scholarly journals Radial Movement Optimization Based Optimal Operating Parameters of a Capacitive Deionization Desalination System

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 964 ◽  
Author(s):  
Hegazy Rezk ◽  
Muhammad Wajid Saleem ◽  
Mohammad Ali Abdelkareem ◽  
Mujahed Al-Dhaifallah
Keyword(s):  
Structural Parameters ◽  
Optimization Technique ◽  
Water Concentration ◽  
Accurate Method ◽  
Optimization Techniques ◽  
Capacitive Deionization ◽  
Objective Functions ◽  
Optimum Conditions ◽  
Radial Movement ◽  
Decision Variables

The productivity of the capacitive deionization (CDI) system is enhanced by determining the optimum operational and structural parameters using radial movement optimization (RMO) algorithm. Six different parameters, i.e., pool water concentration, freshwater recovery, salt ion adsorption, lowest concentration point, volumetric (based on the volume of deionized water), and gravimetric (based on salt removed) energy consumptions are used to evaluate the performance of the CDI process. During the optimization process, the decision variables are represented by the applied voltage, capacitance, flow rate, spacer volume, and cell volume. Two different optimization techniques are considered: single-objective and multi-objective functions. The obtained results by RMO optimizer are compared with those obtained using a genetic algorithm (GA). The results demonstrated that the RMO optimization technique is useful in exploring all possibilities and finding the optimum conditions for operating the CDI unit in a faster and accurate method.

An Objective Function for Optimization Techniques in Simultaneous Multiple-Element Analysis by Inductively Coupled Plasma Spectrometry

1982 ◽  
Vol 36 (1) ◽  
pp. 37-40 ◽  
Author(s):  
J. J. Leary ◽  
A. E. Brookes ◽  
A. F. Dorrzapf ◽  
D. W. Golightly
Keyword(s):  
Objective Function ◽  
Inductively Coupled Plasma ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Objective Functions ◽  
Element Analysis ◽  
Plasma Spectrometry ◽  
Inductively Coupled ◽  
Inductively Coupled Plasma Spectrometry

This work considers several composite objective functions and uses the sequential simplex optimization technique to evaluate the performance of a proposed objective function in locating optimal instrumental operating conditions for simultaneous multiple-element determinations by inductively coupled plasma spectrometry. The proposed objective function, combined with a generalized approach to optimization, can be applied to any group of analysis elements and is of value in routine optimization procedures for simultaneous multiple-element methods of analysis.

scholarly journals Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

2021 ◽  
Vol 13 (24) ◽  
pp. 13709
Author(s):  
Chandrasekaran Venkatesan ◽  
Raju Kannadasan ◽  
Dhanasekar Ravikumar ◽  
Vijayaraja Loganathan ◽  
Mohammed H. Alsharif ◽  
...  
Keyword(s):  
Distribution Systems ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Hybrid Technique ◽  
Objective Functions ◽  
Distributed Generations ◽  
Multi Objective ◽  
Metaheuristic Optimization

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

Application of Meta-Heuristic Optimization Techniques for Design Optimization of a Robotic Gripper

2019 ◽  
Vol 10 (3) ◽  
pp. 107-133 ◽  
Author(s):  
Golak Bihari Mahanta ◽  
Amruta Rout ◽  
Deepak BBVL ◽  
Bibhuti Bhusan Biswal
Keyword(s):  
Geometric Modeling ◽  
Geometric Model ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Design Parameters ◽  
Optimized Design ◽  
Objective Functions ◽  
Decision Variables ◽  
Pick And Place ◽  
Metaheuristic Techniques

Robotic grippers play a key player in the industrial robotics application such as pick and place, and assembly. In this article, geometric modeling of a robotic gripper is proposed and a plan is outlined to obtain optimized design parameters of the robotic gripper using various meta-heuristics techniques. The proposed system was solved in two-step methodology as geometric modeling followed by the formulation of objective functions. The developed two objective functions of the robotic gripper are complex and act as the multi-objective constraint optimization problem. Seven decision variables are chosen to develop the geometric model, and the proposed objective function for the robotic gripper is solved using different metaheuristic techniques such as ABC, FA, TLBO, ACO, and PSO algorithm. A statistical study conducted considering the 100 independent run for all the algorithms.

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

An efficient procedure to predict the dynamic loads for piston liner systems in marine engines

2021 ◽  
pp. 146808742199698
Author(s):  
Lyu Xiuyi ◽  
Abdullah Azam ◽  
Wang Yuechang ◽  
Lu Xiqun ◽  
Li Tongyang ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Structural Parameters ◽  
Computation Time ◽  
Primary Source ◽  
Cylinder Liner ◽  
Accurate Method ◽  
Friction Behavior ◽  
Accuracy Requirement ◽  
Precise Prediction ◽  
Friction Prediction

The piston ring-cylinder liner (PRCL) is one of the most important parts of marine diesel engines and contributes 25% to 50% of total friction loss. The lubrication simulation analysis of the PRCL system is a challenging task. Complete understanding and precise prediction of lubrication loads is a key to understanding the friction behavior of PRCL systems as the accuracy of the friction prediction depends upon precise prediction of lubrication loads. Therefore, this paper focuses on the gas pressure calculation which is the primary source of lubrication loads. The procedure presented combines the advantages of two mainstream methods to predict loads in the PRCL system. The result is a significant reduction in the computation time without compromising on accuracy. Firstly, a comparison of both approaches is presented which suggests that each technique has its limitations (one is time-bound, and one is accuracy-bound). Then, the results from both calculation methods are verified against literature and a parametric study is performed to identify the key structural parameters of PRCL system that affect the calculation efficiency. Finally, a correlation coefficient is introduced into the analysis to combine the two approaches which then identifies the conditions under which the use of the faster method becomes invalid and replaces it with the more accurate approach. This ensures optimum performance of the calculation procedure by switching between the fast and the accurate method depending upon the accuracy requirement under given conditions, thereby, simplifying the dynamic and lubrication model of PRCL systems. The study has direct implications for the tribological design of the PRCL interface.

scholarly journals Optimal Allocation of Multiple Types of Distributed Generations in Radial Distribution Systems Using a Hybrid Technique

2021 ◽  
Vol 13 (12) ◽  
pp. 6644
Author(s):  
Ali Selim ◽  
Salah Kamel ◽  
Amal A. Mohamed ◽  
Ehab E. Elattar
Keyword(s):  
Power System ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Optimal Allocation ◽  
Optimization Technique ◽  
Mathematical Formulation ◽  
Optimization Techniques ◽  
Sensitivity Factor ◽  
Hybrid Technique ◽  
Analytical Technique

In recent years, the integration of distributed generators (DGs) in radial distribution systems (RDS) has received considerable attention in power system research. The major purpose of DG integration is to decrease the power losses and improve the voltage profiles that directly lead to improving the overall efficiency of the power system. Therefore, this paper proposes a hybrid optimization technique based on analytical and metaheuristic algorithms for optimal DG allocation in RDS. In the proposed technique, the loss sensitivity factor (LSF) is utilized to reduce the search space of the DG locations, while the analytical technique is used to calculate initial DG sizes based on a mathematical formulation. Then, a metaheuristic sine cosine algorithm (SCA) is applied to identify the optimal DG allocation based on the LSF and analytical techniques instead of using random initialization. To prove the superiority and high performance of the proposed hybrid technique, two standard RDSs, IEEE 33-bus and 69-bus, are considered. Additionally, a comparison between the proposed techniques, standard SCA, and other existing optimization techniques is carried out. The main findings confirmed the enhancement in the convergence of the proposed technique compared with the standard SCA and the ability to allocate multiple DGs in RDS.

scholarly journals A New Methodology for Solving Trajectory Planning and Dynamic Load-Carrying Capacity of a Robot Manipulator

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
Wanjin Guo ◽  
Ruifeng Li ◽  
Chuqing Cao ◽  
Xunwei Tong ◽  
Yunfeng Gao
Keyword(s):  
Trajectory Planning ◽  
Dynamic Load ◽  
Carrying Capacity ◽  
Robot Manipulator ◽  
Time Interval ◽  
Planning Problem ◽  
Load Carrying Capacity ◽  
Objective Functions ◽  
Decision Variables ◽  
Load Carrying

A new methodology using a direct method for obtaining the best found trajectory planning and maximum dynamic load-carrying capacity (DLCC) is presented for a 5-degree of freedom (DOF) hybrid robot manipulator. A nonlinear constrained multiobjective optimization problem is formulated with four objective functions, namely, travel time, total energy involved in the motion, joint jerks, and joint acceleration. The vector of decision variables is defined by the sequence of the time-interval lengths associated with each two consecutive via-points on the desired trajectory of the 5-DOF robot generalized coordinates. Then this vector of decision variables is computed in order to minimize the cost function (which is the weighted sum of these four objective functions) subject to constraints on joint positions, velocities, acceleration, jerks, forces/torques, and payload mass. Two separate approaches are proposed to deal with the trajectory planning problem and the maximum DLCC calculation for the 5-DOF robot manipulator using an evolutionary optimization technique. The adopted evolutionary algorithm is the elitist nondominated sorting genetic algorithm (NSGA-II). A numerical application is performed for obtaining best found solutions of trajectory planning and maximum DLCC calculation for the 5-DOF hybrid robot manipulator.

Combining Response Surface Method and Metaheuristic Algorithms for Optimizing SPIF Process

2021 ◽  
Vol 11 (4) ◽  
pp. 1-25
Author(s):  
Amr Ahmed Shaaban ◽  
Omar Mahmoud Shehata
Keyword(s):  
Response Surface ◽  
Response Surface Method ◽  
Single Point ◽  
Experimental Studies ◽  
Optimization Techniques ◽  
Parameters Optimization ◽  
Second Phase ◽  
Fe Model ◽  
Optimum Conditions ◽  
Surface Method

Recently, studies have focused on optimization as a method to reach the finest conditions for metal forming processes. This study tests various optimization techniques to determine the optimum conditions for single point incremental forming (SPIF). SPIF is a die-less forming process that depends on moving a tool along a path designed for a specific feature. As it involves various parameters, optimization based on experimental studies would be costly, hence a finite element model (FE-model) for the SPIF process is developed and validated through experimental results. In the second phase, statistical analyses based on the response surface method (RSM) are conducted. The optimum conditions are determined using the desirability optimization method, in addition to two metaheuristic optimization algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO). The results of all optimization techniques are compared to each other and a confirmation test using the FE-model is subsequently performed.

A Strategy for Multi-Point Compressor Blading Using Multi-Objective Optimization

2012 ◽  
Author(s):  
Alireza Fathi ◽  
Abdollah Shadaram ◽  
Mohammad Alizadeh
Keyword(s):  
Incidence Angle ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Design System ◽  
Design Calculation ◽  
Blade Design ◽  
Objective Functions ◽  
Multi Objective ◽  
First Case ◽  
Through Flow

This paper introduces a framework to perform a multi-objective multipoint aerodynamic optimization for an axial compressor blade. This framework considers through-flow design requirements and mechanical and manufacturing constraints. Typically, components of a blade design system include geometry generation tools, optimization algorithms, flow solvers, and objective functions. In particular, optimization algorithms and objective functions are tuned to reduce blade design calculation cost and to match designed blade performance to the through flow design criteria and mechanical and manufacturing constrains. In the present study, geometry parameters of blade are classified to three categories. For each category, a distinct optimization loop is applied. In outer loop, Gradient-based optimization techniques are used to optimize parameters of the second category and a two-dimensional compressible viscous flow code is used to simulate the cascade fluid flow. Surface curvature optimization is carried out in inner loop, and its objective function is defined by integrating the normalized curvature and curvature slope. The genetic algorithm is used to optimize the parameters in the interior loop. To highlight the capabilities of the design method and to develop design know-how, an initial profile is optimized with three different design philosophies. The highest performance improvement in the first case is 15% reduction in loss at design incidence angle. In the second case, 16.5% increase in allowable incidence angle range, improves blade’s performance at off design conditions.

scholarly journals Optimization of the Parameters of RISE Feedback Controller Using Genetic Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani
Keyword(s):  
Genetic Algorithm ◽  
Numerical Simulations ◽  
Control Algorithm ◽  
Chaotic Systems ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
External Disturbances ◽  
Van Der Pol ◽  
Control Methodology

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.

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