1. Introduction

Urban pest species are highly effective and opportunistic in their use of the physical, legal and administrative interstices of the landscape we inhabit, and Aedes mosquitoes are a case in

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

point. Any inaccessible nook or cranny, any vacant land or neglected facility permits adult mosquitoes to hide and, in as little water as might accumulate in the lid of a bottle, find ideal breeding places. Aedes mosquitoes take advantage of the heterogeneous urban ecologies through "skip oviposition," laying a few eggs, spread across the largest possible number of sites. It is an especially well suited strategy for urban environments with abundant, but sparse and even temporary breeding sites [1].

The control of Aedes mosquitoes is failing in most tropical regions [1–3], and human diseases transmitted by this vector, like dengue, chikungunya, yellow fever and Zika, are among the top public health priorities [4, 5]. The strategies for suppressing mosquito populations below a threshold at which they no longer support viral amplification [6–10] has focused on two strategies [7, 11, 12]: (a) negating larval development opportunities by eliminating breeding places and the sites of immature stages; and (b) killing adults by fumigation with insecticides. More recently biomolecular and biogenetic approaches have also been suggested, although their effectiveness under field conditions are uncertain [13, 14]. Therefore new approaches are urgently needed, especially in urban landscapes [15].

Here we propose that an important strategic aspect that is currently overlooked in Aedes control programs: recognition of the complexity of urban ecologies in terms of land ownership, enforcement and accessibility for control interventions. We suggest that a systematic strategy that accounts for this physical complexity is essential to best implement Aedes control.


**7. Operative technique**

In some studies of patients followed over longer time frames, freedom from all valve-related events and freedom from reoperation were improved in patients with mechanical valve prostheses as compared to patients with biological prostheses. [9,16,25] Key of long-term success of mechanical valve prostheses is anticoagulation. Patients that are inconsistent, noncompliant or incapable of managing medications are not good candidates for long-term chronic anticoagulation.[39,41] Also patients with higher levels of education and those from geographic areas with a good medical infrastructure have better compliance with necessary

Many centers used bioprosthetic valves for patients who are older than 70 year, based on data by Akins.[42] In patients younger than 60 years of age, the best solution would be implantation of mechanical valves, based on prosthesis durability and they have low-risk for valve-related events.[21] In decade between 60 and 70 years of age, other factors have to be taken into

receive a biologic valve. Patients under the age of 60 should have a mechanical prosthesis to minimize the risk of structural failure requiring repeat AVR in an octogenarian. Patients between 60 and 65 represent the group in whom there is still considerable debate regarding prosthesis selection. Those patients who have comorbidities such as severe CAD may be less likely to outlive their prosthesis and should receive a biologic valve. A detailed discussion of these risks and benefits of prosthesis selection should occur with all patients and their families

In the early follow-up period, anticoagulation – related hemorrhage is the most common unwanted event for mechanical valve prostheses. Over the first 10 years of follow-up there is a higher incidence of valve-related events in patients with mechanical prostheses as opposed to those with biologic valves.[32] However, in the next 10 to 20 years after AVR, the incidence of valve failure and valve-related complications are much higher at biologic prostheses than those with mechanical valve prostheses. Some series showed that the time to biologic valve failure was only 7.6 years.[43] This failure rate will increase over time. However, freedom from valve-related events is more strongly influenced by pre-existing comorbidities than the

The elderly patient with severe aortic stenosis poses a therapeutic challenge. In considering elderly patients for aortic valve replacement, important factors include the presence of symptoms, physiologic age, patient expectations, anticipated future activities, and comorbid‐ ity. The operation itself carries a higher risk than in younger patients. Extensive calcification of the aorta and annulus as well as fragile tissue presents significant technical difficulties for the surgeon. In addition, particularly in women, the aortic root and annulus may be small and require concomitant enlargement to accommodate the valve prosthesis. Furthermore, pro‐ truding arch atheroma occurs in one-fifth of patients *>* 65 years of age and significantly increases the risk of stroke and mortality during cardiac surgery. Major postoperative complications, nevertheless, remain high, with the incidence of permanent stroke between 4 and 6%. Rehabilitation can also be a problem, as elderly patients take longer to recover from surgery. Survival has clearly improved in these elderly patients with severe symptomatic aortic stenosis who undergo aortic valve replacement. Survival is 80–85% at 1 year and 60–

According to some studies, patients over 65 years at the time of surgery should

medications and anticoagulant monitoring.[31]

prior to entering the operating room. [3,7,22,24,25,37,38]

presence of mechanical prostheses.[21], [22, 25, 31]

account.7

372 Calcific Aortic Valve Disease

Aortic valve replacement is most frequently done through a median sternotomy, meaning the incision is made by cutting through the sternum. Once the pericardium has been opened, the patient is put on a cardiopulmonary bypass machine. This machine takes over the task of breathing for the patient and pumping their blood around while the surgeon replaces the heart valve.

Once the patient is on bypass, a cut is made in the aorta and a crossclamp applied. The surgeon then removes the patient`s diseased aortic valve and a mechanical or biological valve is put in its place. Once the valve is in place and aorta has been closed, the patient is taken off the heartlung machine. Transesophageal echocardiogram can be used to verify that the new valve is functioning property. Pacing wires are usually put in place, so that the heart can be manually paced should any complications arise after surgery. Drainage tubes are also inserted to drain fluids from the chest and pericardium following surgery. These are usually removed within 36-48 hours while the pacing wires are generally left in place until right before the patient is discharged from the hospital.

perfect valve mounting within the aortic annulus, thus reducing the risk of implanting an incompetent valve, postoperative AR and limited durability remain a concern with the free-hand stentless valve insertion technique. This issue may be circumvented with full

[1] Malouf, J. F, Edwards, W. D, Tajik, A. J, & Seward, J. Functional anatomy of the heart. In: Fuster V, O'Rourke RA, Walsh RA, Poole-Wilson P, eds. Hurst's The Heart.

[2] Otto, C. M, Lind, B. K, Kitzman, D. W, et al. Association of aortic valve sclerosis with cardiovascular mortality and morbidity in the elderly. N Eng J Med (1999).

[3] Andrew WangThomas S. Bashore. Valvular Heart Disease. Humana Press, a part of

[4] Gjertsson, P, Caidahl, K, Farasati, M, et al. Preopeartive moderate to severe diastolic dysfunction: A novel Doppler echocardiographic long-term prognosis factor in pa‐

[5] Connolly, H. M, Oh, J. K, Orszulak, T. A, et al. Aortic valve replacement for aortic stenosis with severe left ventricular dysfunction: prognostic indicators. Circulation

[6] Gjertsson, P, Caidahl, K, & Bech-hanssen, O. Left ventricular diastolic dysfunction late after aortic valve replacement in patients with aortic stenosis. Am J Cardiol

[7] Bonow, R. O, Carabello, B. A, & Chatterjee, K. de Leon AC Jr, Faxon DP, Freed MD, Gaasch WH, Lytle BW, Nishimura RA, O'Gara PT, O'Rourke RA, Otto CM, Shah PM, Shanewise JS. ACC/AHA 2006 guidelines for the management of patients with valv‐ ular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Develop

and Ivana Burazor2

Surgical Valve Replacement (Bioprosthetic VS Mechanical)

http://dx.doi.org/10.5772/53687

375

, Dušan Popović1

1 Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia

12th ed. New York, NY: McGraw-Hill Companies, Inc; (2008).

tients with severe aortic stenosis. J Thorac Cardiovasc Surg (2005).

Springer Science Business Media, LLC (2009).

aortic root replacement using a stentless porcine root.[3.49,50]

\*Address all correspondence to<EMAIL_ADDRESS>
**Author details**

**References**

Stamenko Šušak1\*, Lazar Velicki1

2 Clinical Centers, Nis, Serbia

(1997). , 95, 2395-400.

(2005). , 96, 722-7.


**Loop Transfer Recovery for the Grape Juice Concentration Process**

Nelson Aros Oñate1 and Graciela Suarez Segali2 *1Departamento de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de La Frontera, Temuco, 2Departamento de Ingeniería Química, Facultad de Ingeniería, Universidad Nacional de San Juan, San Juan, 1Chile*

*2Argentina*

## **1. Introduction**

280 Challenges and Paradigms in Applied Robust Control

Waller, J.B., Sågfors, M. & Waller, K.E. (1994). Ill-Conditionedness and Process

Zhou, K., Doyle, J.C. & Glover, K. (1996). *Robust and Optimal Control*. Prentice-Hall, Inc.,

*IFAC Symposium*, Kyoto, Japan, 465–470.

Upper Saddle River, New Jersey.

Directionality—The Use of Condition Numbers in Process Control. *Proceedings of*

It is necessary to ensure the quality of concentrated, because it is highly used in the food industry in juices, drinks, sweets, etc. Its application is in full development because it can compete with any other constituent, it is a natural product, and considering that is a very important regional industry, marketing greatly affects the regional economy. Because of this, that it is extremely important to ensure quality and quantity concentrate.

Argentina is one of the principal producers and exporters of concentrated clear grape juices in the world. They are produced mainly in the provinces of San Juan and Mendoza (Argentine Republic) from virgin grape juice and in the major part from sulfited grape juices. The province of San Juan's legislation establishes that a portion of the grapes must be used for making concentrated clear grape juices. This product has reached a high level of penetration in the export market and constitutes an important and growing productive alternative.

An adequate manufacturing process, a correct design of the concentrate plants and an appropriate evaluation of their performance will facilitate optimization of the concentrated juices quality parameters. The plant efficiency is obtained from knowledge of the physics properties of the raw material and products. These properties are fundamental parameters that are used in the designing and calculations on all the equipment used and also in the control process.

The multi-step evaporation (M-SE) is the most important unit operation used in the food industry to concentrate juices of grapes and apples. Even when the main objective of this process is to produce a concentrated product, it should also possess certain organoleptic properties that are critical with respect to its quality and acceptance grade by the customers.

Product requirements and the complex characteristics of the process such as non-linear behavior, input and output constraints, time delays and loop interactions justify the use of an advanced control system.

The rheological behavior influences directly the heat transfer coefficient (Pilati, 1998; Rubio, 1998) and therefore its knowledge is essential together with the influence of temperature on

Loop Transfer Recovery for the Grape Juice Concentration Process 283

On the other hand, sensitivity theory, originally developed by Bode (Bode, 1945), has regained considerable importance, due to the recent work developed by many researchers. This research effort has made evident the fundamental role played by sensitivity theory to highlight design tradeoffs and to analyze, qualitatively, control system performance. One of the fields of active research is the analysis of different design strategies from to point of view of sensibility properties. From this perspective one of the richest strategies is the optimal linear quadratic regulator (LQR). It is well known that the sensitivity of a LQR Loop is always less than one (Anderson, 1971). However, it is also known that when the state is not directly fed back but reconstructed through an observer this property is normally lost. In fact, the situation more general, since the recovery problem appears every time a control

It has been shown that when the plant is minimum-phase, a properly design Kalman filter provides complete recovery of the input sensitivity achieved by LQR will full state feedback (Doyle, 1979). Either full or partial-order filter may be used. On the other hand it is also known that it is generally impossible to obtain LTR if we use observers for a plant with unstable zeros. An exception to this rule arises in MIMO Systems when input directions are

On the other hand, it is known that the only way to obtain full recovery for a general nonminimum-phase plant is to increase the number of independent measurements. This idea has been suggested in conjunction with the use of reduced-order Kalman filters (Friedland,

The additional independent measurements are used to modify the structure of the open loop transfer function. The standard LTR procedure is applied and it is the implemented combining the resulting full-order Kalman filter with the additional measurements optimally weighted. The idea is obviously to feed back only a subset of the state, for that reason we speak of 'partial' state feedback. The basic approach assumes that all states are available for measurement. However in this paper, it is also shown how to do ଶ optimization on the amount of recovery of the input sensitivity when a given set of measurements is available. This situation is important since in many additional situations there are limitations regarding which variables can be measured and how many additional sensors can be used. This connects the recovery theory with the issue of additional measurements raised in the context of practical ideas for control design, as illustrated in the control of the inverted pendulum; see (Middleton, 1990). The theory supporting the proposal is built on some import technical results which allow for computing the amount of

Figure 1 show the input and output streams in a vertical generic effect evaporator with long tubes. The solution to be concentrated circulates inside the tubes, while the steam, used to heat the solution, circulates inside the shell around the tubes. The evaporator operates in parallel mode. The solution to be concentrated and the steam are fed to the first effect by the bottom and by the upper section of the shell, respectively. Later on, the concentrated solution from the first effect is pumped to the bottom of the second effect, and so on until the fourth effect. On the other hand, the vaporized solvent from each effect serve as heater in the next one. Each effect has a baffle in the upper section that serves as a drops splitter for the solution dragged by the vapor. The vapor from the fourth effect is sent to a condenser

design based on state feedback design is implemented through observers.

orthogonal to non-minimum phase zero directions (Zhang, 1990).

recovery, as a function of frequency (Zhang, 1990).

**2. Process description**

1989).

its value. The juices (concentrate and intermediate products) physical properties, such as density, viscosity, boiling point elevation, specific heat and coefficient of thermal expansion, are affected by their solid content and their temperature. For this reason, it is necessary to know the physical properties values, as a function of the temperature and the solids content, during the manufacture process.

The principal solids constituents of clear grape juices are sugars and its concentration affects directly the density, viscosity and refraction index. Tables were developed to relate reducing sugar contents, refractometric values and density of pure solutions, at 20ºC, for concentrate ranges from 0% to 85% w/w and sucrose solutions for different range concentrations 0% to 70% and a temperature range from 0 to 100ºC (AOAC, 1995).

Barbieri (1980) worked with white concentrated clear grape juice in a falling film multiple effect evaporators. They obtained 18.2, 27.3, 38.6, 48.6 and 64.6ºBrix samples. They measured density, viscosity and boiling point elevation as a function of soluble solids concentration and temperature. They presented the results in plots with predictive equations for the properties which were studied.

Di Leo (1988) published density, refraction index and viscosity data for a rectified concentrated grape juice and an aqueous solution of a 1:1 glucose/levulose mixture, for a soluble solids concentrate range from 60 to 71% (in increments of 0.1%) and 20ºC.

Pandolfi, (1991) studied physical and chemical characteristics of grape juices produced in Mendoza and San Juan provinces, Argentina. They determined density at 20ºC in sulfited grape juices of 20–22ºBx and concentrated grape juices of 68–72 ºBx. They obtained no information on intermediate concentrations or other temperatures.

In general, the clarified juice concentrates have a Newtonian behavior (Ibarz 1993; Rao 1984; Saenz, 1986; Saravacos, 1970), although some authors have found a small pseudoplasticity in the flow of grape concentrates, from the variety Concord (Vitis labrusca) for concentrations above 55ºBx. It has been attributed to the presence of some soluble solids, mostly pectins and tartrates (Moressi, 1984; Saravacos, 1970). Other authors consider the juice concentrates as Newtonian, even at high soluble solids concentrations of 60–70ºBx (Barbieri, 1980; Di Leo, 1988; Rao, 1984; Schwartz, 1986).

If we analyze the temperature influence on this product's viscosity, it seems which is directly related with soluble solids concentration; the higher the concentration, the higher is the variation of the viscosity with temperature (Rao, 1984; Saravacos, 1970; Bayindirli, 1992; Crapiste, 1988; Constela, 1989).

Schwartz (1986) determined clear grape juice viscosity at 20, 30, 40 and 50ºC, for 30, 40, 50, 60 and 66% soluble solids concentration, but did not publish the experimental data. These authors presented the correlation constants values of the Arrhenius equation for temperature, a potential and an exponential model between viscosity and solids concentration for each temperature studied.

The physical property that represents density change in a material, due to an increase in its temperature at constant pressure, is called the coefficient of thermal expansion. The importance of this parameter can be seen in the effect that density change in the product can have over heat transfer during the process. There is not publish data on the coefficient of the thermal expansion for grape juices and their concentrates. The existing information did not cover all the temperature and concentration ranges that are used in the evaporation process, or else cover to pure sugar solutions, or grape juices of other varieties and/ or originating in other geographical zones.

282 Challenges and Paradigms in Applied Robust Control

its value. The juices (concentrate and intermediate products) physical properties, such as density, viscosity, boiling point elevation, specific heat and coefficient of thermal expansion, are affected by their solid content and their temperature. For this reason, it is necessary to know the physical properties values, as a function of the temperature and the solids content,

The principal solids constituents of clear grape juices are sugars and its concentration affects directly the density, viscosity and refraction index. Tables were developed to relate reducing sugar contents, refractometric values and density of pure solutions, at 20ºC, for concentrate ranges from 0% to 85% w/w and sucrose solutions for different range concentrations 0% to

Barbieri (1980) worked with white concentrated clear grape juice in a falling film multiple effect evaporators. They obtained 18.2, 27.3, 38.6, 48.6 and 64.6ºBrix samples. They measured density, viscosity and boiling point elevation as a function of soluble solids concentration and temperature. They presented the results in plots with predictive equations for the

Di Leo (1988) published density, refraction index and viscosity data for a rectified concentrated grape juice and an aqueous solution of a 1:1 glucose/levulose mixture, for a

Pandolfi, (1991) studied physical and chemical characteristics of grape juices produced in Mendoza and San Juan provinces, Argentina. They determined density at 20ºC in sulfited grape juices of 20–22ºBx and concentrated grape juices of 68–72 ºBx. They obtained no

In general, the clarified juice concentrates have a Newtonian behavior (Ibarz 1993; Rao 1984; Saenz, 1986; Saravacos, 1970), although some authors have found a small pseudoplasticity in the flow of grape concentrates, from the variety Concord (Vitis labrusca) for concentrations above 55ºBx. It has been attributed to the presence of some soluble solids, mostly pectins and tartrates (Moressi, 1984; Saravacos, 1970). Other authors consider the juice concentrates as Newtonian, even at high soluble solids concentrations of 60–70ºBx (Barbieri, 1980; Di Leo,

If we analyze the temperature influence on this product's viscosity, it seems which is directly related with soluble solids concentration; the higher the concentration, the higher is the variation of the viscosity with temperature (Rao, 1984; Saravacos, 1970; Bayindirli, 1992;

Schwartz (1986) determined clear grape juice viscosity at 20, 30, 40 and 50ºC, for 30, 40, 50, 60 and 66% soluble solids concentration, but did not publish the experimental data. These authors presented the correlation constants values of the Arrhenius equation for temperature, a potential and an exponential model between viscosity and solids

The physical property that represents density change in a material, due to an increase in its temperature at constant pressure, is called the coefficient of thermal expansion. The importance of this parameter can be seen in the effect that density change in the product can have over heat transfer during the process. There is not publish data on the coefficient of the thermal expansion for grape juices and their concentrates. The existing information did not cover all the temperature and concentration ranges that are used in the evaporation process, or else cover to pure sugar solutions, or grape juices of other varieties and/ or originating in

soluble solids concentrate range from 60 to 71% (in increments of 0.1%) and 20ºC.

information on intermediate concentrations or other temperatures.

during the manufacture process.

properties which were studied.

1988; Rao, 1984; Schwartz, 1986).

Crapiste, 1988; Constela, 1989).

other geographical zones.

concentration for each temperature studied.

70% and a temperature range from 0 to 100ºC (AOAC, 1995).

On the other hand, sensitivity theory, originally developed by Bode (Bode, 1945), has regained considerable importance, due to the recent work developed by many researchers. This research effort has made evident the fundamental role played by sensitivity theory to highlight design tradeoffs and to analyze, qualitatively, control system performance. One of the fields of active research is the analysis of different design strategies from to point of view of sensibility properties. From this perspective one of the richest strategies is the optimal linear quadratic regulator (LQR). It is well known that the sensitivity of a LQR Loop is always less than one (Anderson, 1971). However, it is also known that when the state is not directly fed back but reconstructed through an observer this property is normally lost. In fact, the situation more general, since the recovery problem appears every time a control design based on state feedback design is implemented through observers.

It has been shown that when the plant is minimum-phase, a properly design Kalman filter provides complete recovery of the input sensitivity achieved by LQR will full state feedback (Doyle, 1979). Either full or partial-order filter may be used. On the other hand it is also known that it is generally impossible to obtain LTR if we use observers for a plant with unstable zeros. An exception to this rule arises in MIMO Systems when input directions are orthogonal to non-minimum phase zero directions (Zhang, 1990).

On the other hand, it is known that the only way to obtain full recovery for a general nonminimum-phase plant is to increase the number of independent measurements. This idea has been suggested in conjunction with the use of reduced-order Kalman filters (Friedland, 1989).

The additional independent measurements are used to modify the structure of the open loop transfer function. The standard LTR procedure is applied and it is the implemented combining the resulting full-order Kalman filter with the additional measurements optimally weighted. The idea is obviously to feed back only a subset of the state, for that reason we speak of 'partial' state feedback. The basic approach assumes that all states are available for measurement. However in this paper, it is also shown how to do ଶ optimization on the amount of recovery of the input sensitivity when a given set of measurements is available. This situation is important since in many additional situations there are limitations regarding which variables can be measured and how many additional sensors can be used. This connects the recovery theory with the issue of additional measurements raised in the context of practical ideas for control design, as illustrated in the control of the inverted pendulum; see (Middleton, 1990). The theory supporting the proposal is built on some import technical results which allow for computing the amount of recovery, as a function of frequency (Zhang, 1990).

## **2. Process description**

Figure 1 show the input and output streams in a vertical generic effect evaporator with long tubes. The solution to be concentrated circulates inside the tubes, while the steam, used to heat the solution, circulates inside the shell around the tubes. The evaporator operates in parallel mode. The solution to be concentrated and the steam are fed to the first effect by the bottom and by the upper section of the shell, respectively. Later on, the concentrated solution from the first effect is pumped to the bottom of the second effect, and so on until the fourth effect. On the other hand, the vaporized solvent from each effect serve as heater in the next one. Each effect has a baffle in the upper section that serves as a drops splitter for the solution dragged by the vapor. The vapor from the fourth effect is sent to a condenser

$$\frac{dM\_l}{dt} = \mathcal{W}\_{l-1} - \mathcal{W}\_{sl} - \mathcal{W}\_l \tag{1}$$

$$\frac{d(\mathcal{W}\_l X\_l)}{dt} = \mathcal{W}\_{l-1} X\_{l-1} - \mathcal{W}\_l X\_l \tag{2}$$

$$\frac{d(M\_l h\_l)}{dt} = W\_{l-1} h\_{l-1} - W\_l h\_l - W\_{sl} H\_{sl} + A\_l U\_l (T\_{sl-1} - T\_l) \tag{3}$$

$$\mathcal{W}\_{sl} = \frac{\mathcal{U}\_l A\_l (\mathbf{r}\_{sl-1} - \mathbf{r}\_l)}{H\_{sl-1} - h\_{cl}} \tag{4}$$

$$H\_{\rm sl} = 2509.2888 + 1.6747 \cdot T\_{\rm sl} \tag{5}$$

$$h\_{cl} = 4.1868 \cdot T\_{sl} \tag{6}$$

$$\mathcal{L}\_{pl} = 0.80839 - 4.3416 \cdot 10^{-3} \cdot X\_l + 5.6063 \cdot 10^{-4} \cdot T\_l \tag{7}$$

$$h\_l = 0.80839 \cdot T\_l - 4.3416 \cdot 10^{-3} \cdot X\_l T\_l + 2.80315 \cdot 10^{-4} \cdot T\_l^2 \tag{8}$$

$$U\_l = \frac{{}^{490}\!\!\!\!D^{0.87} \cdot \mathcal{W}^{3.6/L}\_{sl}}{\mu^{0.25}\_l \cdot \Delta T^{0.1}\_l} \tag{9}$$

$$
\mu\_l = \mu\_0 \cdot e^{\frac{\mathcal{A} \cdot \mathcal{X}\_l}{\mathbf{1}^{\otimes 0} - B \cdot \mathcal{X}\_l}} \tag{10}
$$

$$A = C\_1 + \frac{c\_2}{T\_\ell} \tag{11}$$

$$B = \mathcal{C}\_3 + \mathcal{C}\_4 \cdot T\_l \tag{12}$$

$$
\dot{\mathbf{x}} = \mathbf{A}\mathbf{x}(\mathbf{t}) + \mathbf{B}\mathbf{u}(\mathbf{t}) + \mathbf{v}(\mathbf{t}) \tag{13}
$$

$$\mathbf{y}(\mathbf{t}) = \mathbf{C}\mathbf{x}(\mathbf{t})\tag{14}$$

$$\mathbf{G(s)} = \mathbf{C(sI - A)^{-1}B} \tag{15}$$

$$\mathbf{u}(\mathbf{t}) = -F\mathbf{x}(\mathbf{t})\tag{16}$$

$$\mathcal{S}\_0(\mathbf{s}) = |I + H(\mathbf{s})|^{-1} \tag{17}$$

$$H(\mathbf{s}) = F(\mathbf{s}I - \mathcal{A})^{-1}\mathcal{B} \tag{18}$$

$$\mathbf{u}(\mathbf{t}) = -F\mathbf{\hat{x}}(\mathbf{t})\tag{19}$$

$$\hat{\mathbf{x}}(\mathbf{t}) = \mathbf{A}\hat{\mathbf{x}}(\mathbf{t}) + \mathbf{B}\mathbf{u}(\mathbf{t}) + L\{\mathbf{y}(\mathbf{t}) - \mathbf{C}\hat{\mathbf{x}}(\mathbf{t})\} \tag{20}$$

$$L = \sum \mathbf{C}^T \tag{21}$$

$$
\mathbf{A}\boldsymbol{\Sigma} + \boldsymbol{\Sigma}\mathbf{A}^T - \boldsymbol{\Sigma}\mathbf{C}^T\boldsymbol{C}\boldsymbol{\Sigma} + \mathbf{Q} = \mathbf{0} \tag{22}
$$

$$\mathbf{S}\_{obs}(\mathbf{s}) = \{I + [I + F(\mathbf{s}I - A + LC)^{-1}B]^{-1}F(\mathbf{s}I - A + LC)^{-1}LC(\mathbf{s}I - A)^{-1}B\}^{-1} \tag{23}$$

$$\mathcal{S}\_{obs}(\mathbf{s}) = \mathcal{S}\_o(\mathbf{s}) \{ I + F(\mathbf{s}I - \mathbf{A} + L\mathbf{C})^{-1} \mathbf{B} \} \tag{24}$$

$$\mathcal{S}\_{obs}(\mathbf{s}) = \mathcal{S}\_o(\mathbf{s}) \{ I + F\phi [B - L(sI - A + LC)^{-1} \mathcal{C}\phi B] \} \tag{25}$$

$$
\phi = (\mathbf{s}I - \mathcal{A})^{-1} \tag{26}
$$

$$E(\mathbf{s}) = \mathbf{S}\_o^{-1} \{ \mathbf{S}\_{obs} - \mathbf{S}\_o \} \tag{27}$$

$$E(\mathbf{s}) = F(\mathbf{s}I - \mathbf{A} + L\mathbf{C})^{-1}\mathbf{B} \tag{28}$$

$$\dot{\mathbf{x}} = \mathbf{A}\mathbf{x}(t) + \mathbf{B}\{\mathbf{u}(t) + \mathbf{U}\mathbf{w}(t)\} + \mathbf{v}(t) \tag{29}$$

$$\mathcal{S}\_{obs}(\mathbf{s}) \to \mathcal{S}\_o(\mathbf{s}) \tag{30}$$

$$\mathcal{G}(\mathbf{s}) = \mathcal{G}\_{\mathbf{m}}(\mathbf{s}) \mathcal{B}\_{\mathbf{z}}(\mathbf{s}) \tag{31}$$

$$\mathbf{G(s)} = \mathbf{C(sI - A)^{-1}B\_mB\_x(s)}\tag{32}$$

$$\mathbf{B}\_{\mathbf{z}}(\mathbf{s}) = \mathbf{B}\_{\mathbf{z}\_1}(\mathbf{s})\mathbf{B}\_{\mathbf{z}\_2}(\mathbf{s})\cdots\mathbf{B}\_{\mathbf{z}\_l}(\mathbf{s})\tag{33}$$

$$\mathcal{B}\_m = \mathcal{B}\_m^l \tag{34}$$

$$\mathcal{B}\_{\mathbf{z}\_l}(\mathbf{s}) = I - \frac{2\Re e(\mathbf{z}\_l)}{\mathbf{s} + \mathbf{z}\_l^\*} \eta\_l \eta\_l^H \tag{35}$$

$$B\_m^l = B\_m^{l-1} - 2\Re e\{\mathbf{z}\_l\} \xi\_l \eta\_l^H \tag{36}$$

$$
\begin{bmatrix}
\mathbf{z}\_l \mathbf{I} - \mathbf{A} & -\mathbf{B}\_m^{t-1} \\
\end{bmatrix}
\begin{bmatrix}
\xi\_l \\
\eta\_l
\end{bmatrix} = \mathbf{0} \tag{37}
$$

$$B\_m^k = \underbrace{\prod\_{l=1}^k \{I - 2\Re e(\mathbf{z}\_l)(\mathbf{z}\_l I - A)^{-1}\}}\_{\equiv \dot{M}\_k} \mathbf{B} \tag{38}$$

$$\xi\_k = (\mathbf{z}\_k I - A)^{-1} \mathbf{M}\_{k-1} \mathbf{B} \tag{39}$$

$$\mathbf{M\_0} = I \tag{40}$$

$$M\_1 = I - 2\Re e(\mathbf{z}\_1)(\mathbf{z}\_1 I - A)^{-1} \tag{41}$$

$$\mathcal{S}\_{obs}(\mathbf{s}) = \mathcal{S}\_o(\mathbf{s}) \{ I + E(\mathbf{s}) \} \tag{42}$$

$$E(\mathbf{s}) := F(\mathbf{s}I - \mathbf{A})^{-1} \{ \mathbf{B} - \mathbf{q} \mathbf{B}\_{\mathbf{m}} \mathbf{W} (I + \mathbf{q} \mathbf{C} \boldsymbol{\phi} \mathbf{B}\_{\mathbf{m}} \mathbf{W})^{-1} \mathbf{C} \boldsymbol{\phi} \mathbf{B}\_{\mathbf{m}} \mathbf{B}\_{\mathbf{z}}(\mathbf{s}) \} \tag{43}$$

$$E(\mathbf{s}) = F(\mathbf{s}I - \mathbf{A})^{-1} \{ \mathbf{B} - \mathbf{B}\_{\text{m}} \mathbf{B}\_{\text{z}}(\mathbf{s}) \} \tag{44}$$

$$E(\mathbf{s}) = E^l(\mathbf{s}) = \sum\_{k=1}^l \frac{2\Re e\langle \mathbf{z}\_k \rangle}{s + \mathbf{z}\_k^\*} F\xi\_k \eta\_k^H B\_\mathbf{z}^{k-1}(\mathbf{s}) \tag{45}$$

$$\lim\_{q \to \text{ss}} \mathbb{S}\_{obs}(\mathbf{s}) = \mathbb{S}\_o(\mathbf{s}) \left\{ \mathbf{1} + \frac{2x}{s \cdot \mathbf{z}} H(\mathbf{z}) \right\} \tag{46}$$

$$H(\mathbf{z}) = F(\mathbf{z}I - \mathcal{A})^{-1}\mathcal{B} \tag{47}$$

$$\mathcal{S}\_{\Gamma}(\mathbf{s}) = \mathcal{S}\_{o}(\mathbf{s}) \left\{ I + \sum\_{k=1}^{l} F(I - \Gamma) \xi\_{k} \eta\_{k}^{H} \mathcal{W}\_{k}(\mathbf{s}) \right\} \tag{53}$$

$$\text{The } \mathcal{L}\_{\text{int}} \text{ is the } \mathcal{L}\_{\text{int}} \text{-module.}$$

$$\mathcal{W}\_k(\mathbf{s}) = \frac{2\mathfrak{R}e(\mathbf{z}\_k)}{\mathbf{s} + \mathbf{z}\_k^\*} \mathcal{B}\_\mathbf{z}^{k-1}(\mathbf{s}) \tag{54}$$

$$\mathbf{B}\_{\mathbf{z}}^{k-1}(\mathbf{s}) = \prod\_{l=1}^{k} \mathbf{B}\_{\mathbf{z}\_l}(\mathbf{s}) \tag{55}$$

$$B\_{\mathbf{z}\_l}(\mathbf{s}) = \frac{-\mathbf{s} + \mathbf{z}\_l}{\mathbf{s} + \mathbf{z}\_l^\*} \tag{56}$$

$$\mathcal{S}\_{\Gamma}(\mathbf{s}) = \mathcal{S}\_{o}(\mathbf{s}) \left[ I + \sum\_{k=1}^{l} F(I - \Gamma) \xi\_{k} W\_{k}(\mathbf{s}) \right] \tag{57}$$

$$FT\zeta\_k = F\xi\_k \quad k = \mathbf{1}, \mathbf{2}, \cdots, l \tag{58}$$

$$
\begin{bmatrix}
\boldsymbol{\alpha}\_{1\_1} & \cdots & \boldsymbol{\alpha}\_{\lambda\_1} \\
\vdots & \ddots & \vdots \\
\boldsymbol{\alpha}\_{1\_l} & \cdots & \boldsymbol{\alpha}\_{\lambda\_l}
\end{bmatrix}
\begin{bmatrix}
\boldsymbol{\mathcal{V}}\_1 \\
\vdots \\
\boldsymbol{\mathcal{V}}\_\lambda
\end{bmatrix} = \begin{bmatrix}
\boldsymbol{\Sigma}\_{l=1}^n \boldsymbol{\alpha}\_{l\_1} \\
\vdots \\
\boldsymbol{\Sigma}\_{l=1}^n \boldsymbol{\alpha}\_{l\_l}
\end{bmatrix} \tag{59}
$$

$$\mathfrak{a}\_{\mathfrak{k}\_{\mathbf{k}}} = \mathfrak{f}\_{\mathfrak{k}} \xi\_{\mathbf{k}\_{\mathbf{k}}}; \qquad \mathfrak{i} = 1, 2, \dots, n \tag{60}$$

$$F = \begin{bmatrix} f\_1 & f\_2 & \cdots & f\_n \end{bmatrix} \tag{61}$$

$$\xi\_{\mathbf{k}} = [\xi\_{\mathbf{k}\_1} \quad \xi\_{\mathbf{k}\_2} \quad \cdots \quad \xi\_{\mathbf{k}\_n}]^{\mathsf{T}} \tag{62}$$

$$\lim\_{q \to \text{ss}} \mathbf{S}\_{\Gamma}(\mathbf{s}) = \mathbf{S}\_{o}(\mathbf{s}) \left\{ \mathbf{1} + \frac{2x}{s + \mathbf{z}^\*} H(\mathbf{z}) - \frac{2x}{s + \mathbf{z}^\*} H'(\mathbf{z}) \right\} \tag{63}$$

$$H'(\mathbf{z}) = F\Gamma(\mathbf{z}I - \mathbf{A})^{-1}\mathbf{B} \tag{64}$$

$$
\Gamma = \mathbf{y} \Gamma'\tag{65}
$$

$$
\Gamma' = \text{diag}\{\mathbf{1}, \mathbf{0}, \cdots, \mathbf{0}\} \tag{66}
$$

$$\mathbf{y} \,\, \mathbf{y} \,\, \, = \, \frac{\mathbf{F} (\mathbf{z}\mathbf{l} - \mathbf{A})^{-1} \mathbf{B}}{\mathbf{F} \mathbf{I}^{\prime} (\mathbf{z}\mathbf{l} - \mathbf{A})^{-1} \mathbf{B}} \,\, \tag{67}$$

$$\mathbf{y}^{\mathbf{o}} = \arg\min\_{\mathbf{y} \in \mathsf{R}e^{\lambda}} \mathbf{J}(\mathbf{y}) \tag{68}$$

$$\mathbf{J} = \int\_0^\infty |\mathbf{E}\_\Gamma(\mathbf{j}\mathbf{w})|^2 \,\mathbf{d}\mathbf{w} \tag{69}$$

$$\mathbf{J} = \int\_0^\infty \left\| \mathbf{G}\_1(\mathbf{j}\mathbf{w}) + \mathbf{y}^\mathbf{T} \mathbf{G}\_2(\mathbf{j}\mathbf{w}) \right\|^2 \,\mathrm{d}\mathbf{w} \tag{70}$$

$$G\_1(f\mathbf{w}) = F(f\mathbf{w}I - A + LC)^{-1}\mathbf{B} \tag{71}$$

$$\mathbf{y}^{\mathsf{T}} \mathbf{G}\_2(f\mathbf{w}) = F\Gamma(f\mathbf{w}I - A + LC)^{-1}\mathbf{B} \tag{72}$$

$$\mathbf{y}^{\mathbf{o}} = -\left[\int\_{0}^{\infty} \mathbf{G}\_{2}(\mathbf{j}\mathbf{w}) [\mathbf{G}\_{2}^{\*}(\mathbf{j}\mathbf{w})]^{\mathbf{T}} \mathbf{d}\mathbf{w}\right]^{-1} \Re\left\{\int\_{0}^{\infty} \mathbf{G}\_{1}(\mathbf{j}\mathbf{w}) \mathbf{G}\_{2}^{\*}(\mathbf{j}\mathbf{w}) \mathbf{d}\mathbf{w}\right\} \tag{73}$$

$$\mathbf{y}^{\mathbf{o}} = -\frac{\mathfrak{Re}\{\int\_{0}^{\infty} \mathbf{G}\_{1}(\|\mathbf{w}\|) \mathbf{G}\_{2}^{\*}(\|\mathbf{w}\|) \mathbf{dw}\}}{\int\_{0}^{\infty} \|\mathbf{G}\_{2}(\|\mathbf{w}\|) \|^{2} \mathbf{dw}}\tag{74}$$

Loop Transfer Recovery for the Grape Juice Concentration Process 295

Controlled system response for optimal regulator, whereas white noise disturbances, as well as step-like variation to the inlet concentration to 50, and then a step 100 is added to the feed

(a)

(b)

Fig. 8. Controlled system response to changes in the shocks in type of step and white noise

(blue for changes of +5% - green changes -5%)

**5.2 Close loop**

temperature at the entrance.

Fig. 5. Behavior of the concentration in the evaporator to a change of a step in the temperature of steam power (up 5% - decrease of 5%)

Fig. 6. Behavior of the concentration in the evaporator to a change of a step in the concentration of power (increase of5% - decrease of 5%)

Fig. 7. Behavior of the concentration in the evaporator to a change of a step in the temperature of the input solution (5% increase - decrease of 5%)

## **5.2 Close loop**

294 Challenges and Paradigms in Applied Robust Control

(a) (b)

(a) (b)

(a) (b)

Fig. 7. Behavior of the concentration in the evaporator to a change of a step in the

temperature of the input solution (5% increase - decrease of 5%)

Fig. 6. Behavior of the concentration in the evaporator to a change of a step in the

Fig. 5. Behavior of the concentration in the evaporator to a change of a step in the

temperature of steam power (up 5% - decrease of 5%)

concentration of power (increase of5% - decrease of 5%)

Controlled system response for optimal regulator, whereas white noise disturbances, as well as step-like variation to the inlet concentration to 50, and then a step 100 is added to the feed temperature at the entrance.

Fig. 8. Controlled system response to changes in the shocks in type of step and white noise (blue for changes of +5% - green changes -5%)

Loop Transfer Recovery for the Grape Juice Concentration Process 297

Controlled system response for optimal regulator, whereas white noise disturbances, as well as like step variation of 5% for the inlet concentration to 50 and then to 100 adds a step is 5%

(a)

(b)

Fig. 10. LQG controlled system response to changes in the type shocks of step and white

noise (blue for changes of +5% - green changes -5%)

**5.3 LQG- design**

of the feed temperature at the entrance.

Fig. 9. Efforts to control the controlled system to changes in the shocks in type of step and white noise (blue for changes of +5% - green changes -5%)

## **5.3 LQG- design**

296 Challenges and Paradigms in Applied Robust Control

(a)

(b)

Fig. 9. Efforts to control the controlled system to changes in the shocks in type of step and

white noise (blue for changes of +5% - green changes -5%)

Controlled system response for optimal regulator, whereas white noise disturbances, as well as like step variation of 5% for the inlet concentration to 50 and then to 100 adds a step is 5% of the feed temperature at the entrance.

Fig. 10. LQG controlled system response to changes in the type shocks of step and white noise (blue for changes of +5% - green changes -5%)

Loop Transfer Recovery for the Grape Juice Concentration Process 299

(a)

(b)

Fig. 12. Controlled system response LQG / LTR to changes in the type shocks of step and

white noise (blue for changes of +5% - green changes -5%)

**5.4 LQG/LTR**

Fig. 11. Control efforts for the LQG-controlled system to changes in the type shocks of step and white noise (blue for changes of +5% - green changes -5%)

## **5.4 LQG/LTR**

298 Challenges and Paradigms in Applied Robust Control

(a)

(b)

Fig. 11. Control efforts for the LQG-controlled system to changes in the type shocks of step

and white noise (blue for changes of +5% - green changes -5%)

(a)

Fig. 12. Controlled system response LQG / LTR to changes in the type shocks of step and white noise (blue for changes of +5% - green changes -5%)

Loop Transfer Recovery for the Grape Juice Concentration Process 301

The authors gratefully acknowledge the financial support of the "Universidad de La Frontera"- Chile DIUFRO DI07-0102, "Universidad Nacional de San Juan"- Argentina,

(Anderson, 1971) Anderson B.D.O., Moore J.B., *Lineal Optimal Control Control*. Prentice Hall.

(Armaou, 2002) Armaou A., Christofides P.D., "Dynamic Optimization of Dissipative PDE

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(Bayindirli, 1992) Bayindirli, L. "Mathematical Analysis Of Variation Of Density And

(Bode, 1945) Bode H.W., *Network Analysis and Feedback Amplifier Design*. Van Nostrand, New York. (Camacho, 1999) Camacho E., Bordons F.C., *Model Predictive Control*. Springer-Verlag. (Crapiste, 1988) Crapiste, G.H. and Lozano, J.E. "Effect Of Concentration And Pressure On

(Constela, 1989). Constenla D.T., Crapiste G.H. and Lozano J.E. "Thermophysical Properties

(Di Leo, 1988) Di Leo, F. "Caratteristiche Fisico-chimiche dei Mosti Concentrati Rettificati.

(Doyle, 1979) Doyle J.C., Stein G., "Robustness with observers". *IEEE Trans. on Auto. Control,*

(Enns, 1984) Enns D., *Model reduction for control systems design*. Ph. D. Thesis. Stanford University. (El-Farra, 2003) El-Farra N.H., Armaou A., Christofides P.D., "Analysis and Control of Parabolic PDE Systems with Input Constraints". *Automatica 39 – 4, pp. 715-725*. (Freudenberg, 1988) Freudenberg J., Looze D., *Frequency Domain Properties of Scalar and*

(Friedland, 1989) Friedland B., "On the properties of reduced-orden Kalman filters". *IEEE*

(Ibarz, 1993) Ibarz, A., & Ortiz, J. "Reología de Zumos de Melocotón". Alimentación,

(Kam, 1999) Kam K.M., Tade M.O., "Case studies on the modelling and control of evaporation systems". *XIX Interamerican Congress of Chemical Engineering COBEQ.*  (Kam, 2000) Kam K.M., Tade M.O., "Simulated Nonlinear Control Studies of Five Effect Evaporator Models". *Computers and Chemical Engineering, Vol. 23, pp. 1795 - 1810.*  (Kaya, 2002) Kaya A., Belibagh K.B., "Rheology of solid Gaziantep Pekmez". *Journal of Food*

*básica de estadística vitivinícola argentina, Mendoza*. Varios números.

Equipos y Tecnología. Octubre, 81–86, Instituto Nacional de Vitivinicultura. *Síntesis*

Systems Using Nonlinear Order Reduction". Chemical Engineering Science 57 - 24,

Viscosity Of Apple Juice With Temperature And Concentration". *Journal. Of Food*

The Boiling Point Rise Of Apple Juice And Related Sugar Solutions". *Journal Food*

Of Clarified Apple Juice As A Function Of Concentration And Temperature".

Project FI-I900. They are also grateful for the cooperation of "Mostera Rio San Juan".

(AOAC, 1995) *Official Methods of Analysis. Reference Tables Appendix C*

*Rivista de Viticol. e di Enologia. Conegliano*, No 1, 10–18.

*Valutazione gleucometrica". Vignevini*, 15(1/2), 43–45.

*Multivariable Feedback Systems*. Springer Verlag, Berlín.

*Trans. on Auto. Control, Vol. AC-34, March.*

*Engineering, Vol. 54, pp. 221-226.*

*Processing and Preservation* (16), 23-28.

*Journal. Food Science.*(54), 663-669.

**7. Acknowledgment**

Engleewood Cliffs, N.J.

pp. 5083-5114.

*Science*. (53), 865-869

*Vol. AC-24, April.*

**8. References**

Fig. 13. Efforts to control the controlled system LQG / LTR to changes in the type shocks of step and white noise (blue for changes of +5% - green changes -5%)

## **6. Conclusions**

Looking at the results presented in figures 4 to 7, show that it is appropriate to consider as manipulated variables steam temperature and feed rate of the solution to concentrate, and as measurable disturbance and characteristic of the system to the concentration the solution concentrated and the inlet temperature of food. You can check the analysis of these figures that the evaporation process presents a complex dynamic, high delay, coupling between the variables, high nonlinearities.

From the results shown in figures 8 to 13, on the behavior of the controlled system verifies that the design LQG/LTR has a better performance especially when control efforts are softer. Partly, it validates the robustness of the proposed control system, despite having analyzed only the rejection of disturbances, since these regulatory systems at the show a good response.

## **7. Acknowledgment**

The authors gratefully acknowledge the financial support of the "Universidad de La Frontera"- Chile DIUFRO DI07-0102, "Universidad Nacional de San Juan"- Argentina, Project FI-I900. They are also grateful for the cooperation of "Mostera Rio San Juan".

## **8. References**

300 Challenges and Paradigms in Applied Robust Control

(a)

(b) Fig. 13. Efforts to control the controlled system LQG / LTR to changes in the type shocks of

Looking at the results presented in figures 4 to 7, show that it is appropriate to consider as manipulated variables steam temperature and feed rate of the solution to concentrate, and as measurable disturbance and characteristic of the system to the concentration the solution concentrated and the inlet temperature of food. You can check the analysis of these figures that the evaporation process presents a complex dynamic, high delay, coupling between the

From the results shown in figures 8 to 13, on the behavior of the controlled system verifies that the design LQG/LTR has a better performance especially when control efforts are softer. Partly, it validates the robustness of the proposed control system, despite having analyzed only the rejection of disturbances, since these regulatory systems at the show a good response.

step and white noise (blue for changes of +5% - green changes -5%)

**6. Conclusions**

variables, high nonlinearities.

(Camacho, 1999) Camacho E., Bordons F.C., *Model Predictive Control*. Springer-Verlag.

**Part 4**

**Power Plant and Power System Control**


**3. Historical review**

Educational technology (Edtech) is the process of integrating technology into education to promote various learning environments and opportunities for students to learn using technology for their common tasks. EdTech is a term used to describe the process of integrating a mix of hardware and software to improve the results of students and quality of education, aiming to improve student outcomes and reduce the burden on teachers. EdTech tools range from VR headsets that help students learn certain subjects to data-based platforms that help teachers identify learning habits and create personalized lesson plans for students.

Distance learning has evolved over the last three centuries into what is currently referred to as "online learning." Distance education started in England in the middle of the nineteenth century under the name of the "correspondence courses" that involved sending hard copy documents by mail to students and that were subjected to long time delays, so that distance education was dependent on the delivery systems and tools available at the time: first, the postal system, and later on followed by radio and television course delivery systems through licensed radio and television stations.

As information and communication technologies have advanced, online education has become more feasible technologically, economically, and operationally. Universities offering online programs can reduce infrastructure for classrooms, offices, cafeterias, dorms, and libraries, and increase the number of nontraditional students who are working full time; the advanced state of technology is making this easy to implement. Dziuban, Picciano, Graham, and Moskal [8] describe the evolution of online education in four phases: 1990s (internet-propelled distance education), 2000–2007 (increasing use of learning management systems—LMS), 2008–2012 (growth of massive open online courses—MOOCs), and beyond, with growth of online higher education enrolments outpacing traditional higher education enrolments.

Students in online course experience some concerns: isolation from peers and professors, worries about mastering new technology and software, potential for negative perception of online degrees by employers, and potential for reduced quality of instruction relative to the same courses taught as traditional face-to-face classes.

These concerns were a failure of online programs to meet expectations, which led to the concept of "blended" or "hybrid" programs that surfaced in 1999/2000 and combined face-to-face classes with online classes, hoping to synergize the advantages of both. Online and blended programs incorporate available technologies in offering asynchronous and synchronous/real-time delivery options and tools such as online discussion boards, chat rooms, and video conferencing.

Palvia et al. [9] summarized the requirements to implement online learning that include:

### **4. The online course revolution**

In the last few years, we have seen a revolution in the way that people learn. The online course revolution is underway. More and more people are choosing to take courses online, and the reasons are numerous. There are many advantages to taking courses online, from the comfort of your own home to the ability to learn at your own pace. One of the biggest advantages is the cost. Online courses are often much cheaper than traditional courses since there are no physical materials to purchase or maintenance costs. In addition, many online courses offer discounts for those who enroll early or pay in advance. Another advantage is convenience. You can take classes online at any time of day or night and from anywhere in the world. All you need is an internet connection. This is ideal for busy people who cannot commit to a traditional class schedule. There are also a variety of courses available online. Whether you want to learn about history or take a cooking class, you can find an online system that meets your needs. Moreover, if you have trouble understanding a concept, you can usually find a video or audio explanation online. The online course revolution is related to education technology because it allows for more affordable, flexible, and convenient learning methods. It is estimated that there are now more than one million online courses available, which will increase in the future [10].

There are a few things to keep in mind when taking an online course. First and foremost, set aside enough time to devote to the class [11]. It can be easy to get behind if you are not used to working on coursework outside of a traditional classroom setting. Secondly, be sure to establish a good working relationship with your professor. You may not have face-to-face interaction, but communicating with your instructor is still important, and you can and should ask questions when needed [12]. Third, take advantage of online resources. Many online courses have forums where students can interact and help each other [13]. Finally, be sure to stay organized and keep on top of deadlines. Again, getting behind in an online course can be easy, but if you manage your time well and stay organized, you can succeed.

*Perspective Chapter: Education Technology (EdTech) and the Online Course Revolution DOI: http://dx.doi.org/10.5772/intechopen.109227*

### **5. Edtech and the online era**

The online era and Edtech have changed how we learn and teach. One of the most significant technological changes is in how we access information and learn new things. In the past, if you wanted to learn something, you would have to go to a library and find a book to read [14]. Alternatively, if you wanted to learn something online, you would have to visit a website and look through the information there. Technology allows us to access information and learn new things in many ways. We can learn about things by watching videos or reading articles online. Additionally, virtual reality experiences, gaming, and interacting with people of other places and cultures are all examples of how Edtech can now be used. These technologies allow students to explore new things and learn about different cultures. They can also help students who have difficulty attending school or have issues with traditional learning methods. This is because these technologies allow students to work on projects and learn at their own pace and in their own style.

### **6. Edtech business models**

Today's elite higher education institutions are pricey and generally for profit. It is now even more challenging to create a quick turnaround and long-lasting infrastructure that can accommodate rising educational demand [15]. However, EdTech has decreased the need for study-related relocation and assisted in saving on lodging costs. Online learning, facilitated by advanced academic tools, has also made learning materials more accessible and inexpensive.

The field of Edtech is home to a variety of business models. eLearning for students preparing for entrance exams is one such effective and appealing strategy. Platforms linking students and universities are also available online for those with the necessary tools [16]. The Kahn Academy is another online model for K-12 through universitylevel eLearning on many subjects. Degree programs provided by organizations through distance learning or online courses that offer short certificate programs are another EdTech business model approach [17]. Furthermore, gamification has lately gained popularity. Parents might find this business model confusing, but EdTech actors have ingeniously exploited gaming as a teaching tool.

Similarly, there are several additional business models, including knowledgesharing websites, online discussion boards where insights are shared, and tools for creating instructive movies. Authorities should naturally be concerned about the effects of EdTech, given its fast expansion and widespread acceptance [16]. EdTech companies must follow rules and regulations that are relevant to the education sector.

### **7. Theory related to EdTech and online courses**

One theory related to EdTech and online courses is that of the technology-mediated learning theory [18]. Technology-mediated learning theory posits that technology can mediate and facilitate learning. This theory has been used to explain how online courses can provide an effective and efficient learning experience for students. It suggests that online courses provide a more customized and individualized learning experience than traditional classroom-based instructions. Additionally, online courses allow more collaboration and interaction between students and instructors. Lastly,

online courses might provide a more flexible learning environment, which can benefit students with different learning styles or who have trouble attending traditional classes [18]. Technology-mediated learning theory provides a valuable perspective on how online courses can help students learn effectively and efficiently. While technologymediated learning theory is relevant to both EdTech and online courses, there are certain limitations to the theory that should be considered when designing or using online courses. Technology-mediated learning theory considers individual differences in learners [18]. This means that different students will likely respond differently to the same type of online course. Therefore, the theory suggests that educators should consider learners' differences since the online instruction is customized and individualized.

Similarly, A theory called online collaborative learning (OCL) was put out by Linda Harasim and emphasizes the advent of digital technology to offer learning settings that promote teamwork and information development [14]. According to Harasim, OCL is a new theory of learning that emphasizes collaborative learning, knowledge creation, and internet use to transform institutional, nonformal, and informal education for the knowledge age [14]. Internet-based and widespread interconnected education has advantages for learning and teaching [14]. Harasim adopts some of Alberto Barabasi's viewpoints on the influence of networks [16]. Idea organizing, idea generating, and intellectual convergence are the three stages of knowledge formation in OCL that occur throughout group discourse.

## **8. The link between EdTech and online courses**

In the twenty-first century, EdTech has been revolutionized by the rise of online courses. By taking advantage of the internet and modern technology, online courses offer a flexible and convenient way to learn. They are also often more affordable than traditional courses. The online course revolution has made learning more accessible than ever before. With online courses, people can learn at their own pace and in their own time. They can also choose from a wide range of courses, which means that there is something for everyone. The online course revolution is transforming education. It is related to EdTech in many ways. For one, online courses are typically more affordable than traditional courses [19]. They are also more convenient, as they can be taken anytime and from anywhere. Additionally, online courses often provide more flexibility in terms of pacing and content than traditional courses. Finally, online courses are often more engaging and interactive than traditional ones [20], as they use multimedia resources and allow for more collaboration between students and instructors. Online courses also offer educators opportunities to improve their skills. Educators can gain new knowledge and understanding of pedagogy, technology, and course design by taking online courses themselves [14].

### **9. The importance of online courses**

Online courses are very important because they can help individuals learn new things and improve their skills. Online courses allow students to learn at their own pace and on their own time schedule [21]. This means that students can take courses when it is convenient for them and learn at their speed. Another reason why online courses are so important is that they can help save money. Online courses do not

#### *Perspective Chapter: Education Technology (EdTech) and the Online Course Revolution DOI: http://dx.doi.org/10.5772/intechopen.109227*

require paying for transportation or accommodation. Students can also save money on textbooks and other materials [21]. Finally, online courses are often more flexible than traditional courses. They can provide a better school-life balance that enables students to attend class whenever it is most convenient. This means they can fit around work or family life [21]. If a student has children, for example, they may be able to take the course during the evenings when children are asleep.

Moreover, a variety of programs are available for online learning. Students have a smaller choice of courses in traditional schooling than in online learning [15]. Through online programs, students can acquire additional knowledge beyond their course material and critical career skills. According to Andreyanova et al. [22], online platforms offer countless options and online learning includes many different possibilities.

Students can improve on the knowledge acquired in the classroom by watching videos online on any topic at any time. They may study different aspects from building a website to learning a new language through online programs. Moreover, they have the opportunity to pause and replay online videos over and over to better understand the most challenging concepts.

Although students can learn online by simply watching videos, authors believe that some subjects are better learned in person and others are better learned online. Welding, organic chemistry, and diplomacy can all be studied online; however, they are all better learned in person. Although they might give a solid foundation, online programs should not be the main source of information on these topics. Authors perspective is that I would not want to undergo surgery by an expert who has only taken the course online because that sort of skill is only developed through actual practice. Additionally, I would not want someone who had only completed an online degree to build my house. I want someone with hands-on experiential learning combined with fundamental architectural knowledge. Finally, scientists who have actually worked in laboratories have gained a wealth of real-world information and are better positioned to fill new, breakthrough demands.

## **10. Why online courses are more effective**

The internet has revolutionized the way we learn. It has allowed us to access more information than ever before and the ability to learn at our own pace. Online courses have become more prevalent in recent years, offering a flexible and convenient learning method. There are several reasons why online courses are becoming more effective [21]. One reason is that online courses can be customized to meet the learner's expectations. In the past, courses were often designed to cater to all in a manner that may not have been suitable for everyone. With online courses, students can choose which topics they want to explore and can start and stop the course at any time. This allows them to focus on their interests and avoid boredom or frustration. Another advantage of online courses is that they are accessible from anywhere in the world [21]. This makes them ideal for those unable to attend class in person.

With the ability to study at their own pace in a relaxed setting, eLearning has changed education in learning institutions and for the working class [23]. The eLearning will undoubtedly significantly impact education in the foreseeable future on matters related to the delivery of educational content. As per IBM (International Business Machines), students who enroll in online learning programs benefit more than those who attend traditional face-to-face classroom sessions [16]. Students may study at a

speed that they think is more comfortable in online classes since they have complete control over their education. Compared to traditional classroom settings, students in online courses learn more quickly and retain more knowledge because they have the opportunity to repeat the same concept over and over [23]. They can advance more quickly through the course's known sections but must proceed gradually through the sections that necessitate additional attention over time. Compared to studying in a regular classroom setting, people spend 40–60% less time on the online type of learning [16]. Additionally, most eLearning approaches let students split their course time in any way they see appropriate.

Another area of improved effectiveness lies in online courses employing a range of evaluation instruments. Numerous evaluation options are available with online learning, and teachers may customize these evaluations for specific students or student groups [22]. Educators are not limited to solely utilizing traditional evaluations, such as examinations and quizzes, to ascertain their learners' progress and the extent of their progress [15]. To show their comprehension, students can produce online presentations for the classroom, write essays and peer review processes, participate in group projects synchronously and asynchronously, or generate essays and peer evaluations [24]. It keeps the game exciting and appealing for both learners and their teachers by offering a range of evaluations.