Size of the Q-table at the final generation
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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He was a visiting Professor in Georgia Institute of Technology in 2002 and 2005. He has published more than 225 papers in scientific journals. He received IEICE Electronics Society Award in 2004, IEICE Achievement Award in 2013, and IEEJ Outstanding Achievement Award in 2014. He served as Vice President of JSAP in 2014-2015. 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Recently reinforcement learning has received much attention as a learning method (Sutton, 1988, Watkins & Dayan, 1992). It does not need a priori knowledge and has higher capability of reactive and adaptive behaviors. However there are some significant problems in applying it to real problems. Some of them are deep cost of learning and large size of action-state space. The Q-learning (Watkins & Dayan, 1992), known as one of effective reinforcement learning, has difficulty in accomplishing learning tasks when the size of action-state space is large. Therefore the application of the usual Q-learning is restricted to simple tasks with the small action-state space. Due to the large action-state space, it is difficult to apply the Q-learning directly to real problems such as control problem for robots with many redundant degrees of freedom or multiple agents moving cooperatively one another.
\n\t\t\tIn order to cope with such difficulty of large action-state space, various structural and dividing algorithms of the action-state space were proposed (Holland, 1986; Svinin et al., 2001; Yamada et al., 2001). In the dividing algorithm, the state space is divided dynamically, however, the action space is fixed so that it is impossible to apply the algorithm to the task with a large action space. In the classifier system, “don’t care” attribute is introduced in order to create general rules. But, that causes partially observable problems. Furthermore, an ensemble system of general and special rules should be prepared in advance.
\n\t\t\tConsidering these points, Ito & Matsuno (2002) proposed a GA-based Q-learning method called “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA).” In their algorithm, a genetic algorithm is employed to reconstruct an action-state space which is learned by Q-learning. That is, the size of the action-state space is reduced by the genetic algorithm in order to apply Q-learning to the learning process of that space. They applied their algorithm to a control problem of multi-legged robot which has many redundant degrees of freedom and a large action-state space. By applying their restriction method for the action-state space, they successfully obtained the control rules for a multi-legged robot by their QDSEGA. However, the way to apply a genetic algorithm in their approach seems so straightforward. Therefore we have proposed a crossover for QDSEGA (Murata & Yamaguchi, 2005; Murata & Yamaguchi, 2008). Through their computer simulations on a control problem of a multi-legged robot, they could make about 50% reduction of the number of generations to obtain a target state of the problem.
\n\t\t\tIn this chapter, we apply the QDSEGA with the neighboring crossover to control multiple agents. An application of QDSEGA to multiple agent system has been considered (Ito and Gofuku, 2003; Ito et al., 2004) though, they still applied genetic operators straightforward. We apply the neighboring crossover to Multi Agent Simulations (MAS) problem and show its effectiveness to reduce the number of actions in a Q-table. We also propose a deletion algorithm to make more compact Q-table in MAS problem. We employ the application in Ito et al. (2004) where a Q-table is developed for homogeneous multiple agents. Computer simulation results show that the size of Q-table can be reduced by introducing the proposed neighboring crossover and the deletion algorithm.
\n\t\tIn this section, we briefly explain the outline of QDSEGA (Ito & Matsuno, 2002; Ito & Gofuku, 2003; Ito et al., 2004; Murata & Yamaguchi, 2005). QDSEGA has two dynamics. One is a learning dynamics based on Q-learning and the other is a structural dynamics based on Genetic Algorithm. Figure 1 shows the outline of QDSEGA. In QDSEGA, each action is represented by an individual of a genetic algorithm. According to actions defined by a set of individuals, an action-state space called Q-table is created. Q-learning is applied to the created Q-table. Then the learned Q-table is evaluated through simulations. A fitness value for each action is assigned according to Q-table. After that, each individual (i.e., each action) is modified through genetic operations such as crossover and mutation. We show some details in these steps in the following subsections, and show our proposed method for crossover and a deletion algorithm in the next section.
\n\t\t\tEach individual expresses a selectable action on the learning dynamics. It means that a set of individuals is selected by genetic operations, and a learning dynamics is applied to the subset. After the evaluation of the subset of actions, a new subset is restructured by genetic operations.
\n\t\t\t\tOutline of QDSEGA
Q-table created from a set of individuals
An action-state space called Q-table is created from the set of individuals. When several individuals are the same code, only one action is used in the action-state space to avoid the redundancy of actions. Figure 2 shows this avoidance process.
\n\t\t\tIn QDSEGA, the conventional Q-learning (Watkins & Dayan, 1992) is employed as a learning dynamics. The dynamics of Q-learning are written as follows:
\n\t\t\t\twhere \n\t\t\t\t\t\t
The fitness \n\t\t\t\t\t\t
where \n\t\t\t\t\t\t
\n\t\t\t\t\t(a) Fitness of Q-table\n\t\t\t\t
\n\t\t\t\tThe fitness of Q-table \n\t\t\t\t\t\t
First, calculate the maximum and minimum value of each state as follows:
\n\t\t\t\tThen \n\t\t\t\t\t\t
where p is a constant value which means the ratio of reward to penalty. After this normalization process, we fix the action \n\t\t\t\t\t\t
where \n\t\t\t\t\t\t
\n\t\t\t\t\t(b) Fitness of Frequency of Use\n\t\t\t\t
\n\t\t\t\tThe fitness of frequency of use \n\t\t\t\t\t\t
where \n\t\t\t\t\t\t
\n\t\t\t\t\tIto & Matsuno (2002) says “the method of the selection and reproduction is not main subject so the conventional method is used.” They employed a crossover that exchanges randomly selected bits between the parent individuals according to the crossover probability\n\t\t\t\t\t\t
Since they did not modify genetic operators for QDSEGA, we have proposed a crossover operation for the multi-legged robot control problem (MRC problem) in (Murata & Yamaguchi, 2005; Murata & Yamaguchi, 2008). We developed a neighboring crossover for QDSEGA for MRC problems.
\n\t\t\t\tThe crossover employed in QDSEGA (Ito & Matsuno, 2002) causes drastic change in the phenotype of a solution since randomly selected bits are changed between two solutions. If the change of solution in phenotype is so drastic, the good part of the solution may be broken. In order to avoid causing such drastic change among solutions, we proposed a crossover between similar parent solutions. We define the similarity by the number of the same genes in the same locus of a chromosome. We introduced a parameter \n\t\t\t\t\t\t
This kind of the restriction for the crossover has been proposed in the research area of distributed genetic algorithms (DGAs). Researches on DGAs can be categorized into two areas: coarse-grained genetic algorithms (Tanese, 1989; Belding, 1995) and fine-grained genetic algorithms (Mandelick & Spiessens, 1989; Muhlenbein et al., 1991; Murata et al., 2000). In the coarse-grained GAs, a population, that is ordinarily a single, is divided into several subpopulations. Each of these subpopulations is individually governed by genetic operations such as crossover and mutation, and subpopulations communicate each other periodically. Algorithms in this type are called the island model because each subpopulation can be regarded as an island. On the other hand, several individuals are locally governed by genetic operations in fine-grained GAs. In a fine-grained GA, each individual exists in a cell, and genetic operations are applied to an individual with individuals in neighboring cells. The DGAs are known to have an advantage to keep the variety of individuals during the execution of an algorithm, and avoid converging prematurely.
\n\t\t\t\tWhile we don’t define any solution space such as cells or islands in our proposed crossover, our restriction in crossover operation may have the same effect of keeping variety in a population and attain the effective search.
\n\t\t\tWe consider a transportation task shown in Ito & Gofuku (2003) and Ito et al. (2004). Figure 3 shows a transportation task used in this chapter. There is a world with 25 cells and a goal cell shown in “G” where five agents exist in Cell 0 and Cell 4. The aim of the transportation task is to convey a load shown in “L1” to the goal cell. In order to carry “L1” to “G”, the
\n\t\t\t\tTransportation task
other load shown in “L2” should be removed from Cell 22. Simultaneously, the door of “G” should be opened before carrying “L1”. To open the door, the switch shown in “SW” should be pushed by an agent in Cell 23.
\n\t\t\t\tEach load has a mobile direction. As shown in Figure 3, “L1” can be moved only in the vertical direction, and “L2” only in the horizontal direction. To move a load, more than one agent should push it toward the same movable direction. Therefore, to convey “L1” to “G”, agents should remove “L2” from Cell 22, open the door, and move “L1” to the goal.
\n\t\t\t\tIn order to control actions of each agent, Ito & Gofuku, (2003); Ito et al., (2004) employed their QDSEGA where the state of the agent is handled as a chromosome of an individual to which genetic operators are applied. Figure 4 shows the chromosome representation of the agent location in Figure 3. Each chromosome consists of genes with the same number of agents. The figure in each gene shows the identification number of cell where the agent locates. Since the agents with odd number locate in Cell 0, all genes for those agents have 0 as its value.
\n\t\t\t\tChromosome representation for the agents in Figure 3\n\t\t\t\t\t\t
An example of Q-table with a set of chromosomes
Q-table of the Q-learning is generated using a set of chromosomes. Figure 5 shows an example of Q-table that shows the relations of agent locations.
\n\t\t\t\tIn Figure 5, each column in the Q-table shows a chromosome generated by genetic operations. Rows of the table consist of the same chromosomes of the columns. That is, the chromosomes in the rows act as states in the Q-table, and the chromosomes in the column act as actions the agent can take in the fired state. When the ten agents locates in the start position (Cell 0 or Cell 4 as in Fig. 3), the current position is shown as (0, 4, 0, 4, 0, 4, 0, 4, 0, 4) in the table. If the target position (10, 3, 11, 4, 3, 13, 21, 11, 18, 22) is selected as an action from the current position, Agent “1” moves from Cell 0 to Cell 10, Agent “2” moves from Cell 4 to Cell 3, and so on.
\n\t\t\t\tWith this Q-table, Q-learning is applied. In order to move each agent to the target position, Ito & Gofuku, (2003) and Ito et al., (2004) proposed the following rules.
\n\t\t\t\t\n\t\t\t\t\t
where \n\t\t\t\t\t\t
\n\t\t\t\t\t
Since the collision between agents is assumed to avoid using traffic rules, Ito & Gofuku, (2003); Ito et al., (2004) considered only the collision between an agent and a load. If the load can not be carried by the agent alone, it should stop until other agents come.
\n\t\t\t\t\n\t\t\t\t\t
If the way that the agent has to go is not the direction to which the load can be moved, the agent should stop beside the load.
\n\t\t\t\t\n\t\t\t\t\t
When we observe a Q-table developed by QDSEGA, some actions or chromosomes are not used in moving multiple agents. That is, unnecessary actions are generated through genetic operations. In order to make a compact Q-table, we mark the chromosomes that are not used for a prespecified term in Q-Learning process.
\n\t\t\tIn this section, we show the simulation results to compare the conventional QDSEGA and the QDSEGA with the neighboring crossover shown in Subsection 2.5 and the deletion algorithm in Subsection 3.3. The neighboring crossover can be applied to the parent solutions that have the same genes more than\n\t\t\t\t\t\t
We employed the same parameter specifications as shown in Ito & Gofuku (2003) and Ito et al. (2004) except the learning rate and the discount rate in Equation (1). We found better specifications for those parameters by preliminary simulations:
\n\t\t\t\t[Genetic Algorithm]
\n\t\t\t\tThe number of individuals: 300,
\n\t\t\t\tSelection: Roulette selection,
\n\t\t\t\tType of crossover: uniform crossover,
\n\t\t\t\tThe probability of crossover: 0.2,
\n\t\t\t\tType of mutation: change the value among valid cell number,
\n\t\t\t\tThe probability of mutation: 0.001,
\n\t\t\t\tThe number of generations: 100,
\n\t\t\t\tWeights in Equation (2):\n\t\t\t\t\t\t
Weights in Equation (7):\n\t\t\t\t\t\t
[Q-learning]
\n\t\t\t\tReward:When “L1” reaches the goal, \n\t\t\t\t\t\t
When “L1” moves up or “L2” is removed, \n\t\t\t\t\t\t
When “L1” moves down or “L2” blocks the course of “L1”, \n\t\t\t\t\t\t
When any agent can not move to the target position, \n\t\t\t\t\t\t
Learning rate in Equation (1):\n\t\t\t\t\t\t
Discount rate in Equation (1):\n\t\t\t\t\t\t
The number of trials of each learning dynamics: 10,000.
\n\t\t\t\n\t\t\t\t\tFigures 6 and 7 show that the average reward for the obtained Q-table and an average number of actions (or situations) in Q-table. The average reward for Q-table is calculated over the last 100 trials among 10,000 trials. The maximum average reward is 130. These figures show that the proposed QDSEGA with \n\t\t\t\t\t\t
In order to obtain a compact Q-table with high average reward, we apply our proposed neighboring crossover after the average reward becomes larger than 100. Since the neighboring crossover is applied to the similar parent solutions, that crossover often produces the offspring that is the same chromosome. This causes the reduction of the size of Q-table. As shown in Figure 7, the number of actions in the Q-table reduced rather than the previous QDSEGA. However, this reduction may prevent improving the performance in the average reward.
\n\t\t\t\tGain attained by Q-learning generated by QDSEGA
The number of actions in Q-table generated by QDSEGA
\n\t\t\t\t\tFigures 8 and 9 show that the average reward and the average number of actions in Q-table. From these figures, we can see that the proposed QDSEGA can keep the high average reward with any value of\n\t\t\t\t\t\t
Gain attained by Q-learning generated by QDSEGA applied the neighboring crossover when obtaining 100 reward
The number of actions in Q-table generated by QDSEGA applied the neighboring crossover when obtaining 100 reward
Although the neighboring crossover has an effect to reduce the number of actions, there are some actions that are not used in moving agents. Therefore, we apply the deletion algorithm in Subsection 3.3. Figures 10 and 11 show the results of QDSEGA with neighboring crossover and the deletion algorithm. From these figures, we can see that the deletion algorithm does not degrade the performance in the average reward but have a fine effect to reduce the number of actions. By combining the neighboring crossover and the deletion algorithm, we could obtain more compact control table with high performance than using the previous algorithms.
\n\t\t\t\tGain attained by Q-learning generated by QDSEGA applied the neighboring crossover and the deletion algorithm when obtaining 100 reward
The number of actions in Q-table generated by QDSEGA applied the neighboring crossover and the deletion algorithm when obtaining 100 reward
\n\t\t\t\t\tTable 1 shows the average number of actions obtained at the final generation. From this table, we can see that the number of actions is reduced by the neighboring crossover and the deletion algorithm. Especially the deletion algorithm could reduce it without degrading the performance of the developed control table using neighboring crossover.
\n\t\t\t\tAfter obtaining a compact control table, we can examine the states and actions that are used to reach the goal. We can see that in order to achieve the task to bring “L1” to the goal, only two actions are required from the initial states shown in Figure 3. For example, the two actions in Figure 12 are enough to convey “L1” to the goal with ten agents. Figure 13 shows the states or positions of the agents according to the obtained states shown in Figure 12.
\n\t\t\t\tWithout Deletion Algorithm | \n\t\t\t\t\t\t\tPrevious | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t\t6 | \n\t\t\t\t\t\t\t8 | \n\t\t\t\t\t\t
# of actions | \n\t\t\t\t\t\t\t222.2 | \n\t\t\t\t\t\t\t210.8 | \n\t\t\t\t\t\t\t196.9 | \n\t\t\t\t\t\t\t149.2 | \n\t\t\t\t\t\t\t96.5 | \n\t\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t|||||
With Deletion Algorithm | \n\t\t\t\t\t\t\tPrevious | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t\t6 | \n\t\t\t\t\t\t\t8 | \n\t\t\t\t\t\t
# of actions | \n\t\t\t\t\t\t\t46.6 | \n\t\t\t\t\t\t\t39.0 | \n\t\t\t\t\t\t\t28.0 | \n\t\t\t\t\t\t\t30.2 | \n\t\t\t\t\t\t\t25.1 | \n\t\t\t\t\t\t
Size of the Q-table at the final generation
Succession of the states to achieve the goal
Achievement of carrying the load to the goal
In this chapter, we show the effectiveness of the neighboring crossover and the deletion algorithm especially in reducing the size of the Q-table. By reducing the Q-table, it becomes easy to read the Q-table that is required for attaining the objective to reach the goal and minimizes the memory to store the developed control table.
\n\t\t\tAs for other further study, we can bring other objective functions to achieve the goal. In Figures 6, 8, and 10, we compared the average reward as shown in the previous study (Ito and Gofuku, 2003, Ito et al., 2004). From these figures, we could minimize the total moving cost of all the agents to achieve the goal.
\n\t\t\tFurthermore, Ito and Gofuku (2003) examined the effectiveness of QDSEGA for multi-agent system with heterogeneous ability. We can show the effectiveness of the neighboring crossover in that problem too.
\n\t\tThis work was partially supported by the MEXT, Japan under Collaboration with Local Communities Project for Private Universities starting 2005.
\n\t\tRapidly growing population and industrialization have increased the energy request. It is very important that this increased energy requirement is from sustainable and environmentally friendly resources. At this point, biomass energy stands out with it being sustainable, environmentally friendly, and an inexhaustible resource that can be obtained anywhere. Especially in rural areas, it is becoming the most promising energy source due to its positive effects on socioeconomic developments [1]. In addition to this, woody biomass is estimated to meet approximately 2–18% of primary energy consumption in 2050 [2].
Biomass is vegetative organisms that plants produce and store from organic matter using photosynthesis using solar energy. Bioenergy is used to define energy and energy-related products produced from biomass. Biomass is formed as a result of the combination of sunlight and carbon dioxide and water in the atmosphere with photosynthesis reaction [1, 3].
where
Biomass has been the most common and crucial energy source that is used in heating and cooking for thousands of years. Thus, wood is still the most widely used and richest biomass energy source. Today, plants, agriculture and forest residues, organic household waste, industrial waste, and algae are used as biomass sources. The biomass sources can be used in wide areas such as producing heat, electricity, fuel, and some chemicals [4].
When technical, economic, environmental, and social effects of alternative energy sources such as biomass, wind, hydroelectric, solar, and geothermal energy are evaluated, it is concluded that the most suitable alternative energy is biomass energy. The most important reason for this is that its social benefit is the highest among others [5]. In addition to this, the use of bioenergy has considerably the potential to reduce emissions of greenhouse gases. Bioenergy produces approximately the same amount of carbon dioxide as fossil energy sources, but net carbon emission is zero since the plant uses carbon dioxide by photosynthesis during the day [4].
Three sides of Turkey are surrounded by seas so that it has different climates. Besides, it is located in the center of the triangle connecting the continents of Asia, Europe, and Africa [6]. In 2015 the amount of carbon absorbed by forests in Turkey is 1.9 billion tons. In addition to this, oxygen production was annually calculated as 42 million tons [7]. Turkey has a very rich fauna and plant species source due to moderate climatic conditions. For this reason, it is among the countries rich in biodiversity. Turkey’s territory is covered with 27.6% of forests, 31.1% of agricultural land, 18.6% of pasture, 21.3% of other areas, and 1.4% of water. The distribution ratios of the land situation in Turkey are shown in Figure 1 [8].
The distribution ratios of the land situation in Turkey [8].
The objective of this study is not only to address the current situation of Turkey’s forest sources and their bioenergy potential but also to present the recent methods of biomass utilization in the applications. This book section exhibits forest bioenergy potential of Turkey and discusses the biomass conversion methods, products, and applications in terms of the production process and usage of the products in the field. This section aims to attract attention to the forests’ bioenergy source and help to seek proper investments for the government and investors regarding forest biomass energy potential.
Turkey is a transit point that connects the Asian and European continents. It is at the center of the triangle formed by the continents of Asia, Europe, and Africa. Besides, Turkey is surrounded by seas on three sides so it has a different climate. As a result, this situation makes it a rich country in terms of animal and plant diversity. Turkey’s forests cover about 30% of the land area and have an equivalent of 11,000 plant species to plant diversity. Furthermore, 3708 of these plant species consist of endemic plant species. When we evaluate the forests of our country as tree species and the area they cover, the first three ranks are 18 species and 6,476,277 ha of oak (Quercus spp.), 5,420,524 ha of red pine (Pinus brutia), and 4,202,298 ha of larch (Pinus nigra) takes the forests. These are followed by beech (Fagus orientalis and Fagus sylvatica), scots pine (Pinus sylvestris), and fir (Abies nordmanniana and Abies cilicica) forests. The classification of Turkey’s forests is as follows: the Black Sea Region, the North Anatolia Forests which constitute 25% of the forests in Turkey, is the most forested area in Turkey, followed by Thrace, West and Middle Black Sea Forests, Eastern Black Sea Forests, Mediterranean Forests, and Central, East, and South East Anatolia Forests [6]. Turkey’s forest asset map and the distribution of the forests are given in Figure 2.
Map of Turkey’s forest assets [8].
Turkey has an ecologically rich diversity due to its geographical location and climatic diversity. The effects of forests on this ecological diversity and wealth are very important. Turkey has 78 million hectares of surface area. In addition to this, forest areas cover by 28.6% percentage except for treeless forest areas [7]. The ratio of land area and the amount of woodland in Turkey is given in Figure 3.
The ratio of land area and the amount of woodland in Turkey [7].
Forest areas can be divided into two classes as grove and coppice according to their operation types. Turkey’s forests are composed of 88% of grove forest areas (19.6 million hectares) and 12% of coppice forest areas (2.7 million hectares) [7]. The rates of the forest areas according to the operation types are given in Figure 4.
The rates of the forest areas according to the operation types [7].
Turkey’s forest lands’ main function distribution is composed of 50% economic, 42% ecological, and 8% sociocultural [7]. Distribution rates according to the main functions of forest areas are given in Figure 5.
Distribution rates according to the main functions of forest areas [7].
According to Turkey’s forest lands taken into consideration for 42 years, the field of forest area size and change of forest wealth have increased through the years. Forest areas increased by 2.1 million hectares in 42 years. Activities such as protection, development, afforestation, and precautions for forests have been effective on this increase [7]. The amount of the forest area of Turkey through the years and the rate of the country land are given in Table 1.
Years | Field (ha) | Rate (%) |
---|---|---|
1973 | 20.199.296 | 26.1 |
1999 | 20.763.248 | 26.7 |
2004 | 21.188.747 | 27.2 |
2012 | 21.678.134 | 27.7 |
2015 | 22.342.935 | 28.6 |
Forest area change by years [7].
Turkey’s forest assets are 20.2 million hectares in 1972 and reached 22.3 million hectares in 2015. In parallel with this, the wood wealth in forests increased from 0.9 billion m3 in 1972 to 1.2 billion m3 in 2003, to 1.6 billion m3 in 2015. In respect to this, between 1973 and 2015, there has been an increase of 700 million m3 in the tree wealth of the country’s forests. In this increase, afforestation studies, migration of citizens living around the forest, and improvement of forest areas have been very effective [7]. The amount of coniferous, broad-leaved, mixed grove, and coppice forest areas of the forest asset in 2012 is given in Table 2.
Qualification | Coniferous tree (ha) | Broad-leaved tree (ha) | Mixed grove (ha) | Total grove (ha) | Coppice forest (ha) | Total forest (ha) |
---|---|---|---|---|---|---|
Productive | 6.792.336 | 2.156.746 | 1.332.464 | 10.281.728 | 1.276.940 | 11.558.668 |
Degraded | 4.983.059 | 950.319 | 1.045.486 | 6.978.864 | 3.140.602 | 10.119.466 |
Total | 11.775.395 | 3.107.066 | 2.378.131 | 17.260.592 | 4.417.542 | 21.678.135 |
Turkey’s amount of forest assets in 2012 [8].
According to the forest renovation plan in 2013–2015 in Turkey, the amount of forest area in 2015 was estimated to be 22.3 million hectares. The amount and rates of the distribution of the forest areas according to the operation types, the forest area, tree wealth, and annual current increase status are given in Table 3. Turkey’s average annual amount of revenue derived from forests planted in 2015 is calculated as the volume-shelled body. This value was calculated as approximately 15.94 million m3 from grove forests and 2.37 million m3 from coppice forests. As a result, it was calculated as 18.31 million m3 of total forest area [7].
Operation types | Normal forest | Degraded forest | Total | |||
ha | % | ha | % | ha | % | |
Forest area distribution | ||||||
Grove | 11.919.061 | 54 | 7.700.657 | 34 | 19.619.718 | 88 |
Coppice | 785.087 | 3 | 1.938.130 | 9 | 2.723.217 | 12 |
Total | 12.704.148 | 57 | 9.638.787 | 43 | 22.342.935 | 100 |
Operation types | Normal forest | Degraded forest | Total | |||
m3 | % | m3 | % | m3 | % | |
Distribution of tree wealth | ||||||
Grove | 1.506.131.410 | 93 | 59.996.731 | 4 | 1.566.128.141 | 97 |
Coppice | 33.692.118 | 2 | 11.953.934 | 1 | 45.646.052 | 3 |
Total | 1.539.823.528 | 95 | 71.950.665 | 5 | 1.611.774.193 | 100 |
Distribution of annual current increase | ||||||
Grove | 42.322.876 | 92 | 1.484.455 | 3 | 43.807.331 | 95 |
Coppice | 1.511.561 | 3 | 585.191 | 2 | 2.096.752 | 5 |
Total | 43.834.437 | 95 | 2.069.646 | 5 | 45.904.083 | 100 |
The situation of forest areas according to their operation types [7].
Turkey’s 13.9 million hectares of forest area (62%) is pure forest. In this amount, the rate of tree species entering the mixture is less than 10%. Besides, approximately 8.4 million hectares of forest (38%) is mixed forest [7]. The distribution of forest ratio of species to general forest area is given in Figure 6.
Proportion of forest areas by tree type [7].
Turkey’s forest areas consist of 33% broad-leaved forests (oak, beech, alder, chestnut tree species such as beech), 48% coniferous forests (tree species such as Turkish pine, crimean pine, scots pine, fir, spruce, cedar), 19% coniferous + broad-leaved mixed forests. Oak occupies the largest area in the forests (5.9 million ha), followed by Turkish pine, crimean pine, beech, scots pine, juniper, fir, cedar, spruce, stone pine, alder, chestnut, hornbeam, poplar, lime tree, ash tree, and eucalyptus [7]. Distribution values of forest areas by tree species are given for 2018 in Table 4.
Total forest area by tree species (2018) | Forest form | ||
---|---|---|---|
Tree type groups | Total (ha) | Productive (ha) | Degraded (ha) |
Oak (Quercus spp.) | 5,938,527 | 2,435,265 | 3,503,262 |
Turkish pine (Pinus brutia) | 5,686,009 | 3,527,063 | 2,158,946 |
Crimean pine (Pinus nigra) | 4,304,821 | 2,787,424 | 1,517,397 |
Beech (Fagus orientalis) | 1,935,730 | 1,665,997 | 269,733 |
Scots pine (Pinus sylvestris) | 1,538,304 | 901,606 | 636,698 |
Juniper (Juniperus) | 963,217 | 223,097 | 740,120 |
Fir (Abies spp.) | 593,201 | 391,842 | 201,359 |
Turkish cedar (Cedrus libani) | 487,819 | 252,590 | 235,229 |
Oriental spruce (Picea orientalis) | 327,890 | 234,224 | 93,666 |
Stone pine (Pinus pinea) | 164,798 | 131,548 | 33,250 |
Alder (Alnus spp.) | 149,215 | 115,646 | 33,569 |
Chestnut (Castanea sativa) | 89,941 | 69,727 | 20,214 |
Hornbeam (Carpinus spp.) | 35,609 | 28,872 | 6737 |
Poplar (Populus spp.) | 16,430 | 6587 | 9843 |
Lime tree (Tilia spp.) | 12,803 | 10,637 | 2166 |
Ash tree (Populus spp.) | 7359 | 6854 | 505 |
Eucalyptus (Eucalyptus spp.) | 1434 | 1383 | 51 |
Other species | 368,826 | 192,784 | 176,042 |
Total | 22,621,935 | 12,983,148 | 9,638,787 |
Distribution of forest areas by tree species 2018 [9].
Turkey’s forest wealth distribution, the current value increment distribution, and distribution of forest areas by the year 2005–2018 are given as follows. Figure 7 shows forest wealth distribution in 2018. The forest was composed of 95% productive high forest. Figure 8 shows distribution of forest wealth between 2005 and 2018. The productive high forest percentage increased by 88–95% in 13 years. Figure 9 indicates distribution of increment in 2018. Figure 10 indicates distribution of increment between 2005 and 2018. Figure 11 shows distribution of forest areas in 2018. Figure 12 shows change of forest areas by years 2005–2018 [9].
Forest wealth distribution in 2018 [9].
Distribution of forest wealth between 2005 and 2018 [9].
Distribution of increment in 2018 [9].
Distribution of increment between 2005 and 2018 [9].
Distribution of forest areas in 2018 [9].
Change of forest areas by years 2005–2018 [9].
In the last 30 years, an increase of approximately 990.000 ha has been achieved in forest areas with afforestation studies and increasing environmental awareness. Thus, not only superficial increase but also quality increase in forest areas was observed [6].
Annual increment in volume of forests can be explained by the increase in total height and diameter of the tree in a cubic meter (m3) during the growth period of the trees. Thus, the annual current increment was 28.1 million m3 in total and 1.4 m3 in a hectare in 1973. In addition to this, the annual current increment was calculated as 45.9 million m3 in total and 2.1 m3 in hectare in 2015. The reason for this increase is due to the increment in tree wealth and forest areas with the maintenance to forests [7]. In Figure 13, the wood biomass source that can be obtained from forests is given as a model.
Model of wood biomass source that can be obtained from forests [2].
Revenue in forestry is the annual revenue amount and is calculated in m3. The amount of revenue in 2015 was determined as 15.942.459 m3 in grove forests and 2.372.162 m3 from coppice forests, with a total of 18.314.621 m3 [7]. The change in forest revenue amounts by years is given in Figure 14.
Forest revenue by years [7].
The total amount of trees and annual revenue growth of forest areas can be considered as the biomass potential of forests. Thus, the amount of production and unprocessed wood production according to the tree species shows the potential of the production of firewood. Tables 5 and 6 show the amount of wood that can be produced as fuel according to the forest area and tree types.
Description | Unit | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|---|
Turkey (industrial wood) | m3 | 14,923,209 | 16,637,598 | 17,009,998 | 15,521,622 | 19,080,137 |
Turkey (fuel wood) | m3 | 5,257,994 | 5,022,986 | 4,877,067 | 4,359,646 | 4,890,455 |
Fuel wood (coniferous) | m3 | 2,120,632 | 2,176,826 | 2,203,385 | 1,926,629 | 2,442,696 |
Fuel wood (non-coniferous) | m3 | 3,137,362 | 2,846,160 | 2,673,682 | 2,433,017 | 2,447,759 |
Volume of wood between 2014–2018 [9].
Range of products | Coniferous | ||||||||
Cedrus | Juniperus | Pinus brutia | Other pinus | Picea | Abies | Other coniferous | Total | ||
Sewn shell body volume (m3) | 177,316 | 29,959 | 6,541,644 | 7,443,431 | 636,806 | 2,067,953 | 408,451 | 17,305,560 | |
Fuel wood | High forest (from allowable cut) | 33,739 | 12,092 | 717,120 | 400,748 | 57,043 | 27,731 | 31,358 | 1,279,831 |
Coppice (from allowable cut) | 4651 | 318 | 8893 | 8926 | 5155 | 1141 | 29,084 | ||
Site clearance, wreck, etc. | 5491 | 754 | 292,649 | 662,736 | 43,024 | 90,171 | 38,959 | 1,133,781 | |
Total | 43,881 | 13,164 | 1,018,659 | 1,072,410 | 100,067 | 123,057 | 71,458 | 2,442,696 | |
Range of products | Non-coniferous | ||||||||
Quercus | Carpinus | Fagus | Populus | Alnus | Other non-coniferous | Total | Final total | ||
Sewn shell body volume(m3) | 2,286,005 | 301,162 | 4,032,484 | 138,236 | 49,932 | 324,418 | 7,132,237 | 24,437,797 | |
Fuel wood | High forest (from allowable cut) | 506,016 | 57,851 | 358,467 | 4559 | 8200 | 139,551 | 1,074,608 | 2,354,439 |
Site clearance, wreck, etc. | 209,091 | 5334 | 332,170 | 7432 | 1605 | 38,958 | 594,590 | 1,728,371 | |
Total | 1,323,625 | 69,497 | 793,986 | 12,373 | 9805 | 238,473 | 2,447,759 | 4,890,455 |
Production amounts of the tree species for volume of wood per species in 2018 [9].
It is estimated that available biomass energy potential from waste is about 8.6 million tons of oil equivalent (toe) in Turkey. Furthermore, it is anticipated that these waste biomass have a biogas potential of 1.5–2 Mtoe [10].
The total amount of waste originating from forests was calculated as 4.8 million tons (1.5 Mtoe) in Turkey. The gasification plant capacity that can be installed is estimated to be 600 MW [11]. The energy value of forest waste is estimated to be 859.899 toe/year in Turkey. The number of biomass electricity generation plant in Turkey is 128 units [12]. Wood biomass potential in forests depends on factors such as forest biomass increase, forest area, and forest growth [2]. Therefore, the bioenergy potential is also highly dependent on factors such as forest biomass increase, obtained from forest wastes, forest area, and growth of the forest. Figure 15 shows the distribution of the amount of Turkey’s forest wastes based on the amount of biomass.
Distribution of Turkey’s forest waste amount [11].
The majority of biomass energy is used for cooking and heating in households. Approximately 6.5 million houses use wood as the main fuel for heating purposes in Turkey. Moreover, in the paper industry, approximately 60% of the factories’ energy needs are obtained from waste wood [5].
There are main processes such as direct combustion, gasification, alcoholic fermentation, pyrolysis, liquefaction, anaerobic digestion, hydrogasification, and transesterification where energy is obtained from biomass. These processes have their own advantages according to the biomass source and the type of energy obtained. If biomass is converted using modern technologies and energy conversion efficiency is ensured, biomass energy could be a primary energy source in the future [4].
Demirbaş [13] classified wood as a second-generation biofuel in the study. Besides, examples of these biofuels are bio-alcohols, bio-oil, bio hydrogen, and bio Fischer-Tropsch diesel. In addition to these, using alternative fuels from biomass as fuel additives can improve fuel properties such as cetane and octane number, viscosity, and density in diesel and gasoline engines. Thus, fuels produced from biomass can be used as alternative fuels in internal combustion engines [14].
Biomass conversion techniques can be applied on biomass materials to obtain solid, liquid, and gaseous fuels. After the conversion process, fuels can be produced with the main products such as biodiesel, biogas, bioethanol, and pyrolytic gas. Besides, by-products such as fertilizer and hydrogen can be also obtained [15]. Alternative biofuels such as biogas and bioethanol fuels can be obtained by the biomass conversion process. Bioethanol can be used instead of oil, and biogas can be used instead of natural gas [16]. Conversion techniques using biomass sources, fuels obtained using these techniques, and application areas are given in Table 7.
The biomass processing process can be divided into two classes: thermal and biochemical. It can be divided into three subtitles as direct combustion, gasification, and pyrolysis in the thermal process. The biochemical process can be classified under two subtitles as fermentation and anaerobic digestion. Figure 16 shows the methods and products obtained in the processing processes of biomass [16].
Biomass processing method and products [16].
Transformation of biomass in thermal process [17].
The majority of modern bioenergy plants are use biomass for obtaining heat and power. Developing gasification and pyrolysis bio-oil technology offers much more efficient energy conversion with turbine and combined cycle technologies [3]. Thermal process can be examined under three main headings: burning, gasification, and pyrolysis.
Pyrolysis process is the simplest and oldest method for biomass to gas from. Pyrolysis process is a physical and chemical situation that occurs by heating organic substances up to 500–600°C without oxygen. In this process, gas components, volatile condensates, charcoal, and ash are released. When it rises to high temperature, wood gas and components gas are released by heating the wood up to 900°C in an oxygen-free environment. As a result of pyrolysis, substances such as gases, water, organic compounds, tar, and charcoal are obtained [15, 18].
Pyrolysis is the method of obtaining solid, liquid, and gas products by breaking down the biomass with heat. Slow pyrolysis is a well-known method widely used in the production of charcoal. Fast pyrolysis is the method in which biomass converts more than 75% of liquid bio-oil at high temperatures. This bio-oil chemical composition obtained is very similar to biomass. This bio-oil can be used as renewable fuel in gas turbines, diesel engines, or boilers. Bio-oil has about 60% calorific value of conventional fuel oils by volume [3].
Bio-oil is a liquid fuel obtained by the thermochemical process of biomass. Bio-oil obtained from wood is liquid and dark brown-colored. Its density is 1200 kg/m3 and it is more than the density of biomass and fuel oil. Bio-oil water content is 14–33 wt% by mass and cannot be removed by traditional methods such as distillation. Higher heating value (HHV) is 27 MJ/kg, and it is lower than traditional fuel oil (43–46 MJ/kg) [13]. The conversion of biomass into product by pyrolysis and the process steps of the products obtained are given in Figure 18.
Conversion of biomass to products by pyrolysis [13].
Transformation processes of biomass into products by gasification [19].
Products obtained from biomass by Fischer-Tropsch synthesis [20].
Gasification technology is one of the oldest conversion processes, and it has been used for more than 200 years [19]. The gasification process is the method for achieved combustible gas by dissolving solids like carbon-containing biomass at high temperatures. The process up to approximately 500°C in the gasification of organic substances is the pyrolysis phase. Here, carbon, gases (calorific value can be up to 20 MJ/m3), and tar are obtained. When heating up to 1000°C, carbon reacts with water vapor to produce CO and H2. Depending on the variable oxygen rate in the raw material, additional oxygen input may not be required for the gasification process. Gasification takes place in a reducing atmosphere with low air oxygen or steam injection. During this process, biomass is burned with the air supplied to the fuel cell under control, and the resulting products include combustible gases such as hydrogen, methane, as well as carbon monoxide, carbon dioxide, and nitrogen. Thus, combustible gases such as carbon monoxide, hydrogen, methane, and low amounts of other gases with low or medium calorific value are obtained. After cleaning the gas, it can be used as fuel in gas turbines, gasoline engines, dual fuel diesel engines, or in fuel cells after purification [3, 15, 18].
Biomass gasification technology provides the opportunity to convert renewable biomass resources into clean gaseous fuels or synthesis gases. Heat or electricity is produced from these produced gases. In addition to these, there is the potential to produce liquid transportable fuel, hydrogen, or chemicals from them. Gasification is a promising energy conversion technology with its flexible, efficient, and environmentally adaptable features [17]. Besides, the most important feature of gasification is its high electrical efficiency. In the future, it is expected to be used instead of natural gas or diesel fuel in gas turbines or fuel cells, industrial boilers, and furnaces, to replace gasoline or diesel in internal combustion engines [19].
The process of biomass giving a fast chemical reaction with oxygen is called burning. As a result of combustion, heat, carbon dioxide, water vapor and some metal oxides are given to the environment [15]. The biomass and full combustion components are given in Figure 21.
The components of the biomass [21].
Industrial and commercial combustion plants can burn a wide variety of fuels, from tree biomass to urban solid waste. Furnaces are the simplest combustion technology, and biomass burns in a combustion chamber. Combustion technology can be divided into two main categories as grate burner and fluid bed burner. In biomass combustion plants, a high-temperature and high-pressure steam is obtained as a result of combustion. This steam is passed through the turbine and converted into electrical energy with efficiency in the range of 17–25%. It can be increased up to 85% with efficient cogeneration systems [3, 19].
Pellets are generally solid wood particles with a cylindrical diameter of 10 mm and a length of less than 35 mm. Pellets produced from wood or waste wood are used to generate electricity in cogeneration systems, for heating in residences and industry. Wood pellets are the fuel with the highest thermal value after coal [22]. A comparison of the higher heat values of biomass and coal are given in Table 8.
Biomass | Conversion method | Fuels | Application fields |
---|---|---|---|
Forest wastes | Anaerobic digestion | Biogas | Electric power production, heating |
Agricultural wastes | Pyrolysis | Ethanol | Heating, transport vehicles |
Energy crops | Direct combustion | Hydrogen | Heating |
Animal waste | Fermentation and anaerobic digestion | Methane | Transport vehicles, heating |
Garbage (organic) | Gasification | Methanol | Jet engines |
Algae | Hydrolysis | Synthetic oil, rockets | |
Energy forests | Biophotolysis | Automotive gas oil | Drying |
Vegetable and animal oils | Esterification reaction | Diesel fuel | Transport vehicles, heating, greenhouse cultivation |
Biomass, biomass conversion methods, fuels, and application areas [15].
Fuel form | HHV (MJ/kg) |
---|---|
Wood | 10–20 |
Vineyard pruning | 14–18 |
Rice husk | 12–14 |
Sawdust | 12 |
Wood pellets | 20 |
Coal | 28 |
A comparison of the higher heat values of biomass and coal [22].
It contains hemicellulose and lignin in different amounts in the biomass. Glucose can be obtained from cellulose using enzymes with chemical hydrolysis or after enzymatic hydrolysis with chemical processes. This process must be done with extreme care, as glucose can sometimes degrade during chemical hydrolysis. By fermentation of glucose, many chemical products can be obtained such as ethanol, acetone, and butanol which are equivalent to products from crude oil [15].
Anaerobic digestion is a biological process and can take place in a completely oxygen-free environment. It is done by microorganisms that can live in an oxygen-free environment. The process is given in Eq. (2):
Biomass can be separated by microorganisms through fermentation in an oxygen-free environment. End of the fermentation process, a valuable fertilizer, and gases such as methane and carbon dioxide products can be obtained [15].
Anaerobic digestion (AD) is a process of producing flammable gas, consisting of methane and carbon dioxide at a rate of 60:40 using microbes in an oxygen-free environment. Therefore, the biogas production process is a complex and sensitive process that contains many microorganism groups. Biogas is a flammable gas formed by decomposing biological wastes in an oxygen-free environment. Biogas approximately contains 50–60% methane gas. Biogas is a colorless, flammable gas. In addition to this, biogas consists of its main components such as methane and carbon dioxide. Besides, it contains a small amount of hydrogen sulfide, nitrogen, oxygen, and carbon monoxide. Generally, 40–60% of organic matter is converted to biogas. The general composition of biogas consists of 60% CH4 and 40% CO2, and its thermal value is 17–25 MJ/m3. The remaining waste is an odorless solid or liquid waste suitable for use as fertilizer. After producing methane gas, methane gas can be used instead of LPG with very small changes. This gas can be used in spark ignition engines, gas turbines, and fuel cells [3, 16, 23, 24]. The components of biogas are given in Table 9.
Methane, CH4 | 55–75% |
Carbon dioxide, CO2 | 25–45% |
Carbon monoxide, CO | 0–0.3% |
Nitrogen, N2 | 1–5% |
Hydrogen, H2 | 0–3% |
Hydrogen sulfide, H2S | 0.1–0.5% |
Oxygen, O2 | Traces % |
Composition of biogas [24].
Biogas is a gaseous fuel as an alternative to natural gas. Thus, it can be used in the following fields: direct heating, motor fuel, turbine fuel power generation, fuel cells, additives for natural gas, and in the production of chemicals [23]. Flow diagrams of biogas production facilities are given in Figures 22 and 23.
Flow diagram of the biogas production facility [24].
Flow diagram of dry fermentation [24].
Hydrogen and oxygen can be obtained by the biophotolysis process using some microscopic algae. These algae use solar energy in seawater, so they can work as a kind of solar cell. Thus, the microscopic algae can separate seawater photosynthetically to hydrogen and oxygen [15].
The study concludes that biomass energy in Turkey is seen as one of the most sustainable and promising renewable energy sources. Forest bioenergy potential can be converted to alternative biofuels. This process consists of the most common biomass conversion methods such as thermal processes, biological processes, and biophotolysis processes. The thermal processes consist of pyrolysis, gasification, and combustion, while the biological processes are fermentation and anaerobic digestion. Thus, forest bioenergy potential can be used for producing energy. In this regard, forest wastes or forest biomass can be turned into pellets and used in electricity generation in power plants. In addition to this, pyrolysis, gasification, fermentation and anaerobic digestion methods, alcohol, and biogas can be produced from forest wastes and used in the residential industry and transportation. Especially, bio-oils and bio-alcohols can be used in internal combustion engines, furnaces, or boilers as fuel. Besides, biogas also is used as a fuel in households or industry. Thus, Turkey can be reduced to its dependence on foreign energy demand due to the advantage of the rich forest resources. Besides, it is obvious that rich forest resources will contribute to both the ecological and socioeconomic structures of countries. Overall, the rich forest biomass potential is not only contributed to countries’ economic field but also the ecological and socio-economic.
The author would like to thank Amasya University.
CH2O | carbohydrate |
CH4 | methane |
HHV | higher heating value (MJ/kg) |
LPG | liquefied petroleum gas |
Mtoe | million tons of oil equivalent |
MW | megawatt |
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She performed research in perioperative autotransfusion and obtained the degree of PhD in 1993 publishing Peri-operative autotransfusion by means of a blood cell separator.\nBlood transfusion had her special interest being the president of the Haemovigilance Chamber TRIP and performing several tasks in local and national blood bank and anticoagulant-blood transfusion guidelines committees. Currently, she is working as an associate professor and up till recently was the dean at the Albert Schweitzer Hospital Dordrecht. She performed (inter)national tasks as vice-president of the Concilium Anaesthesia and related committees. \nShe performed research in several fields, with over 100 publications in (inter)national journals and numerous papers on scientific conferences. \nShe received several awards and is a member of Honour of the Dutch Society of Anaesthesia.",institutionString:null,institution:{name:"Albert Schweitzer Hospital",country:{name:"Gabon"}}},{id:"83089",title:"Prof.",name:"Aaron",middleName:null,surname:"Ojule",slug:"aaron-ojule",fullName:"Aaron Ojule",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Port Harcourt",country:{name:"Nigeria"}}},{id:"295748",title:"Mr.",name:"Abayomi",middleName:null,surname:"Modupe",slug:"abayomi-modupe",fullName:"Abayomi Modupe",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/no_image.jpg",biography:null,institutionString:null,institution:{name:"Landmark University",country:{name:"Nigeria"}}},{id:"94191",title:"Prof.",name:"Abbas",middleName:null,surname:"Moustafa",slug:"abbas-moustafa",fullName:"Abbas Moustafa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94191/images/96_n.jpg",biography:"Prof. Moustafa got his doctoral degree in earthquake engineering and structural safety from Indian Institute of Science in 2002. He is currently an associate professor at Department of Civil Engineering, Minia University, Egypt and the chairman of Department of Civil Engineering, High Institute of Engineering and Technology, Giza, Egypt. He is also a consultant engineer and head of structural group at Hamza Associates, Giza, Egypt. Dr. Moustafa was a senior research associate at Vanderbilt University and a JSPS fellow at Kyoto and Nagasaki Universities. He has more than 40 research papers published in international journals and conferences. He acts as an editorial board member and a reviewer for several regional and international journals. His research interest includes earthquake engineering, seismic design, nonlinear dynamics, random vibration, structural reliability, structural health monitoring and uncertainty modeling.",institutionString:null,institution:{name:"Minia University",country:{name:"Egypt"}}},{id:"84562",title:"Dr.",name:"Abbyssinia",middleName:null,surname:"Mushunje",slug:"abbyssinia-mushunje",fullName:"Abbyssinia Mushunje",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Fort Hare",country:{name:"South Africa"}}},{id:"202206",title:"Associate Prof.",name:"Abd Elmoniem",middleName:"Ahmed",surname:"Elzain",slug:"abd-elmoniem-elzain",fullName:"Abd Elmoniem Elzain",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Kassala University",country:{name:"Sudan"}}},{id:"98127",title:"Dr.",name:"Abdallah",middleName:null,surname:"Handoura",slug:"abdallah-handoura",fullName:"Abdallah Handoura",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Supérieure des Télécommunications",country:{name:"Morocco"}}},{id:"91404",title:"Prof.",name:"Abdecharif",middleName:null,surname:"Boumaza",slug:"abdecharif-boumaza",fullName:"Abdecharif Boumaza",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Abbès Laghrour University of Khenchela",country:{name:"Algeria"}}},{id:"105795",title:"Prof.",name:"Abdel Ghani",middleName:null,surname:"Aissaoui",slug:"abdel-ghani-aissaoui",fullName:"Abdel Ghani Aissaoui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/105795/images/system/105795.jpeg",biography:"Abdel Ghani AISSAOUI is a Full Professor of electrical engineering at University of Bechar (ALGERIA). He was born in 1969 in Naama, Algeria. He received his BS degree in 1993, the MS degree in 1997, the PhD degree in 2007 from the Electrical Engineering Institute of Djilali Liabes University of Sidi Bel Abbes (ALGERIA). He is an active member of IRECOM (Interaction Réseaux Electriques - COnvertisseurs Machines) Laboratory and IEEE senior member. He is an editor member for many international journals (IJET, RSE, MER, IJECE, etc.), he serves as a reviewer in international journals (IJAC, ECPS, COMPEL, etc.). He serves as member in technical committee (TPC) and reviewer in international conferences (CHUSER 2011, SHUSER 2012, PECON 2012, SAI 2013, SCSE2013, SDM2014, SEB2014, PEMC2014, PEAM2014, SEB (2014, 2015), ICRERA (2015, 2016, 2017, 2018,-2019), etc.). His current research interest includes power electronics, control of electrical machines, artificial intelligence and Renewable energies.",institutionString:"University of Béchar",institution:{name:"University of Béchar",country:{name:"Algeria"}}},{id:"99749",title:"Dr.",name:"Abdel Hafid",middleName:null,surname:"Essadki",slug:"abdel-hafid-essadki",fullName:"Abdel Hafid Essadki",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"École Nationale Supérieure de Technologie",country:{name:"Algeria"}}},{id:"101208",title:"Prof.",name:"Abdel Karim",middleName:"Mohamad",surname:"El Hemaly",slug:"abdel-karim-el-hemaly",fullName:"Abdel Karim El Hemaly",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/101208/images/733_n.jpg",biography:"OBGYN.net Editorial Advisor Urogynecology.\nAbdel Karim M. A. El-Hemaly, MRCOG, FRCS � Egypt.\n \nAbdel Karim M. A. El-Hemaly\nProfessor OB/GYN & Urogynecology\nFaculty of medicine, Al-Azhar University \nPersonal Information: \nMarried with two children\nWife: Professor Laila A. Moussa MD.\nSons: Mohamad A. M. El-Hemaly Jr. MD. Died March 25-2007\nMostafa A. M. El-Hemaly, Computer Scientist working at Microsoft Seatle, USA. \nQualifications: \n1.\tM.B.-Bch Cairo Univ. June 1963. \n2.\tDiploma Ob./Gyn. Cairo Univ. April 1966. \n3.\tDiploma Surgery Cairo Univ. Oct. 1966. \n4.\tMRCOG London Feb. 1975. \n5.\tF.R.C.S. Glasgow June 1976. \n6.\tPopulation Study Johns Hopkins 1981. \n7.\tGyn. Oncology Johns Hopkins 1983. \n8.\tAdvanced Laparoscopic Surgery, with Prof. Paulson, Alexandria, Virginia USA 1993. \nSocieties & Associations: \n1.\t Member of the Royal College of Ob./Gyn. London. \n2.\tFellow of the Royal College of Surgeons Glasgow UK. \n3.\tMember of the advisory board on urogyn. FIGO. \n4.\tMember of the New York Academy of Sciences. \n5.\tMember of the American Association for the Advancement of Science. \n6.\tFeatured in �Who is Who in the World� from the 16th edition to the 20th edition. \n7.\tFeatured in �Who is Who in Science and Engineering� in the 7th edition. \n8.\tMember of the Egyptian Fertility & Sterility Society. \n9.\tMember of the Egyptian Society of Ob./Gyn. \n10.\tMember of the Egyptian Society of Urogyn. \n\nScientific Publications & Communications:\n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Asim Kurjak, Ahmad G. Serour, Laila A. S. Mousa, Amr M. Zaied, Khalid Z. El Sheikha. \nImaging the Internal Urethral Sphincter and the Vagina in Normal Women and Women Suffering from Stress Urinary Incontinence and Vaginal Prolapse. Gynaecologia Et Perinatologia, Vol18, No 4; 169-286 October-December 2009.\n2- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nFecal Incontinence, A Novel Concept: The Role of the internal Anal sphincter (IAS) in defecation and fecal incontinence. Gynaecologia Et Perinatologia, Vol19, No 2; 79-85 April -June 2010.\n3- Abdel Karim M. El Hemaly*, Laila A. S. Mousa Ibrahim M. Kandil, Fatma S. El Sokkary, Ahmad G. Serour, Hossam Hussein.\nSurgical Treatment of Stress Urinary Incontinence, Fecal Incontinence and Vaginal Prolapse By A Novel Operation \n"Urethro-Ano-Vaginoplasty"\n Gynaecologia Et Perinatologia, Vol19, No 3; 129-188 July-September 2010.\n4- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n5- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n6- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n7-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n8-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n9-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n10-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n11-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n12- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n13-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n14- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Mohamad A. Rizk and Mohamad A.K.M.El Hemaly.\n Urethro-plasty, a Novel Operation based on a New Concept, for the Treatment of Stress Urinary Incontinence, S.U.I., Detrusor Instability, D.I., and Mixed-type of Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/urethro-plasty_01\n\n15-Ibrahim M. Kandil, Abdel Karim M. El Hemaly, Mohamad M. Radwan: Ultrasonic Assessment of the Internal Urethral Sphincter in Stress Urinary Incontinence. The Internet Journal of Gynecology and Obstetrics. 2003. Volume 2 Number 1. \n\n\n16-Abdel Karim M. El Hemaly. Nocturnal Enureses: A Novel Concept on its pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecolgy/?page=articles/nocturnal_enuresis\n\n17- Abdel Karim M. El Hemaly. Nocturnal Enureses: An Update on the pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecology/?page=/ENHLIDH/PUBD/FEATURES/\nPresentations/ Nocturnal_Enuresis/nocturnal_enuresis\n\n18-Maternal Mortality in Egypt, a cry for help and attention. The Second International Conference of the African Society of Organization & Gestosis, 1998, 3rd Annual International Conference of Ob/Gyn Department � Sohag Faculty of Medicine University. Feb. 11-13. Luxor, Egypt. \n19-Postmenopausal Osteprosis. The 2nd annual conference of Health Insurance Organization on Family Planning and its role in primary health care. Zagaziz, Egypt, February 26-27, 1997, Center of Complementary Services for Maternity and childhood care. \n20-Laparoscopic Assisted vaginal hysterectomy. 10th International Annual Congress Modern Trends in Reproductive Techniques 23-24 March 1995. Alexandria, Egypt. \n21-Immunological Studies in Pre-eclamptic Toxaemia. Proceedings of 10th Annual Ain Shams Medical Congress. Cairo, Egypt, March 6-10, 1987. \n22-Socio-demographic factorse affecting acceptability of the long-acting contraceptive injections in a rural Egyptian community. Journal of Biosocial Science 29:305, 1987. \n23-Plasma fibronectin levels hypertension during pregnancy. The Journal of the Egypt. Soc. of Ob./Gyn. 13:1, 17-21, Jan. 1987. \n24-Effect of smoking on pregnancy. Journal of Egypt. Soc. of Ob./Gyn. 12:3, 111-121, Sept 1986. \n25-Socio-demographic aspects of nausea and vomiting in early pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 35-42, Sept. 1986. \n26-Effect of intrapartum oxygen inhalation on maternofetal blood gases and pH. Journal of the Egypt. Soc. of Ob./Gyn. 12:3, 57-64, Sept. 1986. \n27-The effect of severe pre-eclampsia on serum transaminases. The Egypt. J. Med. Sci. 7(2): 479-485, 1986. \n28-A study of placental immunoreceptors in pre-eclampsia. The Egypt. J. Med. Sci. 7(2): 211-216, 1986. \n29-Serum human placental lactogen (hpl) in normal, toxaemic and diabetic pregnant women, during pregnancy and its relation to the outcome of pregnancy. Journal of the Egypt. Soc. of Ob./Gyn. 12:2, 11-23, May 1986. \n30-Pregnancy specific B1 Glycoprotein and free estriol in the serum of normal, toxaemic and diabetic pregnant women during pregnancy and after delivery. Journal of the Egypt. Soc. of Ob./Gyn. 12:1, 63-70, Jan. 1986. Also was accepted and presented at Xith World Congress of Gynecology and Obstetrics, Berlin (West), September 15-20, 1985. \n31-Pregnancy and labor in women over the age of forty years. Accepted and presented at Al-Azhar International Medical Conference, Cairo 28-31 Dec. 1985. \n32-Effect of Copper T intra-uterine device on cervico-vaginal flora. Int. J. Gynaecol. Obstet. 23:2, 153-156, April 1985. \n33-Factors affecting the occurrence of post-Caesarean section febrile morbidity. Population Sciences, 6, 139-149, 1985. \n34-Pre-eclamptic toxaemia and its relation to H.L.A. system. Population Sciences, 6, 131-139, 1985. \n35-The menstrual pattern and occurrence of pregnancy one year after discontinuation of Depo-medroxy progesterone acetate as a postpartum contraceptive. Population Sciences, 6, 105-111, 1985. \n36-The menstrual pattern and side effects of Depo-medroxy progesterone acetate as postpartum contraceptive. Population Sciences, 6, 97-105, 1985. \n37-Actinomyces in the vaginas of women with and without intrauterine contraceptive devices. Population Sciences, 6, 77-85, 1985. \n38-Comparative efficacy of ibuprofen and etamsylate in the treatment of I.U.D. menorrhagia. Population Sciences, 6, 63-77, 1985. \n39-Changes in cervical mucus copper and zinc in women using I.U.D.�s. Population Sciences, 6, 35-41, 1985. \n40-Histochemical study of the endometrium of infertile women. Egypt. J. Histol. 8(1) 63-66, 1985. \n41-Genital flora in pre- and post-menopausal women. Egypt. J. Med. Sci. 4(2), 165-172, 1983. \n42-Evaluation of the vaginal rugae and thickness in 8 different groups. Journal of the Egypt. Soc. of Ob./Gyn. 9:2, 101-114, May 1983. \n43-The effect of menopausal status and conjugated oestrogen therapy on serum cholesterol, triglycerides and electrophoretic lipoprotein patterns. Al-Azhar Medical Journal, 12:2, 113-119, April 1983. \n44-Laparoscopic ventrosuspension: A New Technique. Int. J. Gynaecol. Obstet., 20, 129-31, 1982. \n45-The laparoscope: A useful diagnostic tool in general surgery. Al-Azhar Medical Journal, 11:4, 397-401, Oct. 1982. \n46-The value of the laparoscope in the diagnosis of polycystic ovary. Al-Azhar Medical Journal, 11:2, 153-159, April 1982. \n47-An anaesthetic approach to the management of eclampsia. Ain Shams Medical Journal, accepted for publication 1981. \n48-Laparoscopy on patients with previous lower abdominal surgery. Fertility management edited by E. Osman and M. Wahba 1981. \n49-Heart diseases with pregnancy. Population Sciences, 11, 121-130, 1981. \n50-A study of the biosocial factors affecting perinatal mortality in an Egyptian maternity hospital. Population Sciences, 6, 71-90, 1981. \n51-Pregnancy Wastage. Journal of the Egypt. Soc. of Ob./Gyn. 11:3, 57-67, Sept. 1980. \n52-Analysis of maternal deaths in Egyptian maternity hospitals. Population Sciences, 1, 59-65, 1979. \nArticles published on OBGYN.net: \n1- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Laila A. S. Mousa and Mohamad A.K.M.El Hemaly.\nUrethro-vaginoplasty, an innovated operation for the treatment of: Stress Urinary Incontinence (SUI), Detursor Overactivity (DO), Mixed Urinary Incontinence and Anterior Vaginal Wall Descent. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/ urethro-vaginoplasty_01\n\n2- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamed M. Radwan.\n Urethro-raphy a new technique for surgical management of Stress Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/\nnew-tech-urethro\n\n3- Abdel Karim M. El Hemaly, Ibrahim M Kandil, Mohamad A. Rizk, Nabil Abdel Maksoud H., Mohamad M. Radwan, Khalid Z. El Shieka, Mohamad A. K. M. El Hemaly, and Ahmad T. El Saban.\nUrethro-raphy The New Operation for the treatment of stress urinary incontinence, SUI, detrusor instability, DI, and mixed-type of urinary incontinence; short and long term results. \nhttp://www.obgyn.net/urogyn/urogyn.asp?page=urogyn/articles/\nurethroraphy-09280\n\n4-Abdel Karim M. El Hemaly, Ibrahim M Kandil, and Bahaa E. El Mohamady. Menopause, and Voiding troubles. \nhttp://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly03/el-hemaly03-ss\n\n5-El Hemaly AKMA, Mousa L.A. Micturition and Urinary\tContinence. Int J Gynecol Obstet 1996; 42: 291-2. \n\n6-Abdel Karim M. El Hemaly.\n Urinary incontinence in gynecology, a review article.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/abs-urinary_incotinence_gyn_ehemaly \n\n7-El Hemaly AKMA. Nocturnal Enuresis: Pathogenesis and Treatment. \nInt Urogynecol J Pelvic Floor Dysfunct 1998;9: 129-31.\n \n8-El Hemaly AKMA, Mousa L.A.E. Stress Urinary Incontinence, a New Concept. Eur J Obstet Gynecol Reprod Biol 1996; 68: 129-35. \n\n9- El Hemaly AKMA, Kandil I. M. Stress Urinary Incontinence SUI facts and fiction. Is SUI a puzzle?! http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly/el-hemaly-ss\n\n10-Abdel Karim El Hemaly, Nabil Abdel Maksoud, Laila A. Mousa, Ibrahim M. Kandil, Asem Anwar, M.A.K El Hemaly and Bahaa E. El Mohamady. \nEvidence based Facts on the Pathogenesis and Management of SUI. http://www.obgyn.net/displayppt.asp?page=/English/pubs/features/presentations/El-Hemaly02/el-hemaly02-ss\n\n11- Abdel Karim M. El Hemaly*, Ibrahim M. Kandil, Mohamad A. Rizk and Mohamad A.K.M.El Hemaly.\n Urethro-plasty, a Novel Operation based on a New Concept, for the Treatment of Stress Urinary Incontinence, S.U.I., Detrusor Instability, D.I., and Mixed-type of Urinary Incontinence.\nhttp://www.obgyn.net/urogyn/urogyn.asp?page=/urogyn/articles/urethro-plasty_01\n\n12-Ibrahim M. Kandil, Abdel Karim M. El Hemaly, Mohamad M. Radwan: Ultrasonic Assessment of the Internal Urethral Sphincter in Stress Urinary Incontinence. The Internet Journal of Gynecology and Obstetrics. 2003. Volume 2 Number 1. \n\n13-Abdel Karim M. El Hemaly. Nocturnal Enureses: A Novel Concept on its pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecolgy/?page=articles/nocturnal_enuresis\n\n14- Abdel Karim M. El Hemaly. Nocturnal Enureses: An Update on the pathogenesis and Treatment.\nhttp://www.obgyn.net/urogynecology/?page=/ENHLIDH/PUBD/FEATURES/\nPresentations/ Nocturnal_Enuresis/nocturnal_enuresis",institutionString:null,institution:{name:"Al Azhar University",country:{name:"Egypt"}}},{id:"113313",title:"Dr.",name:"Abdel-Aal",middleName:null,surname:"Mantawy",slug:"abdel-aal-mantawy",fullName:"Abdel-Aal Mantawy",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ain Shams University",country:{name:"Egypt"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:5681},{group:"region",caption:"Middle and South America",value:2,count:5161},{group:"region",caption:"Africa",value:3,count:1683},{group:"region",caption:"Asia",value:4,count:10200},{group:"region",caption:"Australia and Oceania",value:5,count:886},{group:"region",caption:"Europe",value:6,count:15610}],offset:12,limit:12,total:1683},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{topicId:"8"},books:[{type:"book",id:"10454",title:"Technology in Agriculture",subtitle:null,isOpenForSubmission:!0,hash:"dcfc52d92f694b0848977a3c11c13d00",slug:null,bookSignature:"Dr. Fiaz Ahmad and Prof. Muhammad Sultan",coverURL:"https://cdn.intechopen.com/books/images_new/10454.jpg",editedByType:null,editors:[{id:"338219",title:"Dr.",name:"Fiaz",surname:"Ahmad",slug:"fiaz-ahmad",fullName:"Fiaz Ahmad"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10502",title:"Aflatoxins",subtitle:null,isOpenForSubmission:!0,hash:"34fe61c309f2405130ede7a267cf8bd5",slug:null,bookSignature:"Dr. Lukman Bola Abdulra'uf",coverURL:"https://cdn.intechopen.com/books/images_new/10502.jpg",editedByType:null,editors:[{id:"149347",title:"Dr.",name:"Lukman",surname:"Abdulra'uf",slug:"lukman-abdulra'uf",fullName:"Lukman Abdulra'uf"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10504",title:"Crystallization",subtitle:null,isOpenForSubmission:!0,hash:"3478d05926950f475f4ad2825d340963",slug:null,bookSignature:"Dr. Youssef Ben Smida and Dr. Riadh Marzouki",coverURL:"https://cdn.intechopen.com/books/images_new/10504.jpg",editedByType:null,editors:[{id:"311698",title:"Dr.",name:"Youssef",surname:"Ben Smida",slug:"youssef-ben-smida",fullName:"Youssef Ben Smida"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10552",title:"Montmorillonite",subtitle:null,isOpenForSubmission:!0,hash:"c4a279761f0bb046af95ecd32ab09e51",slug:null,bookSignature:"Prof. Faheem Uddin",coverURL:"https://cdn.intechopen.com/books/images_new/10552.jpg",editedByType:null,editors:[{id:"228107",title:"Prof.",name:"Faheem",surname:"Uddin",slug:"faheem-uddin",fullName:"Faheem Uddin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10572",title:"Advancements in Chromophore and Bio-Chromophore Research",subtitle:null,isOpenForSubmission:!0,hash:"4aca0af0356d8d31fa8621859a68db8f",slug:null,bookSignature:"Dr. Rampal Pandey",coverURL:"https://cdn.intechopen.com/books/images_new/10572.jpg",editedByType:null,editors:[{id:"338234",title:"Dr.",name:"Rampal",surname:"Pandey",slug:"rampal-pandey",fullName:"Rampal Pandey"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10581",title:"Alkaline Chemistry and Applications",subtitle:null,isOpenForSubmission:!0,hash:"4ed90bdab4a7211c13cd432aa079cd20",slug:null,bookSignature:"Dr. Riadh Marzouki",coverURL:"https://cdn.intechopen.com/books/images_new/10581.jpg",editedByType:null,editors:[{id:"300527",title:"Dr.",name:"Riadh",surname:"Marzouki",slug:"riadh-marzouki",fullName:"Riadh Marzouki"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10582",title:"Chemical Vapor Deposition",subtitle:null,isOpenForSubmission:!0,hash:"f9177ff0e61198735fb86a81303259d0",slug:null,bookSignature:"Dr. Sadia Ameen, Dr. M. 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