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Intelligent Environmental Data Monitoring for Pollution Management /

Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution mana...

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Detalles Bibliográficos
Clasificación:TD174 .I58 2021
Otros Autores: Bhattacharyya, Siddhartha (Editor ), Mondal, Naba Kumar (Editor ), Platos, Jan (Editor ), Kromer, Pavel (Editor ), Snasel, Vaclav (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London ; San Diego, CA : Academic Press, an imprint of Elsevier, [2021]
Colección:Intelligent data centric systems.
Temas:

MARC

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245 0 0 |a Intelligent Environmental Data Monitoring for Pollution Management /  |c edited by Siddhartha Bhattacharyya, Naba Kumar Mondal, Jan Platos, Vaclav Snasel, and Pavel Kromer. 
264 1 |a London ;  |a San Diego, CA :  |b Academic Press, an imprint of Elsevier,  |c [2021] 
300 |a 1 online resource (346 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Intelligent Data-Centric Systems: Sensor Collected Intelligence 
588 0 |a Print version record. 
520 |a Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. 
505 0 |a Intro -- Intelligent Environmental Data Monitoring for Pollution Management -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- Chapter 1: Batch adsorption process in water treatment -- 1. Introduction -- 2. Batch experiments -- 3. Factors affecting adsorption process -- 4. Mechanism in batch adsorption -- 4.1. Bulk solution transport -- 4.2. Film diffusion transport -- 4.3. Pore transport -- 4.4. Surface diffusion -- 5. Adsorption isotherm -- 5.1. Langmuir isotherm -- 5.2. Freundlich adsorption isotherm -- 5.3. Sips isotherm -- 5.4. BET isotherm 
505 8 |a 5.5. The extended Langmuir isotherm -- 6. Adsorption kinetics -- 6.1. Pseudo-first-order equation -- 6.2. Pseudo-second-order equation -- 6.3. Elovich model -- 6.4. Intraparticle diffusion -- 6.5. Kinetics of finite bath and infinite bath experiment -- 7. Thermodynamics -- 8. Desorption studies -- 9. Conclusion -- Acknowledgments -- References -- Chapter 2: Removal of heavy metals from industrial effluents by using biochar -- 1. Introduction -- 2. Industrial effluents and heavy metal pollution -- 3. Conventional processes of removal heavy metals from effluent -- 3.1. Chemical precipitation 
505 8 |a 3.2. Ion exchange -- 3.3. Membrane filtration -- 3.4. Photocatalysis process -- 3.5. Electrodialysis -- 3.6. Electrochemical treatments -- 3.7. Adsorption -- 4. Biochar: The adsorbent -- 5. Preparation of biochar -- 6. Properties of biochar -- 7. Removal of heavy metals by biochar -- 8. Conclusion -- Acknowledgment -- References -- Chapter 3: Nanoparticles: A new tool for control of mosquito larvae -- 1. Introduction -- 2. Nanoparticle synthesis -- 2.1. Biological synthesis of nanoparticles -- 2.1.1. Plant-mediated nanoparticle synthesis -- 2.1.2. Micro-organism-mediated nanoparticle synthesis 
505 8 |a 3. Nanoparticle characterizations -- 3.1. UV-visible spectrophotometry -- 3.2. Fourier transform infrared spectroscopy -- 3.3. Transmission electron microscopy -- 3.4. Scanning electron microscopy -- 3.5. Energy-dispersive X-ray spectroscopy -- 3.6. X-ray diffraction -- 3.7. Dynamic light scattering -- 4. Application -- 4.1. Larvicidal activity of nanoparticles -- 4.2. Toxicological effect of nanoparticles on nontarget organism -- 5. Research gap -- 6. Conclusion -- Acknowledgments -- References 
505 8 |a Chapter 4: Biosorption-driven green technology for the treatment of heavy metal(loids)-contaminated effluents -- 1. Introduction -- 2. Heavy metal(loid)s -- 3. Conventional treatment process for metal(loid)s removal from wastewater -- 4. Biosorption -- 5. Mechanisms of biosorption -- 6. Advantages of biosorption process -- 7. Factors affecting biosorption -- 8. Biosorption isotherm and model -- 9. Biosorbent -- 9.1. Types of biosorbent -- 9.1.1. Bacteria -- 9.1.2. Fungi -- 9.1.3. Algae -- 9.1.4. Agricultural waste -- 10. Modification of biosorbent 
588 0 |a Online resource; title from digital title page (viewed on February 04, 2021). 
650 0 |a Pollution prevention. 
650 6 |a Pollution  |x Pr�evention.  |0 (CaQQLa)201-0285941 
650 7 |a Pollution prevention  |2 fast  |0 (OCoLC)fst01070229 
700 1 |a Bhattacharyya, Siddhartha,  |e editor. 
700 1 |a Mondal, Naba Kumar,  |e editor. 
700 1 |a Platos, Jan,  |e editor. 
700 1 |a Kromer, Pavel,  |e editor. 
700 1 |a Snasel, Vaclav,  |e editor. 
776 0 8 |i Print version:  |a Bhattacharyya, Siddhartha.  |t Intelligent Environmental Data Monitoring for Pollution Management.  |d San Diego : Elsevier Science & Technology, �2020  |z 9780128196717 
830 0 |a Intelligent data centric systems.