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A Hybrid Modelling Framework for Dynamic Modelling of Bioprocesses
One of the main hurdles in the computer-aided design and optimization of industrial bioprocesses is the limited capability of models to …
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Ensemble Kalman Filter for estimation of intracellular nucleotide sugars from extracellular metabolites in monoclonal antibodies
The emergence of Quality by Design (QbD) and Process Analytical Technology (PAT) paradigm supported by the FDA imposes a strong …
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Hybrid data-driven and first principles monitoring applied to the Tennessee Eastman process
In this work we present a hybrid monitoring approach for fault detection using the Tennessee Eastman (TE) process. We benchmark our …
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Investigating physics-informed neural networks for bioprocess hybrid model construction
Integrating physical knowledge and machine learning is a cost-efficient solution to modelling complex biochemical processes when the …
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Convex Q-learning: Reinforcement learning through convex programming
Over the last decade, Reinforcement Learning (RL) has received significant attention as it promises novel and efficient solutions to …
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Data-driven coordination of expensive black-boxes
Coordinating decision-making capacities using optimization is a key factor in the success of chemical companies. However, this …
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Piecewise Smooth Hybrid System Identification for Model Predictive Control
Complex systems which exhibit different dynamics based on their operating region pose challenges for data driven control because a …
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An adaptive data-driven modelling and optimization framework for complex chemical process design
Current advances in computer-aided chemical process design and synthesis take advantage of surrogate modelling and superstructure …
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Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation
Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by …
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