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parameters</title> <meta name="description" content="Search results for: optimization parameters"> <meta name="keywords" content="optimization parameters"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="optimization parameters"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 11154</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: optimization parameters</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10974</span> Analysis of Diabetes Patients Using Pearson, Cost Optimization, Control Chart Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Poovarasan"> R. Poovarasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=congenital%20diabetics" title=" congenital diabetics"> congenital diabetics</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20relationship" title=" linear relationship"> linear relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=monotonic%20function" title=" monotonic function"> monotonic function</a>, <a href="https://publications.waset.org/abstracts/search?q=ranking%20samples" title=" ranking samples"> ranking samples</a>, <a href="https://publications.waset.org/abstracts/search?q=pediatric" title=" pediatric"> pediatric</a> </p> <a href="https://publications.waset.org/abstracts/72132/analysis-of-diabetes-patients-using-pearson-cost-optimization-control-chart-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72132.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10973</span> Multidisciplinary and Multilevel Design Methodology of Unmanned Aerial Vehicles using Enhanced Collaborative Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pedro%20F.%20Albuquerque">Pedro F. Albuquerque</a>, <a href="https://publications.waset.org/abstracts/search?q=Pedro%20V.%20Gamboa"> Pedro V. Gamboa</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20A.%20Silvestre"> Miguel A. Silvestre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present work describes the implementation of the Enhanced Collaborative Optimization (ECO) multilevel architecture with a gradient-based optimization algorithm with the aim of performing a multidisciplinary design optimization of a generic unmanned aerial vehicle with morphing technologies. The concepts of weighting coefficient and a dynamic compatibility parameter are presented for the ECO architecture. A routine that calculates the aircraft performance for the user defined mission profile and vehicle’s performance requirements has been implemented using low fidelity models for the aerodynamics, stability, propulsion, weight, balance and flight performance. A benchmarking case study for evaluating the advantage of using a variable span wing within the optimization methodology developed is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multidisciplinary" title="multidisciplinary">multidisciplinary</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel" title=" multilevel"> multilevel</a>, <a href="https://publications.waset.org/abstracts/search?q=morphing" title=" morphing"> morphing</a>, <a href="https://publications.waset.org/abstracts/search?q=enhanced%20collaborative%20optimization" title=" enhanced collaborative optimization"> enhanced collaborative optimization</a> </p> <a href="https://publications.waset.org/abstracts/18259/multidisciplinary-and-multilevel-design-methodology-of-unmanned-aerial-vehicles-using-enhanced-collaborative-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18259.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">929</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10972</span> Application the Queuing Theory in the Warehouse Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaroslav%20Masek">Jaroslav Masek</a>, <a href="https://publications.waset.org/abstracts/search?q=Juraj%20Camaj"> Juraj Camaj</a>, <a href="https://publications.waset.org/abstracts/search?q=Eva%20Nedeliakova"> Eva Nedeliakova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of optimization of store management is not only designing the situation of store management itself including its equipment, technology and operation. In optimization of store management we need to consider also synchronizing of technological, transport, store and service operations throughout the whole process of logistic chain in such a way that a natural flow of material from provider to consumer will be achieved the shortest possible way, in the shortest possible time in requested quality and quantity and with minimum costs. The paper deals with the application of the queuing theory for optimization of warehouse processes. The first part refers to common information about the problematic of warehousing and using mathematical methods for logistics chains optimization. The second part refers to preparing a model of a warehouse within queuing theory. The conclusion of the paper includes two examples of using queuing theory in praxis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=queuing%20theory" title="queuing theory">queuing theory</a>, <a href="https://publications.waset.org/abstracts/search?q=logistics%20system" title=" logistics system"> logistics system</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20methods" title=" mathematical methods"> mathematical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=warehouse%20optimization" title=" warehouse optimization"> warehouse optimization</a> </p> <a href="https://publications.waset.org/abstracts/34523/application-the-queuing-theory-in-the-warehouse-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34523.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">593</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10971</span> Parametric Analysis and Optimal Design of Functionally Graded Plates Using Particle Swarm Optimization Algorithm and a Hybrid Meshless Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Foad%20Nazari">Foad Nazari</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mahmood%20Hosseini"> Seyed Mahmood Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Abolbashari"> Mohammad Hossein Abolbashari</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hassan%20Abolbashari"> Mohammad Hassan Abolbashari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20design" title="optimal design">optimal design</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20frequency" title=" natural frequency"> natural frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=FG%20plate" title=" FG plate"> FG plate</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20meshless%20method" title=" hybrid meshless method"> hybrid meshless method</a>, <a href="https://publications.waset.org/abstracts/search?q=MLPG%20method" title=" MLPG method"> MLPG method</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN%20approach" title=" ANN approach"> ANN approach</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/55056/parametric-analysis-and-optimal-design-of-functionally-graded-plates-using-particle-swarm-optimization-algorithm-and-a-hybrid-meshless-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55056.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">367</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10970</span> Desing of PSS and SVC to Improve Power System Stability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Samkan">Mahmoud Samkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the design and assessment of new coordination between Power System Stabilizers (PSSs) and Static Var Compensator (SVC) in a multimachine power system via statistical method are proposed. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is handled as an optimization problem. The Bacterial Swarming Optimization (BSO), which synergistically couples the Bacterial Foraging (BF) with the Particle Swarm Optimization (PSO), is employed to seek for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is enhanced. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO (BSOPSS) and optimized SVC controller based on BSO (BSOSVC). Moreover, a statistical T test is executed to validate the robustness of coordinated controller versus uncoordinated one. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SVC" title="SVC">SVC</a>, <a href="https://publications.waset.org/abstracts/search?q=PSSs" title=" PSSs"> PSSs</a>, <a href="https://publications.waset.org/abstracts/search?q=multimachine%20power%20system" title=" multimachine power system"> multimachine power system</a>, <a href="https://publications.waset.org/abstracts/search?q=coordinated%20design" title=" coordinated design"> coordinated design</a>, <a href="https://publications.waset.org/abstracts/search?q=bacteria%20swarm%20optimization" title=" bacteria swarm optimization"> bacteria swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20assessment" title=" statistical assessment "> statistical assessment </a> </p> <a href="https://publications.waset.org/abstracts/36661/desing-of-pss-and-svc-to-improve-power-system-stability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36661.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10969</span> Groundwater Level Prediction Using hybrid Particle Swarm Optimization-Long-Short Term Memory Model and Performance Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sneha%20Thakur">Sneha Thakur</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanjeev%20Karmakar"> Sanjeev Karmakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title="long short-term memory">long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20level" title=" groundwater level"> groundwater level</a> </p> <a href="https://publications.waset.org/abstracts/171101/groundwater-level-prediction-using-hybrid-particle-swarm-optimization-long-short-term-memory-model-and-performance-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171101.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10968</span> Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diogo%20Silva">Diogo Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadul%20Rodor"> Fadul Rodor</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Moraes"> Carlos Moraes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PSO" title="PSO">PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=QPSO" title=" QPSO"> QPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20functions" title=" multidimensional functions"> multidimensional functions</a> </p> <a href="https://publications.waset.org/abstracts/81790/particle-swarm-optimization-and-quantum-particle-swarm-optimization-to-multidimensional-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10967</span> Optimal Allocation of Distributed Generation Sources for Loss Reduction and Voltage Profile Improvement by Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Zaheer%20Babar">Muhammad Zaheer Babar</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Kashif"> Amer Kashif</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Rizwan%20Javed"> Muhammad Rizwan Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays distributed generation integration is best way to overcome the increasing load demand. Optimal allocation of distributed generation plays a vital role in reducing system losses and improves voltage profile. In this paper, a Meta heuristic technique is proposed for allocation of DG in order to reduce power losses and improve voltage profile. The proposed technique is based on Multi Objective Particle Swarm optimization. Fewer control parameters are needed in this algorithm. Modification is made in search space of PSO. The effectiveness of proposed technique is tested on IEEE 33 bus test system. Single DG as well as multiple DG scenario is adopted for proposed method. Proposed method is more effective as compared to other Meta heuristic techniques and gives better results regarding system losses and voltage profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Distributed%20generation%20%28DG%29" title="Distributed generation (DG)">Distributed generation (DG)</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi%20Objective%20Particle%20Swarm%20Optimization%20%28MOPSO%29" title=" Multi Objective Particle Swarm Optimization (MOPSO)"> Multi Objective Particle Swarm Optimization (MOPSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization%20%28PSO%29" title=" particle swarm optimization (PSO)"> particle swarm optimization (PSO)</a>, <a href="https://publications.waset.org/abstracts/search?q=IEEE%20standard%20Test%20System" title=" IEEE standard Test System"> IEEE standard Test System</a> </p> <a href="https://publications.waset.org/abstracts/42467/optimal-allocation-of-distributed-generation-sources-for-loss-reduction-and-voltage-profile-improvement-by-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42467.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">454</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10966</span> Thinned Elliptical Cylindrical Antenna Array Synthesis Using Particle Swarm Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Bera">Rajesh Bera</a>, <a href="https://publications.waset.org/abstracts/search?q=Durbadal%20Mandal"> Durbadal Mandal</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajib%20Kar"> Rajib Kar</a>, <a href="https://publications.waset.org/abstracts/search?q=Sakti%20P.%20Ghoshal"> Sakti P. Ghoshal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes optimal thinning of an Elliptical Cylindrical Array (ECA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Particle Swarm Optimization (PSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. The PSO is used to determine the optimal set of ‘ON-OFF’ elements that provides a radiation pattern with maximum SLL reduction. Optimization is done without prefixing the value of First Null Beam Width (FNBW). The variation of SLL with element spacing of thinned array is also reported. Simulation results show that the number of array elements can be reduced by more than 50% of the total number of elements in the array with a simultaneous reduction in SLL to less than -27dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thinned%20array" title="thinned array">thinned array</a>, <a href="https://publications.waset.org/abstracts/search?q=Particle%20Swarm%20Optimization" title=" Particle Swarm Optimization"> Particle Swarm Optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Elliptical%20Cylindrical%20Array" title=" Elliptical Cylindrical Array"> Elliptical Cylindrical Array</a>, <a href="https://publications.waset.org/abstracts/search?q=Side%20Lobe%20Label." title=" Side Lobe Label."> Side Lobe Label.</a> </p> <a href="https://publications.waset.org/abstracts/4068/thinned-elliptical-cylindrical-antenna-array-synthesis-using-particle-swarm-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4068.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">443</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10965</span> Networked Implementation of Milling Stability Optimization with Bayesian Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christoph%20Ramsauer">Christoph Ramsauer</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Karandikar"> Jaydeep Karandikar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tony%20Schmitz"> Tony Schmitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Friedrich%20Bleicher"> Friedrich Bleicher</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machining stability is an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the Vienna University of Technology, Vienna, Austria. The recorded data from a milling test cut is used to classify the cut as stable or unstable based on the frequency analysis. The test cut result is fed to a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculates the probability of stability as a function of axial depth of cut and spindle speed and recommends the parameters for the next test cut. The iterative process between two transatlantic locations repeats until convergence to a stable optimal process parameter set is achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machining%20stability" title="machining stability">machining stability</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor" title=" sensor"> sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/135659/networked-implementation-of-milling-stability-optimization-with-bayesian-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135659.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">206</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10964</span> Dynamic Construction Site Layout Using Ant Colony Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yassir%20AbdelRazig">Yassir AbdelRazig</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolutionary optimization methods such as genetic algorithms have been used extensively for the construction site layout problem. More recently, ant colony optimization algorithms, which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to benchmark combinatorial optimization problems. This paper proposes a formulation of the site layout problem in terms of a sequencing problem that is suitable for solution using an ant colony optimization algorithm. In the construction industry, site layout is a very important planning problem. The objective of site layout is to position temporary facilities both geographically and at the correct time such that the construction work can be performed satisfactorily with minimal costs and improved safety and working environment. During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the construction site layout problem. This paper proposes an ant colony optimization model for construction site layout. A simple case study for a highway project is utilized to illustrate the application of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony" title="ant colony">ant colony</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20site%20layout" title=" construction site layout"> construction site layout</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a> </p> <a href="https://publications.waset.org/abstracts/28641/dynamic-construction-site-layout-using-ant-colony-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10963</span> A Comparison of Sequential Quadratic Programming, Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization for the Design and Optimization of a Beam Column</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nima%20Khosravi">Nima Khosravi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an integrated optimization technique with concurrent use of sequential quadratic programming, genetic algorithm, and simulated annealing particle swarm optimization for the design and optimization of a beam column. In this research, the comparison between 4 different types of optimization methods. The comparison is done and it is found out that all the methods meet the required constraints and the lowest value of the objective function is achieved by SQP, which was also the fastest optimizer to produce the results. SQP is a gradient based optimizer hence its results are usually the same after every run. The only thing which affects the results is the initial conditions given. The initial conditions given in the various test run were very large as compared. Hence, the value converged at a different point. Rest of the methods is a heuristic method which provides different values for different runs even if every parameter is kept constant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beam%20column" title="beam column">beam column</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20quadratic%20programming" title=" sequential quadratic programming"> sequential quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a> </p> <a href="https://publications.waset.org/abstracts/58973/a-comparison-of-sequential-quadratic-programming-genetic-algorithm-simulated-annealing-particle-swarm-optimization-for-the-design-and-optimization-of-a-beam-column" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58973.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10962</span> A Weighted Sum Particle Swarm Approach (WPSO) Combined with a Novel Feasibility-Based Ranking Strategy for Constrained Multi-Objective Optimization of Compact Heat Exchangers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milad%20Yousefi">Milad Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Moslem%20Yousefi"> Moslem Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricarpo%20Poley"> Ricarpo Poley</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Nordin%20Darus"> Amer Nordin Darus</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-stablished evolutionary algorithm, particle swarm optimization (PSO), weighted sum approach and a novel constraint handling strategy is presented in this study. Since, the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the difficulties of a trial-and-error process for setting the penalty parameters is solved in this algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heat%20exchanger" title="Heat exchanger">Heat exchanger</a>, <a href="https://publications.waset.org/abstracts/search?q=Multi-objective%20optimization" title=" Multi-objective optimization"> Multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Particle%20swarm%20optimization" title=" Particle swarm optimization"> Particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=NSGA-II%20Constraints%20handling." title=" NSGA-II Constraints handling."> NSGA-II Constraints handling.</a> </p> <a href="https://publications.waset.org/abstracts/17215/a-weighted-sum-particle-swarm-approach-wpso-combined-with-a-novel-feasibility-based-ranking-strategy-for-constrained-multi-objective-optimization-of-compact-heat-exchangers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17215.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">555</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10961</span> NaOH/Pumice and LiOH/Pumice as Heterogeneous Solid Base Catalysts for Biodiesel Production from Soybean Oil: An Optimization Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joy%20Marie%20Mora">Joy Marie Mora</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Daniel%20De%20Luna"> Mark Daniel De Luna</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsair-Wang%20Chung"> Tsair-Wang Chung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Transesterification reaction of soybean oil with methanol was carried out to produce fatty acid methyl esters (FAME) using calcined alkali metal (Na and Li) supported by pumice silica as the solid base catalyst. Pumice silica catalyst was activated by loading alkali metal ions to its surface via an ion-exchange method. Response surface methodology (RSM) in combination with Box-Behnken design (BBD) was used to optimize the operating parameters in biodiesel production, namely: reaction temperature, methanol to oil molar ratio, reaction time, and catalyst concentration. Using the optimized sets of parameters, FAME yields using sodium and lithium silicate catalysts were 98.80% and 98.77%, respectively. A pseudo-first order kinetic equation was applied to evaluate the kinetic parameters of the reaction. The prepared catalysts were characterized by several techniques such as X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET) sorptometer, and scanning electron microscopy (SEM). In addition, the reusability of the catalysts was successfully tested in two subsequent cycles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alkali%20metal" title="alkali metal">alkali metal</a>, <a href="https://publications.waset.org/abstracts/search?q=biodiesel" title=" biodiesel"> biodiesel</a>, <a href="https://publications.waset.org/abstracts/search?q=Box-Behnken%20design" title=" Box-Behnken design"> Box-Behnken design</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20catalyst" title=" heterogeneous catalyst"> heterogeneous catalyst</a>, <a href="https://publications.waset.org/abstracts/search?q=kinetics" title=" kinetics"> kinetics</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=pumice" title=" pumice"> pumice</a>, <a href="https://publications.waset.org/abstracts/search?q=transesterification" title=" transesterification"> transesterification</a> </p> <a href="https://publications.waset.org/abstracts/62783/naohpumice-and-liohpumice-as-heterogeneous-solid-base-catalysts-for-biodiesel-production-from-soybean-oil-an-optimization-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62783.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">306</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10960</span> Experimental Design and Optimization of Diesel Oil Desulfurization Process by Adsorption Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Firoz%20Kalam">M. Firoz Kalam</a>, <a href="https://publications.waset.org/abstracts/search?q=Wilfried%20Schuetz"> Wilfried Schuetz</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Hendrik%20Bredehoeft"> Jan Hendrik Bredehoeft </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thiophene sulfur compounds' removal from diesel oil by batch adsorption process using commercial powdered activated carbon was designed and optimized in two-level factorial design method. This design analysis was used to find out the effects of operating parameters directing the adsorption process, such as amount of adsorbent, temperature and stirring time. The desulfurization efficiency was considered the response or output variable. Results showed that the stirring time had the largest effects on sulfur removal efficiency as compared with other operating parameters and their interactions under the experimental ranges studied. A regression model was generated to observe the closeness between predicted and experimental values. The three-dimensional plots and contour plots of main factors were generated according to the regression results to observe the optimal points. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activated%20carbon" title="activated carbon">activated carbon</a>, <a href="https://publications.waset.org/abstracts/search?q=adsorptive%20desulfurization" title=" adsorptive desulfurization"> adsorptive desulfurization</a>, <a href="https://publications.waset.org/abstracts/search?q=factorial%20design" title=" factorial design"> factorial design</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</a> </p> <a href="https://publications.waset.org/abstracts/84261/experimental-design-and-optimization-of-diesel-oil-desulfurization-process-by-adsorption-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84261.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10959</span> Crashworthiness Optimization of an Automotive Front Bumper in Composite Material</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Boria">S. Boria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20material" title="composite material">composite material</a>, <a href="https://publications.waset.org/abstracts/search?q=crashworthiness" title=" crashworthiness"> crashworthiness</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20analysis" title=" finite element analysis"> finite element analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/72849/crashworthiness-optimization-of-an-automotive-front-bumper-in-composite-material" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10958</span> Global Optimization Techniques for Optimal Placement of HF Antennas on a Shipboard</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustafa%20Ural">Mustafa Ural</a>, <a href="https://publications.waset.org/abstracts/search?q=Can%20Bayseferogulari"> Can Bayseferogulari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, radio frequency (RF) coupling between two HF antennas on a shipboard platform is minimized by determining an optimal antenna placement. Unlike the other works, the coupling is minimized not only at single frequency but over the whole frequency band of operation. Similarly, GAO and PSO, are used in order to determine optimal antenna placement. Throughout this work, outputs of two optimization techniques are compared with each other in terms of antenna placements and coupling results. At the end of the work, far-field radiation pattern performances of the antennas at their optimal places are analyzed in terms of directivity and coverage in order to see that. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20compatibility" title="electromagnetic compatibility">electromagnetic compatibility</a>, <a href="https://publications.waset.org/abstracts/search?q=antenna%20placement" title=" antenna placement"> antenna placement</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm%20optimization" title=" genetic algorithm optimization"> genetic algorithm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/108667/global-optimization-techniques-for-optimal-placement-of-hf-antennas-on-a-shipboard" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108667.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">236</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10957</span> Global Direct Search Optimization of a Tuned Liquid Column Damper Subject to Stochastic Load</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansour%20H.%20Alkmim">Mansour H. Alkmim</a>, <a href="https://publications.waset.org/abstracts/search?q=Adriano%20T.%20Fabro"> Adriano T. Fabro</a>, <a href="https://publications.waset.org/abstracts/search?q=Marcus%20V.%20G.%20De%20Morais"> Marcus V. G. De Morais</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of an undamped primary system under white noise excitation. Finally, a numerical example considering a simplified wind turbine model is given to illustrate the efficacy of the TLCD. Results from the random vibration analysis are shown for four types of random excitation wind model where the response PSDs obtained showed good vibration attenuation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20pattern%20search" title="generalized pattern search">generalized pattern search</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20optimization" title=" parameter optimization"> parameter optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20vibration%20analysis" title=" random vibration analysis"> random vibration analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20suppression" title=" vibration suppression"> vibration suppression</a> </p> <a href="https://publications.waset.org/abstracts/68674/global-direct-search-optimization-of-a-tuned-liquid-column-damper-subject-to-stochastic-load" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68674.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">275</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10956</span> A Computational Study of N–H…O Hydrogen Bonding to Investigate Cooperative Effects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Setareh%20Shekarsaraei">Setareh Shekarsaraei</a>, <a href="https://publications.waset.org/abstracts/search?q=Marjan%20Moridi"> Marjan Moridi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasser%20L.%20Hadipour"> Nasser L. Hadipour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, nuclear magnetic resonance spectroscopy and nuclear quadrupole resonance spectroscopy parameters of 14N (Nitrogen in imidazole ring) in N–H…O hydrogen bonding for Histidine hydrochloride monohydrate were calculated via density functional theory. We considered a five-molecule model system of Histidine hydrochloride monohydrate. Also, we examined the trends of environmental effect on hydrogen bonds as well as cooperativity. The functional used in this research is M06-2X which is a good functional and the obtained results have shown good agreement with experimental data. This functional was applied to calculate the NMR and NQR parameters. Some correlations among NBO parameters, NMR, and NQR parameters have been studied which have shown the existence of strong correlations among them. Furthermore, the geometry optimization has been performed using M062X/6-31++G(d,p) method. In addition, in order to study cooperativity and changes in structural parameters, along with increase in cluster size, natural bond orbitals have been employed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrogen%20bonding" title="hydrogen bonding">hydrogen bonding</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20functional%20theory%20%28DFT%29" title=" density functional theory (DFT)"> density functional theory (DFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20bond%20orbitals%20%28NBO%29" title=" natural bond orbitals (NBO)"> natural bond orbitals (NBO)</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperativity%20effect" title=" cooperativity effect"> cooperativity effect</a> </p> <a href="https://publications.waset.org/abstracts/18049/a-computational-study-of-n-ho-hydrogen-bonding-to-investigate-cooperative-effects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">456</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10955</span> Approaches to Reduce the Complexity of Mathematical Models for the Operational Optimization of Large-Scale Virtual Power Plants in Public Energy Supply</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Weber">Thomas Weber</a>, <a href="https://publications.waset.org/abstracts/search?q=Nina%20Strobel"> Nina Strobel</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Kohne"> Thomas Kohne</a>, <a href="https://publications.waset.org/abstracts/search?q=Eberhard%20Abele"> Eberhard Abele</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CHP" title="CHP">CHP</a>, <a href="https://publications.waset.org/abstracts/search?q=Energy%204.0" title=" Energy 4.0"> Energy 4.0</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20storage" title=" energy storage"> energy storage</a>, <a href="https://publications.waset.org/abstracts/search?q=MILP" title=" MILP"> MILP</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20power%20plant" title=" virtual power plant"> virtual power plant</a> </p> <a href="https://publications.waset.org/abstracts/95734/approaches-to-reduce-the-complexity-of-mathematical-models-for-the-operational-optimization-of-large-scale-virtual-power-plants-in-public-energy-supply" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95734.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">178</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10954</span> An Enhanced Particle Swarm Optimization Algorithm for Multiobjective Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Houda%20Abadlia">Houda Abadlia</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Smairi"> Nadia Smairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Ghedira"> Khaled Ghedira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multiobjective Particle Swarm Optimization (MOPSO) has shown an effective performance for solving test functions and real-world optimization problems. However, this method has a premature convergence problem, which may lead to lack of diversity. In order to improve its performance, this paper presents a hybrid approach which embedded the MOPSO into the island model and integrated a local search technique, Variable Neighborhood Search, to enhance the diversity into the swarm. Experiments on two series of test functions have shown the effectiveness of the proposed approach. A comparison with other evolutionary algorithms shows that the proposed approach presented a good performance in solving multiobjective optimization problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title="particle swarm optimization">particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migration" title=" migration"> migration</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20neighborhood%20search" title=" variable neighborhood search"> variable neighborhood search</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a> </p> <a href="https://publications.waset.org/abstracts/99544/an-enhanced-particle-swarm-optimization-algorithm-for-multiobjective-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99544.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10953</span> Optimization of Solar Rankine Cycle by Exergy Analysis and Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20Akbari">R. Akbari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Ehyaei"> M. A. Ehyaei</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Shahi%20Shavvon"> R. Shahi Shavvon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, solar energy is used for energy purposes such as the use of thermal energy for domestic, industrial and power applications, as well as the conversion of the sunlight into electricity by photovoltaic cells. In this study, the thermodynamic simulation of the solar Rankin cycle with phase change material (paraffin) was first studied. Then energy and exergy analyses were performed. For optimization, a single and multi-objective genetic optimization algorithm to maximize thermal and exergy efficiency was used. The parameters discussed in this paper included the effects of input pressure on turbines, input mass flow to turbines, the surface of converters and collector angles on thermal and exergy efficiency. In the organic Rankin cycle, where solar energy is used as input energy, the fluid selection is considered as a necessary factor to achieve reliable and efficient operation. Therefore, silicon oil is selected for a high-temperature cycle and water for a low-temperature cycle as an operating fluid. The results showed that increasing the mass flow to turbines 1 and 2 would increase thermal efficiency, while it reduces and increases the exergy efficiency in turbines 1 and 2, respectively. Increasing the inlet pressure to the turbine 1 decreases the thermal and exergy efficiency, and increasing the inlet pressure to the turbine 2 increases the thermal efficiency and exergy efficiency. Also, increasing the angle of the collector increased thermal efficiency and exergy. The thermal efficiency of the system was 22.3% which improves to 33.2 and 27.2% in single-objective and multi-objective optimization, respectively. Also, the exergy efficiency of the system was 1.33% which has been improved to 1.719 and 1.529% in single-objective and multi-objective optimization, respectively. These results showed that the thermal and exergy efficiency in a single-objective optimization is greater than the multi-objective optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exergy%20analysis" title="exergy analysis">exergy analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rankine%20cycle" title=" rankine cycle"> rankine cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20and%20multi-objective%20function" title=" single and multi-objective function"> single and multi-objective function</a> </p> <a href="https://publications.waset.org/abstracts/110507/optimization-of-solar-rankine-cycle-by-exergy-analysis-and-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110507.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10952</span> The Gasoil Hydrofining Kinetics Constants Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Patrascioiu">C. Patrascioiu</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20Matei"> V. Matei</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Nicolae"> N. Nicolae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes the experiments and the kinetic parameters calculus of the gasoil hydrofining. They are presented experimental results of gasoil hidrofining using Mo and promoted with Ni on aluminum support catalyst. The authors have adapted a kinetic model gasoil hydrofining. Using this proposed kinetic model and the experimental data they have calculated the parameters of the model. The numerical calculus is based on minimizing the difference between the experimental sulf concentration and kinetic model estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrofining" title="hydrofining">hydrofining</a>, <a href="https://publications.waset.org/abstracts/search?q=kinetic" title=" kinetic"> kinetic</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/14522/the-gasoil-hydrofining-kinetics-constants-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14522.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">437</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10951</span> Optimization, Yield and Chemical Composition of Essential Oil from Cymbopogon citratus: Comparative Study with Microwave Assisted Extraction and Hydrodistillation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irsha%20Dhotre">Irsha Dhotre</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cymbopogon citratus is generally known as Indian Lemongrass and is widely applicable in the cosmetic, pharmaceutical, dairy puddings, and food industries. To enhance the quality of extraction, microwave-oven-aided hydro distillation processes were implemented. The basic parameter which influences the rate of extraction is considered, such as the temperature of extraction, the time required for extraction, and microwave-oven power applied. Locally available CKP 25 Cymbopogon citratus was used for the extraction of essential oil. Optimization of Extractions Parameters and full factorial Box–Behnken design (BBD) evaluated by using Design expert 13 software. The regression model revealed that the optimum parameters required for extractions are a temperature of 35℃, a time of extraction of 130 minutes, and microwave-oven power of 700 W. The extraction efficiency of yield is 4.76%. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis confirmed the significant components present in the extraction of lemongrass oil. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Box%E2%80%93Behnken%20design" title="Box–Behnken design">Box–Behnken design</a>, <a href="https://publications.waset.org/abstracts/search?q=Cymbopogon%20citratus" title=" Cymbopogon citratus"> Cymbopogon citratus</a>, <a href="https://publications.waset.org/abstracts/search?q=hydro%20distillation" title=" hydro distillation"> hydro distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=microwave-oven" title=" microwave-oven"> microwave-oven</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a> </p> <a href="https://publications.waset.org/abstracts/160880/optimization-yield-and-chemical-composition-of-essential-oil-from-cymbopogon-citratus-comparative-study-with-microwave-assisted-extraction-and-hydrodistillation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160880.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10950</span> A Hybrid Derivative-Free Optimization Method for Pass Schedule Calculation in Cold Rolling Mill</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammadhadi%20Mirmohammadi">Mohammadhadi Mirmohammadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Safian"> Reza Safian</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Haddad"> Hossein Haddad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an innovative solution for complex multi-objective optimization problem which is a part of efforts toward maximizing rolling mill throughput and minimizing processing costs in tandem cold rolling. This computational intelligence based optimization has been applied to the rolling schedules of tandem cold rolling mill. This method involves the combination of two derivative-free optimization procedures in the form of nested loops. The first optimization loop is based on Improving Hit and Run method which focus on balance of power, force and reduction distribution in rolling schedules. The second loop is a real-coded genetic algorithm based optimization procedure which optimizes energy consumption and productivity. An experimental result of application to five stand tandem cold rolling mill is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=derivative-free%20optimization" title="derivative-free optimization">derivative-free optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Improving%20Hit%20and%20Run%20method" title=" Improving Hit and Run method"> Improving Hit and Run method</a>, <a href="https://publications.waset.org/abstracts/search?q=real-coded%20genetic%20algorithm" title=" real-coded genetic algorithm"> real-coded genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=rolling%20schedules%20of%20tandem%20cold%20rolling%20mill" title=" rolling schedules of tandem cold rolling mill"> rolling schedules of tandem cold rolling mill</a> </p> <a href="https://publications.waset.org/abstracts/18442/a-hybrid-derivative-free-optimization-method-for-pass-schedule-calculation-in-cold-rolling-mill" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">696</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10949</span> Multiobjective Optimization of Wastwater Treatment by Electrochemical Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malek%20Bendjaballah">Malek Bendjaballah</a>, <a href="https://publications.waset.org/abstracts/search?q=Hacina%20Saidi"> Hacina Saidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarra%20Hamidoud"> Sarra Hamidoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to model and optimize the performance of a new electrocoagulation (E.C) process for the treatment of wastewater as well as the energy consumption in order to extrapolate it to the industrial scale. Through judicious application of an experimental design (DOE), it has been possible to evaluate the individual effects and interactions that have a significant influence on both objective functions (maximizing efficiency and minimizing energy consumption) by using aluminum electrodes as sacrificial anode. Preliminary experiments have shown that the pH of the medium, the applied potential and the treatment time with E.C are the main parameters. A factorial design 33 has been adopted to model performance and energy consumption. Under optimal conditions, the pollution reduction efficiency is 93%, combined with a minimum energy consumption of 2.60.10-3 kWh / mg-COD. The potential or current applied and the processing time and their interaction were the most influential parameters in the mathematical models obtained. The results of the modeling were also correlated with the experimental ones. The results offer promising opportunities to develop a clean process and inexpensive technology to eliminate or reduce wastewater, <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocoagulation" title="electrocoagulation">electrocoagulation</a>, <a href="https://publications.waset.org/abstracts/search?q=green%20process" title=" green process"> green process</a>, <a href="https://publications.waset.org/abstracts/search?q=experimental%20design" title=" experimental design"> experimental design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/158692/multiobjective-optimization-of-wastwater-treatment-by-electrochemical-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158692.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10948</span> Heat Transfer Process Parameter Optimization in SI/Ge Using TAGUCHI Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Evln%20Ranga%20Charyulu">Evln Ranga Charyulu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20P.%20Venu%20Madhavarao"> S. P. Venu Madhavarao</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Udaya%20kumar"> S. Udaya kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20V.%20S.%20S.%20N.%20V.%20G.%20Krishna%20Murthy"> S. V. S. S. N. V. G. Krishna Murthy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the advent of new nanometer process technologies, it is possible to integrate billion transistors on a single substrate. When more and more functionality included there is the possibility of multi-million transistors switching simultaneously consuming more power and dissipating more power along with more leakage of current into the substrate of porous silicon or germanium material. These results in substrate heating and thermal noise generation coupled to signals of interest. The heating process is represented by coupled nonlinear partial differential equations in porous silicon and germanium. By identifying heat sources and heat fluxes may results in designing of ultra-low power circuits. The PDEs are solved by finite difference scheme assuming that boundary layer equations in porous silicon and germanium. Local heat fluxes along the vertical isothermal surface immersed in porous SI/Ge are considered. The parameters considered for optimization are thermal diffusivity, thermal expansion coefficient, thermal diffusion ratio, permeability, specific heat at constant temperatures, Rayleigh number, amplitude of wavy surface, mass expansion coefficient. The diffusion of heat was caused by the concentration gradient. Thermal physical properties are homogeneous and isotropic. By using L8, TAGUCHI method the parameters are optimized. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title="heat transfer">heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=pde" title=" pde"> pde</a>, <a href="https://publications.waset.org/abstracts/search?q=taguchi%20optimization" title=" taguchi optimization"> taguchi optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=SI%2FGe" title=" SI/Ge "> SI/Ge </a> </p> <a href="https://publications.waset.org/abstracts/33557/heat-transfer-process-parameter-optimization-in-sige-using-taguchi-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33557.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10947</span> Response Surface Methodology for the Optimization of Paddy Husker by Medium Brown Rice Peeling Machine 6 Rubber Type</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Bangphan">S. Bangphan</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Bangphan"> P. Bangphan</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Ketsombun"> C. Ketsombun</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Sammana"> T. Sammana </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brown%20rice" title="brown rice">brown rice</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology%20%28RSM%29" title=" response surface methodology (RSM)"> response surface methodology (RSM)</a>, <a href="https://publications.waset.org/abstracts/search?q=peeling%20machine" title=" peeling machine"> peeling machine</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=paddy%20husker" title=" paddy husker"> paddy husker</a> </p> <a href="https://publications.waset.org/abstracts/17663/response-surface-methodology-for-the-optimization-of-paddy-husker-by-medium-brown-rice-peeling-machine-6-rubber-type" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17663.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">574</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10946</span> Computationally Efficient Stacking Sequence Blending for Composite Structures with a Large Number of Design Regions Using Cellular Automata</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ellen%20Van%20Den%20Oord">Ellen Van Den Oord</a>, <a href="https://publications.waset.org/abstracts/search?q=Julien%20Marie%20Jan%20Ferdinand%20Van%20Campen"> Julien Marie Jan Ferdinand Van Campen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article introduces a computationally efficient method for stacking sequence blending of composite structures. The computational efficiency makes the presented method especially interesting for composite structures with a large number of design regions. Optimization of composite structures with an unequal load distribution may lead to locally optimized thicknesses and ply orientations that are incompatible with one another. Blending constraints can be enforced to achieve structural continuity. In literature, many methods can be found to implement structural continuity by means of stacking sequence blending in one way or another. The complexity of the problem makes the blending of a structure with a large number of adjacent design regions, and thus stacking sequences, prohibitive. In this work the local stacking sequence optimization is preconditioned using a method found in the literature that couples the mechanical behavior of the laminate, in the form of lamination parameters, to blending constraints, yielding near-optimal easy-to-blend designs. The preconditioned design is then fed to the scheme using cellular automata that have been developed by the authors. The method is applied to the benchmark 18-panel horseshoe blending problem to demonstrate its performance. The computational efficiency of the proposed method makes it especially suited for composite structures with a large number of design regions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite" title="composite">composite</a>, <a href="https://publications.waset.org/abstracts/search?q=blending" title=" blending"> blending</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=lamination%20parameters" title=" lamination parameters"> lamination parameters</a> </p> <a href="https://publications.waset.org/abstracts/76009/computationally-efficient-stacking-sequence-blending-for-composite-structures-with-a-large-number-of-design-regions-using-cellular-automata" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76009.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">227</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10945</span> Simulation and Controller Tunning in a Photo-Bioreactor Applying by Taguchi Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hosein%20Ghahremani">Hosein Ghahremani</a>, <a href="https://publications.waset.org/abstracts/search?q=MohammadReza%20Khoshchehre"> MohammadReza Khoshchehre</a>, <a href="https://publications.waset.org/abstracts/search?q=Pejman%20Hakemi"> Pejman Hakemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study involves numerical simulations of a vertical plate-type photo-bioreactor to investigate the performance of Microalgae Spirulina and Control and optimization of parameters for the digital controller by Taguchi method that MATLAB software and Qualitek-4 has been made. Since the addition of parameters such as temperature, dissolved carbon dioxide, biomass, and ... Some new physical parameters such as light intensity and physiological conditions like photosynthetic efficiency and light inhibitors are involved in biological processes, control is facing many challenges. Not only facilitate the commercial production photo-bioreactor Microalgae as feed for aquaculture and food supplements are efficient systems but also as a possible platform for the production of active molecules such as antibiotics or innovative anti-tumor agents, carbon dioxide removal and removal of heavy metals from wastewater is used. Digital controller is designed for controlling the light bioreactor until Microalgae growth rate and carbon dioxide concentration inside the bioreactor is investigated. The optimal values of the controller parameters of the S/N and ANOVA analysis software Qualitek-4 obtained With Reaction curve, Cohen-Con and Ziegler-Nichols method were compared. The sum of the squared error obtained for each of the control methods mentioned, the Taguchi method as the best method for controlling the light intensity was selected photo-bioreactor. This method compared to control methods listed the higher stability and a shorter interval to be answered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photo-bioreactor" title="photo-bioreactor">photo-bioreactor</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20and%20optimization" title=" control and optimization"> control and optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Light%20intensity" title=" Light intensity"> Light intensity</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a> </p> <a href="https://publications.waset.org/abstracts/13380/simulation-and-controller-tunning-in-a-photo-bioreactor-applying-by-taguchi-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13380.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimization%20parameters&page=6" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimization%20parameters&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimization%20parameters&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimization%20parameters&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=optimization%20parameters&page=4">4</a></li> <li class="page-item"><a class="page-link" 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