Integrating Artificial Intelligence and Nanotechnology in Environmental Biotechnology: Monitoring, Remediation, and Waste Management

This chapter explores the convergence of artificial intelligence (AI) and nanotechnology within environmental biotechnology, emphasizing their combined potential to enhance environmental monitoring, pollution remediation, and waste management efforts. A range of AI models, such as deep neural networks, support vector machines, random forests, and hybrid approaches, are assessed for their accuracy, scalability, and efficiency in real-time environmental evaluation. The focus is on the implementation of nano-AI sensors, integrating nanomaterials with artificial intelligence to enhance sensitivity and selectivity in identifying contaminants in air, water, and soil. Case studies feature intelligent waste bins that utilize AI for improved waste segregation and collection, alongside AI-enhanced vehicle routing and real-time monitoring to boost operational efficiency. This chapter explores the application of AI in forecasting energy demand, enhancing renewable energy systems, and overseeing power grid management. Significant obstacles recognized encompass the necessity for extensive, high-calibre datasets, safeguarding data, and the intricacies involved in updating models within real-time systems. The future of environmental biotechnology is expected to showcase advanced automation and intelligent systems that harness the synergistic capabilities of AI and nanotechnology to tackle intricate environmental issues with enhanced accuracy and sustainability.