scholarly journals On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified Bee Colony Algorithm

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 15
Author(s):  
Vasiliy V. Grigoriev ◽  
Oleg Iliev ◽  
Petr N. Vabishchevich
Keyword(s):  
Parameter Identification ◽  
Reactive Transport ◽  
Optimization Problems ◽  
Bees Algorithm ◽  
Implicit Time ◽  
Element Approximation ◽  
Pore Scale ◽  
Convex Functional ◽  
Admissible Sets ◽  
Bee Colony

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelblau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Pore-scale investigation on multiphase reactive transport for the conversion of levulinic acid to γ-valerolactone with Ru/C catalyst

2021 ◽  
pp. 130917
Author(s):  
Xiangqian Wei ◽  
Wenzhi Li ◽  
Qiying Liu ◽  
Weitao Sun ◽  
Siwei Liu ◽  
...  
Keyword(s):  
Reactive Transport ◽  
Levulinic Acid ◽  
Pore Scale

A Novel Pore-Scale Thermal-Fracture-Acidizing Model With Heterogeneous Rock Properties

SPE Journal ◽  
2016 ◽  
Vol 21 (01) ◽  
pp. 280-292 ◽  
Author(s):  
John Lyons ◽  
Hadi Nasrabadi ◽  
Hisham A. Nasr-El-Din
Keyword(s):  
Mass Transfer ◽  
Reactive Transport ◽  
Reaction Rate ◽  
Oil And Gas ◽  
Injection Rate ◽  
Rock Properties ◽  
Thermal Fracture ◽  
Pore Scale ◽  
Heterogeneous Rock ◽  
Acid Reaction

Summary Fracture acidizing is a well-stimulation technique used to improve the productivity of low-permeability reservoirs and to bypass deep formation damage. The reaction of injected acid with the rock matrix forms etched channels through which oil and gas can then flow upon production. The properties of these etched channels depend on the acid-injection rate, temperature, reaction chemistry, mass-transport properties, and formation mineralogy. As the acid enters the formation, it increases in temperature by heat exchange with the formation and the heat generated by acid reaction with the rock. Thus, the reaction rate, viscosity, and mass transfer of acid inside the fracture also increase. In this study, a new thermal-fracture-acidizing model is presented that uses the lattice Boltzmann method to simulate reactive transport. This method incorporates both accurate hydrodynamics and reaction kinetics at the solid/liquid interface. The temperature update is performed by use of a finite-difference technique. Furthermore, heterogeneity in rock properties (e.g., porosity, permeability, and reaction rate) is included. The result is a model that can accurately simulate realistic fracture geometries and rock properties at the pore scale and that can predict the geometry of the fracture after acidizing. Three thermal-fracture-acidizing simulations are presented here, involving injection of 15 and 28 wt% of hydrochloric acid into a calcite fracture. The results clearly show an increase in the overall fracture dissolution because of the addition of temperature effects (increasing the acid-reaction and mass-transfer rates). It has also been found that by introducing mineral heterogeneity, preferential dissolution leads to the creation of uneven etching across the fracture surfaces, indicating channel formation.

scholarly journals A Shuffle-Based Artificial Bee Colony Algorithm for Solving Integer Programming and Minimax Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1211
Author(s):  
Ivona Brajević
Keyword(s):  
Integer Programming ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Minimax Problems ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Effective Performance ◽  
Complex Optimization ◽  
Overall Performance ◽  
Integer Programming Problems

The artificial bee colony (ABC) algorithm is a prominent swarm intelligence technique due to its simple structure and effective performance. However, the ABC algorithm has a slow convergence rate when it is used to solve complex optimization problems since its solution search equation is more of an exploration than exploitation operator. This paper presents an improved ABC algorithm for solving integer programming and minimax problems. The proposed approach employs a modified ABC search operator, which exploits the useful information of the current best solution in the onlooker phase with the intention of improving its exploitation tendency. Furthermore, the shuffle mutation operator is applied to the created solutions in both bee phases to help the search achieve a better balance between the global exploration and local exploitation abilities and to provide a valuable convergence speed. The experimental results, obtained by testing on seven integer programming problems and ten minimax problems, show that the overall performance of the proposed approach is superior to the ABC. Additionally, it obtains competitive results compared with other state-of-the-art algorithms.

scholarly journals Pore-Scale Modeling of Mineral Growth and Nucleation in Reactive Flow

2022 ◽  
Vol 3 ◽  
Author(s):  
Vitalii Starchenko
Keyword(s):  
Reactive Transport ◽  
Surface Reaction ◽  
Nucleation Theory ◽  
Crystal Nucleation ◽  
Classical Nucleation Theory ◽  
Reactive Flow ◽  
Natural Environments ◽  
Pore Scale ◽  
Arbitrary Lagrangian Eulerian ◽  
Mineral Precipitation

A fundamental understanding of mineral precipitation kinetics relies largely on microscopic observations of the dynamics of mineral surfaces exposed to supersaturated solutions. Deconvolution of tightly bound transport, surface reaction, and crystal nucleation phenomena still remains one of the main challenges. Particularly, the influence of these processes on texture and morphology of mineral precipitate remains unclear. This study presents a coupling of pore-scale reactive transport modeling with the Arbitrary Lagrangian-Eulerian approach for tracking evolution of explicit solid interface during mineral precipitation. It incorporates a heterogeneous nucleation mechanism according to Classical Nucleation Theory which can be turned “on” or “off.” This approach allows us to demonstrate the role of nucleation on precipitate texture with a focus at micrometer scale. In this work precipitate formation is modeled on a 10 micrometer radius particle in reactive flow. The evolution of explicit interface accounts for the surface curvature which is crucial at this scale in the regime of emerging instabilities. The results illustrate how the surface reaction and reactive fluid flow affect the shape of precipitate on a solid particle. It is shown that nucleation promotes the formation of irregularly shaped precipitate and diminishes the effect of the flow on the asymmetry of precipitation around the particle. The observed differences in precipitate structure are expected to be an important benchmark for reaction-driven precipitation in natural environments.

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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