Common Pitfalls When Applying Machine Learning
Richard Braatz, Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA
There have been significant advances in machine learning over the last twenty years, which have produced more accurate predictions in a variety of well-validated industrial case studies. On the other hand, there are many ways to incorrectly apply machine learning methods, usually resulting in models that are much less accurate than believed and have poor predictive value. This presentation describes a half dozen common pitfalls that occur when applying machine learning, and ways to avoid such pitfalls. Following these recommendations produce machine learning models that have higher predictive value.
Industrial AI for Agility, Guidance and Automation
Heiko Claussen, AspenTech, Bedford, MA
In the rapidly evolving field of chemical engineering, Industrial AI is becoming increasingly important. Industrial AI can accelerate the speed and accuracy of design, operation, and maintenance of chemical assets. To be effectively used in industrial contexts, guardrails comprised of chemical industry first principles, and explainability of the path to results will make the AI explainable and trustworthy. Within that framework, applications are already providing successful results, that create value in terms of agility, guidance, and automation. This presentation, led by Heiko Claussen, the Co-Chief Technology Officer at Aspen Technology, will delve into the transformative impact of these elements on the industry. Attendees will gain insights into how agility can enhance innovation and operational efficiency, how advanced guidance systems can improve decision-making processes, and how automation can streamline complex workflows and the handling of large data sets. The session will also explore real-world case studies demonstrating the successful implementation of these technologies, providing a comprehensive understanding of their benefits and challenges. Join us to discover how embracing agility, guidance, and automation can drive innovation and excellence in chemical engineering.
