AI for Waste-to-Energy Systems in Urban Areas: Circular Economy Modeling & Emissions Reduction

About This Course

In this 3-module course, participants will learn how AI can transform waste-to-energy (WTE) systems in urban areas, focusing on circular economy modeling, emissions reduction, and smart urban development. You’ll explore the integration of AI tools in optimizing waste management, enhancing energy production, and reducing emissions for sustainable cities. This course is packed with practical, real-world examples, showing how AI solutions can help create cleaner, more efficient, and eco-friendly urban systems.

Aim

To provide participants with the tools and knowledge to apply AI in waste-to-energy systems, enhancing resource recovery, reducing emissions, and implementing circular economy principles for smarter and more sustainable urban development.

Course Structure

Module 1 — Introduction to Waste-to-Energy Systems & AI Applications

• Overview of waste-to-energy systems: what they are and how they help cities grow sustainably

• Types of WTE technologies: incineration, biogas, pyrolysis, and their roles in energy production

• The role of waste-to-energy in sustainable urban development and overcoming waste challenges

• Introduction to AI applications in waste management: how AI optimizes sorting, collection, and treatment processes

• Case studies: Real-world examples of AI implementation in waste management and energy generation

Module 2 — Circular Economy Modeling & AI-Driven Emissions Tracking

• Circular economy principles and their relevance to waste management systems in cities

• Integrating circular economy models into urban planning and waste management for resource efficiency

• AI-based tools for tracking circularity, waste diversion, and resource recovery

• Emissions reduction strategies in waste-to-energy: using AI for lifecycle emissions tracking

• AI techniques for forecasting and optimizing emissions

• Case studies: Lifecycle emissions reduction and tracking using AI for sustainability

Module 3 — AI-Enhanced Optimization of Waste-to-Energy Systems & Future Trends

• Optimizing WTE systems with machine learning algorithms: enhancing energy production and waste processing efficiency

• Predictive maintenance and real-time monitoring of WTE systems using AI-powered models

• Data-driven decision-making for energy generation and waste management processes

• Future trends in AI for waste-to-energy integration: emerging technologies and innovations in AI-driven solutions

• How AI contributes to future urban sustainability goals and how cities can implement these tools for long-term success

Who Should Enrol?

• Environmental professionals, researchers, and students in urban planning, engineering, and sustainability

• Anyone interested in smart urban solutions, waste management, and circular economy

• Urban planners and engineers working on sustainable city projects

• No prior AI or waste-to-energy experience required, though helpful