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Optimal Compression for Identically Prepared Qubit States

Physical Review Letters 2016年第9期117卷 090502-090502页

作者： Yuxiang Yang Giulio Chiribella Masahito Hayashi Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong Canadian Institute for Advanced Research CIFAR Program in Quantum Information Science Toronto Ontario M5G 1Z8 Canada Graduate School of Mathematics Nagoya University Nagoya Japan Centre for Quantum Technologies National University of Singapore Singapore 117543 Singapore

We establish the ultimate limits to the compression of sequences of identically prepared qubits. The limits are determined by Holevo’s information quantity and are attained through use of the optimal universal clonin... 详细信息

关键词： Quantum algorithms Quantum communication Quantum information processing Quantum measurements Quantum memories

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Evaluation of Three Machine Learning Algorithms as Classifiers of Premature Ventricular Contractions on ECG beats.

Evaluation of Three Machine Learning Algorithms as Classifie...

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Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges Conference

作者： M. M. Casas R. L. Avitia M. A. Reyna A. Cárdenas Bioengineering Graduate Program Autonomous University of Baja California. Mexicali BC Mex Department of Bioengineering and Environmental Health Autonomous University of Baja California Mexicali BC Mex Mathematics and Computer Science Department University of Missouri-St Louis MO USA

ISBN: (纸本)9781509024872

According to the World Health Organization, cardiovascular diseases (CVD) are the main cause of death worldwide. An estimated 17.5 million people died from CVD in 2012, representing 31% of all global deaths. The electrocardiogram (ECG) is a central tool for the pre-diagnosis of heart diseases. Many advances on ECG arrhythmia classification have been developed in the last century;however, there is still research to identify malignant waveforms on ECG beats. The premature ventricular complexes (PVC) are known to be associated with malignant ventricular arrhythmias and in sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. In this work, we extracted 80 different features from 108,653 ECG classified beats of the MIT-BIH database in order to classify the Normal, PVC and other kind of ECG beats. We evaluated three classifier algorithms based on Machine Learning with different parameters and we got a total of 14 models. We used the F1 score and we compared predictive values as a measured of classifier evaluations. Results show that we could have a F1 scores near to the unit, showing the models are higher than 93% of performance.

关键词： Electrocardiogram (ECG) Premature Ventricular Contraction (PVC) Arrhythmia classifier Logistic Regression (LG) Neural Network (NN) Support Vector Machines (SVM)

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Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Iqbal Dahlan Abdullah H Hartono Cut Ita Erliana S Sriadhi Nurmawati M Adhi Prasnowo Abdurrozzaq Hasibuan Akbar Iskandar Kundharu Saddhono Abdussakir Hastuti Musa Muhammad Ikhsan Setiawan Youmeni Sagala Rumiris Siahaan Eka Mayastika Sinaga Anggraini Syahputri Feby Milanie Henry Aspan Muammar Khaddafi Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Department of Industrial Engineering Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Engineering Universitas Islam Sumatera UtaraMedan-Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Mathematics Universitas Muhammadiyah Parepare Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia STIE Bina Karya Tebing Tinggi Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

The development of information and communication technology at this time has touched all sides of life. Almost no side of human life is not touched by this information technology, which is more synonymous with the world of computerization. No exception also with the computerization that is popular in the corporate environment and government agencies. If in ancient times everything was still done manually. However, at this time all leaders and management have realized the importance of this information technology products that can facilitate them in carrying out the day-to-day corporate functions. Decision Making System of goods purchasing is one of the determinants of the accumulation of goods. If the decision is taken right then stockpiling of goods can be avoided. In this thesis the authors designed a web-based system that helps Management in making the decision of the purchase amount of goods. the system was developed by using Microsoft Visual Studio 2010 and using SQL Server database processing applications. The end result of system design is expected to help minimize the accumulation of goods.

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Retraction: Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method (J. Phys.: Conf. Ser. 1114 012082)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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The cluster heritage project with XMM-Newton: Mass assembly and thermodynamics at the endpoint of structure formation I. programme overview

arXiv

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arXiv 2020年

作者： Arnaud, M. Ettori, S. Pratt, Gabriel W. Rossetti, M. Eckert, D. Gastaldello, F. Gavazzi, R. Kay, S.T. Lovisari, L. Maughan, B.J. Pointecouteau, E. Sereno, M. Bartalucci, I. Bonafede, A. Bourdin, H. Cassano, R. Duffy, R.T. Iqbal, A. Maurogordato, S. Rasia, E. Sayers, J. Andrade-Santos, F. Aussel, H. Barnes, D.J. Barrena, R. Borgani, S. Burkutean, S. Clerc, N. Corasaniti, P.-S. Cuillandre, J.-C. de Grandi, S. de Petris, M. Dolag, K. Donahue, M. Ferragamo, A. Gaspari, M. Ghizzardi, S. Gitti, M. Haines, C.P. Jauzac, M. Johnston-Hollitt, M. Jones, C. Kéruzoré, F. LeBrun, A.M.C. Mayet, F. Mazzotta, P. Melin, J.-B. Molendi, S. Nonino, M. Okabe, N. Paltani, S. Perotto, L. Pires, S. Radovich, M. Rubino-Martin, J.-A. Salvati, L. Saro, A. Sartoris, B. Schellenberger, G. Streblyanska, A. Tarrío, P. Tozzi, P. Umetsu, K. van der Burg, R.F.J. Vazza, F. Venturi, T. Yepes, G. Zarattini, S. AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France INAF Osservatorio di Astrofisica e Scienza dello Spazio via Pietro Gobetti 93/3 Bologna40129 Italy INFN Sezione di Bologna viale Berti Pichat 6/2 BolognaI-40127 Italy INAF IASF-Milano via A. Corti 12 MilanoI-20133 Italy Department of Astronomy University of Geneva ch. d'Écogia 16 VersoixCH-1290 Switzerland CNRS Sorbonne Université UMR 7095 Institut d'Astrophysique de Paris 98 bis Boulevard Arago Paris75014 France Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy School of Natural Sciences The University of Manchester ManchesterM13 9PL United Kingdom Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States HH Wills Physics Laboratory University of Bristol Tyndall Ave BristolBS8 1TL United Kingdom IRAP Université de Toulouse CNRS CNES UPS 9 av du colonel Roche BP44346 Toulouse Cedex 431028 France Dipartimento di Fisica e Astronomia Universitá di Bologna Via Gobetti 92/3 Bologna40121 Italy INAF Istituto di Radio Astronomia Via Gobetti 101 Bologna40129 Italy Hamburger Sternwarte Gojenbergsweg 112 Hamburg21029 Germany Università degli studi di Roma Tor Vergata via della ricerca scientifica 1 Roma00133 Italy Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France INAF - Osservatorio Astronomico di Trieste via Tiepolo 11 TriesteI-34131 Italy IFPU - Institute for Fundamental Physics of the Universe via Beirut 2 Trieste34151 Italy California Institute of Technology PasadenaCA91125 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States C/ Vía Láctea s/n La Laguna TenerifeE-38205 Spain Universidad de La Laguna Departamento de Astrofísica C/ Astrofísico Francisco Sánchez s/n La Laguna TenerifeE-38206 Spain D

The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a three-mega-second Multi-Year Heritage programme to obtain X-ray observations of a minimally-biased, signal-to-noise-limited sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. The programme, described in detail in this paper, aims to study the ultimate products of structure formation in time and mass. It is composed of a census of the most recent objects to have formed (Tier-1: 0.05 14 M 500 14 M), together with a sample of the highest mass objects in the Universe (Tier-2: z 500 > 7.25 × 1014 M). The programme will yield an accurate vision of the statistical properties of the underlying population, measure how the gas properties are shaped by collapse into the dark matter halo, uncover the provenance of non-gravitational heating, and resolve the major uncertainties in mass determination that limit the use of clusters for cosmological parameter estimation. We will acquire X-ray exposures of uniform depth, designed to obtain individual mass measurements accurate to 15 − 20% under the hydrostatic assumption. We present the project motivations, describe the programme definition, and detail the ongoing multi-wavelength observational (lensing, SZ, radio) and theoretical effort that is being deployed in support of the project. Copyright © 2020, The Authors. All rights reserved.

关键词： Thermodynamics

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A Data Mining Analysis of Asthma Risk Factors

A Data Mining Analysis of Asthma Risk Factors

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International Conference on Applied Computing and Information Technology/International Conference on Computational science and Intelligence (ACIT-CSI)

作者： Alma Pochini Ben M. Williams Hasanboy M. Isomitdinov Gongzhu Hu Departments of Finance Law & Departments of Mathematics Data Mining Graduate Certificate Program Department of Economics Department of Computer Science Central Michigan University Michigan USA

Asthma is a chronic inflammatory disease of the airways. Although causes of asthma may directly related to environment factors such as dust mites, pollens, cold air, etc.,we would like to find out if demographic, social and behavior factors also play a role. In this paper, we present a study of the behavioral rick factors in relation to people getting asthma. This study applies data mining techniques to the 2013 Behavioral Risk Factor Surveillance System (BRFSS) dataset of over 490,000 observations with 336 variables. The results reveal some relationships between some of these variables and asthma that may help asthma prevention.

关键词： Logistics Diseases Data mining Neural networks Regression tree analysis

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Optimization Process of Glycerol Esterification Using Real Time Adaptive Control

Optimization Process of Glycerol Esterification Using Real T...

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International Conference on Advanced computer science and Information Systems

作者： Iwan Aang Soenandi Ani Suryani Taufik Djatna Irzaman Post Graduate Program of Agro-industrial Technology Bogor Agricultural University Dramaga Indonesia Lecturer Faculty of Engineering and Computer Science Krida Wacana Christian University Post Graduate Program of Agro-industrial Technology Bogor Agricultural University. Dramaga Indonesia Post Graduate Program of Agro-industrial Technology and Graduate Program of Computer Science Bogor Agricultural University. Dramaga Indonesia Department of Physics Faculty Mathematics and Natural Sciences Bogor Agricultural University. Dramaga Indonesia

ISBN: (纸本)9781509003648

The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification process by minimizing the time needed for the process and maximizing the yield of Mono-glycerides. This optimization has gained importance for boosting the esterification industry and improving the production efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency, a lower energy consumption and a better product quality. This paper presents the optimization with a computational algorithm in real time and adaptive control (RTAC), as compared to the conventional (traditional) methods to monitor and control of glycerol esterification processes. The identification of esterification status based on temperature and time are evaluated to strengthen the optimization. An adaptive method as feature selection to select wavelength IR sensors at specified intervals was carried out with Relief algorithm and Adaptive Pillar K-means clustering method to set the parameter control was proposed in this paper. Many combinations were evaluated from real time condition process, to achieve the best optimization results. The experimental results demonstrate that real time adaptive control can be achieved by using three clusters, which are heating up, stabilizing and finishing. In RTAC, each cluster has its own parameter to set the control point by the servo motor that was attached to magnetic stirrer-heater. By using optimization parameter for each cluster, esterification process time can be shortened by 15-20 minutes with a higher yield (7% or more), lower range stirrer rotation (300rpm-450 rpm) and a lower final temperature of 200℃ - 210℃.

关键词： Optimization Real Time Adaptive Control Glycerol Esterification

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