11045nam 22005173 450 991111906320332120251127080514.00-443-26780-49780443267802(MiAaPQ)EBC32428072(Au-PeEL)EBL32428072(CKB)43713391400041(OCoLC)1555343471(EXLCZ)994371339140004120251127d2025 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplications of Artificial Intelligence in Removal of Emerging Contaminants Sustainable Approach for Environmental Clean-Up and Circular Economy1st ed.Chantilly :Elsevier,2025.©2025.1 online resource (471 pages)0-443-26779-0 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.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.Environmental innovationsGenerated by AIPollution control equipmentGenerated by AIEnvironmental innovationsPollution control equipment628.5028563Sillanpää Mika767426MiAaPQMiAaPQMiAaPQBOOK9911119063203321Applications of Artificial Intelligence in Removal of Emerging Contaminants4800413UNINA