CTV contributed to the 36th European Symposium on Computer Aided Process Engineering, held in Sheffield from June 21–24.
Alexander Wolf presented “A Neural Model of Pinch-Based Multicomponent Distillation Columns for Applications in Flowsheeting”, introducing a neural-network shortcut model for fast and robust prediction of distillation column products in conceptual process design. The model is designed for flowsheeting and optimization tasks, where many reliable column evaluations are required.
Aparna Muraleedharan contributed to the poster “Beyond Tennessee Eastman: Benchmarking Deep Anomaly Detection on Real-World Pilot-Scale Continuous Distillation Data”, which examined anomaly detection methods using realistic pilot-plant datasets.
Together, these contributions highlight CTV’s work on machine-learning-based methods for chemical process design, optimization, and operation.