CODE: OG37
DURATION: 5 Days/10 Days
CERTIFICATIONS: CPD
This course provides a comprehensive introduction to the application of Artificial Intelligence (AI) and Machine Learning (ML) in Predictive Emissions Monitoring Systems (PEMS). Participants will explore how advanced algorithms and data-driven approaches can be leveraged to model, predict, and optimize emissions in industrial processes, reducing reliance on traditional continuous emissions monitoring systems (CEMS). Through a blend of theoretical foundations and real-world case studies, participants will gain insights into emissions modeling, data preprocessing, model training, validation, and deployment for compliance, operational efficiency, and sustainability goals.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the principles of predictive emissions monitoring and its advantages over traditional methods.
Learn key AI and ML techniques for emissions modeling and forecasting.
Acquire skills in handling industrial emissions data, including cleaning, preprocessing, and feature engineering.
Develop, evaluate, and validate predictive models for real-world applications.
Explore case studies of successful PEMS implementations and emerging trends in regulatory compliance.
At the end of this course, you’ll understand:
This course is designed for environmental engineers, data scientists, process engineers, compliance managers, and professionals working in industries such as power generation, oil & gas, chemical manufacturing, and heavy industries. It is also suitable for researchers, technology enthusiasts and other individuals who are keen to understand how AI and ML can be applied to environmental monitoring and regulatory compliance.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
