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optimization</title> <meta name="description" content="Search results for: process optimization"> <meta name="keywords" content="process optimization"> <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="process optimization"> <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> 17475</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: process optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17475</span> Parametric Optimization of Electric Discharge Machining Process Using Taguchi's Method and Grey Relation Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pushpendra%20S.%20Bharti">Pushpendra S. Bharti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process yield of electric discharge machining (EDM) is directly related to optimal combination(s) of process parameters. Optimization of process parameters of EDM is a multi-objective optimization problem owing to the contradictory behavior of performance measures. This paper employs Grey Relation Analysis (GRA) method as a multi-objective optimization technique for the optimal selection of process parameters combination. In GRA, multi-response optimization is converted into optimization of a single response grey relation grade which ultimately gives the optimal combination of process parameters. Experiments were carried out on die-sinking EDM by taking D2 steel as work piece and copper as electrode material. Taguchi's orthogonal array L36 was used for the design of experiments. On the experimental values, GRA was employed for the parametric optimization. A significant improvement has been observed and reported in the process yield by taking the parametric combination(s) obtained through GRA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electric%20discharge%20machining" title="electric discharge machining">electric discharge machining</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20relation%20analysis" title=" grey relation analysis"> grey relation analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=material%20removal%20rate" title=" material removal rate"> material removal rate</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/61577/parametric-optimization-of-electric-discharge-machining-process-using-taguchis-method-and-grey-relation-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61577.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">409</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">17474</span> Review on Optimization of Drinking Water Treatment Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Farhaoui">M. Farhaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Derraz"> M. Derraz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the drinking water treatment processes, the optimization of the treatment is an issue of particular concern. In general, the process consists of many units as settling, coagulation, flocculation, sedimentation, filtration and disinfection. The optimization of the process consists of some measures to decrease the managing and monitoring expenses and improve the quality of the produced water. The objective of this study is to provide water treatment operators with methods and practices that enable to attain the most effective use of the facility and, in consequence, optimize the of the cubic meter price of the treated water. This paper proposes a review on optimization of drinking water treatment process by analyzing all of the water treatment units and gives some solutions in order to maximize the water treatment performances without compromising the water quality standards. Some solutions and methods are performed in the water treatment plant located in the middle of Morocco (Meknes). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coagulation%20process" title="coagulation process">coagulation process</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=turbidity%20removal" title=" turbidity removal"> turbidity removal</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20treatment" title=" water treatment"> water treatment</a> </p> <a href="https://publications.waset.org/abstracts/44937/review-on-optimization-of-drinking-water-treatment-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44937.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">422</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">17473</span> Optimization of Electrocoagulation Process Using Duelist Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Totok%20R.%20Biyanto">Totok R. Biyanto</a>, <a href="https://publications.waset.org/abstracts/search?q=Arif%20T.%20Mardianto"> Arif T. Mardianto</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Farid%20R.%20R."> M. Farid R. R.</a>, <a href="https://publications.waset.org/abstracts/search?q=Luthfi%20Machmudi"> Luthfi Machmudi</a>, <a href="https://publications.waset.org/abstracts/search?q=kandi%20mulakasti"> kandi mulakasti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this research is optimizing the electrocoagulation process design as a post-treatment for biologically vinasse effluent process. The first principle model with three independent variables that affect the energy consumption of electrocoagulation process i.e. current density, electrode distance, and time of treatment process are chosen as optimized variables. The process condition parameters were determined with the value of pH, electrical conductivity, and temperature of vinasse about 6.5, 28.5 mS/cm, 52 oC, respectively. Aluminum was chosen as the electrode material of electrocoagulation process. Duelist algorithm was used as optimization technique due to its capability to reach a global optimum. The optimization results show that the optimal process can be reached in the conditions of current density of 2.9976 A/m2, electrode distance of 1.5 cm and electrolysis time of 119 min. The optimized energy consumption during process is 34.02 Wh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=vinasse%20effluent" title=" vinasse effluent"> vinasse effluent</a>, <a href="https://publications.waset.org/abstracts/search?q=electrocoagulation" title=" electrocoagulation"> electrocoagulation</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title=" energy consumption"> energy consumption</a> </p> <a href="https://publications.waset.org/abstracts/61721/optimization-of-electrocoagulation-process-using-duelist-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61721.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">469</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">17472</span> Flowsheet Development, Simulation and Optimization of Carbon-Di-Oxide Removal System at Natural Gas Reserves by Aspen–Hysys Process Simulator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ruhul%20Amin">Mohammad Ruhul Amin</a>, <a href="https://publications.waset.org/abstracts/search?q=Nusrat%20Jahan"> Nusrat Jahan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Natural gas is a cleaner fuel compared to the others. But it needs some treatment before it is in a state to be used. So natural gas purification is an integral part of any process where natural gas is used as raw material or fuel. There are several impurities in natural gas that have to be removed before use. CO2 is one of the major contaminants. In this project we have removed CO2 by amine process by using MEA solution. We have built up the whole amine process for removing CO2 in Aspen Hysys and simulated the process. At the end of simulation we have got very satisfactory results by using MEA solution for the removal of CO2. Simulation result shows that amine absorption process enables to reduce CO2 content from NG by 58%. HYSYS optimizer allowed us to get a perfect optimized plant. After optimization the profit of existing plant is increased by 2.34 %.Simulation and optimization by Aspen-HYSYS simulator makes available us to enormous information which will help us to further research in future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aspen%E2%80%93Hysys" title="Aspen–Hysys">Aspen–Hysys</a>, <a href="https://publications.waset.org/abstracts/search?q=CO2%20removal" title=" CO2 removal"> CO2 removal</a>, <a href="https://publications.waset.org/abstracts/search?q=flowsheet%20development" title=" flowsheet development"> flowsheet development</a>, <a href="https://publications.waset.org/abstracts/search?q=MEA%20solution" title=" MEA solution"> MEA solution</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20gas%20optimization" title=" natural gas optimization"> natural gas optimization</a> </p> <a href="https://publications.waset.org/abstracts/28865/flowsheet-development-simulation-and-optimization-of-carbon-di-oxide-removal-system-at-natural-gas-reserves-by-aspen-hysys-process-simulator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28865.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">498</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">17471</span> Reinforcement Learning for Quality-Oriented Production Process Parameter Optimization Based on Predictive Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=policy%20optimization" title=" policy optimization"> policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=actor%20critic%20approach" title=" actor critic approach"> actor critic approach</a> </p> <a href="https://publications.waset.org/abstracts/160123/reinforcement-learning-for-quality-oriented-production-process-parameter-optimization-based-on-predictive-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160123.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">17470</span> A New Tool for Global Optimization Problems: Cuttlefish Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adel%20Sabry%20Eesa">Adel Sabry Eesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Adnan%20Mohsin%20Abdulazeez%20Brifcani"> Adnan Mohsin Abdulazeez Brifcani</a>, <a href="https://publications.waset.org/abstracts/search?q=Zeynep%20Orman"> Zeynep Orman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cuttlefish%20Algorithm" title="Cuttlefish Algorithm">Cuttlefish Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithms" title=" bio-inspired algorithms"> bio-inspired algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20optimization%20problems" title=" global optimization problems"> global optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/11956/a-new-tool-for-global-optimization-problems-cuttlefish-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11956.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">563</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">17469</span> The Application of Artificial Neural Network for Bridge Structures Design Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Angga%20S.%20Fajar">Angga S. Fajar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Aminullah"> A. Aminullah</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Kiyono"> J. Kiyono</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20A.%20Safitri"> R. A. Safitri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bridge%20structures" title="bridge structures">bridge structures</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation" title=" back propagation"> back propagation</a> </p> <a href="https://publications.waset.org/abstracts/58189/the-application-of-artificial-neural-network-for-bridge-structures-design-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58189.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">372</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">17468</span> Digestion Optimization Algorithm: A Novel Bio-Inspired Intelligence for Global Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akintayo%20E.%20Akinsunmade">Akintayo E. Akinsunmade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digestion optimization algorithm is a novel biological-inspired metaheuristic method for solving complex optimization problems. The algorithm development was inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to eﬀectively and eﬃciently search for optimal solutions. This algorithm was tested for optimal solutions on seven diﬀerent types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solution of the test functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithm" title="bio-inspired algorithm">bio-inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmark%20optimization%20functions" title=" benchmark optimization functions"> benchmark optimization functions</a>, <a href="https://publications.waset.org/abstracts/search?q=digestive%20system%20in%20human" title=" digestive system in human"> digestive system in human</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20development" title=" algorithm development"> algorithm development</a> </p> <a href="https://publications.waset.org/abstracts/194133/digestion-optimization-algorithm-a-novel-bio-inspired-intelligence-for-global-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194133.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">8</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">17467</span> Study of the Effect of Inclusion of TiO2 in Active Flux on Submerged Arc Welding of Low Carbon Mild Steel Plate and Parametric Optimization of the Process by Using DEA Based Bat Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sheetal%20Kumar%20Parwar">Sheetal Kumar Parwar</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Deb%20Barma"> J. Deb Barma</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Majumder"> A. Majumder</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Submerged arc welding is a very complex process. It is a very efficient and high performance welding process. In this present study an attempt have been done to reduce the welding distortion by increased amount of oxide flux through TiO2 in submerged arc welding process. Care has been taken to avoid the excessiveness of the adding agent for attainment of significant results. Data Envelopment Analysis (DEA) based BAT algorithm is used for the parametric optimization purpose in which DEA Data Envelopment Analysis is used to convert multi response parameters into a single response parameter. The present study also helps to know the effectiveness of the addition of TiO2 in active flux during submerged arc welding process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BAT%20algorithm" title="BAT algorithm">BAT algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiment" title=" design of experiment"> design of experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=submerged%20arc%20welding" title=" submerged arc welding"> submerged arc welding</a> </p> <a href="https://publications.waset.org/abstracts/30180/study-of-the-effect-of-inclusion-of-tio2-in-active-flux-on-submerged-arc-welding-of-low-carbon-mild-steel-plate-and-parametric-optimization-of-the-process-by-using-dea-based-bat-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30180.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">639</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">17466</span> A Review on Parametric Optimization of Casting Processes Using Optimization Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhrugesh%20Radadiya">Bhrugesh Radadiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Shah"> Jaydeep Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Indian foundry industry, there is a need of defect free casting with minimum production cost in short lead time. Casting defect is a very large issue in foundry shop which increases the rejection rate of casting and wastage of materials. The various parameters influences on casting process such as mold machine related parameters, green sand related parameters, cast metal related parameters, mold related parameters and shake out related parameters. The mold related parameters are most influences on casting defects in sand casting process. This paper review the casting produced by foundry with shrinkage and blow holes as a major defects was analyzed and identified that mold related parameters such as mold temperature, pouring temperature and runner size were not properly set in sand casting process. These parameters were optimized using different optimization techniques such as Taguchi method, Response surface methodology, Genetic algorithm and Teaching-learning based optimization algorithm. Finally, concluded that a Teaching-learning based optimization algorithm give better result than other optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=casting%20defects" title="casting defects">casting defects</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=parametric%20optimization" title=" parametric optimization"> parametric optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=TLBO%20algorithm" title=" TLBO algorithm"> TLBO algorithm</a> </p> <a href="https://publications.waset.org/abstracts/21826/a-review-on-parametric-optimization-of-casting-processes-using-optimization-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21826.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">728</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">17465</span> Parametric Optimization of Wire Electric Discharge Machining (WEDM) for Aluminium Metal Matrix Composites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Rajyalakhmi">G. Rajyalakhmi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Karthik"> C. Karthik</a>, <a href="https://publications.waset.org/abstracts/search?q=Gerson%20Desouza"> Gerson Desouza</a>, <a href="https://publications.waset.org/abstracts/search?q=Rimmie%20Duraisamy"> Rimmie Duraisamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this present work, metal matrix composites with combination of aluminium with (Sic/Al2O3) were fabricated using stir casting technique. The objective of the present work is to optimize the process parameters of Wire Electric Discharge Machining (WEDM) composites. Pulse ON Time, Pulse OFF Time, wire feed and sensitivity are considered as input process parameters with responses Material Removal Rate (MRR), Surface Roughness (SR) for optimization of WEDM process. Taguchi L18 Orthogonal Array (OA) is used for experimentation. Grey Relational Analysis (GRA) is coupled with Taguchi technique for multiple process parameters optimization. ANOVA (Analysis of Variance) is used for finding the impact of process parameters individually. Finally confirmation experiments were carried out to validate the predicted results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parametric%20optimization" title="parametric optimization">parametric optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=particulate%20reinforced%20metal%20matrix%20composites" title=" particulate reinforced metal matrix composites"> particulate reinforced metal matrix composites</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi-grey%20relational%20analysis" title=" Taguchi-grey relational analysis"> Taguchi-grey relational analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=WEDM" title=" WEDM"> WEDM</a> </p> <a href="https://publications.waset.org/abstracts/16565/parametric-optimization-of-wire-electric-discharge-machining-wedm-for-aluminium-metal-matrix-composites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16565.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">580</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">17464</span> On Mathematical Modelling and Optimization of Emerging Trends Processes in Advanced Manufacturing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agarana%20Michael%20C.">Agarana Michael C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Akinlabi%20Esther%20T."> Akinlabi Esther T.</a>, <a href="https://publications.waset.org/abstracts/search?q=Pule%20Kholopane"> Pule Kholopane</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Innovation in manufacturing process technologies and associated product design affects the prospects for manufacturing today and in near future. In this study some theoretical methods, useful as tools in advanced manufacturing, are considered. In particular, some basic Mathematical, Operational Research, Heuristic, and Statistical techniques are discussed. These techniques/methods are very handy in many areas of advanced manufacturing processes, including process planning optimization, modelling and analysis. Generally the production rate requires the application of Mathematical methods. The Emerging Trends Processes in Advanced Manufacturing can be enhanced by using Mathematical Modelling and Optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modelling" title="mathematical modelling">mathematical modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20trends" title=" emerging trends"> emerging trends</a>, <a href="https://publications.waset.org/abstracts/search?q=advanced%20manufacturing" title=" advanced manufacturing"> advanced manufacturing</a> </p> <a href="https://publications.waset.org/abstracts/158822/on-mathematical-modelling-and-optimization-of-emerging-trends-processes-in-advanced-manufacturing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158822.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">296</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">17463</span> Optimization of Process Parameters in Wire Electrical Discharge Machining of Inconel X-750 for Dimensional Deviation Using Taguchi Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mandeep%20Kumar">Mandeep Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hari%20Singh"> Hari Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents the optimization aspects of a Wire Electrical Discharge Machining operation using Inconel X-750 as work material. The objective considered in this study is minimization of the dimensional deviation. Six input process parameters of WEDM namely spark gap voltage, pulse-on time, pulse-off time, wire feed rate, peak current and wire tension, were chosen as variables to study the process performance. Taguchi's design of experiments methodology has been used for planning and designing the experiments. The analysis of variance was carried out for raw data as well as for signal to noise ratio. Four input parameters and one two-factor interaction have been found to be statistically significant for their effects on the response of interest. The confirmation experiments were also performed for validating the predicted results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANOVA" title="ANOVA">ANOVA</a>, <a href="https://publications.waset.org/abstracts/search?q=DOE" title=" DOE"> DOE</a>, <a href="https://publications.waset.org/abstracts/search?q=inconel" title=" inconel"> inconel</a>, <a href="https://publications.waset.org/abstracts/search?q=machining" title=" machining"> machining</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a> </p> <a href="https://publications.waset.org/abstracts/48085/optimization-of-process-parameters-in-wire-electrical-discharge-machining-of-inconel-x-750-for-dimensional-deviation-using-taguchi-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48085.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">204</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">17462</span> Application of the Global Optimization Techniques to the Optical Thin Film Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20Li">D. Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical thin films are used in a wide variety of optical components and there are many software tools programmed for advancing multilayer thin film design. The available software packages for designing the thin film structure may not provide optimum designs. Normally, almost all current software programs obtain their final designs either from optimizing a starting guess or by technique, which may or may not involve a pseudorandom process, that give different answers every time, depending upon the initial conditions. With the increasing power of personal computers, functional methods in optimization and synthesis of optical multilayer systems have been developed such as DGL Optimization, Simulated Annealing, Genetic Algorithms, Needle Optimization, Inductive Optimization and Flip-Flop Optimization. Among these, DGL Optimization has proved its efficiency in optical thin film designs. The application of the DGL optimization technique to the design of optical coating is presented. A DGL optimization technique is provided, and its main features are discussed. Guidelines on the application of the DGL optimization technique to various types of design problems are given. The innovative global optimization strategies used in a software tool, OnlyFilm, to optimize multilayer thin film designs through different filter designs are outlined. OnlyFilm is a powerful, versatile, and user-friendly thin film software on the market, which combines optimization and synthesis design capabilities with powerful analytical tools for optical thin film designers. It is also the only thin film design software that offers a true global optimization function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optical%20coatings" title="optical coatings">optical coatings</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20software" title=" design software"> design software</a>, <a href="https://publications.waset.org/abstracts/search?q=thin%20film%20design" title=" thin film design"> thin film design</a> </p> <a href="https://publications.waset.org/abstracts/80917/application-of-the-global-optimization-techniques-to-the-optical-thin-film-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80917.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">316</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">17461</span> Discretization of Cuckoo Optimization Algorithm for Solving Quadratic Assignment Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Kazemi">Elham Kazemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Quadratic%20Assignment%20Problem%20%28QAP%29" title="Quadratic Assignment Problem (QAP)">Quadratic Assignment Problem (QAP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Discrete%20Cuckoo%20Optimization%20Algorithm%20%28DCOA%29" title=" Discrete Cuckoo Optimization Algorithm (DCOA)"> Discrete Cuckoo Optimization Algorithm (DCOA)</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20algorithms" title=" meta-heuristic algorithms"> meta-heuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/25249/discretization-of-cuckoo-optimization-algorithm-for-solving-quadratic-assignment-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25249.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">517</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">17460</span> Optimization of the Transfer Molding Process by Implementation of Online Monitoring Techniques for Electronic Packages</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Burcu%20Kaya">Burcu Kaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan-Martin%20Kaiser"> Jan-Martin Kaiser</a>, <a href="https://publications.waset.org/abstracts/search?q=Karl-Friedrich%20Becker"> Karl-Friedrich Becker</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanja%20Braun"> Tanja Braun</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus-Dieter%20Lang"> Klaus-Dieter Lang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Quality of the molded packages is strongly influenced by the process parameters of the transfer molding. To achieve a better package quality and a stable transfer molding process, it is necessary to understand the influence of the process parameters on the package quality. This work aims to comprehend the relationship between the process parameters, and to identify the optimum process parameters for the transfer molding process in order to achieve less voids and wire sweep. To achieve this, a DoE is executed for process optimization and a regression analysis is carried out. A systematic approach is represented to generate models which enable an estimation of the number of voids and wire sweep. Validation experiments are conducted to verify the model and the results are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dielectric%20analysis" title="dielectric analysis">dielectric analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20packages" title=" electronic packages"> electronic packages</a>, <a href="https://publications.waset.org/abstracts/search?q=epoxy%20molding%20compounds" title=" epoxy molding compounds"> epoxy molding compounds</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20molding%20process" title=" transfer molding process"> transfer molding process</a> </p> <a href="https://publications.waset.org/abstracts/46904/optimization-of-the-transfer-molding-process-by-implementation-of-online-monitoring-techniques-for-electronic-packages" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46904.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">17459</span> Simulation-Based Optimization Approach for an Electro-Plating Production Process Based on Theory of Constraints and Data Envelopment Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mayada%20Attia%20Ibrahim">Mayada Attia Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evaluating and developing the electroplating production process is a key challenge in this type of process. The process is influenced by several factors such as process parameters, process costs, and production environments. Analyzing and optimizing all these factors together requires extensive analytical techniques that are not available in real-case industrial entities. This paper presents a practice-based framework for the evaluation and optimization of some of the crucial factors that affect the costs and production times associated with this type of process, energy costs, material costs, and product flow times. The proposed approach uses Design of Experiments, Discrete-Event Simulation, and Theory of Constraints were respectively used to identify the most significant factors affecting the production process and simulate a real production line to recognize the effect of these factors and assign possible bottlenecks. Several scenarios are generated as corrective strategies for improving the production line. Following that, data envelopment analysis CCR input-oriented DEA model is used to evaluate and optimize the suggested scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroplating%20process" title="electroplating process">electroplating process</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20of%20experiment" title=" design of experiment"> design of experiment</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20optimization" title=" performance optimization"> performance optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=theory%20of%20constraints" title=" theory of constraints"> theory of constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a> </p> <a href="https://publications.waset.org/abstracts/145646/simulation-based-optimization-approach-for-an-electro-plating-production-process-based-on-theory-of-constraints-and-data-envelopment-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145646.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">17458</span> Sensitivity Analysis during the Optimization Process Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Rubio">M. A. Rubio</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Urquia"> A. Urquia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Genetic algorithms (GA) are applied to the solution of high-dimensional optimization problems. Additionally, sensitivity analysis (SA) is usually carried out to determine the effect on optimal solutions of changes in parameter values of the objective function. These two analyses (i.e., optimization and sensitivity analysis) are computationally intensive when applied to high-dimensional functions. The approach presented in this paper consists in performing the SA during the GA execution, by statistically analyzing the data obtained of running the GA. The advantage is that in this case SA does not involve making additional evaluations of the objective function and, consequently, this proposed approach requires less computational effort than conducting optimization and SA in two consecutive steps. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity" title=" sensitivity"> sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithms" title=" genetic algorithms"> genetic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20calibration" title=" model calibration"> model calibration</a> </p> <a href="https://publications.waset.org/abstracts/62152/sensitivity-analysis-during-the-optimization-process-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62152.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">436</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">17457</span> Multiparametric Optimization of Water Treatment Process for Thermal Power Plants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Balgaisha%20%20Mukanova">Balgaisha Mukanova</a>, <a href="https://publications.waset.org/abstracts/search?q=Natalya%20Glazyrina"> Natalya Glazyrina</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Glazyrin"> Sergey Glazyrin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The formulated problem of optimization of the technological process of water treatment for thermal power plants is considered in this article. The problem is of multiparametric nature. To optimize the process, namely, reduce the amount of waste water, a new technology was developed to reuse such water. A mathematical model of the technology of wastewater reuse was developed. Optimization parameters were determined. The model consists of a material balance equation, an equation describing the kinetics of ion exchange for the non-equilibrium case and an equation for the ion exchange isotherm. The material balance equation includes a nonlinear term that depends on the kinetics of ion exchange. A direct problem of calculating the impurity concentration at the outlet of the water treatment plant was numerically solved. The direct problem was approximated by an implicit point-to-point computation difference scheme. The inverse problem was formulated as relates to determination of the parameters of the mathematical model of the water treatment plant operating in non-equilibrium conditions. The formulated inverse problem was solved. Following the results of calculation the time of start of the filter regeneration process was determined, as well as the period of regeneration process and the amount of regeneration and wash water. Multi-parameter optimization of water treatment process for thermal power plants allowed decreasing the amount of wastewater by 15%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=direct%20problem" title="direct problem">direct problem</a>, <a href="https://publications.waset.org/abstracts/search?q=multiparametric%20optimization" title=" multiparametric optimization"> multiparametric optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20parameters" title=" optimization parameters"> optimization parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20treatment" title=" water treatment"> water treatment</a> </p> <a href="https://publications.waset.org/abstracts/27581/multiparametric-optimization-of-water-treatment-process-for-thermal-power-plants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27581.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">387</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">17456</span> Comparative Study of Deep Reinforcement Learning Algorithm Against Evolutionary Algorithms for Finding the Optimal Values in a Simulated Environment Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akshay%20Paranjape">Akshay Paranjape</a>, <a href="https://publications.waset.org/abstracts/search?q=Nils%20Plettenberg"> Nils Plettenberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Schmitt"> Robert Schmitt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20process%20optimization" title=" production process optimization"> production process optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20optimization" title=" real-time optimization"> real-time optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid-MPO" title=" hybrid-MPO"> hybrid-MPO</a> </p> <a href="https://publications.waset.org/abstracts/159906/comparative-study-of-deep-reinforcement-learning-algorithm-against-evolutionary-algorithms-for-finding-the-optimal-values-in-a-simulated-environment-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159906.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">112</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">17455</span> Genetic Algorithm Optimization of a Small Scale Natural Gas Liquefaction Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20I.%20Abdelhamid">M. I. Abdelhamid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20O.%20Ghallab"> A. O. Ghallab</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20S.%20Ettouney"> R. S. Ettouney</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20El-Rifai"> M. A. El-Rifai </a> </p> <p class="card-text"><strong>Abstract:</strong></p> An optimization scheme based on COM server is suggested for communication between Genetic Algorithm (GA) toolbox of MATLAB and Aspen HYSYS. The structure and details of the proposed framework are discussed. The power of the developed scheme is illustrated by its application to the optimization of a recently developed natural gas liquefaction process in which Aspen HYSYS was used for minimization of the power consumption by optimizing the values of five operating variables. In this work, optimization by coupling between the GA in MATLAB and Aspen HYSYS model of the same process using the same five decision variables enabled improvements in power consumption by 3.3%, when 77% of the natural gas feed is liquefied. Also on inclusion of the flow rates of both nitrogen and carbon dioxide refrigerants as two additional decision variables, the power consumption decreased by 6.5% for a 78% liquefaction of the natural gas feed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stranded%20gas%20liquefaction" title="stranded gas liquefaction">stranded gas liquefaction</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=COM%20server" title=" COM server"> COM server</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20nitrogen%20expansion" title=" single nitrogen expansion"> single nitrogen expansion</a>, <a href="https://publications.waset.org/abstracts/search?q=carbon%20dioxide%20pre-cooling" title=" carbon dioxide pre-cooling"> carbon dioxide pre-cooling</a> </p> <a href="https://publications.waset.org/abstracts/65318/genetic-algorithm-optimization-of-a-small-scale-natural-gas-liquefaction-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65318.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">448</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">17454</span> Curve Fitting by Cubic Bezier Curves Using Migrating Birds Optimization Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mitat%20Uysal">Mitat Uysal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title=" Bezier curves"> Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20optimization" title=" heuristic optimization"> heuristic optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=migrating%20birds%20optimization" title=" migrating birds optimization"> migrating birds optimization</a> </p> <a href="https://publications.waset.org/abstracts/78026/curve-fitting-by-cubic-bezier-curves-using-migrating-birds-optimization-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78026.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">336</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">17453</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">17452</span> A Robust Optimization Model for Multi-Objective Closed-Loop Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Y.%20Badiee">Mohammad Y. Badiee</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Golestani"> Saeed Golestani</a>, <a href="https://publications.waset.org/abstracts/search?q=Mir%20Saman%20Pishvaee"> Mir Saman Pishvaee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years consumers and governments have been pushing companies to design their activities in such a way as to reduce negative environmental impacts by producing renewable product or threat free disposal policy more and more. It is therefore important to focus more accurate to the optimization of various aspect of total supply chain. Modeling a supply chain can be a challenging process due to the fact that there are a large number of factors that need to be considered in the model. The use of multi-objective optimization can lead to overcome those problems since more information is used when designing the model. Uncertainty is inevitable in real world. Considering uncertainty on parameters in addition to use multi-objectives are ways to give more flexibility to the decision making process since the process can take into account much more constraints and requirements. In this paper we demonstrate a stochastic scenario based robust model to cope with uncertainty in a closed-loop multi-objective supply chain. By applying the proposed model in a real world case, the power of proposed model in handling data uncertainty is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title="supply chain management">supply chain management</a>, <a href="https://publications.waset.org/abstracts/search?q=closed-loop%20supply%20chain" title=" closed-loop supply chain"> closed-loop supply chain</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=goal%20programming" title=" goal programming"> goal programming</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title=" robust optimization"> robust optimization</a> </p> <a href="https://publications.waset.org/abstracts/39139/a-robust-optimization-model-for-multi-objective-closed-loop-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39139.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">415</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">17451</span> A Review on Robot Trajectory Optimization and Process Validation through off-Line Programming in Virtual Environment Using Robcad</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashwini%20Umale">Ashwini Umale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. An optimized trajectory of multi-axis robot is important and directly influences the Performance of the executing task. Optimal is defined to be the minimum time to transition from the current speed to the set speed. In optimization of trajectory through virtual environment explores the most suitable way to represent robot motion from virtual environment to real environment. This paper aims to review the research of trajectory optimization in virtual environment using simulation software Robcad. Improvements are to be expected in trajectory optimization to generate smooth and collision free trajectories with minimization of overall robot cycle time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trajectory%20optimization" title="trajectory optimization">trajectory optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=forward%20kinematics%20and%20reverse%20kinematics" title=" forward kinematics and reverse kinematics"> forward kinematics and reverse kinematics</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20constraints" title=" dynamic constraints"> dynamic constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=robcad%20simulation%20software" title=" robcad simulation software"> robcad simulation software</a> </p> <a href="https://publications.waset.org/abstracts/17300/a-review-on-robot-trajectory-optimization-and-process-validation-through-off-line-programming-in-virtual-environment-using-robcad" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17300.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">505</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">17450</span> Gas Lift Optimization to Improve Well Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20A.%20G.%20H.%20Abdalsadig">Mohamed A. G. H. Abdalsadig</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Nourian"> Amir Nourian</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20G.%20Nasr"> G. G. Nasr</a>, <a href="https://publications.waset.org/abstracts/search?q=Meisam%20Babaie"> Meisam Babaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gas lift optimization is becoming more important now a day in petroleum industry. A proper lift optimization can reduce the operating cost, increase the net present value (NPV) and maximize the recovery from the asset. A widely accepted definition of gas lift optimization is to obtain the maximum output under specified operating conditions. In addition, gas lift, a costly and indispensable means to recover oil from high depth reservoir entails solving the gas lift optimization problems. Gas lift optimization is a continuous process; there are two levels of production optimization. The total field optimization involves optimizing the surface facilities and the injection rate that can be achieved by standard tools softwares. Well level optimization can be achieved by optimizing the well parameters such as point of injection, injection rate, and injection pressure. All these aspects have been investigated and presented in this study by using experimental data and PROSPER simulation program. The results show that the well head pressure has a large influence on the gas lift performance and also proved that smart gas lift valve can be used to improve gas lift performance by controlling gas injection from down hole. Obtaining the optimum gas injection rate is important because excessive gas injection reduces production rate and consequently increases the operation cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20rate" title=" production rate"> production rate</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoir%20pressure%20effect" title=" reservoir pressure effect"> reservoir pressure effect</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20injection%20rate%20effect" title=" gas injection rate effect"> gas injection rate effect</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20injection%20pressure" title=" gas injection pressure"> gas injection pressure</a> </p> <a href="https://publications.waset.org/abstracts/46454/gas-lift-optimization-to-improve-well-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46454.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">413</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">17449</span> Reliability Enhancement by Parameter Design in Ferrite Magnet Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Won%20Jung">Won Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Emri"> Wan Emri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ferrite magnet is widely used in many automotive components such as motors and alternators. Magnets used inside the components must be in good quality to ensure the high level of performance. The purpose of this study is to design input parameters that optimize the ferrite magnet production process to ensure the quality and reliability of manufactured products. Design of Experiments (DOE) and Statistical Process Control (SPC) are used as mutual supplementations to optimize the process. DOE and SPC are quality tools being used in the industry to monitor and improve the manufacturing process condition. These tools are practically used to maintain the process on target and within the limits of natural variation. A mixed Taguchi method is utilized for optimization purpose as a part of DOE analysis. SPC with proportion data is applied to assess the output parameters to determine the optimal operating conditions. An example of case involving the monitoring and optimization of ferrite magnet process was presented to demonstrate the effectiveness of this approach. Through the utilization of these tools, reliable magnets can be produced by following the step by step procedures of proposed framework. One of the main contributions of this study was producing the crack free magnets by applying the proposed parameter design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ferrite%20magnet" title="ferrite magnet">ferrite magnet</a>, <a href="https://publications.waset.org/abstracts/search?q=crack" title=" crack"> crack</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20optimization" title=" process optimization"> process optimization</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/14217/reliability-enhancement-by-parameter-design-in-ferrite-magnet-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14217.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">517</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">17448</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">17447</span> Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samia%20Sadouki%20Chibani">Samia Sadouki Chibani</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelkamel%20Tari"> Abdelkamel Tari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithms" title="bio-inspired algorithms">bio-inspired algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=elephant%20herding%20optimization" title=" elephant herding optimization"> elephant herding optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=QoS%20optimization" title=" QoS optimization"> QoS optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service%20composition" title=" web service composition"> web service composition</a> </p> <a href="https://publications.waset.org/abstracts/64274/elephant-herding-optimization-for-service-selection-in-qos-aware-web-service-composition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64274.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">327</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">17446</span> Modeling and Optimization of Nanogenerator for Energy Harvesting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fawzi%20Srairi">Fawzi Srairi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abderrahmane%20Dib"> Abderrahmane Dib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the desire for a self-powered micro and nanodevices has attracted a great interest of using sustainable energy sources. Further, the ultimate goal of nanogenerator is to harvest energy from the ambient environment in which a self-powered device based on these generators is needed. With the development of nanogenerator-based circuits design and optimization, the building of new device simulator is necessary for the study and the synthesis of electromechanical parameters of this type of models. In the present article, both numerical modeling and optimization of piezoelectric nanogenerator based on zinc oxide have been carried out. They aim to improve the electromechanical performances, robustness, and synthesis process for nanogenerator. The proposed model has been developed for a systematic study of the nanowire morphology parameters in stretching mode. In addition, heuristic optimization technique, namely, particle swarm optimization has been implemented for an analytic modeling and an optimization of nanogenerator-based process in stretching mode. Moreover, the obtained results have been tested and compared with conventional model where a good agreement has been obtained for excitation mode. The developed nanogenerator model can be generalized, extended and integrated into simulators devices to study nanogenerator-based circuits. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20potential" title="electrical potential">electrical potential</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristic%20algorithms" title=" heuristic algorithms"> heuristic algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20modeling" title=" numerical modeling"> numerical modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=nanogenerator" title=" nanogenerator"> nanogenerator</a> </p> <a href="https://publications.waset.org/abstracts/60114/modeling-and-optimization-of-nanogenerator-for-energy-harvesting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60114.pdf" target="_blank" class="btn btn-primary 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