Applications of Artificial Intelligence in Removal of Emerging Contaminants : Sustainable Approach for Environmental Clean-Up and Circular Economy

• Autore: Sillanpää Mika
• Pubblicazione: Chantilly : , : Elsevier, , 2025; ©2025
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (471 pages)
• Disciplina: 628.5028563
• Soggetto topico: Environmental innovations; Pollution control equipment
• Nota di contenuto: Front Cover -- Applications of Artificial Intelligence in Removal of Emerging Contaminants -- Applications of Artificial Intelligence in Removal of Emerging Contaminants: Sustainable Approach for Environmental Clean-up and Circular Economy -- Copyright -- Contents -- Contributors -- Preface -- 1 - Artificial intelligence in pollution emission control of municipal solid waste incineration process: Status and future ... -- 1. Introduction -- 2. Problem description of MSWI pollution control in terms of AI -- 2.1 Process description -- 2.2 Control mode in developed and developing countries -- 2.3 Grim situation of industrial process control safety under cloud-edge-end collaboration -- 2.4 Challenges faced by MSWI enterprises -- 2.5 Difficulties in the implementation and application of "AI+MSWI" technology -- 2.5.1 Controlled object modeling of combustion process -- 2.5.2 Real-time detection modeling of operational indicators -- 2.5.3 Adaptive self-organizing control of combustion process -- 2.5.4 Operational status perception and fault diagnosis -- 2.5.5 Collaborative operation optimization of whole-process incineration -- 2.5.6 AI modeling, control, and optimization algorithm validation -- 3. Research status of MSWI pollution emission control -- 3.1 Unidirectional data security collection status in MSWI process -- 3.2 AI algorithm research status in MSWI process -- 3.2.1 AI modeling algorithm -- 3.2.1.1 Numerical simulation and AI hybrid-driven AI modeling algorithm -- 3.2.1.2 Difficult-to-measure trace pollutant detection modeling algorithm -- 3.2.1.3 Easy-to-measure traditional pollutant prediction modeling algorithm -- 3.2.2 AI control algorithm -- 3.2.2.1 On-site control algorithm -- 3.2.2.2 Off-site control algorithm -- 3.2.3 AI maintenance algorithm -- 3.2.3.1 Flame combustion state recognition algorithm. ; 3.2.3.2 Pollutant emission warning algorithm -- 3.2.4 AI optimization algorithm -- 3.2.4.1 Controlled variable setpoint value optimization algorithm -- 3.2.4.2 Manipulated variable output value optimization algorithm -- 3.3 AI algorithm verification platform status in MSWI process -- 4. Prospects status of AI in MSWI pollution emission control -- 4.1 Multipurpose AI modeling algorithm -- 4.2 Multiposition AI control algorithm -- 4.3 Multilevel AI maintenance algorithm -- 4.4 Multiobjective AI optimization algorithm -- 4.5 Multifunction AI algorithm verification platform -- 5. Future AI security empower cloud-edge-end systems for whole-process operation optimization -- 5.1 Multimodal data AI modeling system -- 5.1.1 Multimodal data modeling structured processing subsystem -- 5.1.2 Difficult-to-measure parameter detection subsystem -- 5.1.3 Key process parameter prediction subsystem -- 5.1.4 Combustion state quantification and identification subsystem -- 5.1.5 Environmental indicators optimization modelling subsystem -- 5.1.6 Manipulated variables optimization modelling subsystem -- 5.2 End-side industrial site DCS auxiliary system -- 5.2.1 Operating parameter assisted decision-making subsystem -- 5.2.2 Process monitoring subsystem -- 5.3 Edge-side security isolation and AI control system -- 5.3.1 Edge-side data collection forward isolation subsystem -- 5.3.2 Edge-side AI security empowerment control subsystem -- 5.3.3 Edge-side operation parameter reverse transmission subsystem -- 5.4 Cloud-side security isolation and AI optimization system -- 5.4.1 Cloud-side security forward access subsystem -- 5.4.2 Cloud-side AI security empowerment optimization subsystem -- 5.4.3 Cloud-side secure reverse transmission subsystem -- 6. Conclusion -- References -- Further reading. ; 2 - Predicting the compressive strength of sustainable cement mortar composite incorporating biochar: A machine learning mo ... -- 1. Introduction -- 1.1 Background of this study -- 2. Biochar: Definition, process, properties, and applications -- 2.1 Biomass -- 2.2 Biochar -- 2.3 The production of BC -- 2.3.1 Pyrolysis -- 2.3.2 Gasification -- 2.3.3 Hydrothermal carbonization -- 2.3.4 Other thermochemical methods -- 2.4 Properties of BC -- 2.4.1 Chemical characteristics -- 2.4.2 Physical characteristics -- 3. Machine learning: Background, methods, and uses -- 3.1 Background history of ML -- 3.2 Theory/methods of ML -- 3.3 Category of ML applicable for predictive tasks -- 3.3.1 Artificial neural networks -- 3.3.2 Random forest -- 3.3.3 Support vector machine -- 3.3.4 eXtreme gradient boosting -- 3.3.5 Hybrid ML algorithms -- 4. Cement mortar composite with BC -- 4.1 Properties and characteristics of BC cement composite -- 4.1.1 Fresh properties -- 4.1.2 Mechanical properties -- 4.2 Machine learning in predicting mechanical attributes of cement composites -- 5. Conclusions -- References -- 3 - Mapping European innovation and policy landscapes using deep learning: Technologies for sustainable wastewater management -- 1. Introduction -- 2. Background -- 2.1 Urban wastewater treatment directive -- 2.2 Data and source -- 2.3 Global patent grant trends -- 3. Materials and methods -- 3.1 Patent and policy analytics -- 3.2 KeyBERT -- 3.3 Patent and policy mapping -- 4. Results and key discussions -- 5. Cautions and conclusion -- References -- 4 - Artificial intelligence in removal of emerging contaminants: A sustainable approach for environmental cleanup and circu ... -- 1. Introduction -- 1.1 Introduction to the role of AI in environmental cleanup -- 1.2 Objectives of the review -- 1.2.1 To evaluate the potential of AI in removing ECs. ; 1.2.2 To explore the sustainability and circular economy aspects of AI-driven solutions -- 1.2.3 To identify future research directions -- 2. Emerging contaminants: Sources, types, and environmental impact -- 2.1 Sources of emerging contaminants -- 2.2 Types and characteristics of emerging contaminants -- 2.3 Environmental and health impacts -- 3. Traditional methods for removal of emerging contaminants -- 3.1 Chemical methods -- 3.2 Biological methods -- 3.3 Limitations of traditional methods -- 4. Artificial intelligence in environmental cleanup -- 4.1 Overview of AI techniques -- 4.2 AI in environmental monitoring and assessment -- 4.3 AI in process optimization and control -- 4.4 Case studies and applications -- 5. AI-Driven technologies for removing emerging contaminants -- 5.1 Neural networks and deep learning -- 5.2 Expert systems and decision support -- 5.3 Hybrid AI systems -- 6. Sustainability and circular economy considerations -- 6.1 Environmental sustainability of AI solutions -- 6.2 Economic sustainability and cost-benefit analysis -- 6.3 Circular economy principles -- 6.4 AI in supporting circular economy models -- 6.4.1 AI and climate change mitigation -- 6.4.2 AI in public health and safety -- 6.4.3 AI in resource management -- 6.4.4 Integration with other emerging technologies -- 7. Economic impacts and job creation -- 8. Challenges and future directions -- 8.1 Research gap in AI for environmental cleanup and circular economy -- 9. Conclusion -- 9.1 Summary of key findings -- 9.2 Implications for practice and policy -- 9.3 Final thoughts -- References -- Further reading -- 5 - Application of conventional methods and artificial intelligence tools for wastewater treatment: Recent advancements and ... -- 1. Introduction -- 1.1 Background and importance of wastewater treatment -- 1.1.1 Overview of global wastewater treatment challenges. ; 1.2 Evolution of wastewater treatment techniques -- 1.2.1 Emergence and role of AI in wastewater treatment -- 1.3 Objectives and scope of the review -- 1.3.1 Purpose of comparing conventional methods with AI tools -- 2. Conventional methods of wastewater treatment -- 2.1 Overview of conventional treatment processes -- 2.1.1 Primary, secondary, and tertiary treatment stages -- 2.2 Physical treatment methods -- 2.2.1 Screening -- 2.2.2 Grit removal -- 2.2.3 Sedimentation -- 2.2.4 Filtration -- 2.3 Chemical treatment methods -- 2.3.1 Coagulation, flocculation, chlorination, ozonation -- 2.3.2 Coagulation -- 2.3.3 Flocculation -- 2.3.4 Chlorination -- 2.3.5 Ozonation -- 2.4 Biological treatment methods -- 2.4.1 Activated sludge process, biofiltration, anaerobic digestion -- 2.4.2 Activated sludge process -- 2.4.3 Biofiltration -- 2.4.4 Anaerobic digestion -- 2.5 Advanced treatment methods -- 2.5.1 Membrane filtration -- 2.5.2 UV radiation -- 2.5.3 Advanced oxidation processes (AOPs) -- 2.6 Challenges and limitations of conventional methods -- 2.6.1 Efficiency, cost, environmental impact, and scalability -- 3. Artificial intelligence in wastewater treatment -- 3.1 Introduction to AI and its relevance in wastewater treatment -- 3.1.1 Definition and basic concepts of AI -- 3.1.2 AI applications in environmental engineering -- 3.2 AI tools and techniques -- 3.2.1 Machine learning -- 3.2.2 Deep learning -- 3.2.3 Neural networks -- 3.2.4 Fuzzy logic -- 3.2.5 Genetic algorithms -- 3.3 Applications of AI in wastewater treatment -- 3.3.1 Monitoring and control systems -- 3.3.2 Predictive modeling and optimization -- 3.3.3 Real-time decision-making and automation -- 4. Comparative analysis -- 4.1 Efficiency and effectiveness -- 4.1.1 Treatment performance: removal efficiency and water quality -- 4.1.2 Response to varying wastewater characteristics. ; 4.2 Cost analysis.
• Sommario/riassunto: Applications of Artificial Intelligence in Removal of Emerging Contaminants: Sustainable Approach for Environmental Clean-up and Circular Economy focuses on the exploitation of various Artificial Intelligence (AI) tools to treat emerging contaminants and the restoration of contaminated sites.
• Titolo autorizzato: Applications of Artificial Intelligence in Removal of Emerging Contaminants
• ISBN: 0-443-26780-4; 9780443267802
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911119063203321
• Lo trovi qui: Univ. Federico II