Proximate and ultimate analyses of raw Zhundong coal samples.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"3093",leadTitle:null,fullTitle:"Wave Propagation Theories and Applications",title:"Wave Propagation",subtitle:"Theories and Applications",reviewType:"peer-reviewed",abstract:"A wave is one of the basic physics phenomena observed by mankind since ancient time. 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He received the B.S. in agro-meteorology from Bogor Agricultural University, in 1988, M.S. degrees in physics from University of Indonesia, in 1998, and Ph.D. degree in physics from Bandung Technology University, Bandung, in 2005.\n\nSince 1997, he is a resident lecturer of Physics Physics Department, Bogor Agricultural University. From 2007 to 2011, He is Physics Head of Department, Bogor Agricultural University. From 2011, He is Head of Material Physics Laboratory, Physics Department, Bogor Agricultural University. He is the author of 16 Scopus indexed journal articles and a book chapter. His research interests include sensors engineering, semiconductor device physics and new material characterization, and ferroelectrics. 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Thermal energy storage is widely used in industrial and civilian applications, so it occupies an extremely important position in the field of energy storage technology. Phase change materials (PCM) will absorb or release a large amount of latent heat for energy storage when the state of matter changes. As a raw material for the preparation of phase change materials, coal has great energy storage density, and the output temperature and energy are relatively stable, so it has good application prospects [2]. Zhundong coalfield is the largest compositive basin in China, which has a large reserve of 390 Gt and is estimated to meet China’s next 100-year coal consumption requirement [3]. In addition, Zhundong coal is more environmentally friendly than other types of coal owing to its extremely low sulfur and ash contents, which has a great significance to coal industry [4, 5]. Therefore, the investigation on Zhundong coal has theoretical and practical significance for the utilization of coal resources of China.
Thermal pyrolysis is the first stage reaction in most of coal thermal conversion processes (combustion, gasification, and liquefaction) and it is also one of the most promising technologies for clean and effective utilization of low-rank coal. During the pyrolysis process, various non-condensible gas and liquid phase matter such as tar are generated, which will lead to the weightlessness of coal samples. The weight losses of the coal range from 10 to 55%, which is affected by pyrolysis temperature, coal size, pyrolysis time and other factors such as pressure, atmosphere, heating rate and mineral content, etc. [6, 7]. The heating rate and pyrolysis temperature have an important influence on the pyrolysis characteristics of coal. The heating rate affects the concentration gradient of the product inside the particle and the ratio of gas phase substance to tar in the volatiles. The pyrolysis temperature determines the pyrolysis of the macromolecule reaction rate in the coal structure and the total release of volatile matters. A thermogravimetric analyzer (TGA) is routinely employed by many researchers to characterize moisture and volatile contents of various coals. It enables the user to monitor the weight change of a certain quality of coal samples as a function of time and temperature, which endue this method to incorporate the advantages of simple operation and good repeatability [8]. From the TG curve, we can obtain the key parameters such as the temperature of initial point of pyrolysis volatilization, the maximum release rate of volatile matter, and its corresponding temperature.
It is necessary to obtain an accurate knowledge of the pyrolysis process, since all the models of coal thermal conversion processes involve pyrolysis, and the kinetic behavior of these products in pyrolysis is fundamental to the optimization of their use. However, the actual phenomena of pyrolysis are complex and the kinetic parameters of which are difficult to obtain. To date, a large number of kinetic models have been proposed in the literature [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. The models for processing TGA data and explaining the kinetic process of coal pyrolysis include first-order single reaction model (SRM), isoconversional or model-free methods, distributed activation energy model (DAEM). The SRM is the simplest, and it is applicable only over a narrow range of temperatures, thus limiting its applicability. Isoconversional or model-free methods are also used to process TGA data in which the kinetics parameters can be obtained without an explicit kinetics model [9]. Friedman method [10], Kissinger method [11], Flynn-Wall-Ozawa [12], and Miura-Maki method [13] are some of the outstanding methods in this classification. Several TGA curves at different heating rates for the same value of conversion are required for these models to determine activation energy for each conversion point. In recent years, nonlinear regressions (least square optimization) are increasingly used to process the pyrolysis process of coals. More often than not, the distributed activation energy model (DAEM), originally developed by Pitt [14], has become the distinguished representative of them. It assumes that the coal pyrolysis should include a large number of independent, parallel, and irreversible
In the present paper, TGA experiments with pyrolysis of Zhundong coal at different heating rate have been carried out. The data was first processed using various isoconversional methods and then processed using the distributed activation energy model (DAEM). An m-nth-DAEM was developed to predict the weight loss of Zhundong coal, considering that
The experimental tests were with a TGA (Netzsch Model STA449F3). Before the pyrolysis experiments, coals were grounded and the finer particle fractions in a size range 0.075–0.15 (mm) were selected. Table 1 shows the results of the proximate and ultimate analyses of the coal samples. Small samples were used to ensure uniform heating and to avoid problems of transport phenomena through the sample bed in the crucible. The instrument was first purged for 30 min with nitrogen to remove any remains of air, then further heated from room temperature to 1000°C at a heating rate of 10
Proximate analysis (%) | Ultimate analysis (%) | |||||||
---|---|---|---|---|---|---|---|---|
Mad | Aad | Vad | FCad | Cd | Hd | Od | Nd | St,d |
9.96 | 15.46 | 22.49 | 52.09 | 70.55 | 3.49 | 7.64 | 0.62 | 0.53 |
Proximate and ultimate analyses of raw Zhundong coal samples.
Typical decomposition data of Zhundong coal obtained from the TGA experiments [9] are shown in Figure 1. The key characteristic parameters related to the main pyrolysis process are summarized in Table 2. The initial decomposition temperature is denoted as
TGA curves of Zhundong coal samples during pyrolysis at different heating rates taken from literature [
10 | 425.39 | 718.95 | 0.90 | 35.8 |
20 | 458.89 | 731.25 | 1.75 | 36.4 |
30 | 482.89 | 735.25 | 2.85 | 38.0 |
50 | 489.86 | 752.75 | 5.34 | 37.9 |
Pyrolysis characteristic values of coal samples.
For non-isothermal and heterogeneous systems, the thermal analysis kinetics is usually evolved from the theory of isothermal and homogeneous systems. The kinetics of reactions in isothermal and homogeneous systems for solids are generally described by Eq. (1)
where V is total mass of volatiles released at time ‘t’,
In the above formula,
Thus,
Assuming
For different reaction models, there are differences in the expression of
The core issue of the general reaction kinetics of coal pyrolysis is determining the three factors
The above is the basic knowledge of thermal analysis dynamics. Next, two methods, that is, the isoconversional model and the distributed activation energy model (DAEM), will be used to align the TGA data of Zhundong coal pyrolysis.
The basic assumption of the isoconversional method is: under different conditions of temperature rise, the activation energy corresponding to the same conversion rate remains the same [18], and the pyrolysis is specified to be a first-order reaction, that is,
The activation energy and pre-exponential factor for each conversion point can be obtained by analyzing several TGA curves at different heating rates for the same value of conversion
Friedman model is a typical representative of the temperature differential method. Taking the logarithm of the Eq. (5) on both sides of the equal sign to obtain,
The above formula is the general formula of the Friedman method. Thus, the plot of
Combining Eq. (5), Eq. (8) can be rewritten as
\nwhere \n
Taking the first two items in parentheses on the right side of Eq. (11), we can get the approximate expression of \n
Performing logarithmic operation on both sides of the Eq. (12),
\nFor a given solid fuel, in the temperature range of the thermal reaction stage, the value of \n
Substituting Eq. (14) into Eq. (13), it gives,
\nUsing the Taylor series expansion for the two logarithmic expressions to the right of Eq.(15) and taking a first-order approximate expression of each logarithmic, the following expression can be obtained:
\nSubstituting Eq. (17) into Eq. (10) yields,
\nEq. (18) can be rewritten as,
\nEq. (19) is the integral form of the FWO model. The plot of \n
Taking the first item in parentheses on the right side of Eq. (11), the approximate expression of \n
Substituting Eq. (20) into Eq. (10) yields,
\nPerforming Logarithmic operations on the two sides of Eq. (21) and then substituting \n
Eq. (22) is the holonomic form of the KAS model. Using Eq. (22), we can estimate both \n
In the Miura-Maki integral method, the approximation of the temperature integral is the same as the KAS method. Rearranging Eq. (22) and inserting Eq. (8) gives,
\nTo derive the Miura-Maki integral method, the assumption of distributable activation energy model is used to approximate the right side of Eq. (23) as a step function at \n
Inserting the value of \n
Eq. (25) is the final expression of Miura-Maki method. The linear expression used to calculate the activation energy is similar to the KAS method, but there are some differences in the calculation results of the pre-exponential factor.
\nGenerally speaking, the core methods of the above four models for calculating activation energy and pre-exponential factor are the same. The procedure to estimate \n
Measure \n
Calculate the values of \n
Plot \n
The Friedman model is a representative of the differential method in the isoconversional method. The FWO model, KAS model, and Miura-Maki method are three typical integration methods. The integral equations all involve a general temperature integral, \n
In order to obtain the key parameters of the Friedman, FWO, KAS, and Miura-Maki models, the value of \n
Comparison of activation energy and pre-exponential factor obtained with four different methods for Zhundong coal samples.
Method | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n
---|---|---|---|---|
FWO | \n55 | \n237 | \n120.16 | \n1.58E + 10 | \n
KAS | \n48.5 | \n231.6 | \n9.46 | \n1E + 10 | \n
Miura-Maki | \n48.5 | \n231.6 | \n100.48 | \n3.82 E + 9 | \n
Friedman | \n69.7 | \n247 | \n528.75 | \n5.07E + 9 | \n
Summary of the kinetic parameters obtained from different methods.
\n\n | \n0.15 | \n0.25 | \n0.35 | \n0.45 | \n0.55 | \n0.65 | \n0.75 | \n0.85 | \n0.95 | \n
---|---|---|---|---|---|---|---|---|---|
FWO | \n0.999 | \n0.998 | \n0.998 | \n0.999 | \n0.999 | \n0.998 | \n0.990 | \n0.973 | \n0.927 | \n
KAS/Miura-Maki | \n0.998 | \n0.996 | \n0.997 | \n0.999 | \n0.998 | \n0.998 | \n0.988 | \n0.969 | \n0.916 | \n
Friedman | \n0.999 | \n0.996 | \n0.997 | \n0.998 | \n0.997 | \n0.993 | \n0.973 | \n0.974 | \n0.881 | \n
Linearly dependent coefficients for different methods at various \n
Pyrolysis activation energy distributions
Comparison of calculation results from FWO method (
The DAEM assumes that the original materials of coal contain plenty of constituents, which are numbered
To some solid state reactions, the pre-exponential factor is connected with the temperature through the following relationship,
\nCombining Eq. (27), the integrating solution to Eq. (26) may be rewritten as,
\nConsidering the continuous reactants have different activation energies, the DAEM devotes to describe the energy distribution of the infinite complicated set of reactions, that is \n
The integration of Eq. (29) brings about,
\nThe value of \n
By integrating Eq. (31), the following expression can be obtained,
\nSubstituting Eq. (30) into Eq. (32), the equation yields,
\nA double model [15] was developed assuming that the pyrolysis process occurs in two steps with different kinetic behaviors. The pyrolysis process is divided into two steps: the tar and light hydrocarbon gas formation during the primary pyrolysis and the char condensation, cross-linking reactions, and a further gas production during the secondary pyrolysis. Two sets of parallel reactions occur, sharing the same pre-exponential factor but not the same distributed activation energy. The distribution function of activation of energy \n
where \n
In the 1-DAEM case, there are four parameters to be estimated: the mean activation energy \n
In this work, the model-fitting exercise was carried out using MATLAB TM (R2014b). It is essential to use an appropriate method to compute the inner \n
To deal with the outer \n
The kinetic parameters of the DAEM are determined using an objective function. An effective criterion for achieving the objective function is to minimize the differences of squares. The functional form of the objective function used in this paper is,
\nwhere \n
In the distributed activation energy models (DAEMS) adopted here, the pre-exponential factor \n
The DAEMs were then applied to fit with the experimental data of \n
Comparison of experimental data and simulated data with the 1-DAEM methods: (a) Gaussian distribution; (b) logistic distribution.
\n | \n\n | \n\n\n | \n\n\n | \nOF | \n||
---|---|---|---|---|---|---|
\n\n | \nGaussian | \n169.42 | \n24.45 | \n−0.444 | \n1 | \n0.0493 | \n
Logistic | \n175.11 | \n79.84 | \n−0.400 | \n1 | \n0.2148 | \n|
\n\n | \nGaussian | \n172.96 | \n18.73 | \n0.127 | \n5.766 | \n0.0058 | \n
Logistic | \n172.96 | \n19.35 | \n0.132 | \n5.786 | \n0.0060 | \n
Estimated values of kinetic parameters [1-DAEMs with Gaussian/logistic distribution functions].
Comparison of experimental data and simulated data with the 2-DAEM methods: (a) Gaussian distribution; (b) logistic distribution.
Model | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \n\n\n | \nOF | \n||
---|---|---|---|---|---|---|---|---|---|
\n\n | \nGaussian | \n150.00 | \n20.00 | \n173.00 | \n36.85 | \n−0.500 | \n1 | \n0.400 | \n0.0492 | \n
Logistic | \n151.02 | \n41.89 | \n171.08 | \n25.06 | \n−0.514 | \n1 | \n0.304 | \n0.0160 | \n|
\n\n | \nGaussian | \n150.00 | \n28.22 | \n173.00 | \n26.85 | \n−0.502 | \n2.872 | \n0.646 | \n0.0001 | \n
Logistic | \n150.00 | \n29.57 | \n181.83 | \n34.50 | \n−0.513 | \n2.826 | \n0.765 | \n0.0005 | \n
Estimated values of kinetic parameters [2-DAEMs with Gaussian/logistic distribution functions].
Distribution activation energy curves as a function of activation energy for 2-nth-DAEM with Gaussian distribution functions.
The kinetic parameters extracted with 2-nth-DAEM with Gaussian distribution functions (best models to describe data at 20 \n
Comparison of predicted results with the experimental data at other heating rates with 2-nth-DAEM with Gaussian distribution functions: (a)
As a raw material for the preparation of phase change materials, coal is of great significance to energy utilization. In this work, Zhundong coal was taken as an example to investigate the pyrolysis process of it by means of thermogravimetric tests. In order to evolve appropriate kinetic parameters to characterize the pyrolysis process of coal, various methods and potential assumptions for processing TGA data were rigorously analyzed. The four isoconversional methods, that is, Friedman, FWO, KAS, and Miura-Maki method were used to calculate the activation energy and pre-exponential factor of Zhundong coal samples, respectively. It was found that the FWO model gave the best fit for the experimental data. During the pyrolysis process, the distribution of the activation energy of Zhundong coal, \n
This work is currently supported by the National Natural Science Foundation of China through contract No.51606153 and China Postdoctoral Science Foundation (2017M3200).
\nE | activation energy (kJ·mol−1) |
E0 | mean activation energy (kJ·mol−1) |
fE | distribution curve of the activation energy E |
i | the number of constituents in original materials |
k | rate coefficient s−1 |
k0 | pre-exponential factor s−1 |
n | The reaction order |
R | universal gas constant (kJ·mol−1·K−1) |
R∞ | maximum weight loss rate (%·min−1) |
T | temperature (K) |
T0 | initial decomposition temperature (°C) |
Tp | peak temperature (°C) |
V | total mass of volatiles released at time ‘t’ (%) |
Vf | the corresponding weight loss percentage when the pyrolysis reaches the final temperature (%) |
V∗ | total amount of volatile yields (%) |
α | Conversion |
β | heating rate (K∙min−1) |
γ | Constant |
ω | weighing factor |
σ | standard deviation (kJ·mol−1) |
Subscript | |
cal | calculated value |
exp | experimental value |
Water is the most abundant and widely distributed chemical compound in the world [1]. Though water in the natural state is one of the purest compounds known, there is difficulty finding a freshwater source that has not been altered by man [2, 3]. The use of drinking water with high physical, chemical and microbiological qualities is vital in poultry production as one single water source serves several birds. Hence, any problem in water quality would affect a great number of birds [4, 5, 6]. Birds consume approximately 1.6–2.0 times as much water as feed on a weight basis [7]; therefore, any deviation in water quality could have more pronounced effect on poultry health and production than feed [7, 8].
Water is an essential nutrient in animal nutrition and plays roles in poultry metabolism including thermal homeostasis [9], food digestion and absorption, nutrients transport, and waste products elimination from the body [10]. It also makes up 70 to 80% of lean body mass by weight in birds [11, 12, 13].
Drinking water constitutes the greatest source of water to poultry and it is made available in drinkers [1]. It is of great concern to poultry producers due to its great variability in quality and potential for contamination [5]. It could be from tap, stored in rooftop tanks, or underground (well) water [7]. The physico-chemical parameters established as indicators of water quality include taste, color, odor, pH, electrical conductivity (EC), hardness, alkalinity, salinity, and presence of cations and anions [14]. High-quality drinking water has been defined as water that contains inclusions, which promote vitality and lack inclusions causing morbidity and mortality [15, 16]. Because no water in nature is 100% pure, different water sources will have varying degree of water inclusions, which directly or indirectly affect poultry performance and welfare [17].
This is the water available in the feed. However, feeding of wet mashes to poultry has not been recommended for use in large scale commercial poultry production, on the basis that it does not offer any nutritional advantage and is difficult to apply [18].
Metabolic water refers to water created inside a living organism through their metabolism, by oxidizing energy-containing substances in their feed [19]. Birds excrete uric acid and can have a net gain of water from the metabolism of protein [20]. Migratory birds have been reported to rely exclusively on metabolic water production while making non-stop flights [21].
Tables 1 and 2 [22] and Table 3 [23] provided data on typical water consumption levels for layers, broilers and turkeys, respectively, at 21°C.
Production stage | Age/rate of production | Liters of water/1000 birds |
---|---|---|
4 weeks | 100 | |
Layer pullet | 12 weeks | 160 |
18 weeks | 200 | |
Laying hens | 50% production | 220 |
90% production | 270 |
Typical daily water consumption for layers at 21°C [22].
Age (weeks) | Liters of water/1000 birds |
---|---|
1 | 65 |
2 | 120 |
3 | 180 |
4 | 245 |
5 | 290 |
6 | 330 |
7 | 355 |
8 | 370 |
Typical daily water consumption for broilers at 21°C (liters per 1000 mixed sex birds) [22].
Age (weeks) | Galons of water/1000 birds | |
---|---|---|
Toms | Hens | |
1 | 10 | 10 |
2 | 20 | 15 |
3 | 30 | 20 |
4 | 40 | 30 |
5 | 55 | 40 |
6 | 75 | 60 |
7 | 100 | 80 |
8 | 125 | 100 |
9 | 150 | 115 |
10 | 170 | 130 |
11 | 190 | 140 |
12 | 210 | 150 |
13 | 215 | 160 |
14 | 220 | 170 |
15 | 220 | |
16 | 220 |
Typical daily water consumption (galons) for turkeys at 21°C (liters per 1000 mixed sex birds) [23].
In quail chicks, the water requirement has been reported to change with increase in age, quantity and quality of feed dry matter. This has been documented to be 3:1–4:2 g/g body weight at 12–29 days of age following stabilization at around 2 g/g body weight. The water feed ratio for the above period are 2:0–2:3 respectively [24, 25].
Birds, like most mammals, are considered to be homeothermic, and they maintain their deep body temperature at about the same level over a wide range of ambient temperatures [26]. Water intake will vary depending on age, environmental temperature, relative humidity, certain diet constituents, type of drinkers used and rate of growth [27]. Water consumption can be limited if the water is too hot or is contaminated with excess minerals [28, 29]. Water and feed consumption rates are interdependent, so reduced water intake can also lead to reduced feed intake [30].
It has been reported that the thermoneutral temperature for broiler chicks up to 7 days of age ranges between 28 and 35°C, and that temperatures higher than these may induce hyperthermia and dehydration, leading to a lower feed consumption and delayed growth [31, 32, 33]. On the other hand, a lower environmental temperature induces hypothermia and may lead to pulmonary hypertension in broilers.
Chicks exposed to low ambient temperature (20°C) had lower water intake than chicks brooded at high environmental temperature (35°C) as reported by Moraes et al. [34]. These were related to the heat conserving behavior of these birds, since at low ambient temperature (20°C) they clustered to maintain optimal heat thus reducing the frequency to the feeder and drinker. Similar decrease in water intake has been reported in birds above 2 weeks of age but with increased feed intake and metabolism patterns [35]. Since poultry are homeotherms that can live comfortably only in a relatively narrow zone of thermoneutrality [36], they are forced to increase feed consumption under low temperatures in order to balance their body temperatures [37].
Broilers subjected to acute heat stress have been reported to show higher water intake. The water intake increases in order to maintain thermoregulatory balance [38], as heat stress induces high water loss through the respiratory. This acts as a means to achieve efficient thermoregulation through evaporative cooling. In critical heat stress situations, water loss may cause marked changes in the thermoregulatory balance of poultry [39] and this may result in death.
Increase in environmental temperature tends to cause an increase in water intake while decrease in environmental temperature causes decrease in water intake. Hence, in addition to its nutritional role, water is more important for thermoregulation in poultry especially during hot conditions.
The authors declare no conflict of interest.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. 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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. 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