and is even consumer-generated within private households. Food waste mostly contains high-value components such as phytochemicals, proteins, flavor compounds, polysaccharides, and fibers, which can be reused as nutraceuticals and functional ingredients. Adsorption is a practicable separation method for purification, along with bulk separation where surface characteristics and pore structures are the main properties in determining equilibrium rate. Managing waste materials on the whole is often unsatisfactory, especially in developing countries, and the unreasonable disposal of waste is a major issue worldwide.
\r\n
\r\n\tThe following issues will be of particular interest for this book: effects of waste on environment and health, biomedical waste - storage, management, treatment, and disposal, biomedical waste contamination, food waste, potential applications of low-cost sorbents in agricultural and food sectors, biosorbents and bioadsorbents, adsorption of modified agricultural and biological wastes (biosorption), compounds recovered from food waste, and agricultural and food waste-derived sorbents.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"fef68f549e98b68c60ae17bb2b3c64e4",bookSignature:"Dr. Parisa Ziarati",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/9842.jpg",keywords:"Biomedical waste, Food waste, Classification, Hazardous waste, Sources, Treatment and disposal, Contamination, Bioaccumulation, Sorbents, Sorption, Biosorption, Food waste recovery",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"December 4th 2019",dateEndSecondStepPublish:"March 3rd 2020",dateEndThirdStepPublish:"May 2nd 2020",dateEndFourthStepPublish:"July 21st 2020",dateEndFifthStepPublish:"September 19th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"312371",title:"Dr.",name:"Parisa",middleName:null,surname:"Ziarati",slug:"parisa-ziarati",fullName:"Parisa Ziarati",profilePictureURL:"https://mts.intechopen.com/storage/users/312371/images/system/312371.jpg",biography:"Parisa Ziarati currently works at Nutrition and Food Sciences Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran. She is a hardworking researcher since she has published 168 research articles in leading technical and scientific journals. She is the author of 3 books. She has delivered 138 lectures at national and international conferences on relevant subjects, primarily environmental chemistry. She has also supervised 118 master’s theses and mediated 108 theses as an advisor. She has also published several papers on new findings in phytoremediation, a topic of current and original research attracting commercial interest. Moreover, she has worked exhaustively on turning low-cost waste products (food, agricultural, forestry, industrial, and mine waste) into valuable resources for water / wastewater remediation and pollution prevention. It is notable that remediation of soils contaminated with heavy metals and organics, detoxification and removal of heavy metals from foods, including rice and vegetables by adsorbents / bio adsorbents is her current research passion.",institutionString:"Nutrition and Food Sciences Research Center",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"12",title:"Environmental Sciences",slug:"environmental-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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1. Introduction
The heating, ventilating, and air-conditioning (HVAC) systems have huge different characteristics in control engineering from chemical and steel processes. One of the characteristics is that the equilibrium point (or the operating point) usually varies with disturbances such as outdoor temperature (or weather conditions) and thermal loads. The variations of the operating point intend to vary parameters of a plant model. Thus, the HVAC control systems are extremely difficult to obtain an exact mathematical model (Kasahara 2000). Proportional-plus-integral (PI) controllers have been by far the most common control strategy as the complexity of the control problem increased (Åström 1995).
Today, a variable air volume (VAV) system has been universally accepted as means of achieving energy efficient and comfortable building environment. While the VAV control strategies provide a high quality environment for building occupants, the VAV system analysis rarely receives the attention it deserves. As a result, basic control strategies for the VAV system have remained unchanged up to now (Hartman 2003).
In addition, applying the model predictive control method to the HVAC systems, the control performance has been highly improved by pursuing the deviation from the operating point (Taira 2004). According to this report, recognizing the deviation from the operating point and calculating the optimal control inputs about the newly obtained operating point on next sampling time, the control system gives better responses than the traditional feedback control system.
Motivated by these considerations in these reports, we consider the room temperature and humidity controls using the adjustable resets which compensate for thermal loads upsets. One of the primary objectives of the HVAC systems is to maintain the room air temperature and humidity at the setpoint values to a high quality environment for building occupants. The room temperature and humidity control systems may be represented in the same block-diagram form as single-variable, single-loop feedback control systems because this interaction is weak relative to the desired control performance.
In some applications, disturbances can be estimated in advance before they entered the plant. Particularly, in the HVAC systems, it is possible that the outdoor thermometer detects sudden weather changes and the occupant roughly anticipates thermal loads upsets. Using this information, disturbances can be offset by the compensation of the reset, which is the exactly same function as an integral (I) control action. In the previous paper, the compensation method of the reset for PID controllers was proposed and the control system for room air temperature was often effective in reducing thermal loads upsets (Yamakawa 2010).
In this paper, of special interest to us is how to tune PID parameters more effective for the room temperature and humidity control. And the control performances for compensation of the adjustable reset are compared with the traditional method of the fixed reset. Namely, obtaining the approximate operating point using outdoor temperature and thermal loads profiles and adjusting the reset, the stabilization of the control system will be improved. The validation simulations will be demonstrated in terms of three performance indices such as the integral values of the squared errors, total control input, and PID control input.
2. Plant and control system
In this paper, we consider only the cooling mode of operation in summer and therefore refer to this system as a room air cooling system. The definition of variables in Equations is described in NOMENCLATURE.
2.1. Dynamics of air-conditioning system
To explore the application of PID controllers to the room temperature and humidity control system, we consider a single-zone cooling system, as shown in Figure 1. It is due to the fact that cooling and heating modes are found to perform nearly the same under most circumstances. The controlled room (the controlled plant) measures 10 m by 10 m by 2.7 m and is furnished with an air-handling unit (AHU) consisting of the cooling coil and the humidifier to control room air temperature and humidity. In general, since the responses of the AHU are faster than those of the controlled room, the dynamics of the AHU may be neglected for all practical purposes. Thus, as will be seen later, this rough assumption may be fairly validated. The model, however, possesses the important elements (the controlled room and the AHU) to analyze the air-conditioning system.
With this system, the room air temperature (θ ) and relative humidity (φ) are measured with a thermometer and a hygrometer (sensors). The output signals from the sensors are amplified and then fed back to the PID controllers. Using the errors defined as the differences between the setpoint value (θr and φr) and the measured values of the controlled variables (θ and φ), the PID controllers generate the control inputs for the actuators (the supply air damper and the humidifier) so that the errors are reduced. The AHU responds to the control inputs (fs and xs (is adjusted by humidifier h)) by providing the appropriate thermal power and humidity to the supply airflow. Air enters the AHU at a warm temperature, which decreases as air passes the cooling coil, and then the humidifier supplies steam to cooled air if necessary. This occurs in a momentary period because there are a lot of times when the humidifier is not running. In this AHU, a dehumidifier is not installed, so an excessive demand for humidity is difficult to achieve.
Figure 1.
Overall structure of a single-zone cooling system.
2.1.1. Room temperature model
Simplifying this thermal system to be a single-zone space enclosed by an envelope exposed to certain outdoor conditions is of significant interest to treat the fundamental issues in control system design (Zhang 1992, Matsuba 1998, Yamakawa 2009). This simplified thermal system (the room temperature model) can be obtained by applying the principle of energy balance,
Cdθdt=ws(θs−θ)+α(θ0−θ)+qLE1
where
C = overall heat capacity of air-conditioned space [kJ/K],
α = overall transmittance-area factor [kJ/min K],
qL = thermal load from internal heat generation [kJ/min],
ws = ρacpfs [kJ/min K], which is heat of supply air flowrate,
ρa = density of air [kg/m3],
cp = specific heat of air [kJ/kg K],
fs = supply air flowrate [m3/min].
The physical interpretation of Equation 1 is that the rate of change of energy in the room is equal to the difference between the energy supplied to and removed from the room. The first term on the right-hand side is the heat loss which is controlled by the supply air flowrate. The second term is the heat gain through the room envelope, including the warm air infiltration due to the indoor-outdoor temperature differential. The third term is the thermal loads from the internal heat generation and the infiltration. In this simplified model, any other uncontrolled inputs (e.g., ambient weather conditions, solar radiation and inter-zonal airflow, etc) are not considered.
It should be noted that all variables such as θ, θs, θ0, qL and ws in Equation 1 are obviously the function of a time t. For the sake of simplicity the time t is not presented. When realizing a digital controller, a deadtime exists between the sampling operation and the outputting time of control input, thus ws, namely fs, includes a deadtime LP.
These plant parameters have been obtained by experimental results (National Institute for Environment Studies in Tsukuba, Yamakawa 2009). The room dynamics can be approximated by a first-order lag plus deadtime system from the experimental data (Åström 1995, Ozawa 2003). Thus, the plant dynamics including the AHU and the sensor can be represented by,
P(s)=KPTPs+1e−LPs=0.6418s+1e−2.4sE2
Comparing to Equation 1, the plant gain (KP) and the time constant (TP) can be given by,
KP=θsws+α,TP=Cws+α,ws=racpfsE3
Therefore, KP and TP change with the control input (the supply air flowrate fs). Similarly, it is assumed that LP changes with the control input. Namely,
LP=LP0ws+αE4
where LP0 is determined so that LP is equal to 2.4 [min] when fs is equal to 50 [%]. From LP = 2.4 [min], ws = ρacpfs = 10.89 [kJ/min K] and α = 9.69 [kJ/min K], LP0 can be obtained to be equal to 49.4 [kJ/K]. It is easily be found that these parameters are strongly affected by the operating points. Carrying out an open-loop experiment in the HVAC field to measure KP, TP and LP is one way to get the information needed to tune a control loop.
To get some insight into the relations between Equation 1 and Equation 2, we will describe a bilinear system in detail (Yamakawa 2009). Introducing small variations about the operating points and normalizing the variables, Equation 1 has been transformed to a bilinear system with time delayed feedback. A parametric analysis of the stability region has been presented.
The important conclusion is that the stability analysis demonstrated the validity of PID controllers and there was no significant advantage in analyzing a bilinear system for VAV systems. It was fortunate that the linear system like a first-order lag plus a deadtime system derived in Equation 2 often satisfactorily approximated to the bilinear system derived in Equation 1. The linear system is an imaginary system, but it does represent it closely enough for some particular purpose involved in our analysis.
Certainly the linear model derived in Equation 2 can be used to tune the PID controller and the physical model derived in Equation 1 can be used for numerical simulations. Over the range upon which this control analysis is focused, the relations between Equation 1 and Equation 2 are determined to be sufficiently close.
2.1.2. Room humidity model
The room humidity model can be derived by applying the principle of mass balance,
Vdxdt=fs(xs−x)+nρapE5
where
V = room volume (10×10×2.7[m3])
x = absolute humidity of the room [kg/kg (DA)]
xs = absolute humidity of the supply air [kg/kg (DA)]
p = evaporation rate of a occupant (0.00133 [kg/min])
n = number of occupants in the room [-].
Equation 5 states that the rate of change of moisture in the room is equal to the difference between the moisture removed from and added to the room. The first term expresses a dehumidifying effect by the supply air flowrate. The second term is the moisture due to the occupants in the room. The absolute humidity x can be converted to the relative humidity φ as described in the next section.
In the same way as the room temperature model, the humidity model can be approximated by a first-order lag plus deadtime system as shown in Equation 2. Thus, the plant dynamics concerned with the room humidity model can be represented by,
P′(s)=KPhTPhs+1e−LPhs=1.013.5s+1e−2.4sE6
The gain constant KPh and the time constant TPh are given by,
KPh=fsfs=1,
TPh=VfsE7
Thus, KPh and TPh change with the supply air flowrate as same as those represented in the room temperature model. Similarly, the deadtime LPh is assumed to be changed with the supply air flowrate. Thus,
LPh=LPh0fsE8
where LPh0 is the constant. The deadtime LPh of the humidity model is assumed in the same way as one of the temperature model. Thus, the deadtime LPh0 can be calculated by LPh×fs = 2.4×8.33 = 19.99.
Figure 2.
Block diagram for AHU.
The room humidity can be determined by regulating the moisture of the supply air to the room. This implies that the room humidity can be indirectly controlled. Similarly the first-order lag plus a deadtime model by Equation 6 can be used to tune the PID controller and the physical model by Equation 5 can be used in numerical simulations. It does not mean that Equation 5 and 6 are mathematically equivalent.
2.1.3. Air-handling unit (AHU) model
Figure 2 shows the simple block diagram for the AHU that conditions supply air for the room. Air brought back to the AHU from the room is called return air. The portion of the return air discharged to the outdoor air is exhaust air, and a large part of the return air reused is recirculated air. Air brought in intentionally from the outdoor air is outdoor air. The outdoor air and the recirculated air are mixed to form mixed air, which is then conditioned and delivered to the room as supply air.
The AHU consists of a cooling coil, a humidifier, and a fan to control supply air temperature (θs ) and humidity (xs). The mixed air enters the cooling coil at a given temperature θ, which decreases as the air passes through the cooling coil. The temperature of the air leaving the cooling coil is θc. Since the responses of the cooling coil and the humidifier are significantly faster than those of the room (a principal controlled plant), it can be generally assumed that the cooling coil and the humidifier are static systems. Namely, it is common for the cooling coil to be controlled to maintain the supply air temperature at a setpoint value (θsr). Thus, the temperature (θc) and the absolute humidity (xc) of the cooling coil can be given by;
θc=θsrxc={xsi(pw≤pws)0.622pwsP−pws(pw>pws)E9
where θsr is the setpoint of the supply air temperature, pw is the partial pressure of water vapor, pws is the partial pressure of saturated vapor at temperature, P (=101.3 [kPa]) is the total pressure of mixed air, and xsi is the absolute humidity of the air entering the cooling coil. The humidity is divided into two calculations depending on the difference between pws and pw. This constraint means that the relative humidity does not exceed 100 %.
The humidifier is the most important actuator to control the room relative humidity (φ) for heating mode in winter. Nevertheless, we are interested here in examining control characteristics in the operation mode of cooling. Note that the control input h(t) does not have strong effect on the room relative humidity (φ) in cooling mode. From the energy and mass balances, the dynamics of the humidifier can be described by,
qd = load by humidifier ((190.1 – 1.805θh)h) [kJ/min]), and
h = rate of moist air produced in the humidifier.
Considering the steady-state of the dynamics of the humidifier, the supply air temperature θs and the supply air absolute humidity xs can be obtained by,
θs=cpρafsθc+αdθ0+qB+qdcpρafs+αdxs=xc+hfsρaE11
As can be seen in Equation 11, the supply air temperature (θs) can be influenced by the humidifier (h), so that the errors in the reset (fs0) can be produced. Thus, the control performance may be deteriorated.
The air flowrate from the outdoor air is considered 25% of the total supply air flowrate. This ratio will be held constant in this study. Note that the pressure losses and heat gains occurring in the duct have negligible effects on the physical properties of air for simplification. The absolute humidity of mixed air entering the cooling coil can be described by,
fsxsi=0.25fsx0+0.75fsxE12
where x0 and x are the absolute humidity of outdoor air and of indoor air, respectively. All the actual values of the plant parameters used in the numerical simulations are listed in Table 1. Since we assume that the supply air temperature for the cooling coil can be controlled so as to maintain the setpoint value (θsr) of the supply air temperature, the energy-balance of mixed air is not needed to consider.
C
370.44 [kJ/K]
V
270 [m3]
cp
1.3 [kJ/kg K]
(a
1.006 [kg/m3]
(
9.69 [kJ/min K]
(d
0.1932 [kJ/min K]
qL
121.72 [kJ/min]
fsmax
16.66 [m3/min]
fsmin
0.00 [m3/min]
hmax
0.33 [m3/min]
hmin
0.00 [m3/min]
(sr
13.1 [C]
Table 1.
Summary of significant parameters in the development of the room and the AHU
2.1.4. Calculation of relative humidity
In this section, the conversion from the absolute humidity to the relative humidity is briefly explained. The relative humidity is derived from the air temperature and the absolute humidity of the air (ASHRAE 1989; Wexler and Hyland 1983).
First, the air temperature must be converted to the absolute temperature as,
Θa=θa+273.15E13
where θa is the air temperature, and Θa is the absolute temperature of the air.
Second, to evaluate the supply air temperature θc reaches its dew-point temperature, the two partial pressures pw and pws can be conveniently defined. The partial pressure of water vapor pw can be obtained by,
pw=Pxi0.622+xiE14
where xi is the absolute humidity of water vapor and P is the total pressure of mixed air (101.3 [kPa]). And, the partial pressure pws of saturated vapor at temperature Θa can be given by,
Figure 3.
Overall of the temperature-humidity control system.
Finally, the relative humidity φ for the room can be given by,
φ=pwpws×100E16
2.2. Control system
Figure 3 shows a block diagram of the room temperature and humidity control systems using adjustable resets which compensate for thermal loads upsets. In this figure, signals appear as lines and functional relations as blocks. The primary controlled plant is the room. The cooling coil, the humidifier and the damper are defined as the secondary controlled plants (to produce appropriate actuating signals). The following control loops are existed in our room temperature and humidity control system:
Room air temperature control system
Room air humidity control system
The control outputs of interests are room air temperature (θ ) and relative humidity (φ). In order to maintain room air temperature and humidity in desirable ranges, traditional PID controllers have been used to reduce component costs. The control inputs that vary according to the control actions are the supply air flowrate (fs) and the rate of moist air produced in the humidifier (h), which will be discussed in more detail.
2.2.1. Room temperature control system
Taking the PID control algorithm into account, one of control inputs, related to the room air temperature (θ ) can be given by,
fs(t)=kpe(t)+ki∫0te(τ)dτ+kdde(t)dt+fs0(t)E17
where fs0(t) is the manual reset. In electronic controllers, the manual reset is often referred to as “tracking input”. The error e(t) can be defined by,
e(t)=q(t−LP)−qrE18
where θr is the setpoint value of the room air temperature, and LP (= 2.4 [min]) is the deadtime. The PID parameters (the proportional gain kp, the integral gain ki, and the derivative gain kd) can be determined by the well-known tuning method. The inherent disadvantage of the I action, which easily causes instabilities, can be reduced by varying the reset fs0(t) to compensate for thermal loads upsets (disturbances). In some cases of HVAC systems, the reset fs0(t) can be estimated by knowledge of the plant dynamics.
Equation 17 can be given in a discrete-time system when control input and error signal are respectively assumed to be fs(k) and e(k) at time kT (T is the sampling period).
In Equation 21, the supply air temperature (θs), the outdoor temperature (θ0), and the setpoint (θr) can easily be measured. However, thermal loads cannot be specified in advance. Thus, it is recommended that occupants must roughly estimate thermal loads to improve the control performance at adequate sampling interval. For example, three of the rough estimates for compensation can be used as:
the maximum (75%), the medium (50%), and the minimum (25%),
where 100 % means the maximum supply air flowrate 16.66 [m3/min].At any given point of operation, the reset (fs0) to offset thermal loads can be easily calculated using Equation 21. Thus, it can be concluded that the controller with lower I action is superior to that with no I action, and is also called a PD controller.
2.2.2. Room humidity control system
To control the room air relative humidity, another one of control inputs that vary according to the control actions is the rate of moist air produced in the humidifier h(t). The control input can be given by,
h(t)=kpheh(t)+kih∫0teh(τ)dτ+kdhdeh(t)dt+h0(t)E22
where h0(t) is the reset. The error eh(t) can be defined by,
eh(t)=jr−j(t−LPh)E23
where φr is the setpoint value of the room air relative humidity and LPh (= 2.4 [min]) is the deadtime. The hygrometer in the room can detect the room air relative humidity (φ ), but not the absolute humidity (x). Therefore, the relative humidity is used in the error eh(t) for the calculation of the control input h(t). However, the humidity model can be described by the relational expression of the absolute humidity. And, the derivation of the humidity model parameters from the experimental results in terms of the relative humidity may be extremely difficult. As a result, PID parameters (proportional gain kph, integral gain kih, and derivative gain kdh) must be determined by trial and error under the consideration that the absolute humidity cannot be directly measurable. In this study, for the sake of simplicity, it is assumed that the basic relation of the humidity model is invariant even if the variable in the humidity model is changed the absolute humidity into the relative humidity. For this reason, the traditional tuning method (Ziegler and Nichols 1942) for the first-order lag plus deadtime system as shown in Equation 6 (the plant parameters is described by Equations 7 and 8) can be used.
Since the supply air temperature (θs ) can be affected by the rate of moist air produced in the humidifier (h), the reset of the supply air flowrate (fs0) arising from moist air variations must be accounted. This means that good control performance for heating mode can be expected.
The reset h0(t) for the humidifier can be obtained from Equations 5, 9, 11, and 12 as follows: First, taking the humidity model (Equation 5) at the steady-state and the setpoint value xr of the absolute humidity into account, the following equation can be obtained by,
However, as will be seen in Equation 25, the first term is small in comparison to the second term and h0(t) may be negative. The adjustable reset h0(t) can be found to be nearly zero under most circumstances in the present work.
Table 2 provides PID parameters tuned by the traditional ultimate sensitivity method (Ziegler and Nichols 1942) and the empirical modified PID method.
The ultimate sensitivity method is simple and intuitive. It has been still widely used, either in its original form or in some modification. Since it only gives “ball-park” values, it is necessary to make manual tuning to obtain the desired performance. Our empirical modified PID controller can help improve the time response of a control system because thermal loads and operating conditions are changing continuously in HVAC systems.
In modified PID parameters for room air temperature control, the proportional gain (kp) is about 80 % of that of the conventional tuning method. The integral gain (ki) is one-fourth of that of the conventional tuning method. The derivative gain (kd) is nearly the same as that of the conventional tuning method. In modified PID parameters for room air relative humidity control, all gains (kph, kih, and kdh) are nearly one-tenth of those of the conventional tuning method.
3. Simulation results in daily operation
To illustrate the control performance of the room temperature and humidity control systems, several simulation runs are made. Representative outdoor temperature and thermal loads profiles for one-day (between 08:00 in the morning and 08:00 in the next morning) are assumed as shown in Figure 4. These profiles are based on the experimental data obtained from the National Institute for Environmental Studies in Tsukuba, Japan. In the right hand side of Figure 4, the dashed line depicts the artificial estimated value of the thermal load. At the start-up (at 08:00 in the morning), the feedback control system takes over and controls the room air temperature and relative humidity. These simulation runs are carried out under the same conditions mentioned above. Figure 5 depicts the adjustable reset (fs0) of the supply air flowrate for daily operation calculated using Equation 21. The computational interval of 1 hour (60 min) for adjusting the reset is used in this control. These simulation runs are made on MATLAB which is an effective tool for field engineers in control engineering.
The following control configurations are used in our room temperature and humidity control. These abbreviations are common throughout the remainder of this paper.
(a) Temp. control
kp
ki (Ti)
kd (Td)
Conventional PID
11.65
2.55 (4.57)
13.26 (1.16)
Modified PID
8.73
0.8 (10.9)
10 (1.15)
(b) Humidity control
kph
kih (Tih)
kdh (Tdh)
1.22
0.26 (4.65)
1.41 (1.16)
Table 2.
PID parameters. (Ti, Tih: integral times, Td, Tdh: derivative times for temperature and humidity controls, respectively)
Figure 4.
Outdoor temperature and thermal loads profiles.
Figure 5.
Reset of supply air flowrate.
Number of control outputs of interest, Room temperature and humidity control. This refers to the room air temperature and relative humidity control.
Setpoints of control outputs
Regarding the room air temperature θr:
Fixed setpoint, The setpoint θr is fixed at 24 ºC for daily operation.
Variable setpoint, The setpoint θr are varied within the range, that θr is set at the value (θ0 − 4) ºC where θ0 is the outdoor temperature, and θr is limited to the minimum 20 ºC and the maximum 28 ºC.
Regarding the room air relative humidity φr:
The setpoint φr is usually fixed at 55 % for daily operation.
Control strategies for the reset
Conventional PID control, This refers to conventional PID control with the fixed reset (fs0 = 50 %).
Modified PID control, This refers to modified PID control with the adjustable reset (Figure 5).
Performance indices, The control performance should be evaluated by defining three performance indices.
ISE (the integral of squared error), ISE = ∫024e2dt
ICI (the integral of control input), ICI = ∫024fsdt
IPID (the integral of control input produced in PID controller only), IPID =∫024(fs−fs0)dt
Figure 6.
Simulation results of conventional PID.
Room temperature and humidity control
Typical daily simulation results show that the conventional PID and the suitably modified PID controllers can maintain the room air temperature and relative humidity close their respective setpoints irrespective of variable thermal loads. The method of determining PID parameters for the modified controller is practical for room temperature and humidity control systems.
Fixed setpoint
Figure 6 and 7 show the responses to the fixed setpoint of the room air temperature for the cases of the conventional PID and the modified PID controls, respectively.
In Figure 6, there are sudden changes in θ and φ during the initial few hours, which then settle to setpoints. We can expect that, since the transient responses of θ and φ will also change rapidly, θ and φ are very close to their setpoints even though θ0 and qL are varied. The supply air flowrate illustrates instabilities locally due to humidifier working.
When looking over results of Figures 6 and 7, it should be noted that the responses (θ and φ) of the conventional PID control and the modified PID control are somewhat different.
Figure 7.
Simulation results of Modified PID.
Conventional PID
Modified PID
ISE
3.09
7.95
ICI
2.08104
2.08104
IPID
8742
2734
Table 3.
Comparison of control performance indices to fixed setpoint.
Because the reset for the modified PID control can be adjusted very often, it becomes difficult to maintain θ and φ at the setpoints, so θ fluctuates around the setpoint. It is clear that the results for modified PD control cannot represent an improvement over those for the conventional PID control. For small values of the integral gain (ki) for the modified PID control, θ creeps slowly towards the setpoint. However, as will be seen in the near future, this disadvantage may be clearly solved.
Figure 8.
Variable setpoint profile.
Table 3 shows that the results of the validation simulations in terms of three performance indices. For the ISE (tracking accuracy), it is evident that the sharply change of the reset aggravates the tracking accuracy of θ for the modified PID control, but it is enhanced by increasing the integral gain (ki). Further investigation into the total amount of control inputs (ICI and IPID) can lead to some interesting results. It is recognized that the ICI is exactly the same for the two control strategies. The physical interpretation of this fact is that there is no difference of supply air flowrates between two control strategies. However, for the IPID, the modified PID control clearly represents an improvement over the conventional PID control.
As a matter of fact, the merit of the modified PID control becomes obvious when the maximum capacity of the controller is limited.
Variable setpoint
Figure 8 depicts the setpoint profile ((θ0 − 4) [ºC]) of room air temperature depending on the outdoor temperature on a typical day. The responses to the variable setpoint for the cases of the conventional PID and the modified controls are shown in Figure 9 and 10, respectively. The room air temperature and humidity follow their respective setpoint profiles even though thermal loads are variable. It is apparent from Figure 9 that the solid areas indicate rapidly oscillating values due to hunting when the humidifier is positioned between 0 % and 100 %. Subsequently, the room air temperature can be oscillated with the occurrence of such huntings. The same trend is also apparent in the supply air flowartes.
It can be seen from Figure 10 that suitably tuned modified controller can maintain the room air temperature and humidity close to their respective setpoints suppressing such huntings. The effectiveness of the modified PID control can be confirmed. By comparing these responses with those of Figure 6 and 7, it is clear that the humidifier is turned on very often and the hunting of the room air temperature may occur simultaneously.
Fig. 9 and 10 demonstrate locally rapid oscillation of the humidifier when the indoor relative humidity φ becomes below the setpoint 55 %. This is due to the fact that the humidifier is very sensitive to control inputs. There are also many technological problems to be solved when we make positive use of the humidifier in cooling operation.
Figure 9.
Simulation results of conventional PID.
Conventional PID
Modified PID
ISE
5.98
8.83
ICI
1.92104
1.92104
IPID
4276
3005
Table 4.
Comparison of control performance indices to variable setpoint.
Table 4 represents the control performance indices obtained by typical daily simulation results. A comparison with Table 3 shows that there is very little difference in performance between the fixed setpoint and the variable setpoint. For the ISE, the ISE for the modified PID is larger than that for the conventional PID. This means that the I action is effective for not only elimination of offset (steady-state error) but also disturbance attenuation. Tracking accuracy and disturbance attenuation will be enhanced by selecting high integral gain.
For the ICI, it is striking that the ICI values are exactly the same for two control strategies. For the IPID, the modified PID control gives slightly better results than the conventional PID control. It is concluded that the modified PID control should be also incorporated by limiting the maximum control input available to the controller.
Figure 10.
Simulation results of Modified PID.
4. Conclusions
In this paper, the room temperature and humidity control systems with the conventioanl PID control using fixed reset or the modified PID control using adjustable resets which compensate for thermal loads upset are examined. The simulation results for one-day operation based on practical outdoor temperature and thermal loads profiles provide satisfactory control characteristics. The results of validation simulations are demonstrated in terms of three performance indicies (as three integrals of squared error (ISE), control input (ICI), and control input in PID controller only (IPID)).
The results obtained in this study are summarized in the following:
The room air temperature and humidity illustrate instabilities locally due to humidifier working.
By changing the setpoint of the room air temperature on the basis of the outdoor temperatures profile, the control performance can be remarkably improved.
In daily operation, when the reset is adjusted at every hour, the sharply change of the reset aggravate the response of the room air temperature. The response can be improved by proper selection of the computational period.
The proposed control strategy for the adjustable reset cannot be effective for energy-savings, but has a possibility in case that there exists a limitation of the maximum control input available to the controller.
Finally, the results given in this paper were motivated by the desire to obtain satisfactory performance with adjustable reset better than that with fixed reset. Consequently, it is concluded that there is little inherent advantages in designing the modified PID controller with adjustable reset. However, since this modified PID control lightens the total amount of control input produced in the controller, it can be good candidates for the next HVAC controllers.
The work reported here is being continued to validate several conclusions obtained by experimental results.
5. Nomenclature
C= overall heat capacity of air-conditioned space (kJ/K)
Cad= overall thermal capacity of humidifier space (kJ/K)
θsr= setpoint of supply air temperature (in humidifier) (ºC)
θc= supply air temperature (in cooling coil) (ºC)
θ0= outside temperature (ºC)
ρa= density of air (1.3 kg/m3)
cp= specific heat of air (kJ/kg K)
fs= supply air flowrate (m3/min)
fs0= reset of supply air flowrate of room (m3/min)
ws= cp × ρa × fs, heat of supply air flowrate (kJ/min K)
V= room volume (10×10×2.7 m3)
Vd= room volume of humidifier (m3)
x= indoor absolute humidity (kg/kg (DA))
xs= absolute humidity of supply air (kg/kg (DA))
xsi= return air absolute humidity at the inlet of air-handling unit (kg/kg (DA))
x0= outdoor absolute humidity (kg/kg (DA))
φ= indoor relative humidity (%)
φr= setpoint of indoor relative humidity of room (%)
h= rate of moist air produced in humidifier (kg/min)
qL= thermal load from internal heat generation (kJ/min)
qB= fan load (59.43 kJ/min)
qd= load by humidifier ( (190.1 – 1.805θc)h kJ/min)
p= evaporation rate of a occupant (0.00133 kg/min)
P= total pressure of mixed air (101.3 kPa)
pw= partial pressure of water vapor at the inlet of air-handling unit (kPa)
pws= partial pressure of saturated vapor at temperature θc (kPa)
h0= reset of rate of moist air produced in humidifier (kg/min)
n= number of occupants in the room (-)
KP= plant gain of room temperature dynamics
TP= time constant of room temperature dynamics
LP= deadtime of room temperature dynamics
KPh= plant gain of room humidity dynamics
TPh= time constant of room humidity dynamics
LPh= deadtime of room humidity dynamics
Acknowledgments
This research was partially supported by the National Institute for Environment Studies in Tsukuba. The authors would like to acknowledge staffs of Controls Group for their contribution to this study.
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1. Introduction
The increasing use of antibiotics has an important impact on human health by introducing the emergence of resistant bacterial strains, both in humans treated in an indiscriminate manner, and in two other situations as worrying as, which are the presence of these molecules in drinking water and abusive use in agriculture. This has all resulted in the phenomenon of antimicrobial resistance (AMR) [1].
Each year worldwide, 700,000 deaths occur, approximately, due to diseases that had antimicrobial resistance as responsible for the deaths. By 2050, these deaths could reach the terrifying 10 million mark [1].
One of the biggest barriers to antibiotic-resistant infections is that they add significant costs to the any nation’s already overburdened health system [2].
Thus, the paths have been opened for other ways to fight infections and photodynamic therapy (PDT) has stood out with the aim of inactivating not only bacteria, but also fungi, protozoa, and viruses. It is a promising technique, including the treatment of diseases that already have antimicrobial resistance.
In this chapter we will address the theme of advances in research involving microbiological control with photodynamic action, more specifically in the treatment or prevention of diseases of the respiratory tract.
1.1 Antimicrobial resistance
The bacteria have developed several mechanisms to fight against antibiotics action. An important molecular mechanism involves the horizontal transfer of genes from the efflux pumps when the organism acquires a gene that confers the ability to eliminate antibiotics from the intracellular environment [3]. A well-known example is the acquisition of the β-lactamase gene from antibiotic inactivating enzymes, which inactivates β-lactam antibiotics, such as penicillin and cephalosporins [3], where bacteria acquire the ability to inactivate antibiotics through an enzymatic mechanism.
Two interesting aspects are related to cell wall morphology and the ability of bacterial colonies to form biofilms, and interestingly, these aspects are directly related to the cell wall structure of Gram-positive bacteria. The cell-wall glycopolymers from Gram-positive bacteria present an essential role in host-cell adhesion, the first step towards forming a bacterial biofilm. In contrast to Gram-negative, Gram-positive bacteria have a thicker cell wall structure with multiple layers of peptidoglycan. In addition, many Gram-positive bacteria have protective surface structures, typically with glycopolymers bound to peptidoglycan or membrane lipids. These structures include glycopolymers of teicoic acids and branched mycobacterial polymers [4]. Infections caused by Gram-positive bacteria are important for human health and it is worrying that these bacteria are becoming increasingly resistant to existing antibiotics. The teicoic acids wall has multiple functional roles in Gram-positive bacteria including resistance to cationic antimicrobial peptides, such as the vancomycin, a glycopeptide antibiotic. Other cellular processes influenced by this wall include autolysis, cell division, the location of penicillin-binding protein, survival at higher temperatures, biofilm formation and epithelial cell adhesion [5].
Biofilms have an important impact on bacterial infections and also on bacterial resistance. Organisms structured in biofilms exhibit up to 1,000 times more resistance to antibiotics than planktonic cells.
1.2 Mechanisms of antibiotic resistance
Pathogenic bacteria resistant to antibiotics are prevalent in different populations of the environment such as from the soil and water containing encode genes with resistance mechanisms [6], which can be mobilized for new hosts, including humans [7] and, depending on genic expression, may result in significant public health problems [8]. If the microbial mutations are for its benefice, such as antibiotic resistance, they are predominant in the species and transmitted for subsequent generations, making the bacteria predominant antibiotic-resistant [9]. The mechanisms of an antimicrobial resistance may be intrinsic to the microorganisms or even acquired through the transmission of the genetic material or by mutation (which may occur during replication) during the bacterial evolution, whether induced or spontaneous, by mutation mechanisms in a chromosome or transfer genes loci, which can encode inactivate enzymes in antibiotics or even reducing their permeability in cells [10]. The bacterial mutations that can occur are replacement (transition and transversion); deletion (macrodelection and microdeletion); insertion (macroinsertion and microinsection) and inversion, with exchange of pyrimidine or purine, removal of nucleotides, the inclusion of nucleotides, and removal or insertion of DNA, respectively.
Strains resistant to antibiotics can be transmitted between patients in healthcare units, often through healthcare professionals’ contaminated hands, medical-surgical equipment, or inanimate objects from the hospital environment [11]. This type of spread is generally clonal, involving the transmission of a single resistant strain. Outbreaks caused by the clonal spread of an antibiotic-resistant organism have been commonly reported in S. aureus MRSA strains [12]. Patients’ transmission can be clonal in multiple species of strains with different prevalence according to the geographic region [13].
1.3 The worldwide impact of antimicrobial resistance
Infectious diseases are a major cause of human deaths. According to the World Health Organization (WHO), on the top ten global causes of death (2016), chronic obstructive pulmonary disease and lower respiratory infections are occupying the third and fourth places, respectively, behind ischemic heart disease and stroke [14]. It is relevant to note that infectious diseases outperform all types of cancer in terms of mortality, according to WHO data. Figures reported in 2016 indicate that there were 3.190 million deaths due to respiratory infections, with a mortality rate of 43/100,000. Analyzing again the top ten global causes of death but now, in low-income countries (2016), lower respiratory infections were among the leading causes of death across all income groups [14].
It is essential to discover and invest in the development of new antibiotic molecules, following the growing global need. But just as importantly, research into new non-antibiotic approaches for the prevention and protection against infectious diseases is needed and should be encouraged and a high priority research and development project [15].
In the US, the Centers for Disease Control and Prevention (CDC) estimated that antibiotic-resistant infections are responsible for $20 billion a year in additional health care costs, and $35 billion a due to loss of productivity [16]. Thus, a deeper understanding of the mechanisms of resistance to antibiotics is relevant in terms of human health, that is, it saves human lives, but it also reduces an important economic burden for public and private health systems.
Penicillin, discovered by Fleming in 1928, was first tested for the treatment of infectious diseases in the 1930s and became a widespread drug in the 1940s. β-lactam antibiotics, the group to which penicillin belongs, are effectively drugs of choice for the treatment of community-based respiratory diseases, for example, which are usually caused by Gram-positive bacteria, such as Staphylococcus and Streptococcus.
The introduction of new antibiotics in clinical use was quickly followed by the clinical observation of resistant strains and the time between clinical use and resistance has become shorter and shorter. For example, sulfonamides were introduced for clinical use in 1930 and resistant strains appeared in the 1940s. Vancomycin was introduced in 1956 and resistant strains were first reported in 1988. However, for newer antibiotics, such as daptomycin, fidaxomycin and linezolid, resistance was observed in the same year in which clinical use began [17].
2. Antimicrobial photodynamic therapy (aPDT)
The mechanisms of aPDT are basically the same of PDT for tumors, based on the combined action of three elements: a photosensitizer (PS), a light source at appropriate wavelength to excite the PS and molecular oxygen (O2) in the target tissue.
The photodynamic process inactivating microorganisms occurs through the action of reactive oxygen species (ROS) that destroy vital constituents of fungi, bacteria, viruses and protozoa. In 1933 Jablonski published his article explaining the electronic states of a molecule and the transitions between them [18]. In this famous “Jablonski’s diagram”, we understand how a photosensitizer in the singlet ground state, moves to the excited singlet state after absorbing photons from a light source. And through the process named “intersystem crossing”, a spin inversion occurs and then, this molecule goes to the excited triplet state, giving it conditions to transfer energy (type II mechanism) or electron (type I mechanism) to O2, generating ROS.
For antimicrobial purposes, the photodynamic action will take place within the cells or at the extracellular matrix of the microbial biofilm where the photosensitizer molecules are present, the main sites being the outer membrane or cell wall, membrane lipids and lipopolysaccharides. The singlet oxygen produced has a very small radius of action, less than 0.02 μm, so the damage produced by PDT will only be in the presence of the photosensitizer and under photoactivation. As a result, cell death is caused by cell wall or membrane lysis and/or inactivation of proteins or enzymes essential for microbial metabolism [19].
3. aPDT of respiratory tract diseases
3.1 Pharyngotonsillitis
Sore throat is a frequent complaint in outpatient medical consultations and emergencies. Acute pharyngotonsillitis represents a significant source of social disorders in the child population, such as missed classes repeated use of antimicrobials, and can cause complications such as peritonsillar or retropharyngeal abscess, otitis, sinusitis, pneumonia, rheumatic fever, and post-streptococcal glomerulonephritis [20]. Bacterial infections of the respiratory system can be located in the pharynx (pharyngotonsillitis). Viruses cause around 90% of pharyngitis, and 10% are caused by bacteria that have the vast majority associated with Streptococcus pyogenes or Beta-hemolytics of Lancefield group A (EBHGA) [21] however, other bacteria can cause pharyngotonsillitis such as Streptococcus mutans and Streptococcus pyogenes, Staphylococcus aureus, Moraxella catarrhalis, Haemophilus influenzae, Prevotella sp., Bacteroids fragilis, and Fusobacterium sp.
The diagnosis of EBHGA infection should preferably be confirmed microbiologically by rapid antigens detection tests and through oropharyngeal secretion culture. The gold standard for diagnosing oropharyngeal infections by EBHGA is culture [22], which should be done before starting treatment with antibiotics [23]. Clinical samples should be seeded on blood agar plates, which allows a preliminary screening of β-hemolytic colonies. Subsequent confirmation of suspected colonies such as EBHGA can be obtained by several laboratory tests, which are easily and quickly performed and which are still widely applied in clinical microbiology, despite the increasing use of automatic identification systems. EBHGA can be an oropharyngeal colonizing agent and thus, the microbiological investigation must be guided by clinical and epidemiological factors: patient’s age, clinical signs and symptoms, season, and personal exposure to EBHGA [24].
According to the World Health Organization (WHO), approximately 600 million new pharyngotonsillitis cases due to EBHGA occur annually, and of these, 500 thousand may progress with rheumatic fever and about 300 thousand with rheumatic carditis [25]. In developing countries, the occurrence is three times higher than in developed countries. The preliminary diagnosis and treatment of tonsillitis and pharyngitis is a common cause of inappropriate use of antibiotics.
Penicillin is the drug of choice for S. pyogenes infections’ empirical treatment, despite more than 60 years of use. S. pyogenes remains sensitive to penicillin, and resistance tests for penicillins or other beta-lactams approved for its treatment are unnecessary. However, more than 10% of patients report an allergy to penicillin, which leads to the use of cephalosporins, clindamycin, or macrolides as alternative treatments [26]. As rates of resistance to macrolides among isolated S. pyogenes have been increasing in North America and Europe, resistance tests for these substances may be indicated. Sore throat is a symptom that leading people to seek medical attention, and although it spontaneously remits, primary care doctors usually prescribe antibiotics for it. In a systematic review, Spinks and collaborators concluded that antibiotics confer relative benefits in the treatment of sore throat. However, the absolute benefits are modest [27].
The research carried out at Santa Casa Hospital of São Carlos city (São Paulo, Brazil) by the CEPOF - Optics and Photonics Research Center” from University of São Paulo - São Carlos is composed of a clinical trial - “Turmeric and LED in the treatment of sore throat” with objectives as assessing the therapeutic efficacy of PDT with curcumin as an adjunct in the treatment of acute pharyngotonsillitis in adults in the municipality of São Carlos [28]. The photosensitizer used in this study was curcumin (0.75 mg/ml), using two minutes of illumination with a blue light (LED) at 450 nm. The clinical trial is randomized and controlled with adults aged 18 to 45 years diagnosed with acute pharyngotonsillitis. Participants are undergoing a rapid test for the detection of group A beta-hemolytic streptococcus (EBHGA). Participants with streptococcal pharyngotonsillitis are divided into Antibiotic therapy comparison groups in conjunction with photodynamic therapy; and Antibiotic Therapy Group in conjunction with a photodynamic therapy placebo, and the therapeutic response will be evaluated in terms of clinical symptoms (sore throat and fever) and microbiological response, mainly considering the presence of EBHGA in the clinical response.
3.2 Designing antimicrobial-coating for endotracheal tube to prevent ventilator-associated respiratory tract infections
Mechanical ventilation (endotracheal intubation) is an effective intervention performed for breathing support in patients admitted in the intensive care unit, but it is also identified as one of the highest risk factors for developing ventilator-associated pneumonia (VAP) [29]. VAP is a type of nosocomial infection that results in a higher mortality (increase from 20–75%) and morbidity rate, prolonged lengths of hospitalization, and also increased hospitalization costs ($10,000 to $25,000) [30, 31, 32]. Furthermore, each year, approximately 50 million patients in the intensive care unit are intubated with an endotracheal tube (ETT) for breathing support worldwide [33].
Most cases of VAP are caused by the aspiration of infected (bacteria and/or virus) secretions from the oropharynx, although a small number of cases can result from direct bloodstream infection [34]. Moreover, there is a growing concern associated with the ETT as the primary target related to VAP by biofilm formation on its surface [35]. Biofilms are characterized by its resistance to commercial antibiotics that favor resistant microorganisms’ proliferation and make them inaccessible to antimicrobials [36].
Regarding VAP occur by ETT, aspiration occurs when there is distal migration of microorganisms present in the secretions accumulated above the ETT cuff. Moreover, biofilm is formed and attached in the lumen of ETT facilitating the transfer to the sterile bronchial tree [37], as presented in Figure 1.
Figure 1.
Pathogenesis of ventilator-associated pneumonia (VAP). Copyright (2020) National Academy of Sciences.
Currently, there are methods used to prevent VAP based on its pathogenesis such as prevent aspiration of secretions and bacterial colonization of aerodigestive tract. Lastly, strategies include measures to minimize the risk of contaminated equipment but these methods show some practical limitations. In this regard, the development of strategies and new medical devices to avoid VAP is urgently need.
New medical devices based on the development of antimicrobial coated for ETT surface should be considered if they have been able to prevent VAP in well-designed clinical studies and be cost-effective [38]. Along the years, different strategies and antimicrobial coated for ETT surface (e.g. metal/antiseptics, metal/zeolites/d-tyrosine, nanorough/fructose, antimicrobial peptides, antibiotics/antiseptics, photo-based therapy, micropatterned surfaces, nanorough surfaces, and hydrophobic/hydrophilic) have been evaluated aiming to prevent the biofilm formation and VAP [38] (Figure 2).
Figure 2.
Antimicrobial coatings for ETT.
These antimicrobial coated are functionalized on ETT surface via covalently or ionic bonding or creating a matrix on a polymer (e.g. polyvinyl chloride (PVC)) depending on the molecular structure of both antimicrobial and type of polymer-based ETT and the presence of additives on ETT constitution [39].
As a selected example, in 2020, the Optics and Photonics Research Center from University of São Paulo developed a photo-based antimicrobial coating for ETT via functionalization of a natural product (curcumin) photosensitizer on PVC-based ETT surface [40] (Figure 3).
Figure 3.
Curcumin-functionalized endotracheal tube. Copyright (2020) National Academy of Sciences.
This therapeutic approach is based on the photoactivation of curcumin-functionalized endotracheal tube using an optical fiber followed by the production of reactive oxygen species and 1O2 able to destroy biofilm and preventing its formation in the lumen of ETT. In this regard, the authors observed a photoelimination of bacteria biofilm such as E. coli (72%), S. aureus (95%), and P. aeruginosa (73%) previously formed on the ETT surface using a light dose of 50 J/cm2. Moreover, a prevention on formation of S. aureus bacteria biofilm in the lumen of curcumin-functionalized endotracheal tube was observed when it was under illumination (at 450 nm, 35 mW/cm2) [39]. Furthermore, no degradation and leaching for curcumin-functionalized endotracheal tube under different pH values (2.0, 4.5, 7.0, 8.0, and 10.0) were observed. These results pave the way for developing of photosensitizers-functionalized ETT and photodynamic action to combat hospital-acquired infections like VAP [40].
Overall, the development and application of antimicrobials coatings for ETT have shown great promise and continue to progress. Significant results are being obtained with a wide family of the antimicrobial coating, including photosensitizers. From perspective, these in vitro methodologies developed so far could be applied in ex vivo and in vivo tests to evaluate and optimize these antimicrobial medical devices to be applied in clinical trials. In sum, this approach possesses excellent potential to reduce the number of deaths worldwide and decrease healthcare costs.
3.3 Lower respiratory tract infections and current treatment challenges
Lower respiratory infections are the fourth-largest cause of death worldwide and the main cause of death in low-income countries [14]. The most frequent lower respiratory infections are acute bronchitis and bronchiolitis, influenza, and pneumonia [41]. In Brazil, pneumonia is the number one cause of hospitalization [42]. It is also the main worldwide cause of death of children younger than 5 years old [43]. Although the number of hospitalizations has decreased over the past decades, the in-hospital mortality increased, mainly explained by the aging of the population and the occurrence of pneumonia cases that are more difficult to treat [42].
The European Respiratory Society defines pneumonia as an acute illness of the lower respiratory tract that includes cough and at least one other symptom: new focal chest signs, new lung shadowing shown by radiography, otherwise unexplained fever for more than 4 days, or otherwise unexplained tachypnea/dyspnea [41]. Community Acquired Pneumonia (CAP) is contracted from contact with the infection in day-to-day life [41]. It is predominantly bacterial in origin, being Streptococcus pneumoniae its most prevalent pathogen [44]. Other important agents are Haemophilus influenza, Pseudomonas aeruginosa [44, 45]. Also, about 30% of cases are coinfections with viruses [46]. However, in the vast majority of CAP cases, there is no investigation of the etiological agent [42]. In such situations, the treatment is based on the most prevalent microorganisms of that locality [42].
Hospital Acquired Pneumonia (HAP), also called nosocomial pneumonia, is the one that develops after at least 48 hours after the patients’ admission [47]. Its reported mortality rate ranges from 20 to 50%, the highest among nosocomial infections [47]. As mentioned above, ventilator-associated pneumonia (VAP) is the one contracted at least 48–72 hours after endotracheal intubation [41]. The most relevant HAP and VAP agents are also bacteria, like Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and Klebsiella, Acinetobacter, and Enterobacter species [48]. Knowledge of the etiological agents is essential in treating these infections since patients who receive the wrong initial therapy have a high risk of mortality and morbidity [47]. However, the delay in starting the treatment also leads to poor prognostic [47]. A significant concern in HAP and VAP cases is the present of methicillin-resistant Staphylococcus aureus (MRSA), which is associated with elevated mortality rates and treatment costs [49]. Traditionally, the first-choice drug for MRSA infections is vancomycin, which due to its low penetration in the lungs and high renal toxicity, leads to a failure rate the can reach 70% [49].
Even with new drugs like linezolid, tigecycline and ceftaroline, persists the difficulty in increasing the success rate of treatments and the worry with the development of resistance [49, 50]. Linezolid, for example, was approved for clinical use in 2000, and cases of resistance in patients were reported as early as 2002 [51]. In a study from 2014, the occurrence of non-susceptibility to this antibiotic remained relatively low, but several different resistance mechanisms had already been observed by then [51].
Another approach to hinder the burden of pneumonia is vaccination. Two types of vaccines are currently available for S. pneumoniae, the main agent in CAP: the pneumococcal polysaccharide vaccine (PPV) has been recommended for adults since the mid-1980’s, but it lacks efficacy in neonates and infants [52]; the pneumococcal conjugate vaccines (PCVs), designed to overcome that, were first approved in 2000 [53]. However, pneumococcal vaccination faces two main challenges: first, each vaccine is only effective against the serotypes contained in it; second, the reduction of the said serotypes increases the colonization of other serotypes that are not covered by the vaccines, and of other pathogen species like S. aureus and H. influenza [52]. Thus, new vaccines need to be developed continuously, similarly to what happens to antibiotics [52].
In face of so many challenges, PDT using indocyanine green (ICG) and infrared light has been studied in the treatment of bacterial pneumonia. ICG is a water-soluble dye that emits fluorescence when exposed to infrared light [54]. Its absorption peak in human plasma is 805 nm [55]. It is desirable to have the light excitation at this range because it penetrates deeper into biological tissue, since it is less absorbed by water, melanin and hemoglobin [56].
In an in vitro study by Leite et al., the in vitro inactivation of S. pneumoniae was effective using concentrations of ICG as low as 5 μM when combined with a 780 nm laser device or 10 μM when using an 850 nm LED. In these conditions, the treatment was safe for RAW 264.7 macrophages, and seemed to enhance their ability to fight the bacteria [57]. Other studies have also investigated similar protocols for other relevant pneumonia pathogens. Topaloglu et al. found an effective in vitro killing of S. aureus using 84 J/cm2 of light (809 nm) with 6 μg/mL of ICG, and of P. aeruginosa using 125 μg/mL ICG and 252 J/cm2 [58]. Kassab et al. had similar results for S. aureus, and showed that the same protocol, with up to 200 J/cm2 and 10 μM of ICG, was harmless to multiple mammalian cell lines [59].
The first in vivo investigation of the proposed protocol, performed by Geralde et al., found a reduction in the bacterial burden and an increase in the survival rate of SKH-1 hairless mice infected with S. pneumoniae after a single PDI session using ICG 100 μM and 120 J/cm2 of light at 780 nm, with a waiting interval of 3 minutes [60]. In this study, the light exposure did not seem to be harmful to the animals. Additionally, the ICG alone was no different form the control, suggesting that the activation with light was essential to the observed effects. It was then demonstrated that nebulization would be a viable delivery method for ICG to reach the lungs. ICG is compatible with air-jet nebulization in multiple concentrations, and it reaches and distributes in the lungs similarly as intranasal instillation [59, 61]. Additionally, mice exposed to pulmonary PDT using ICG and 216 J/cm2 of light at 808 nm showed no clinical signs of toxicity or histological damage to the lungs, liver or stomach 7 days after the treatment [59]. Replicating such results in larger models and patients might be challenging due to the layers of biological tissue the light needs to go through to reach the target. Nonetheless, aPDT using ICG and infrared light shows good efficacy and safety in pre-clinical studies, and has great potential to become a treatment for lower respiratory infections.
4. Nanotechnology and future perspectives for aPDT
Antimicrobial Photodynamic Therapy is one of the main option that have been investigated against resistant bacteria. However, even with the use of some photosensitizers in the clinic, especially for tumor treatment and already approved by the FDA, some restrictions of these molecules, such as low solubility, little tissue penetration, low specificity and little accumulation in the target cells are some of the characteristics that hinders the greater use of this technique as the gold standard in various diseases [62] Nanotechnologies is one possibility to increase the efficacy of molecules with poor pharmacokinetics and pharmacodynamics properties, including PS [63].
Drug delivery is, therefore, one of the most challenges for aPDT [64]. For this reason, nanotechnology has been used in PDT as a possibility to increase its effect. Nano-systems can be stable (even under light), present good optical properties and high penetration in the tissue, as the skin (for topical application), have more specificity (with surface functionalization) and be more efficient in ROS production [62]. Nanomaterials can be used as PS itself or to load the PS (as carrier), opening several possibilities to conjugate nanotechnology with aPDT.
The nanoparticles used as drug carrier present some advantages in relation to traditional molecules, such as the transport in the blood circulation of hydrophobic substances, the incorporation of some antigen given them desired properties, the facility to enter in the target and yet, it is possible to control drug delivery [64]. Thus, several types of nanoparticles, with different sizes, shapes and functions, have been synthetized in the last years, including for aPDT [65]. They are classified according to their material: inorganic (as metal nanoparticles), organic (as liposomes) and nanocomposites, organic or inorganic [66].
Some nanomaterials have been explored under irradiation, showing photodynamic effect and have been applied in different tests. Gold and Silver nanoparticles, nanomaterials based in silica and silicon, quantum-dots, carbon-based materials and nanoparticles from organic molecules are examples of the materials already used in photodynamic therapy and its multimodal conjugation treatments in several application [62].
The nanosystems also enable the delivery of PS with desirable optical characteristics, such as the use of absorption by two photons or upconversion nanoparticles and can result in high penetration into the tissue. Thus, they can be activated from X-ray to infrared, reaching regions of the body that previously were not possible with traditional PDT. This prospect of applying nano PDT can make this technique extremely useful in the context of respiratory diseases, especially due to the current concern about infections caused by resistant bacteria, the pandemic of the coronavirus, or the next outbreaks that are yet to come [64].
However, it is still necessary to overcome the barrier between in vitro and in vivo studies to reach nanotechnology’s clinical applications. Viral, fungal and bacterial infections characterize a global public health problem and, with the coronavirus pandemic, humanity saw the urgency to invest in new therapeutic possibilities, especially because new pandemics have been predicted. The advent of nanotechnology has helped to provide quick answers to urgent problems [63]. The scientific and clinical community’s joint efforts and their integration into industry are needed to respond quickly to respiratory diseases [67].
APDT is increasingly becoming a viable option for upper and lower airway infections and nanosystems can help to break traditional PDT barriers. The search for highly efficient PS has been one of the main research lines when it comes to improving PDT. Many molecules synthesis methods have been explored, as well as the synthesis of nanoparticles, but they are usually complicated and, especially with nanoparticles, are difficult to apply for large-scale production. Thus, simpler synthesis methods with functionalization of these nanometric systems have been gaining relevance in the scientific community, since it is one of the challenges for the clinical implementation of nano-PDT [68]. It is also necessary to understand the parameters beyond the laboratory, such as dose, irradiation and clinical efficacy [69].
5. Conclusions
Increased resistance to antibiotics has a direct impact on humanity and is one of the most important public health problems worldwide. Especially in the respiratory tract (lower and upper), which involves pharyngotonsillitis, pneumonia and infections by endotracheal tube, new therapeutic possibilities are needed. APDT has been shown to be highly effective against the microorganisms that cause these diseases and several protocols with different photosensitizers and illumination devices have been developed to make aPDT a great therapeutic option. New molecules and nanotechnology have been developed to improve aPDT and break down barriers to clinical applications.
Acknowledgments
The authors would like to thank CEPOF, the Brazilian National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES), and the São Paulo Research Foundation (FAPESP, grants 2013/07276-1, 2018/18188-0, 2016/14033-6, 2019/13569-8).
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"antimicrobial resistance, antimicrobial photodynamic therapy, photochemotherapy, infections of the respiratory tract, endotracheal tube",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/74812.pdf",chapterXML:"https://mts.intechopen.com/source/xml/74812.xml",downloadPdfUrl:"/chapter/pdf-download/74812",previewPdfUrl:"/chapter/pdf-preview/74812",totalDownloads:41,totalViews:0,totalCrossrefCites:0,dateSubmitted:"July 17th 2020",dateReviewed:"December 22nd 2020",datePrePublished:"January 14th 2021",datePublished:null,dateFinished:"January 14th 2021",readingETA:"0",abstract:"Antimicrobial resistance (AMR) and its relevant health consequences have been explicitly framed as a shared global problem and are estimated to be one of the largest causes of death worldwide by 2050. Antimicrobial photodynamic therapy (aPDT) proposes an alternative treatment for localized infections in response to AMR’s ever-growing problem. This technique combines molecular oxygen, a non-toxic photoactivatable photosensitizer (PS), and light of appropriate wavelength, leading to the formation of cytotoxic reactive oxygen species. Besides the ability to inactivate resistant pathogens via a non-selective approach (multiple targets), a relevant advantage of aPDT resides in the fact that no evidence of microorganism resistance has ever been reported to it. In this chapter, we address some efforts to use this technology to kill bacteria in the respiratory tract, from in vitro to clinical applications. We put forward three focuses: pharyngotonsillitis, pneumonia, and preventing secondary infections during the use of a photosensitizer-functionalized endotracheal tube. The results here presented offer a foundation for what may become a much larger clinical approach to treat respiratory tract infections.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/74812",risUrl:"/chapter/ris/74812",signatures:"Natalia M. Inada, Lucas D. Dias, Kate C. Blanco, Giulia Kassab, Hilde H. Buzzá and Vanderlei S. Bagnato",book:{id:"7886",title:"Photodynamic Therapy - from Basic Science to Clinical Research",subtitle:null,fullTitle:"Photodynamic Therapy - from Basic Science to Clinical Research",slug:null,publishedDate:null,bookSignature:"Dr. Natalia Mayumi Inada, Dr. Hilde Buzzá, Dr. Kate Cristina Blanco and Dr. Lucas D. Dias",coverURL:"https://cdn.intechopen.com/books/images_new/7886.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"90788",title:"Dr.",name:"Natalia",middleName:"Mayumi",surname:"Inada",slug:"natalia-inada",fullName:"Natalia Inada"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1 Antimicrobial resistance",level:"2"},{id:"sec_2_2",title:"1.2 Mechanisms of antibiotic resistance",level:"2"},{id:"sec_3_2",title:"1.3 The worldwide impact of antimicrobial resistance",level:"2"},{id:"sec_5",title:"2. Antimicrobial photodynamic therapy (aPDT)",level:"1"},{id:"sec_6",title:"3. aPDT of respiratory tract diseases",level:"1"},{id:"sec_6_2",title:"3.1 Pharyngotonsillitis",level:"2"},{id:"sec_7_2",title:"3.2 Designing antimicrobial-coating for endotracheal tube to prevent ventilator-associated respiratory tract infections",level:"2"},{id:"sec_8_2",title:"3.3 Lower respiratory tract infections and current treatment challenges",level:"2"},{id:"sec_10",title:"4. Nanotechnology and future perspectives for aPDT",level:"1"},{id:"sec_11",title:"5. 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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
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