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Sizing Method of a Storage System for Determining the Performance of a Photovoltaic Pumping System over the Sun

Smart Grid and Renewable Energy 2019年第2期10卷 17-28页

作者： Mohamed Elmamy Mohamed Mahmoud Mr. Soukeyna A. Yahfdhou A. K. Mahmoud I. Youm Applied Research Laboratory for Renewable Energies (LRAER) University of Nouakchott Al-Aasriya (UNA) Mauritania Unit of Electromechanical Research Institute Superior of Technological Education Rosso Mauritania Laboratories of Semiconductor and Solar Energy (LASES) Faculty of Science and Technology Cheikh Anta Diop Dakar Senegal Laboratory of Electronic Computing Telecommunication and Renewable Energies (LEITER) UGB Saint-Louis Senegal Centers of the Study and Research of the Renewable Energy (CERER) Dakar Senegal

The use of renewable energy is growing significantly in the world. In front of the growing demand for electric energy, essentially for the needs of remote, isolated and mountainous regions, photovoltaic systems, especially water pumping systems, are beginning to emerge in large applications. In this sense, the proposed study deals with the problem of the water level regulation in the photovoltaic pumping system. It is in this context that the interest in this paper is dictated by the need to use an existing energy source on the site. Still in this light, it is important to note that, often, the calculation of the size of the GPV that feeds the pumping system and the pump involves a certain degree of uncertainty, mainly due to two main reasons: the first is related to randomness of solar radiation which is often little known and the second is related to the difficulty to estimate the water needs. This is why, on the one hand, the realization of such a system has made it possible to show the possibility of determining the projected quantity for water storage. Similarly, it has shown that the prediction of this quantity of water can be calculated by a simple analytical method based on numerical computation. Thus, it was also shown for this pumping system, thanks to graphical analysis methods, developing autonomy, reliability and good performance. In this sense, this experience opens the door for a practical and economical solution to the problem of lack of water, especially in our regions. Measurements made on the studied system prove that the designed approach improves the efficiency. Finally, it is also expected to draw further conclusions for the operation of these systems in similar sites.

关键词： Water Pumping PV Storage Output

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New collective structures in Au179 and their implications for the triaxial deformation of the Pt178 core

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Physical Review C 2022年第6期106卷 064324-064324页

作者： M. Balogh E. Jajčišinová M. Venhart A. Herzáň J. L. Wood D. T. Joss F. A. Ali K. Auranen S. Bánovská M. Bírová R. J. Carroll D. M. Cox J. G. Cubiss T. Davis M. C. Drummond P. T. Greenlees T. Grahn A. Gredley J. Henderson U. Jakobsson R. Julin S. Juutinen G. Kantay J. Konki P. Konopka M. Leino V. Matoušek A. K. Mistry C. G. McPeake D. O'Donnell R. D. Page J. Pakarinen P. Papadakis J. Partanen P. Peura P. Rahkila P. Ruotsalainen M. Sandzelius J. Sarén B. Saygı M. Sedlák D. Seweryniak C. Scholey J. Sorri A. Špaček S. Stolze M. Taylor A. Thornthwaite J. Uusitalo M. Veselský S. Vielhauer F. P. Wearing Institute of Physics Slovak Academy of Sciences SK-84511 Bratislava Slovakia INFN Laboratori Nazionali di Legnaro IT-35020 Padova Italy Department of Physics Georgia Institute of Technology Atlanta Georgia 30332 USA Department of Physics Oliver Lodge Laboratory University of Liverpool Liverpool L69 7ZE United Kingdom Department of Physics College of Science Education University of Sulaimani P.O. Box 334 Sulaimani Kurdistan Region Iraq Accelerator Laboratory Department of Physics University of Jyväskylä FI-40014 Jyväskylä Finland Department of Physics Lund University 22100 Lund Sweden Department of Physics University of York York YO10 5DD United Kingdom Department of Physics University of York Heslington York YO10 5DD United Kingdom Institut für Kernphysik Technische Universität Darmstadt Darmstadt D-64289 Germany School of Computing Engineering & Physical Sciences University of the West of Scotland Paisley PA1 2BE United Kingdom Ege University Faculty of Science Department of Physics 35100 Bornova Izmir Turkey Physics Division Argonne National Laboratory Argonne Illinois 60439 USA The University of Manchester School of Physics and Astronomy Oxford Road M13 9PL Manchester United Kingdom Institute of Experimental and Applied Physics Czech Technical University Prague 110 00 Czech Republic

The extremely neutron-deficient isotope Au179 has been studied by a combination of in-beam γ-ray and isomeric-decay spectroscopy. For in-beam spectroscopy, the recoil-isomer tagging technique was employed, using the known 3/2−, T1/2=328 ns isomer. A new rotational band, associated with the unfavored signature band of the 1h9/2⊕2f7/2 proton-intruder configuration, was revealed. A previously unknown, high-spin isomeric state with an excitation energy of 1743(17) keV and T1/2=2.16(8)µs was discovered. Five decay paths were identified, some of them feeding previously unknown non-yrast excited states, associated with the 1i13/2 proton-intruder configuration. Calculations based on the particle-plus-triaxial-rotor model were performed to interpret the data. On the basis of these calculations, the new 1h9/2⊕2f7/2 rotational band is interpreted as due to triaxial deformation of the underlying configuration with β2≈0.26 and γ≈27∘. Observed non-yrast states of the positive-parity 1i13/2 intruder configuration are interpreted as due to triaxial deformation with β2≈0.26 and γ≈20∘.

关键词： Collective levels Isomer decays Lifetimes & widths Nuclear structure & decays 150 ≤ A ≤ 189

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Energy crops for sustainable phytoremediation – Thermal decomposition kinetics

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Energy Procedia 2019年 158卷 873-878页

作者： Thuat T. Trinh Sebastian Werle Khanh-Quang Tran Aneta Magdziarz Szymon Sobek Marta Pogrzeba Computing and Design Bionanochemisry Group Faculty of Applied Science Ton Duc Thang University Ho Chi Minh city Vietnam Department of Cicil and Environemenal Engineering Norwegian University of Science and Technology NO-7491Trondheim Norway Silesian University of Technology 22 Konarskiego St 44-100 Gliwice Poland Department of Energy and Process Engineering Norwegian University of Science and Technology Kolbjørn Hejes vei 1B Trondheim Norway AGH University of Science and Technology Al. Mickiewicza 30 30-059 Krakow Poland Institute for Ecology of Industrial Areas 6 Kossutha St 40-844 Katowice Poland

The thermal decomposition kinetics of two energy crops Miscanthus x giganteus and Sida hermaphrodita biomass materials harvested from a phytoremediation study in Polish and Germany have been studied, assuming a three-pseudocomponent model. The kinetic parameters extracted from the study are realistic and within reasonable ranges. The activation energies are within 20 - 103.55 kJ/mol. The reaction orders are within 1.01-1.99. The result shows that the energy crops from the Polish site exhibited higher conversion rates than the samples from Germany. A negative effect of the ash content on the biomass reactivity is observed.

关键词： Kinetic modelling thermal decomposition phytoremediation

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Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

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arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

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FUNCTIONAL CLT FOR THE RANGE OF STABLE RANDOM WALKS

arXiv

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arXiv 2019年

作者： Cygan, Wojciech Sandrić, Nikola Šebek, Stjepan INSTITUT FÜR MATHEMATISCHE STOCHASTIK TECHNISCHE UNIVERSITÄT DRESDEN DRESDEN GERMANY & INSTYTUT MATEMATYCZNY UNIWERSYTET WROCLAWSKI WROCLAW Poland DEPARTMENT OF MATHEMATICS UNIVERSITY OF ZAGREB ZAGREBCROATIA Croatia INSTITUTE OF DISCRETE MATHEMATICS GRAZ UNIVERSITY OF TECHNOLOGY GRAZ Austria DEPARTMENT OF APPLIED MATHEMATICS FACULTY OF ELECTRICAL ENGINEERING AND COMPUTING UNIVERSITY OF ZAGREB ZAGREBCROATIA Croatia

In this note, we establish a functional central limit theorem for the capacity of the range for a class of α-stable random walks on the integer lattice Zd with d > 5α∕2. Using similar methods, we also prove an analogous result for the cardinality of the range when d > 3α∕*** Codes 60F17, 60F05, 60G50, 60G52 Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

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Optimal method of optimizing risk of portfolio

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IOP Conference Series: Materials Science and Engineering 2020年第1期917卷

作者： Dare Jayeola Suliadi Sufahani Opeyemi Akinmoji Mohd Helmy Abd Wahab Syed Zulkarnain Syed Idrus Mustafa Mamat Department of Mathematical Sciences Adekunle Ajasin University PMB 001Akungba Akoko Ondo State Nigeria Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Pagoh Campus 84600 Pagoh Johor Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Faculty of Applied and Human Sciences Universiti Malaysia Perlis 01000 Kangar Perlis Malaysia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin 22200 Besut Terengganu Malaysia

Diversification is a strategic method that investors use to optimize risk of portfolio. It is an opportunity by which investors move from micro-firm into macro-firm. The investors' aim is to make an optimal choice that leads to minimization of risk and maximization of return, but the methods that lead to these objectives are not easily achieved. The purpose of this paper is to propose a method to minimize risk of portfolio. Firstly, this paper investigates the risk reduction strength of each asset and secondly, it explores the impact of each asset in minimizing risk of portfolio. The assets allocations divulge by Black Litterman model are used to estimate risk of both portfolios and assets. We explore DataStream (Yahoo finance) of Gold, Oil and Natural gas which spans from January, 2010 to September, 2016. It is observed that investing on Gold minimizes higher risk and achieve more benefits than other assets in the portfolio. In view of these facts, it means diversifying in gold acts as hedge/safe haven for investors during economic crisis.

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Security Systems with Biometry based on Partial View Facial Images Using Geometrical Features

Security Systems with Biometry based on Partial View Facial ...

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IEEE International Symposium on Wireless Systems (IDAACS-SWS)

作者： Piotr Milczarski Zofia Stawska Shane Dowdall Faculty of Physics and Applied Informatics University of Lodz Lodz Poland Dept. of Visual and Human Centred Computing Dundalk Institute of Technology Dundalk Co Louth Ireland

ISBN: (纸本)9781538675885

In the security systems with a biometrical support it is vital to check non-invasively some features e.g. iris color, gender. In the paper, we have shown the problem of choosing the best set of features in the task of gender classification/recognition. The choice of a minimum set of features, that can give satisfactory results is also important in the biometrical systems where only a part of the face is visible e.g. security check. The minimum set of features can simplify the classification process to make it really fast in IoT security systems, video analysis of faces in the crowd, surveillance video analysis as well as for mobile applications The proposed areas can be taken into account while having veiled or partially obscured faces.

关键词： Face Support vector machines Security Databases Training Internet Testing

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Why is the winner the best?

arXiv

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arXiv 2023年

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M.D. Isensee, F. Adler, T.J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D.G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulmé, A. Jannin, P. Kavur, A.E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martín-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Rädsch, T. Rafael-Patiño, J. Bawa, V. Singh Speidel, S. Sudre, C.H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M.H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Brüngel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C.M. Fuller, C. Gaire, R.R. Galdran, A. Faura, Á. García Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martinčič, T. Morais, P. Naser, M.A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Štepec, D. Subedi, R. Tochon, G. Torres, H.R. Urien, H. Vilaça, J.L. Wahid, K.A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jäger, P.F. Kopp-Schneider, A. Maier-Hein, L. Heidelberg Germany Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Germany Heidelberg Germany School of Computing Faculty of Engineering and Physical Sciences University of Leeds Leeds United Kingdom Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington SeattleWA United States Department of Computer Science University College London London United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Sierre Switzerland Harvard Medical School Brigham and Women’s Hospital BostonMA United States School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Essen Germany University of Nebraska Medical Center OmahaNE United States Department of Internal Medicine III Heidelberg University Hospital Heidelberg Germany Neurobiology Research Unit Copenhagen University Hospital Rigshospitalet C

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. © 2023, CC BY.

关键词： Benchmarking

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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An Intelligent Carbon-Based Prediction of Wastewater Treatment Plants Using Machine Learning Algorithms

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Adsorption Science & technology 2022年 2022卷

作者： Anwer Mustafa Hilal Maha M. Althobaiti Taiseer Abdalla Elfadil Eisa Rana Alabdan Manar Ahmed Hamza Abdelwahed Motwakel Mesfer Al Duhayyim Noha Negm Department of Computer and Self Development Preparatory Year Deanship Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Computer Science College of Computing and Information Technology Taif University P.O.Box 11099 Taif 21944 Saudi Arabia Department of Information Systems-Girls Section College of Science & Arts at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer and Information Sciences Majmaah University Majmaah 11952 Saudi Arabia Department of Natural and Applied Sciences College of Community-Aflaj Prince Sattam Bin Abdulaziz University Saudi Arabia Faculty of Science Mathematics and Computer Science Department Menoufia University Egypt

Purification of polluted water and return back to the agriculture field is the wastewater treatment for plants. Contaminated water causes illness and health emergencies of public. Also, health risk due release of toxic contaminants brings problem to all living beings. At present, sensors are used in waste water treatment and transfer data via internet of things (IoT). Prediction of wastewater quality content which is presence of total nitrogen (T-N) and total phosphorous (T-P) elements, chemical oxygen demand (COD), biochemical demand (BOD), and total suspended solids (TSS) is associated with eutrophication that should be prevented. This may leads to algal bloom and spoils aquatic life which is consumed by human. The presence of nitrogen and phosphorous elements is in the content of wastewater, and these elements are associated with eutrophication which should be prevented. Adsorption of T-N and T-P activated carbon was predictable as one of the most promising methods for wastewater treatment. Many research works have been done. The issues are inefficiency in the prediction of wastewater treatment. To overcome this issue, this paper proposed fusion of B-KNN with the ELM algorithm that is used. The accuracy of the BKNN-ELM algorithm in classification of water quality status produced the highest accuracy of the highest accuracy which is and with rate of accuracy which is 93.56%, and the lowest accuracy is . Experiment evaluation shows that a total suspended solid predicted by proposed model is 91 with accuracy of 93%. The relative error rate of prediction is 12.03 which is lesser than existing models.

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