| Description |
1 online resource (346 pages) |
| Series |
Intelligent Data-Centric Systems: Sensor Collected Intelligence |
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Intelligent data centric systems.
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| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 |
| Notes |
Print version record |
|
Online resource; title from digital title page (viewed on February 04, 2021) |
| Subject |
Pollution prevention.
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Pollution prevention
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| Form |
Electronic book
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| Author |
Bhattacharyya, Siddhartha, editor
|
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Mondal, Naba Kumar, editor
|
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Platos, Jan, editor
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Kromer, Pavel, editor
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Snasel, Vaclav, editor
|
| ISBN |
9780128199244 |
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0128199245 |
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