P/G Pad Placement Optimization: Problem Forumulation for Best IR Drop

2005 ◽  
Author(s):  
A. Dubey
Keyword(s):  
Optimization Problem ◽  
Ir Drop ◽  
Placement Optimization

scholarly journals A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 857
Author(s):  
Jahedul Islam ◽  
Md Shokor A. Rahaman ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Ahshanul Hoqe ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Well Placement ◽  
Suggested Approach ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

A holistic review on artificial intelligence techniques for well placement optimization problem

2020 ◽  
Vol 141 ◽  
pp. 102767 ◽  
Author(s):  
Jahedul Islam ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Moacyr Bartholomeu Laruccia ◽  
Myo Myint ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problem ◽  
Well Placement ◽  
Artificial Intelligence Techniques ◽  
Placement Optimization ◽  
Holistic Review ◽  
Well Placement Optimization

scholarly journals Workpiece Placement Optimization of Six-Revolute Industrial Serial Robots for Machining Operations

2012 ◽  
Author(s):  
Claire Dumas ◽  
Stéphane Caro ◽  
Sébastien Garnier ◽  
Benoît Furet
Keyword(s):  
Machine Tools ◽  
Optimization Problem ◽  
Quality Criterion ◽  
Numerical Control ◽  
Machining Quality ◽  
Manufacturing Operations ◽  
Serial Robots ◽  
Placement Optimization ◽  
Robotic Cells ◽  
Machining Operations

Roboticists are faced with new challenges in robotic-based manufacturing. Up to now manufacturing operations that require both high stiffness and accuracy have been mainly realized by using computer numerical control machine tools. This paper aims to show that manufacturing finishing tasks can be performed with robotic cells knowing the process cutting phenomena and the robot stiffness throughout its Cartesian workspace. It makes sense that the finishing task of large parts would be cheaper with robots. However, machining robots have not been adapted for such operations yet. As a consequence, this paper introduces a methodology that aims to determine the best placement of the workpiece to be machined knowing the cutting forces exerted on the tool and the elastostatic model of the robot. In this vein, a machining quality criterion is proposed and an optimization problem is formulated. The KUKA KR270-2 robot is used as an illustrative example throughout the paper.

Parameters Identification of the Path Placement Optimization Problem for a Redundant Coordinated Robotic Workcell

2018 ◽  
Author(s):  
Mohammad H. FarzanehKaloorazi ◽  
Ilian A. Bonev ◽  
Lionel Birglen
Keyword(s):  
Optimization Problem ◽  
Sample Path ◽  
Parameters Identification ◽  
Optimization Process ◽  
Rotary Table ◽  
Linear Guide ◽  
Placement Optimization ◽  
Innovative Method ◽  
Joint Limits ◽  
Independent Parameters

This paper proposes a method to identify the number of independent parameters in order to optimize the placement of a given path for a coordinated redundant robotic workcell. The workcell consists of a generic 6 DoF serial manipulator and a 1 DoF redundancy provider (RP). The RP is not attached to the serial manipulator, but the workpiece is attached to the RP. Two cases of RPs are investigated, namely a rotary table and a linear guide. In general, 6 parameters are needed in order to place a path on the RP, and 6 parameters to place the RP in the workspace of the serial manipulator. However, because of the symmetricities and the degree of redundancy involved in the problem, not all 12 parameters can independently affect the placement operation. Therefore, it is important to identify the number of independent parameters in order to improve the efficiency of the placement optimization process. This paper presents an innovative method for determining the number of independent parameters for both cases under study, i.e., the rotary table and the linear guide, with and without considering each one’s joint limits. The optimization process is briefly introduced and the results of using all 12 parameters, as opposed to only the independent ones, are compared. Finally, the performance of the rotary table is compared to the linear guide, for a sample path.

A Survey of Nature-Inspired Algorithms With Application to Well Placement Optimization

2020 ◽  
pp. 32-45 ◽  
Author(s):  
Jahedul Islam ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Moacyr Bartholomeu Laruccia ◽  
Myo Myint
Keyword(s):  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Economic Factors ◽  
Well Placement ◽  
Field Development ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Nature Inspired Algorithms ◽  
Metaheuristic Techniques

Well placement optimization is one of the major challenging factors in the field development process in the oil and gas industry. This chapter aims to survey prominent metaheuristic techniques, which solve well the placement optimization problem. The well placement optimization problem is considered as high dimensional, discontinuous, and multi-model optimization problem. Moreover, the computational expenses further complicate the issue. Over the last decade, both gradient-based and gradient-free optimization methods were implemented. Gradient-free optimization, such as the particle swarm optimization, genetic algorithm, is implemented in this area. These optimization techniques are utilized as standalone or as the hybridization of optimization methods to maximize the economic factors. In this chapter, the authors survey the two most popular nature-inspired metaheuristic optimization techniques and their application to maximize the economic factors.

Well Placement Optimization With Cat Swarm Optimization Algorithm Under Oilfield Development Constraints

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Chen Hongwei ◽  
Feng Qihong ◽  
Zhang Xianmin ◽  
Wang Sen ◽  
Zhou Wensheng ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Well Placement ◽  
Swarm Optimization ◽  
Penalization Method ◽  
De Algorithm ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Oilfield Development ◽  
Handling Method

Proper well placement can improve the oil recovery and economic benefits during oilfield development. Due to the nonlinear and complex properties of well placement optimization, an effective optimization algorithm is required. In this paper, cat swarm optimization (CSO) algorithm is applied to optimize well placement for maximum net present value (NPV). CSO algorithm, a heuristic algorithm that mimics the behavior of a swarm of cats, has characteristics of flexibility, fast convergence, and high robustness. Oilfield development constraints are taken into account during well placement optimization process. Rejection method, repair method, static penalization method, dynamic penalization method and adapt penalization method are, respectively, applied to handle well placement constraints and then the optimal constraint handling method is obtained. Besides, we compare the CSO algorithm optimization performance with genetic algorithm (GA) and differential evolution (DE) algorithm. With the selected constraint handling method, CSO, GA, and DE algorithms are applied to solve well placement optimization problem for a two-dimensional (2D) conceptual model and a three-dimensional (3D) semisynthetic reservoir. Results demonstrate that CSO algorithm outperforms GA and DE algorithm. The proposed CSO algorithm can effectively solve the constrained well placement optimization problem with adapt penalization method.

Design and Simulation Placement Optimization Problem of Multi-Surface Reflector of Street Lamps

2012 ◽  
Vol 507 ◽  
pp. 147-151
Author(s):  
Yue E Chen ◽  
Yun Kai Zhou ◽  
Pei Xin Qu
Keyword(s):  
Power Consumption ◽  
Energy Saving ◽  
Optimization Problem ◽  
Optimization Theory ◽  
Point Of View ◽  
Street Lighting ◽  
Placement Optimization ◽  
The People ◽  
Street Lamp ◽  
Save Energy

Street lamps are indispensable social facilities. The power consumption of street lamps is quite astonishing .In this paper, we design a novel multi-surface reflector of street lamp, which is used AUTOCAD to generate reflector’s surface, applied the optimization theory and optical software Tracepro. Multi-surface reflector of street lamps is reasonable in the distribution of light, full using of energy, and has incomparable advantages compared with the traditional reflector. Using the design of multi-surface street lamp and simulation of MATLAB, this article sets up maximum spacing model of one lamp and area of the largest model of two lamp basic on analysis of the cha-racteristics of street lighting. From the lighting brightness and energy saving point of view, we give a reasonable height and spacing of street lamps, so that the design can meet the people on the light-ing requirements, but also can save energy as much as possible.

Exergy and sensibility analysis of each individual effect in a kraft multiple effect evaporator

TAPPI Journal ◽  
2019 ◽  
Vol 18 (10) ◽  
pp. 607-618
Author(s):  
JÉSSICA MOREIRA ◽  
BRUNO LACERDA DE OLIVEIRA CAMPOS ◽  
ESLY FERREIRA DA COSTA JUNIOR ◽  
ANDRÉA OLIVEIRA SOUZA DA COSTA
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Kraft Pulping ◽  
Steam Temperature ◽  
Input Flow ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Multiple Effect ◽  
Sensibility Analysis ◽  
Exergetic Analysis

The multiple effect evaporator (MEE) is an energy intensive step in the kraft pulping process. The exergetic analysis can be useful for locating irreversibilities in the process and pointing out which equipment is less efficient, and it could also be the object of optimization studies. In the present work, each evaporator of a real kraft system has been individually described using mass balance and thermodynamics principles (the first and the second laws). Real data from a kraft MEE were collected from a Brazilian plant and were used for the estimation of heat transfer coefficients in a nonlinear optimization problem, as well as for the validation of the model. An exergetic analysis was made for each effect individually, which resulted in effects 1A and 1B being the least efficient, and therefore having the greatest potential for improvement. A sensibility analysis was also performed, showing that steam temperature and liquor input flow rate are sensible parameters.

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