Statistical summary of input data.
\r\n\tThis volume presents the multifaceted aspects and should allow readers at all levels an entry into the exiting world of Chlamydomonas research.
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Optimization is the process of setting decision variable values in such a way that the objective in question is optimized. The optimal solution is a set of decision variables that maximizes or minimizes the objective function while satisfying the constraints. In general, optimal solution is obtained when the corresponding values of the decision variables yield the best value of the objective function, while satisfying all the model constraints.
Apart from the gradient-based optimization methods, some new optimization methods have also been proposed that help solve complex problems. In the available classifications, these methods are recognized as “intelligent optimization,” “optimization and evolutionary computing,” or “intelligent search.” One of the advantages of these algorithms is that they can find the optimal point without any need to use objective function derivatives. Moreover, compared to the gradient-based methods, they are less likely to be trapped in local optima.
Optimization algorithms are classified into two types: exact algorithms and approximate algorithms. Exact algorithms are capable of precisely finding optimal solutions, but they are not applicable for complicated optimization problems, and their solution time increases exponentially in such problems. Approximate algorithms can find close-to-optimal solutions for difficult optimization problems within a short period of time [1].
There are two types of approximate algorithms: heuristics and metaheuristics. Two main shortcomings of the heuristic algorithms are (1) high possibility of being trapped into local optima and (2) performance degradation in practical applications on complex problems. Metaheuristic algorithms are introduced to eliminate the problems associated with heuristic algorithms. In fact, metaheuristic algorithms are approximate optimization algorithms that enjoy specific mechanisms to exit local optima and can be applied on an extensive range of optimization problems.
The decision-making process consists of three steps: problem formulation, problem modeling, and problem optimization. A variety of optimization models are actually applied to formulate and solve decision-making problems (Figure 1). The most successful models used in this regard include mathematical programming and constraint programming models.
Optimization models.
The optimization methods are presented in Figure 2. Since the problem is complicated, exact or approximate methods are used to solve it. The exact methods provide optimal solutions and guarantee optimality. Approximate methods lead to favorable and near-optimal solutions, but they do not guarantee optimality.
Optimization methods.
Any problem in the real world has the potential to be formulated as an optimization problem. Generally, all optimization problems with explicit objectives can be expressed as nonlinearly constrained optimization problem as presented in Eq. (1).
where
For a vector v, p-norm is denoted by
where p is a positive integer. According to this definition, one can understand that a p-norm satisfies the following conditions: ‖
The eigenvectors for a square matrix
where I is a unitary matrix with the same size as A. All the nontrivial solutions are obtained from Eq. (4).
which can be written as a polynomial in form of Eq. (4).
where
The spectral radius of a square matrix is another important concepts associated with eigenvalues of matrices. Assuming that
which is equal to the maximum absolute value of all eigenvectors. Geometrically speaking, if we draw all the eigenvalues of matrix A on a complex plane and then draw a circle on the plane, in such a way that it encloses all the eigenvalues, then the minimum radius of such a circle is referred to as the spectral radius. Spectral radius is useful in determining the stability or instability of iterative algorithms.
The gradient vector of a multivariate function f (x) is defined according to Eq. (7),
where
where b is a vector constant.
Second derivative of a general function f (x) of a matrix n × n is called the Hessian matrix,
Linear programming problems are usually classified according to the convexity of their defining functions. Geometrically speaking, an object is called convex when for any two points within the object, every point on the straight line connecting them also lies within the object (Figure 3). Mathematically, a set
Convex object (a) and nonconvex object (b).
A function
An interesting feature of the convex function f is that it ensures that the gradient at a point
Mathematical programming includes several concepts. Here, we will first introduce three related concepts: feasible solution, strong local maximum, and weak local maximum.
Point X that satisfies all the constraints of the problem is called a feasible solution. The set of all feasible points will form the feasible region.
Point x is a strong local maximum if f (x) is defined in
Strong and weak local minima and maxima.
The efficiency of an algorithm is usually measured by algorithmic complexities or computational complexities. Such complexities are often referred to as Kolmogorov complexity in literature. For a given problem with complexity of n, this complexity is represented by big-O notations, for example,
where K is a finite and nonzero value. The big-O notation indicates that f is asymptotically equivalent to the order of g. If the limit value is K = 1, it can be argued that f is of the same order as g [1]. The small-o notation is applied when the limit tends to be zero,
In mathematical programming, an easy or tractable problem is a problem that can be solved using a computer algorithm, with a reasonable solution time, as a polynomial function of problem size n. An algorithm is referred to as a P-problem, or a polynomial-time problem, when the number of steps needed to find the solution is represented by a polynomial in terms of n and there is at least one algorithm to solve it.
On the other hand, a hard or intractable problem is a problem whose solution time is an exponential function of n. In case the solution to a polynomial problem is estimated in polynomial time, then it is called a nondeterministic polynomial. But it should be noted that there is no specific rule for making such a guess. As a result, the estimated solutions cannot be guaranteed to be optimal or even near-optimal solutions. In fact, there is no specific algorithm for solving hard-NP problems, and only approximate or heuristic solutions are applicable. Therefore, heuristic and metaheuristic methods can provide us the near-optimal/suboptimal responses with acceptable accuracy.
A given problem can be called NP-complete if it is actually an NP-hard problem, and other NP problems can be reduced to it using certain reduction algorithms. The reduction algorithm has a polynomial time. The traveling salesman problem can be counted as an example of NP-hard problem, which aims to find the shortest route or the lowest traveling cost to visit all n cities once and then return to the starting city.
Two opposite criteria should be taken into account in development of a metaheuristic algorithm: (1) exploration of the search space and (2) exploitation of the best solution (Figure 5).
Metaheuristic algorithm design space.
Promising areas are specified by good solutions obtained. In intensification, the promising regions are explored accurately to find better solutions. In diversification, attempts are made to make sure that all regions of the search space are explored.
In the exploration approach, random algorithms are the best algorithms for searching. Random algorithms generate a random solution in each iteration and completely exploit the search space in this way.
The simulation of any metaheuristic algorithm requires an encoding method. In other words, the problem statement procedure is referred to as representation. Encoding plays a major role in the productivity and efficiency of any metaheuristic algorithm and is recognized as a necessary step in the algorithm. Additionally, the representation efficiency depends on the search operators (neighborhood, recombination, etc.). In fact, when defining a representation, we first need to remember how the problem is evaluated and how the search operator will work. A representation needs to have the following characteristics:
Completeness: It is one of the main characteristics of representation; in the sense that all the solutions of a given problem need to be represented.
Connectivity: It means that a search path must exist between any two solutions in the search space.
Efficiency: Representation should be easily available to search operators.
Representations can be divided into two types in terms of their structure: linear and nonlinear. In this study, linear representation has been used. Some linear representations include the following:
Binary encoding: It is performed using binary alphabets.
Continuous encoding: In continuous optimization problems, encoding is performed based on real numbers.
Discrete encoding: It is used for discrete optimization problems such as the assignment problem.
Permutation encoding: It is used in problems where the objective is to find a permutation.
Random Key: This type of encoding converts real numbers into a permutation.
Diploid representation: In the diploid representation, two values are considered for each subset of the decision vector.
The objective function generates a real number for any solution in the search space. This number describes the quality or the fitness of the solution. The objective function is an important element in development of a metaheuristic algorithm that directs the search toward the best solution. If the objective function is wrongly defined, it will generate unacceptable solutions. In the present work, the objective function is maximization of drilling penetration rate.
Constraint handling is another critical issue for the efficient design of metaheuristic algorithms. In fact, many continuous or discrete optimization problems are constrained. As mentioned earlier, constraints might be linear or nonlinear, equal or unequal. Constraints can mostly be applied to the decision variables or objective function. Some constraint handling strategies are presented in this section; these strategies can be categorized as follows:
Reject strategy: In this approach, infeasible solutions are rejected, and only the feasible ones are taken into account.
Penalizing strategy: In this strategy, infeasible solutions obtained during the search process are preserved in the search space. This strategy is the most popular strategy used to handle constraints. This strategy uses the penalizing strategy to transform problems with constraints into a problem with no constraint.
Repairing strategy: In this strategy, infeasible solutions turn into feasible solutions.
Preserving strategy: In this strategy, specific operators are used to generate feasible solutions alone.
Search strategy is of particular importance in metaheuristic algorithms. This strategy carries out the search process without using the derivative of the problem. Some of the leading search models are listed below.
Golden Section search: This is a technique used to find the extremum (maximum and minimum) of a unimodal function by narrowing the range of values inside which the extremum is known to exist.
Random search: Random search is a numerical optimization method independent of the gradient and hence can be used for noncontinuous or non differentiable functions.
Nelder-Mead method: The Nelder-Mead method, also known as downhill simplex, is usually used for nonlinear optimization. This method is a numerical method that can converge to nonstationary points.
The criteria used for classification of metaheuristic algorithms are as follows:
Nature-inspired vs. nonnature inspiration: Many of the metaheuristic algorithms are inspired by natural processes. Evolutionary algorithms and artificial immune systems, ranging from biological behavior of bee, social behavior of bird flocking, and physical behavior of materials in simulated annealing to human-sociopolitical behavior in imperialist competitive algorithm, belong to these nature-inspired algorithms.
Memory usage versus memoryless methods: Some metaheuristic algorithms are memoryless. These algorithms do not store data dynamically during search time. Simulated annealing lies in this category of algorithms, while some other metaheuristic algorithms use information explored during the search process. Short-term and long-term memory used in tabu search algorithm are of this type.
Deterministic or stochastic: Deterministic metaheuristic algorithms solve optimization problems through deterministic decision-making (such as local search and tabu search). In stochastic metaheuristic algorithms, several stochastic rules are applied to searching. In deterministic algorithms, the initial solution leads to the generation of a final solution similar to the initial one.
Population-based vs. single-point search algorithms: Single-point algorithms (such as simulated annealing) direct and transmit a single solution throughout the search process, while population-based algorithms (such as particle swarm optimization) will involve the whole solution population. Single-point search algorithms apply an exploitive approach; these algorithms have the power to concentrate searching on the local space. Population-based algorithms have exploratory trajectory and allow for more diversified exploration of the search space.
Iterative or greedy approach: In iterative algorithms, the search starts with an initial set of solutions (population), and the solutions vary in each iteration. In greedy algorithms, the search begins with a null solution, and a decision variable is determined at each step until the final solution is obtained. Most metaheuristic algorithms follow an iterative approach.
In this section, firstly, a brief explanation of some of the mostly used metaheuristic algorithms is provided. Next, previous works dealing with prediction and optimization of penetration rate performed by various authors are introduced.
The optimization literature changed dramatically with the advent of metaheuristic algorithms in the 1960s. Alan Turing might be the first to use heuristic algorithms. During the Second World War, Alan Turing and Gordon Welchman managed to design the Bambe machine and finally crack the German Enigma machine in 1940. In 1948, he managed to get a patent for his ideas in the field of intelligent machinery, machine learning, neural network, and evolutionary algorithms.
The genetic algorithm that was developed by John Holland et al. during 1960–1970 is a biological evolutionary model inspired by Charles Darwin’s natural selection and survival of the fittest. Holland was the first to use crossover, recombination, mutation, and selection in comparative studies and artificial systems [2]. Figures 6 and 7 indicate the application of crossover and mutation operators.
The schematic view of crossover at a random point [2].
The schematic view of mutation at a random point [2].
Patrick et al. developed a simulated annealing algorithm to solve optimization problems. When steel is cooled, it develops into a crystallized structure with minimum energy and larger crystalline sizes, and the defects of steel structure are decreased (Figure 8) [3].
Simulated annealing search technique.
The search technique used in this algorithm is a movement-based search, which starts from an initial guess at high temperatures and the system cools down with a gradual decrease in temperature. A new movement or solution is accepted if it is better. Otherwise, it will be accepted as a probable solution so that the system can be freed from the local optima trap [3].
Tabu search was discovered by Glover [4]. It is a memory-based search strategy that searches the memory history as an integrative element. Two important points should be taken into account in this search: (1) how to efficiently use memory and (2) how to integrate the algorithm into other algorithms to develop a superior algorithm. Tabu search is the centralized local search algorithm that uses memory to avoid potential cycles of local solutions to increase search efficiency.
In the algorithm running stages, recent attempts (memory history) are recorded and listed as tabu, such that new solutions should avoid those available in the tabu list. Tabu list is one of the most important concepts in the tabu search method and records the search moves as a recent history, so that any new move must avoid the previous move list. This will also lead to time saving because the previous move is not repeated [4].
When ants find a food source, they use pheromones to mark the food source and the trails to and from it. As more ants cross the same path, that path turns into a preferred path (Figure 9). Thus, several preferred paths will emerge during the process. Using this behavioral property of the ants, scientists have managed to develop a number of robust ant colony optimization methods. Dorigo was known as a pioneer in this field in 1992 [5].
Ant preferred trail formation process.
Sometime later, the particle swarm optimization was developed by [6]. This method is inspired by the collective behavior exhibited by birds, fish, and even humans, which is referred to as swarm intelligence. Particles swarm around the search space based on initial random guess. This swarm communicates the current best and the global best and is updated based on the quality of the solutions. The movement of particles includes two main components: a stochastic component and a deterministic component. A particle is attracted toward the current global best while it has a tendency to move randomly. When a particle finds a location that is better than the previous ones, it updates it as the new best location. Figure 10 shows the schematic view of the motion of particles [7].
Schematic representation of particle motion in the particle swarm method.
The effect of neighbors and herd center on the movement of krill [16].
Harmony search was first developed by Geem et al. [8]. Harmony search is a metaheuristic algorithm inspired by music, which is developed based on the observation that the aim of music is to search for a perfect state of harmony. This harmony in music is analogous to find optimality in an optimization process. When a musician wants to play a piece of music, there are three choices:
• Harmony memory accurately plays a piece of famous music on memory.
• Pitch adjusting plays something similar to a famous piece.
• Randomization sets a random or new note [8].
Honeybee algorithm is another type of optimization algorithm. This algorithm is inspired by the explorative behavior of honeybees, and many variants of this algorithm have already been formulated: honeybee algorithm, virtual bee algorithm, artificial bee colony, and honeybee mating algorithm.
Literature suggests that the honeybee algorithm was first formulated by Sunill Nakrani and Craig Tovey (2004) at Oxford University in order to be used to allocate computers among different clients and web hosting servers [9].
Big Bang-Big Crunch was first presented by Erol and Eksin [10]. This approach relies on theories of the evolution of the universe, namely the Big Bang-Big Crunch evolution theory. In the Big Bang phase, energy dissipation causes a state of disorder or chaos, and randomization is known as the principal feature of this stage. In the Big Crunch stage, however, the randomly distributed particles are drawn into an order [10].
The Firefly algorithm was developed by Yang [11] at Cambridge University based on idealization of the flashing characteristics of fireflies. In order to develop the algorithm, the following three idealized rules are used:
All fireflies are unisex, such that a firefly will be attracted to other fireflies, regardless of their gender.
Attractiveness is proportional to its desired brightness, hence for any of the two flashing fireflies, the less brighter firefly will move toward the more brighter one.
The brightness of a firefly can be determined by the landscape of the objective function [11].
The imperialist competitive algorithm was developed by Atashpaz Gargari and Lucas in 2007. Drawing on mathematical modeling of sociopolitical evolution process, this algorithm provides an approach to solving mathematical optimization problems. During the imperialist competition, weak empires lose their power gradually and are finally eliminated. The imperialist competition makes it possible for us to reach a point where there is only one empire left in the world. This comes to realization when the imperialist competitive algorithm reaches the optimal point of the objective function and stops [12].
Cuckoo search is an optimization algorithm developed by Yang and Deb in 2009. This algorithm is inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds. The following idealized rules are used for more simplicity:
Each cuckoo lays egg once at a time and puts it in a randomly selected nest.
The best nests with high-quality eggs will carry over to the next generation.
The number of hosts nests is fixed, and the egg laid by a cuckoo can be discovered by the host bird with a probability P a ∈ (0,1). In this case, the host bird will either dump the eggs or just leaves the nest to build a new one somewhere else [13].
The bat algorithm is a metaheuristic optimization algorithm developed by Yang [14]. This algorithm is based on the echolocation behavior of microbats with varying pulse rates of emission and loudness. Echolocation is a biological sound tracking system that is used by bats and some other animals, such as dolphins. By idealization of some of the echolocation features, one can develop various bat-inspired algorithms:
All bats use echolocation to sense distance, and they also “know” the difference between food/prey and background barriers in some magical way.
Bats fly randomly with velocity
Although loudness may vary in many ways, it is assumed that loudness variations range from a large (positive)
Charged system search was presented by Kaveh and Talatahari [15] for optimization of mathematical model. Each search agent is referred to as a charged particle, which behaves like a charged sphere with a known radius and a charge proportional to the quality of the produced solution. Thus, the particles are able to exert force on one another and cause other particles move. In addition, exploitation of particle’s previous velocity as a consideration of the particle’s past performance can be effective in changing the particle position. Newtonian mechanic rules were used to precisely determine these changes from the rules used here provided some sort of balance between the algorithm power at the conclusion and search stage [15].
The krill herd algorithm was proposed by Gandomi and Alavi [16] to optimize the mathematical model. This algorithm is classified as a swarm intelligence algorithm. This algorithm is inspired by the herding behavior of krill swarms in the process of food finding. In the krill herd algorithm, minimum distance of the krill individual from food and from the highest density of the herd is considered as the objective functions for krill movement. The specific location of the individual krill varies with time depending on the following three actions: movement induced by other krill individuals; foraging activity; and random diffusion (Figure 11).
Dolphin echolocation was first proposed by Kaveh and Farhoudi as a new optimization method. Scientists believe that dolphins are ranked second (after humans) in terms of smartness and intelligence. This optimization method was developed according to echolocation ability of dolphins [17].
Drilling operations lead to significant costs during the development of oil and gas fields. Therefore, drilling optimization can decrease the costs of a project and hence increase the profit earned from the oil and gas production. In most of the studies, rate of penetration (ROP) has been considered as the objective function of the optimization process. ROP depends on many factors including well depth, formation characteristics, mud properties, rotational speed of the drill string, etc. Several studies have been conducted to gain a profound insight into the effective parameters on ROP. Maurer [18] introduced an equation for ROP, in which it was accounted for rock cratering mechanisms of roller-cone bits. Galle and Woods [19] proposed a mathematical model for estimating ROP, where formation type, weight on bit, rotational speed of bit, and bit tooth wear were taken as input parameters. Mechem and Fullerton [20] proposed a model with input variables of formation drilling ability, well depth, weight on bit, bit rotational speed, mud pressure, and drilling hydraulics. Bourgoyne and Young [21] used multiple regression analysis to develop an analytical model and also investigated the effects of depth, strength, and compaction of the formation, bit diameter, weight on bit, rotational speed of bit, bit wear, and hydraulic interactions associated with drilling. Bourgoyne and Young [21] introduced a technic for selection of optimum values for weight on bit, rotational speed, bit hydraulics, and calculation of formation pressure through multiple regression analysis of drilling data. Tanseu [22] developed a new method of ROP and bit life optimization based on the interaction of raw data, regression, and an optimization method, using the parameters of bit rotational speed, weight on bit, and hydraulic horsepower. Al-Betairi et al. [23] used multiple regression analysis for optimization of ROP as a function of controllable and uncontrollable variables. They also studied the correlation coefficients and multicollinearity sensitivity of the drilling parameters. Maidla and Ohara [24] introduced a computer software for optimum selection of roller-cone bit type, bit rotational speed, weight on bit, and bit wearing for minimizing drilling costs. Hemphill and Clark [25] studied the effect of mud chemistry on ROP through tests conducted with different types of PDC bits and drilling muds. Fear [26] conducted a series of studies using geological and mud logging data and bit properties in order to develop a correlation for estimating ROP. Ritto et al. [27] introduced a new approach for optimization of ROP as a function of rotational speed at the top and the initial reaction force at the bit, vibration, stress, and fatigue limit of the dynamical system. Alum and Egbon [28] conducted a series of studies, which led to the conclusion that pressure loss in the annulus is the only parameter that affects ROP significantly, and finally, they proposed an analytical model for estimation of ROP based on the model introduced by Bourgoyne and Young. Ping et al. [29] utilized shuffled frog leaping algorithm to optimize ROP as a function of bit rotational velocity, weight on bit, and flow rate. Hankins et al. [30] optimized drilling process of already drilled wells with variables of weight on bit, rotational velocity, bit properties, and hydraulics to minimize drilling costs. Shishavan et al. [31] studied a preliminary managed pressure case to minimize the associated risk and decrease the drilling costs. Wang and Salehi [32] used artificial intelligence for prediction of optimum mud hydraulics during drilling operations and performed sensitivity analysis using forward regression. A variety of artificial intelligence works have recently been conducted in civil and oil engineering [33, 34, 35, 36].
In the following sections, a new approach was used for prediction and optimization of ROP, based on artificial neural network (ANN). According to the authors’ knowledge, ANN application on ROP optimization has not been widely used by previous studies. The variables used in this study were well depth (D), weight on bit (WOB), bit rotational velocity (N), the ratio of yield point to plastic viscosity (Yp/PV), and the ratio of 10 min gel strength to 10 s gel strength (10MGS/10SGS). Using ANN technic, several models were developed for prediction of ROP, and the best one was selected according to their performances. Then, an artificial bee colony (ABC) algorithm was used for optimization of ROP based on the selected ANN predictive model, and the drilling parameters were evaluated to determine their effects on ROP.
In the present work, it is aimed to apply neural networks in combination with artificial bee colony (ABC) algorithm on a real case of penetration rate prediction and optimization. The basic definitions regarding the problem of study are provided in the nest subsections. Then, the case used in our work is explained. At the end, ABC algorithm used in the optimization process is described.
Hydrocarbon is the general term used for any substance, which is composed of hydrogen and carbon. From clothing to energy, there are different areas in which hydrocarbons serve as the main material. Hydrocarbons are usually extracted from reservoirs located deep in the formation of the earth’s crust. Underground hydrocarbon reservoirs, which are also known as oil and gas reservoirs, have been exploited since more than one and half a century ago. And there have been several developments in technologies associated with oil and gas industry [37, 38].
The term hydrocarbon reservoir is used for a large volume of rock containing hydrocarbon either in oil or gas form, which is usually found in deep formation in the earth. This type of reservoir is far different from what most of people imagine when they think about. A hydrocarbon reservoir is not a tank or something like that. In fact, it is a rock having numerous pores, which make it capable of storing fluid. There are two types of hydrocarbon reservoirs: conventional and unconventional [39].
A conventional reservoir consists of porous and permeable rock, which is bounded by an impermeable rock, usually called cap rock. Due to the high pressure in the deep layers, the fluid in the reservoir rock tends to move out of the rock toward lower depths, which usually have lower pressures. The role of cap rock is to seal the rock in order to prevent the hydrocarbon from migrating to low-pressure depths.
Conventional reservoirs were the only type of exploited hydrocarbon reservoirs until the recent years. As the conventional reserves became rare and depleted, oil and gas industries started to study the feasibility of production from unconventional reservoirs. Thanks to the recent developments in the related technologies, production of hydrocarbon from unconventional reservoirs has been started in different locations of the earth. The major difference between conventional and unconventional reservoirs is that in unconventional reservoirs, there is no traditional placement of reservoir and cap rock. The reservoir rock has high ?porosity, but because of low permeability, the fluid cannot move out of it and is entrapped into the rock. Since the example of the present work deals with a conventional reservoir, we avoid discussing more about unconventional reservoirs.
In order to produce oil and gas from a reservoir, at the first step, it is required to find a location in which hydrocarbon is accumulated in such a large volume that it can be exploited in an economic way. This exploration step is typically done using seismic technics. In the next step, the location with high probability of having hydrocarbon storage is drilled. The drilled well is called exploration well, and if it reaches a relatively large amount of hydrocarbon, more wells are drilled after preparing a field development plan. The production of the reservoir continues until the production rate falls below an economic criterion, which is usually defined as net present value.
Due to the high pressure of the reservoir rock, the hydrocarbon tends to move toward a lower pressurized region. In order to exploit the entrapped hydrocarbon and providing a flow path, one or more wells are needed. The well is drilled deep into the rocks, and after passing the cap rock, it reaches the reservoir rock. Then, due to the pressure difference between the rock and surface, the hydrocarbons start to move from the reservoir to the surface through the drilled well. Sometimes the pressure difference is not so large that the fluid can reach the surface. In these cases, some technics, called artificial lift methods, are used to increase the energy for delivering the fluid to higher altitude. After extraction of hydrocarbon, it is delivered to treatment facilities and the next steps are designed according to the producer company’s plan.
As mentioned above, exploitation of oil and gas reservoirs typically consists of the three types of operation: exploration, drilling, and production. The drilling phase involves costly operations, which consume a high portion of the capital expenditure of the field development. Therefore, optimizing the operations associated with drilling can reduce the investments significantly, increasing the net present value of the project [40].
In the early years of oil and gas industry, the wells were drilled using percussion table tools. These technics became inefficient as demand for drilling deep and hence more pressurized formations increased. In the early twentieth century, rotary drilling technic was introduced to oil and gas industries and it paved the way for drilling faster and deeper wells.
Rotary drilling simply defines the process in which a sharp bit penetrates into the rock due to its weight and rotational movement [41]. Rotary drilling system comprises prime movers, hoisting equipment, rotary equipment, and circulating equipment, all of which mounted on a rig. The prime mover, usually a diesel engine, provides the power required for the whole rig. Hoisting system is responsible for raising and lowering the drill string in and out of the hole. Rotary equipment supports the rotation of the drill bit by transforming electrical power to rotational movement. In order to transport the cuttings to the surface and also to cool the bit, the circulation equipment provides mud flow that is directed into the drill string down to the bit and returns to surface transporting the debris accumulated in the bottom of the hole.
One of the important factors in drilling process is rate of penetration, which is usually measured in terms of meter per minute or foot per minute. This parameter shows how fast the drilling process has been done, and thus, how much cost has been reduced. Through the survey of previous studies, a series of parameters were identified as having significant effect on rate of penetration during drilling operations. These parameters include rotation speed of the bit, weight on the bit, shut-in pipe pressure, mud circulation rate, yield point and plastic viscosity of the mud, and mud gel strength. In the following, each parameter is briefly described.
Bit rotation speed: In a drilling process, the bit is rotated using rotary table or top drive system. The rotation of the bit is usually measured in rotation per minute (rpm).
Weight on the bit: In order to provide the required downward force for penetrating into the rock, several drill collars are installed before the bit. The parameter is generally called weight on bit (WOB) and measured in thousand of pounds (Klb).
Standpipe pressure: Standpipe pressure (SPP) refers to the total pressure loss due to fluid friction. In detail, SPP is the summation of pressure losses in drill string, annulus, bottom hole assembly, and across the bit. The unit for measuring the SPP is pounds per square inch (psi).
Mud flow rate: In order to lubricate and cool down the bit under drilling process, a mixture of additives mixed in water or oil, which, respectively, are called water-based and oil-based drilling mud, is pumped through the drill pipe down to the bit. Drilling mud also cleans up the bottomhole by transporting the cuttings up to the surface. It also helps penetration rate as it passes bit nozzles and penetrates the rock as a water jet system. Mud flow rate is often expressed in gallons per minute (gpm).
Mud yield point: Yield point, which is usually expressed in lbf/100 ft2, is an indicator for determining the resistance of a fluid to movement. It is a parameter of Bingham plastic model, which is equal to shear stress at zero shear rate. As attractive force among the colloidal particle increases, the mud needs more force to move; hence the yield point is considered higher.
Mud plastic viscosity: Plastic viscosity of the mud is determined by the slope of the shear stress vs. shear rate plot. Higher plastic viscosity indicates more viscous fluid and vice versa. The unit for measurement of plastic viscosity is centipoises.
Mud gel strength: Gel strength is the term that defines the shear stress measured at low shear rate after the drilling mud has been static for a certain period of time, which is 10 s and 10 min in API standard. It indicates ability of the drilling mud to suspend drill solid and weighting material when circulation is ceased. It is measured in lbf/100 ft2 in petroleum engineering applications.
In the present study, a data set obtained from a drilling process in a gas field located in the south of Iran was used. The depth of the well was 4235, which was drilled with one run of roller-cone bit and three runs of PDC bit. The IADC code of the roller-cone bit was 435 M, and PDC bits had codes of M332, M433, and M322. Roller-cone bit was used for about 20% and PDC bits for 80% of the drilled depth. In detail, roller-cone bit was used for the depth interval of 1016–1647 m, PDC (M332) was used for depth interval of 1647–2330 m, PDC (M433) was used for depth interval of 2330–3665 m, and finally, the depth between 3665 and 4235 m was drilled by PDC (M322).
The data set consists of 3180 samples, which were taken every 1 meter of penetration from 1016 to 4235 m. The recorded variables included well depth (D), rotation speed of bit (N), weight on bit (WOB), shut-in pipe pressure (SPP), fluid rate (Q), mud weight (MW), the ratio of yield point to plastic viscosity (Yp/PV), and the ratio of 10 min gel strength to 10 s gel strength (10MGS/10SGS). The statistical summary of the data points is gathered in Table 1.
Parameter (unit) | Minimum value | Maximum value | Mean value |
---|---|---|---|
Well depth (m) | 1016 | 4235 | 2636 |
Rotation speed of bit (rpm) | 91.38 | 192.00 | 150.72 |
Weight on bit (Klb) | 1.02 | 43.26 | 21.59 |
Shut-in pipe pressure (psi) | 898.98 | 4085.82 | 2502.61 |
Fluid rate (gpm/day) | 726.92 | 1054.75 | 865.17 |
The ratio of yield point to plastic viscosity | 0.96 | 2.09 | 1.49 |
The ratio of 10 min gel strength to 10 s gel strength | 1.13 | 1.50 | 1.27 |
Statistical summary of input data.
This algorithm was developed by Karaboga [42] and mimics the behavior of bees when they search for nectar of flowers. In a hive of bees, there are three different types of bees: scouts, employed bees, and onlookers. The scout bees start a random search of the surrounding environment in order to find flowers that secrete nectar. After finding the flowers, they keep the location in their memory. Then, they return to the hive and share their information about their findings through a process called waggle dance. Next, the other group, called employed bees, starts finding the flowers based on the information obtained from the scouts in order to exploit the nectar of the flowers. The number of employed bees is equal to number of food sources. The third group of bees are called onlookers, which remain in the hive waiting for the return of the employed bees in order to exchange information and select the best source based on the dances (fitness of the candidates). In addition, the employed bees of an abandoned food site serves as a scout bee.
Considering an objective function,
where
ABC algorithm has been used in different engineering problems including well placement optimization of petroleum reservoirs [43], optimization of water discharge in dams [44], data classification [45], and machine scheduling [46]. More description on the ABC algorithm can be found in other references [47, 48, 49, 50]. A typical flowchart of ABC algorithm is shown in Figure 12.
Typical flowchart of ABC algorithm.
In the present research, an ANN model was developed to predict the ROP as a function of effective parameters. The neural network is widely used in various engineering fields [51, 52, 53, 54, 55, 56, 57, 58, 59, 60]. In order to train the network, three training functions were used including Levenberg-Markvart (LM), scaled conjugate gradient (SCG), and one-step secant (OSS). The number of hidden layers in the network was one since according to Hornik et al. [61], one hidden layer is capable of solving any type of nonlinear function. The number of neurons in the hidden layer was another parameter to be set. Several equations have been proposed by different authors to determine the optimum number of neurons in a hidden layer, which are represented in Table 2. Ni and No indicate the number of input and output variables, respectively.
Relationships | Reference |
---|---|
[62] | |
(Ni + N0)/2 | [63] |
[64] | |
2Ni/3 | [65] |
[66] | |
2Ni | [67, 68] |
The equations for determining the optimum number of neurons in a hidden layer.
Using the values obtained by equations of Table 2, several ANN models were developed with neurons of 2–16. Then, the models were compared in terms of R2 and RMSE, and the best model was selected [69, 70, 56, 71]. The comparison was done through the method proposed by Zorlu et al. [72]. In this method, the R2 and RMSE of each enveloped model are calculated. Next, the networks are assigned an integer number according to their R2 and RMSE value, in the way that the better result acquires higher number. For example, if the number of models is equal to 8, the model having the best (highest) R2 value acquires 8, and the model having the worst model acquires the value of 1. This procedure also is repeated based on RMSE comparison. Then, the two numbers assigned to each model are summed up, and a total score is obtained for each model. Finally, the model acquiring the highest total value is determined as the best model for the problem of study.
In the present article, three types of learning functions were used for training the network, results of which are presented in Tables 3–5. According to the tables, LM, SCG, and OSS functions acquired the best results, respectively. In order to design an accurate model, the best model of each function was compared. The results of comparison are shown in Figures 13 and 14. As can be seen, the best model of LM function yielded better performance. Thus, this function was selected for designing an ANN for prediction and optimization of ROP.
Model no. | Neuron no. | Train | Test | Train rating | Test rating | Total rank | ||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | |||
1 | 2 | 0.839 | 0.1040 | 0.816 | 0.1076 | 1 | 1 | 1 | 1 | 4 |
2 | 4 | 0.899 | 0.0821 | 0.885 | 0.0893 | 5 | 6 | 4 | 4 | 19 |
3 | 6 | 0.902 | 0.0850 | 0.897 | 0.0818 | 6 | 4 | 8 | 8 | 26 |
4 | 8 | 0.882 | 0.0897 | 0.884 | 0.0886 | 2 | 2 | 3 | 5 | 12 |
5 | 10 | 0.893 | 0.0868 | 0.887 | 0.0910 | 4 | 3 | 5 | 2 | 14 |
6 | 12 | 0.892 | 0.0827 | 0.875 | 0.0907 | 3 | 5 | 2 | 3 | 13 |
7 | 14 | 0.908 | 0.0800 | 0.892 | 0.0885 | 7 | 7 | 6 | 6 | 26 |
8 | 16 | 0.912 | 0.0779 | 0.893 | 0.0863 | 8 | 8 | 7 | 7 | 30 |
The results of the developed ANN models based on LM function.
Model no. | Neuron no. | Train | Test | Train rating | Test rating | Total rank | ||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | |||
1 | 2 | 0.798 | 0.1159 | 0.824 | 0.1002 | 1 | 1 | 3 | 4 | 9 |
2 | 4 | 0.820 | 0.1092 | 0.815 | 0.1083 | 4 | 4 | 2 | 2 | 12 |
3 | 6 | 0.809 | 0.1127 | 0.839 | 0.0949 | 2 | 2 | 6 | 8 | 16 |
4 | 8 | 0.841 | 0.1035 | 0.831 | 0.0993 | 6 | 6 | 4 | 5 | 21 |
5 | 10 | 0.827 | 0.1076 | 0.846 | 0.0982 | 5 | 5 | 7 | 7 | 24 |
6 | 12 | 0.814 | 0.1093 | 0.810 | 0.1093 | 3 | 3 | 1 | 1 | 8 |
7 | 14 | 0.853 | 0.0984 | 0.837 | 0.1065 | 8 | 8 | 5 | 3 | 24 |
8 | 16 | 0.849 | 0.1006 | 0.860 | 0.0985 | 7 | 7 | 8 | 6 | 28 |
The results of the developed ANN models based on SCG function.
Model no. | Neuron no. | Train | Test | Train rating | Test rating | Total rank | ||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | RMSE | R2 | RMSE | R2 | RMSE | R2 | RMSE | |||
1 | 2 | 0.815 | 0.1128 | 0.807 | 0.1033 | 2 | 2 | 4 | 5 | 13 |
2 | 4 | 0.811 | 0.1089 | 0.781 | 0.1254 | 1 | 4 | 1 | 1 | 7 |
3 | 6 | 0.829 | 0.1072 | 0.791 | 0.1086 | 5 | 6 | 2 | 3 | 16 |
4 | 8 | 0.816 | 0.1113 | 0.843 | 0.0976 | 3 | 3 | 8 | 7 | 21 |
5 | 10 | 0.837 | 0.1128 | 0.792 | 0.1057 | 7 | 2 | 3 | 4 | 16 |
6 | 12 | 0.822 | 0.1085 | 0.828 | 0.0971 | 4 | 5 | 5 | 8 | 22 |
7 | 14 | 0.849 | 0.0996 | 0.836 | 0.1098 | 8 | 8 | 6 | 2 | 24 |
8 | 16 | 0.832 | 0.1055 | 0.840 | 0.1006 | 6 | 7 | 7 | 6 | 26 |
The results of the developed ANN models based on OSS function.
The results of R2 for LM, SCG, and OSS functions.
The results of RMSE for LM, SCG, and OSS functions.
In the previous section, an ANN was developed for prediction of ROP using the input data. As mentioned, selecting the most accurate predictive model can significantly affect the performance of optimization. In this section, the performance of the optimization algorithm is evaluated. Then, the ANN model obtained in the previous section is incorporated in the optimization algorithm to optimize the effective parameters for maximizing the penetration rate.
In this section, the best ANN model obtained in the previous section was selected for optimization of ROP using ABC algorithm. In order to evaluate the performance of ABC, two functions were used for minimization by ABC:
The range of variations of x1 and x2 are (−2, 2). Also, the optimal value of this function at the point (1−, 0) is 3.
This function is plotted in Figure 15. The ABC algorithm was used for finding minimum point of the above mentioned function, and the values of −0.33559 and −0.52311 were obtained for Eq. (15). The performance of ABC in finding the minimum point is illustrated in Figure 16.
Function of Eq. (6) plotted in Cartesian coordinates.
Evaluation of ABC algorithm for Eq. (6).
In this section, the ANN predictive model was used for optimization of parameters effective on ROP. Since the well depth increases during drilling, it was not considered as a decision variable. Hence, the parameters of ROP were optimized in some specific depths. It makes sense in the way that the parameters cannot be optimized in each meter of penetration.
The ABC algorithm was used for optimization of ROP effective parameters. After a series of sensitivity analysis, it was concluded that the efficient number of population and iterations are 40 and 500, respectively. Three depths on which optimization applied were 2000, 2500, and 3000. The results of optimization in the selected depths are provided in Tables 6–8.
Parameter | Unit | Initial value | Optimum value |
---|---|---|---|
WOB | Klb | 23.8 | 17.4 |
N | rpm | 181 | 149 |
SPP | psi | 2181.4 | 2783.6 |
Q | bbl/day | 901.67 | 848 |
Yp/PV | — | 1.545 | 1.34 |
10MGS/10SGS | — | 1.33 | 1.16 |
ROP | m/h | 16.77 | 21.66 |
Comparison of real and optimized values for depth of 2000 m.
Parameter | Unit | Initial value | Optimum value |
---|---|---|---|
WOB | Klb | 15.4 | 21.6 |
N | rpm | 157 | 162 |
SPP | psi | 2531.5 | 2481.3 |
Q | bbl/day | 898.45 | 790 |
Yp/PV | — | 2.09 | 1.76 |
10MGS/10SGS | — | 1.2 | 1.09 |
ROP | m/h | 18.52 | 22.85 |
Comparison of real and optimized values for depth of 2500 m.
Parameter | Unit | Initial value | Optimum value |
---|---|---|---|
WOB | Klb | 21.9 | 25.5 |
N | rpm | 142 | 153 |
SPP | psi | 2854.7 | 2927.5 |
Q | bbl/day | 851.7 | 816 |
Yp/PV | — | 1.428 | 1.59 |
10MGS/10SGS | — | 1.25 | 1.11 |
ROP | m/h | 13.94 | 17.30 |
Comparison of real and optimized values for depth of 3000 m.
As can be seen, in each selected depth, value of ROP was increased by about 20–30%. Therefore, by combining artificial intelligence and optimization, suitable patterns for ROP in an oil well in order to increase penetration and reduce costs can be created.
In this chapter, firstly, the basics of optimization are explained to solve problems. Then, an application of neural network combined with ABC algorithm was used for prediction of rate of penetration in a gas well. The data were collected from a gas field located in south of Iran. Seven input parameters were selected as input data to develop a predictive ANN model. For this purpose, three learning functions were compared, and LM function was selected as the best function for designing the predictive model. Next, an ABC algorithm was employed to optimize the effective parameters of ROP for maximizing the penetration rate. Three scenarios were selected for considering the well depth in optimization process. Then, the best models for the depths of 2000, 2500, and 3000 m were obtained, and the results showed 20–30% of improvement in penetration rate.
According to the results of the test, it was concluded that the proposed model is a powerful tool for prediction and optimization of rate of penetration during drilling process. Since the drilling process involves numerous effective parameters, it is almost infeasible to explicitly take into account each parameter. Therefore, use of ANN seems very useful in this complex problem and it helps to predict and optimize the penetration rate in a short period of time and without heavy computational costs.
The 354 alloy belongs to the Al-Si-Cu-Mg system similar to B319 alloy that is widely used for automotive engine blocks [1]. The high silicon content in the 354 alloy improves the alloy castability whereas the presence of Cu and Mg noticeably enhances the yield strength (YS) and the ultimate tensile strength (UTS) of the 354 alloy due to the formation of intermetallic phases, mainly Al2Cu or eutectic Al + Al2Cu, and Mg2Si precipitates [2, 3]. However, segregation behavior of Cu may lead to incipient melting during solution treatment which will apparently reduce the alloy strength [4]. Addition of Mg has a strong affinity to react with Sr, leading to the formation of a complex Mg2SrAl4Si3 intermetallic phase, and hence reducing the effectiveness of Sr as a Si modifying agent [5]. In the absence of Cu, high Fe and Mg contents lead to the formation of π-FeMg3Si6Al8 phase which is difficult to dissolve during the solution treatment process [6, 7]. In the quaternary Al-Si-Cu-Mg alloy system, Q-phase (Al4Mg8Cu2Si6) can coexist with the Al2Cu, Mg2Si, and Si phases depending on the levels of Cu, Mg, and Si [8, 9, 10, 11]. The different factors that may influence the mechanical behavior of cast aluminum alloys are schematically represented in Figure 1 [12].
\nSchematic representation of factors affecting alloy performance [12].
Zirconium may be added to Al alloys in order to refine the grain structure due to the presence of fine coherent dispersoids (mainly Al3Zr) which obstruct dislocation motion and in turn, enhance the elevated temperature mechanical properties of aluminum alloys [13]. In order to increase the volume fraction of Al3Zr precipitates and based on the phase diagram of Al-Zr, the concentration of Zr in the alloys investigated in this study was kept at around 0.3 wt.% [14].
\nThe main purpose of solution heat treatment is to obtain a supersaturated solid solution at high temperatures (below the eutectic temperature). As a result, a homogeneous supersaturated solid solution (SSSS) will form through dissolving the precipitated phases during the solidification process, such as β-Mg2Si, θ-Al2Cu, Q-Al5Cu2Mg8Si6, π-Al9FeMg3Si5 and β-Al5FeSi phases. The β-Mg2Si and θ-Al2Cu phases can be easily dissolved when the optimum solution heat treatment temperature and time are employed. The solution treatment temperature is determined according to the alloy composition and solid solubility limit; however, it must be lower than the melting point of the phases that exist in the as-cast structure to avoid incipient melting of these phases [15, 16].
\nFollowing the empirically developed concept of quality index proposed by Drouzy et al. [17, 18] Cáceres proposed a mathematical model emphasizing the significance of the quality index as follows [17, 19, 20]:
\nwhere quality index Q can be calculated using the relative quality index (q), strain-hardening exponent (n), and the strength coefficient (K).
\nThe present study was undertaken to explore the effect of Zr addition and aging conditions of the as cast tensile bars on:
Characterizing the microstructural features of the investigated alloys,
Exploring the tensile properties at both ambient and elevated temperatures, and
Correlating the tensile properties to the microstructural features to establish the strengthening or softening mechanisms responsible for the observed properties.
It should be noted here that the term “temperature” applies to aging temperatures as well as testing temperature.
\nAlloy 354 modified with 200 ppm of strontium (using Al-10% Sr master alloy) and grain refined using 0.20 wt.%Ti (Al-5%Ti-1%B) was used as the base alloy (alloy A). To this alloy, 0.3%Zr in the form of Al-25wt.%Zr master alloy was added (alloy B). The chemical compositions of both alloys are listed in Table 1. Figure 2 shows the microstructure of the as-received base alloy ingots. Melting and casting procedures were carried out as described elsewhere.
\nMicrostructure (200×) of the base alloy 354 used in this work.
To prepare test bars for the tensile tests, three samples for chemical analysis were also taken at the time of the casting; this was done at the beginning, in the middle, and at the end of the casting process to ascertain the exact chemical composition of each alloy. The experimental work was divided into two stages: Stage I in which the 354 alloy (alloy A) was used, and Stage II where the 354 alloy with 0.3%Zr (alloy B) was used. In Stage I, the melt temperature was kept around 750°C, whereas in Stage II, the melt temperature was superheated to 800°C, to ensure the complete decomposition of the Al-25%Zr master alloy used.
\nTensile bars were solution heat treated at 495°C for 8 h, followed by quenching in warm water at 60°C, after which artificial aging was applied according to the plan listed in Table 2. After aging, the test bars were allowed to cool naturally at room temperature (25°C). All of the samples, whether as-cast, solution heat-treated, or aged, were tested to the point of fracture using an MTS servo-hydraulic mechanical testing machine at a strain rate of 4 × 10−4 s−1.
\n\nAlloy code | \nElement (wt.%) | \n||||||||
---|---|---|---|---|---|---|---|---|---|
Si | \nFe | \nCu | \nMn | \nMg | \nZr | \nTi | \nSr | \nAl | \n|
A | \n9.1 | \n0.12 | \n1.8 | \n0.0085 | \n0.6 | \n— | \n0.18 | \n0.02 | \n87.6 | \n
B | \n9.1 | \n0.12 | \n1.8 | \n0.0085 | \n0.6 | \n0.3 | \n0.18 | \n0.02 | \n87.6 | \n
Chemical composition of the 354 alloys used in this study.
Temperature (°C) | \nAging time (h) and aging condition codes | \n||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | \n4 | \n6 | \n8 | \n10 | \n12 | \n16 | \n20 | \n24 | \n36 | \n48 | \n72 | \n100 | \n|
155 | \n1 | \n2 | \n3 | \n4 | \n5 | \n6 | \n7 | \n8 | \n9 | \n10 | \n11 | \n12 | \n13 | \n
170 | \n14 | \n15 | \n16 | \n17 | \n18 | \n19 | \n20 | \n21 | \n22 | \n23 | \n24 | \n25 | \n26 | \n
190 | \n27 | \n28 | \n29 | \n30 | \n31 | \n32 | \n33 | \n34 | \n35 | \n36 | \n37 | \n38 | \n39 | \n
240 | \n40 | \n41 | \n42 | \n43 | \n44 | \n45 | \n46 | \n47 | \n48 | \n49 | \n50 | \n51 | \n52 | \n
300 | \n53 | \n54 | \n55 | \n56 | \n57 | \n58 | \n59 | \n60 | \n61 | \n62 | \n63 | \n64 | \n65 | \n
350 | \n66 | \n67 | \n68 | \n69 | \n70 | \n71 | \n72 | \n73 | \n74 | \n75 | \n76 | \n77 | \n78 | \n
Artificial aging conditions used for room temperature tension tests.
The yield strength (YS) was calculated according to the standard 0.2% offset strain, and the fracture elongation was calculated as the percent elongation (%El) over 50 mm gauge length, as recorded by the extensometer. The ultimate tensile strength (UTS) was also obtained from the data acquisition system of the MTS machine. The average %El, YS, or UTS values obtained from the five samples tested per condition were considered to be the values representing that specific condition. An extensometer, or strain gage was used in the tests to measure the extent of deformation in the samples.
\nSamples for metallography were sectioned from the tensile-tested bars of all the alloys studied, about 10 mm below the fracture surface. The percentage porosity and eutectic Si-particle characteristics were measured and quantified using an optical microscope linked to a Clemex image analysis system. The microstructures of the polished sample surfaces were examined using an Olympus PMG3 optical microscope. Phase identification was carried out using electron probe microanalysis (EPMA) in conjunction with wavelength dispersive spectroscopic (WDS) analysis, using a JEOL*JXA-889001WD/ED combined microanalyzer operating at 20 kV and 30 nA, where the electron beam size was ~2 μm.
\nMapping of certain specific areas of the polished sample surfaces was also carried out where required, so as to show the distribution of different elements within the phases. The fracture surfaces of tensile-tested samples were also examined using the same SEM, employing the backscattered electron (BSE) detector and EDS system. The fracture behavior was analyzed using the backscattered electron (BSE) images obtained, and analysis of the EDS spectra of phases observed on the fracture surface. Differential scanning calorimetry (DSC) was used to characterize the sequence of reactions occurring during the heating and/or cooling cycles of an alloy sample during a DSC scan which continuously changes with the increasing or decreasing temperature cycle to produce peaks according to the two expected reactions:
Phase formation → heat emission → exothermic peak
Phase dissolution → heat absorption → endothermic peak
For the high temperature tensile tests, samples from selected conditions were tested to fracture using an Instron Universal mechanical testing machine at a strain rate of 4 × 10−4 s−1. The heating furnace installed on the testing machine is an electrical resistance, forced-air box type, having the dimensions 30 × 43 × 30 cm. The yield strength (YS) was calculated according to the standard 0.2% offset strain, and the fracture elongation was calculated as the percent elongation (%El) over the 25.4 mm gauge length as recorded by the extensometer. The ultimate tensile strength (UTS) was obtained from the data acquisition system of the universal machine. In order to reach and stabilize the intended test temperature during the tests, at the time that the samples were mounted in the tensile machine, the furnace was already pre-set at the required temperature; also, these samples were kept mounted in the furnace of the tensile testing machine for 30 min before the start of every test.
\n\nFigure 3 shows the macrostructure revealing the grain size for alloy A, about 200 μm. A complete modification of the silicon particles in the microstructure of alloy A in the as-cast condition can be seen in Figure 4(a). From Figure 4(a) and (b), solution heat treatment has changed the morphology of the silicon particles from faceted to globular. As a consequence of solution heat treatment, there may also be observed a reduction in the number of silicon particles and a reduction in the density of the silicon phase, due to the diffusion of silicon into the aluminum matrix. The white arrows in Figure 4(a) show the rounded shape of the dendrites with grain refining [21], whereas Figure 4(b) reveals the dissolution of the Al2Cu phase observed in Figure 4(a)—circled.
\nMacrograph showing grain size of the tensile bars in the as-cast condition.
Optical microstructure: (a) before, and (b) after solution heat treatment.
Zhu and Liu [22] proposed a model of the granulation of unmodified eutectic Si composed of three major stages during heat treatment: (i) the mass transport of solute, (ii) a discontinuous phase fragmentation, and lastly (iii) spheroidization. During heat treatment, the silicon atoms in the matrix at the Si particle tips diffuse to locations on the curved surfaces of the particles, leading to the dissolution of eutectic silicon at the tips. This transport of silicon atoms ultimately causes the fragmentation and spheroidization of eutectic silicon which is important from strength point of view compared to Si particles with sharp edges which act as sites for stress concentration.
\nThe values of secondary dendrite arm spacing (SDAS), porosity, modification level, and grain size for both the as-cast (AC) and solution heat-treated (SHT) condition are listed in Tables 3 and 4. As can be seen, SHT resulted in (i) no noticeable change in both the SDAS and grain size, (ii) a significant decrease in the particle density due to coarsening of the eutectic Si particles, and (iii) almost complete solubility of Al2Cu in the aluminum matrix. Since the solutionizing temperature was well below the incipient melting temperature, tensile test bars revealed negligible change in the amount of porosity, i.e., no incipient melting.
\nAlloy code condition | \nSDAS (μm) | \nGrain size (μm) | \nPorosity (%) | \nVolume fraction of intermetallics (%) | \n||
---|---|---|---|---|---|---|
EPMA | \n||||||
Av\n*\n\n | \n\n | Av | \nSD\n**\n\n | \nAv | \nSD | \n|
A-AC | \n19.3 | \n201 | \n0.14 | \n0.06 | \n3.08 | \n0.32 | \n
A-SHT | \n23.1 | \n192 | \n0.12 | \n0.05 | \n1.27 | \n0.10 | \n
SDAS, porosity%, grain size, level of modification, and volume fraction of intermetallics for alloy A.
Average.
Standard deviation.
Alloy code condition | \nArea (%) | \nParticle length (μm) | \nRoundness ratio (%) | \nAspect ratio | \nDensity (particles/mm2) | \n|||
---|---|---|---|---|---|---|---|---|
Av | \nAv | \nSD | \nAv | \nSD | \nAv | \nSD | \n||
A-AC | \n14.58 | \n3.522 | \n3.94 | \n0.4302 | \n0.181 | \n2.033 | \n0.8609 | \n39110 | \n
A-SHT | \n10.868 | \n4.286 | \n3.144 | \n0.554 | \n0.1547 | \n1.641 | \n0.5429 | \n12080 | \n
Silicon particle characterization for alloy A.
\nFigure 5 illustrates the effect of aging treatment on the alloy strength parameters. The main observations inferred from this figure can be summarized as follows:
Solution heat treatment and artificial aging at 190°C for 2 h or at 155°C for 100 h resulted in an increase in the alloy strength by ~64% over its as-cast strength.
Aging at 155 or 170°C for a long period of time offered maximum resistance to softening.
The greatest decrease in tensile strength occurred at 240°C (312 MPa at 2 h to 240 MPa at 100 h). Similarly, a significant decrease in strength took place upon aging at 190°C for a lengthy time (from 382 MPa at 2 h to 314 MPa at 100 h) indicating the end of peak-aging or the commencement of over aging.
The greatest reduction in the alloy UTS and YS levels resulted when the tensile bars were aged at a temperature as high as 350°C even for a short period of 2 h.
In comparison to the ascendant and steady strength curves corresponding to aging temperatures of 155°C and 170°C, fluctuations in the strength curves were observed at aging temperatures of 190°C and over, similar to that reported by Reif [23] where a similar alloy was used and an ascendant monotonic strength curve was observed at an aging temperature of 155°C.
Although the highest ductility values were obtained after 2 h aging at 350°C (~5%), none of the aging conditions reached the higher ductility values exhibited by the solution heat-treated condition (~6.5%). This observation may be considered evidence that the mechanical behavior displayed by alloy A is common to that of the Al-Si-Cu-Mg alloys whose strength is obtained at the expense of ductility [24, 25].
Variation in alloy tensile parameters as a function of aging temperature and time: (a) UTS, (b) YS, and (c) %El.
In order to analyze the alloy quality by means of the Quality Index charts, the as-cast and the solution heat treated conditions plus aging conditions at 155°C, 190°C, and 350°C for aging times in the range of 2–100 h were used. From a previous study [5], K was calculated as 500 MPa.
\nThe plastic strain and the quality index (Q ) both exhibit a great improvement following solution heat treatment. The fact that plastic deformation (q) was about 0.31 in the solution-treated condition means that the alloy reached 31% of its maximum quality index value (Q ). The importance of q is that it shows how much a sample is away from its maximum possible ductility q = 1 and indicates that it would be possible to control the microstructure, for example by reducing the SDAS, or the porosity, or intermetallic level to enhance the alloy ductility and hence, the quality index, Q. When the ductility increases sharply from the as-cast to the solution heat treated condition, such changes can be related to the spheroidization of silicon particles and to the uniformity of the microstructure in the solution heat-treated condition, as shown in Figure 6(a).
\n\nQ-charts following: (a) SHT, (b) aging at 155°C, (c) aging at 190°C, and (d) aging at 350°C. Legends in (b) apply for other charts. The curved lines indicate the passage from 2 to 10 to 100 h.
From the data presented in Figure 6(b)–(d), it is evident that the change in crystallographic structure of Al2Cu phase from G-P zones (155°C) to a metastable phase (190°C) to a stable phase (350°C) is the main parameter controlling the alloy performance quality. As can be seen, at each aging temperature, all points fall within a narrow circle due the progress in the formation of the precipitated phase. The broken lines in these figures show the change in the Q-level as a function of aging temperature. The width of the circle deceased from 175 MPa (155°C) to 75 MPa (190°C) to 25 MPa (350°C), representing the hardening and softening behavior of the alloy as a function of the aging temperature and time [26]. Using aging times of 2 and 100 h as reference points, the Q , UTS and %El values are presented in Table 5. As can be seen, the Q values after 2 h are more-or-less same over such a large range of aging temperatures, due to the variation in both UTS and %El. However, aging for 100 h revealed highest value at 190°C compared to 155°C (under aging) and 350°C (over aging). The Q values for test bars aged at 350°C for 100 h is the same due to the balance between UTS and %El.
\nAging temperature (°C) | \n\nQ (MPa) | \nUTS (MPa) | \n%El | \nAging time (h) | \n
---|---|---|---|---|
155 | \n432 | \n300 | \n3.5 | \n2 | \n
190 | \n432 | \n388 | \n1.5 | \n2 | \n
350 | \n406 | \n221 | \n5.3 | \n2 | \n
155 | \n340 | \n387 | \n0.9 | \n100 | \n
190 | \n470 | \n324 | \n1.5 | \n100 | \n
350 | \n406 | \n198 | \n5.3 | \n100 | \n
\nQ , UTS and %El values for alloy A after 2 and 100 h aging at different temperatures.
The heat treatment procedures followed for the alloy B are listed in Table 6. The same treatments were applied for both 25°C and 250°C tensile testing.
\nHeat treatment procedures and parameters | \n|||
---|---|---|---|
Heat treatment | \nSolution treatment | \nQuenching | \nAging | \n
SHT\n*\n\n | \n495°C for 5 h | \nWarm water (60°C) | \nNA | \n
T5 temper | \nN/A | \nN/A | \n180°C for 8 h | \n
T6 temper | \n495°C for 5 h | \nWarm water (60°C) | \n180°C for 8 h | \n
Heat treatment procedures and parameters applied to alloys investigated in stage II.
SHT, solution heat treatment.
\nFigure 7 [27] shows the DSC heating curves of the alloys in the as-cast and SHT conditions, where three explicit peaks could be detected and coded 1, 2, 3. Considering the main parameter is the precipitation of Al2Cu phase particles, thus the height of peak number 1 following SHT compared to that in the as-cast condition plays a crucial role in controlling the alloy performance after aging. In addition, it is an indication of the effectiveness of the SHT process in dissolving the initial Al2Cu phase. In Figure 7, peak # 1 after solutionizing is more or less negligible due to dissolution of most of the Al2Cu phase, as shown in Figure 4(b).
\nPortion of the DSC heating curves of as-cast and as-quenched alloy B samples [27].
The principle phases seen in alloy B are demonstrated in the optical as well as backscattered (BSE) images displayed in Figure 8(a) and (b) [27], respectively. Figure 8(a) exhibits α-Al dendrites separated by eutectic silicon colonies. The phases observed in Figure 8(b) were identified using EDS analysis and reference to the results of Hernandez-Sandoval [28] and Garza-Elizondo [29]. Selective EDS spectra identifying these phases are displayed in Figure 8(c) through Figure 8(d). The existence of Al2Cu phase in the block-like form may be attributed to the presence of Sr. in the alloy which leads to segregation of copper to localized areas [30]. The platelets of the Fe-rich β-Al5FeSi phase are easily recognized in the BSE image, surrounded by the blocky Al2Cu particles. The Mg-rich Q-phase (Al5Cu2Mg8Si6) is found growing out of the Al2Cu phase as seen in the BSE image. The absence of coarse Al3Zr precipitates [31] may be related to superheating that led to considerable dissolution of the Al3Zr phase from the master alloy during the melting process. As a result, the coarse Zr-containing phases are rarely detected since Al3Zr particles act as nucleation spots for these coarse phases. According to Garza-Elizondo [29], coarse Zr-rich particles may be nucleated on the undissolved Al3Zr particles provided by the master alloy, i.e., Al-15 wt.%Zr. In the present study, superheating the melt to 800°C would significantly reduce the numbers of Al3Zr particles in the matrix. The predicted fine zirconium trialuminide (Al3Zr) dispersoids that may be present on a nanoscale would require a high magnification BSE image to be detected.
\n(a) Optical micrograph at 200× magnification, and (b) backscattered electron image of alloy B (354 + 0.3 wt.%Zr), obtained at a low cooling rate of 0.35°C/s, showing the different phases present in the alloy; (c–g) EDS spectra corresponding to Al2Cu, (Al,Si)3(Ti,Zr), Q-Al5Mg8Cu2Si6, (Al,Si)3Zr, and β-Al5FeSi phases observed in (b) [27].
\nFigure 9(a) shows a bright-field (BF) TEM image obtained in a T6-treated sample of alloy B with the electron beam parallel to the [001] zone axis. This figure shows a high density of uniformly distributed needle-like precipitates which are oriented along <110> family of directions and aligned along the [100] planes. The length of these precipitates ranges from 50 to 150 nm, close to the reported size of θ′-Al2Cu plates (50–100 nm long) [32, 33]. Figure 9(b) displays the associated selected area electron diffraction (SAED) pattern obtained from Figure 9(a). The observable discrete diffraction maxima for the precipitates in SAED pattern indicate the presence of θ′-Al2Cu, where the streaks most probably result from the presence of fine S′-Al2CuMg particles. Computer simulation studies [34, 35, 36, 37] on the S′-phase reflections show that they are hidden within the streaks of θ′.
\n(a) Bright-field TEM image of alloy B in T6-treated condition, and (b) the selected area electron diffraction (SAED) pattern.
In the present work, Figure 10(a), the addition of ~0.3 wt.%Zr to the 354-type Al-Si-Cu-Mg cast alloy in the as-cast condition improves the ambient-temperature (25°C) strength values of the Zr-free 354 alloy (alloy A), by ~26 MPa (UTS) and 40 MPa (YS), respectively. Following SHT, the UTS and ductility values remained almost constant at ~300 MPa and ~ 6.3%, respectively, while the yield strength increased by ~33 MPa compared to alloy A. It is believed that the improved strength values of alloy B emphasizes the role of Zr addition in enhancing the ambient-temperature tensile properties through the formation of fine secondary strengthening precipitates (Al3Zr) as reported by many authors [14, 38, 39, 40]. The fact that UTS and YS in the T5 and T6 conditions are very close may be attributed to the strengthening effect of the fine dispersoids, which precipitate during the artificial aging stage of the T5, and T6 treatments as reported in Figure 9.
\n(a) Ambient, and (b, c) high temperature tensile properties of alloy B.
Tensile testing at 250°C, endured a significant softening due to the possible coarsening of the strengthening precipitates (Al2Cu) that existed during tensile testing at room temperature (Figure 11). In addition, the T5 heat treatment did not improve the elevated-temperature strength values of the as-cast alloys but reduced the alloy ductility by ~50%. However, application of the T6 heat treatment noticeably enhanced the strength values of the as-cast condition from about 175 to 225 MPa. Another parameter to consider is the effect of thermal stability. In the present work, some tensile samples were stabilized at 250°C (following the T5 and T6 aging treatment) for a lengthy period of time, i.e., 100 and 200 h. As can be seen from Figure 10(c), the stabilized T5-treated alloy B samples exhibit better strength values (UTS and YS) than those obtained in the stabilized T6-treated condition. However, the ductility values obtained after stabilization of the T5-treated samples are dramatically lower in comparison. Figure 11(a) shows Al2Cu particle size and distribution in a T6 sample stabilized at 250°C for 200 h, whereas Figure 11(b) is the corresponding EDS spectrum.
\n(a) Backscattered electron images showing the size and distribution of precipitates in the T6-treated B alloy after stabilization at 250°C for 200 h; (b) EDS spectrum corresponding to the rod-like particles in (a).
A detailed investigation of the fracture surfaces of tensile bars of alloy B were examined in the T6-treated condition, before and after stabilization for 200 h at 250°C. The T6-temper treatment was selected due to its wide use in the automotive industry. The BSE image shown in Figure 12(a) [41] shows the fracture surface of the tensile-tested alloy in the T6-treated condition. The fracture surface has a dimpled-structure throughout, which indicates the ductile nature of the fracture mode. In addition, the BSE image exhibits the precipitation of Alx(Zr,Ti)Si compound, in the form of star-like shape, as confirmed by the associated EDS spectrum in Figure 12(b). Also, cracks can be spotted in various particles of this compound, as indicated by the arrows. The higher magnification BSE image shown in Figure 11(c) reveals a cracked Alx(Zr,Ti)Si phase particle.
\nFracture surface of T6-treated alloy B: (a) BSE image showing a uniform dimple structure and cracked particles (arrowed), (b) EDS spectrum corresponding to the point of interest in (a), and (c) high magnification BSE image showing a cracked Al-Si-Ti-Zr particle (arrowed) [41].
\nFigure 13(a) [41] shows the fracture surface of the T6-treated B alloy tested at 250°C after stabilization for 200 h at the testing temperature. The dimple structure is coarser compared to that before stabilization at 250°C. This observation would explain the improved ductility of the alloy due to the softening behavior associated with the prolonged elevated-temperature exposure at 250°C. Coarsened precipitates appear in the interiors of the dimples, as indicated by the oval contours in Figure 13(a). The BSE image and the EDS spectrum shown in Figure 13(b) and (c), respectively, confirm the presence of Alx(Zr,Ti)Si phase particles.
\nFracture surface of alloy B: (a, b) BSE images of T6-treated alloy after stabilization at 250°C for 200 h showing a coarse dimpled structure, coarsened precipitates and Alx(Zr,Ti)Si particles involved in the crack initiation process, and (c) corresponding EDS spectrum of the phase of interest as shown in (b) [41].
Based on an analysis of the results presented in this article, the following conclusions may be made:
For the base 354 alloy A, solution heat treatment and artificial aging at 190°C for 2 h or at 155°C for 100 h resulted in an increase in the alloy strength by ~64% over its as-cast strength. Aging at 155 or 170°C for a long period of time offered maximum resistance to softening.
The Zr-rich intermetallic phases appear in two different forms, namely (Al,Si)2(Zr,Ti) in block-like form, and containing high level of silicon, and (Al,Si)3(Zr,Ti) in needle-like form, containing high level of aluminum.
Quality index (Q) charts constructed for alloy 354 characterize the tensile properties in terms of the heat treatment conditions applied. Minimum and maximum Q values, i.e., 259 and 459 MPa, are observed for the as-cast and solution heat-treated conditions, respectively; the yield strength shows a maximum of 345 MPa and a minimum of 80 MPa within the range of aging treatments applied.
DSC runs carried out on alloy B (354 alloy + 0.3%Zr) revealed peak patterns which included differences in peak heights—which reflected the amount of the precipitated phase, and shifts in the transformation temperature.
Melt superheating at 800°C is beneficial in terms of reducing the amount of coarse Zr-rich phases in the alloy structure, as it provides efficient dissolution of the Al3Zr phase from the master alloy during the melting process. Coarse Zr-containing phases are rarely observed due to the limited number of Al3Zr particles available to act as nucleation sites for these coarse phases.
TEM investigations confirm that the investigated alloys are strengthened primarily by θ-Al2Cu and S-Al2CuMg precipitates and their precursors, in addition to a secondary strengthening effect by precipitates in the form of Alx(Zr,Ti)Si which form following the addition of Zr.
Prolonged exposure at 250°C, resulting in coarsening of the strengthening precipitates, causes noticeable reduction in strength values, particularly the yield strength (cf. 160 and 325 MPa), and a remarkable increase in the ductility values (cf. 6.3 and 1.1%).
The strength values (UTS and YS) obtained at room temperature for the stabilized T5-treated alloy samples are comparable to those of the stabilized T6-treated condition, and higher in the case of elevated-temperature tensile testing.
The fracture surface of the T6-treated alloy B after stabilization for 1 h at 250°C reveals a dimpled-structure throughout, indicating the ductile nature of the fracture mode.
The Alx(Zr,Ti)1-xSi complex compound is observed with star-like and blocky morphologies, with cracks appearing in various particles of this compound. By increasing the stabilization time up to 200 h, coarser and deeper dimples are formed, highlighting the improved ductility of the alloy due to the softening behavior associated with the prolonged exposure at 250°C.
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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:5684},{group:"region",caption:"Middle and South America",value:2,count:5166},{group:"region",caption:"Africa",value:3,count:1682},{group:"region",caption:"Asia",value:4,count:10211},{group:"region",caption:"Australia and Oceania",value:5,count:887},{group:"region",caption:"Europe",value:6,count:15616}],offset:12,limit:12,total:1689},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{hasNoEditors:"0",sort:"dateEndThirdStepPublish",topicId:"9"},books:[{type:"book",id:"10260",title:"E-Service",subtitle:null,isOpenForSubmission:!0,hash:"11dab65781b3c4347022c56477311f46",slug:null,bookSignature:"Dr. Kyeong Kang",coverURL:"https://cdn.intechopen.com/books/images_new/10260.jpg",editedByType:null,editors:[{id:"2114",title:"Dr.",name:"Kyeong",surname:"Kang",slug:"kyeong-kang",fullName:"Kyeong Kang"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9226",title:"Intelligent User Interfaces",subtitle:null,isOpenForSubmission:!0,hash:"2540a73b78f2f13158366ac0ab9d62a1",slug:null,bookSignature:"Dr. Rüdiger Heimgärtner",coverURL:"https://cdn.intechopen.com/books/images_new/9226.jpg",editedByType:null,editors:[{id:"135236",title:"Dr.",name:"Rüdiger",surname:"Heimgärtner",slug:"rudiger-heimgartner",fullName:"Rüdiger Heimgärtner"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9980",title:"Vision Sensors",subtitle:null,isOpenForSubmission:!0,hash:"fc472f04a4214bf13db3f693a2c7c323",slug:null,bookSignature:"Dr. Vasanth Iyer",coverURL:"https://cdn.intechopen.com/books/images_new/9980.jpg",editedByType:null,editors:[{id:"301000",title:"Dr.",name:"Vasanth",surname:"Iyer",slug:"vasanth-iyer",fullName:"Vasanth Iyer"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10517",title:"Swarm Intelligence",subtitle:null,isOpenForSubmission:!0,hash:"c184136bf5b833b19f7e12ace5531773",slug:null,bookSignature:"Dr. Mehmet Emin Aydin",coverURL:"https://cdn.intechopen.com/books/images_new/10517.jpg",editedByType:null,editors:[{id:"148497",title:"Dr.",name:"Mehmet",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10576",title:"Factoring Ethics in Technology, Policy Making and Regulation",subtitle:null,isOpenForSubmission:!0,hash:"eff20787f4c5417ea12367e8a6d72e92",slug:null,bookSignature:"Prof. Ali G. 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