arXiv subjects

Ethan Errington

Publications and source records attributed to Ethan Errington.

4 recordsLinked to original sources

arXiv2026

PyOMES: an open-source framework for biochemical process modelling

PyOMES is a Python-based, Open-source Modelling Environment for (bio)chemical process Simulation that aims to simplify the modelling of dynamic (including steady state) processes. This is done in a generalied, modular way to facilitate modelling a broad range of biological, chemical, and biochemical systems under a single modelling framework. PyOMES has been built to be accessible to a broad range of users - ranging from those with little modelling experience, such as experimentalists and students, through to more experienced power-users. Here, an introduction is provided to the PyOMES software including a summary of the design, architecture and vision. Use cases are then provided to demonstrate applicability of PyOMES to a number of (bio)chemical process modelling scenarios. Comparisons of predictions against existing benchmark software (i.e. PHREEQC) demonstrate the robustness of the package. Finally, a summary is provided of future directions for the PyOMES package - highlighting its establishment as a unified modelling framework and its potential for community-driven improvement as future developments.

q-bio.QM↗

arXiv2026

Transfer Learning Architectures for Scalable Multi-Fidelity Bayesian Optimization

Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core. Gaussian processes (GPs) are the default choice, but they scale poorly as data accumulate and assume a smooth landscape that molecular and materials search spaces routinely violate. Transfer learning offers an alternative suited to this regime: it learns a representation from abundant cheap data and adapts it to sparse expensive data. Despite its use in property prediction, transfer learning has not been tested as the engine of a closed-loop optimization. Here we benchmark eleven transfer-learning surrogates against four GP methods under an identical selection rule, fidelity budget, and model size, across nine tasks spanning synthetic functions to real chemistry and materials problems. GPs win on smooth, low-dimensional functions but perform worst on molecular and materials problems, where transfer-learning surrogates reach substantially better solutions using far less computation. Because acquisition policy is held fixed across surrogates, this advantage is attributable to the surrogate itself. Uncertainty-driven exploration is not reliably beneficial, and calibration does not predict optimization performance, so greedy exploitation of the transfer-learned mean is the more robust default. Transfer learning is therefore the surrogate of choice for molecular and materials MFBO.

cs.LG↗

arXiv2026

Valorisation of Fermentation Side-Stream for Waste-to-Mycoprotein: Nutrient Composition, Metabolic Insights and Process Optimisation

Fermentation-derived side streams represent an underutilised resource for sustainable protein production. This study investigates the potential of centrate from industrial Fusarium venenatum fermentation as a nutrient source for fungal biomass generation. Following compositional characterisation, a synthetic centrate medium was formulated and evaluated using a Box-Behnken design combined with response surface methodology. Across 46 experimental runs, cell dry weight (CDW) ranged from 0.22 to 3.87 g per liter, demonstrating a strong dependence on nutrient composition. Ammonia and glucose were identified as the dominant factors influencing biomass production, with significant nonlinear effects. The model predicted a maximum CDW of 4.17 g per liter under optimised conditions, which was experimentally validated at 3.99 g per liter. Carbon conversion efficiency reached up to 29.02%, indicating effective substrate utilisation. These findings demonstrate that fermentation-derived centrate can support substantial fungal growth, while highlighting its potential to enhance nutrient recovery and influence the biochemical composition of sustainable mycoprotein.

eess.SY↗

arXiv2024

A KAN-based Interpretable Framework for Process-Informed Prediction of Global Warming Potential

Accurate prediction of Global Warming Potential (GWP) is essential for assessing the environmental impact of chemical processes and materials. Traditional GWP prediction models rely predominantly on molecular structure, overlooking critical process-related information. In this study, we present an integrative GWP prediction model that combines molecular descriptors (MACCS keys and Mordred descriptors) with process information (process title, description, and location) to improve predictive accuracy and interpretability. Using a deep neural network (DNN) model, we achieved an R-squared of 86% on test data with Mordred descriptors, process location, and description information, representing a 25% improvement over the previous benchmark of 61%; XAI analysis further highlighted the significant role of process title embeddings in enhancing model predictions. To enhance interpretability, we employed a Kolmogorov-Arnold Network (KAN) to derive a symbolic formula for GWP prediction, capturing key molecular and process features and providing a transparent, interpretable alternative to black-box models, enabling users to gain insights into the molecular and process factors influencing GWP. Error analysis showed that the model performs reliably in densely populated data ranges, with increased uncertainty for higher GWP values. This analysis allows users to manage prediction uncertainty effectively, supporting data-driven decision-making in chemical and process design. Our results suggest that integrating both molecular and process-level information in GWP prediction models yields substantial gains in accuracy and interpretability, offering a valuable tool for sustainability assessments. Future work may extend this approach to additional environmental impact categories and refine the model to further enhance its predictive reliability.

cs.LG↗