DoctorAlfred Herritsch

My research interests centre on dynamic, first-principles simulation and engineering digital twins for complex process systems. I develop physics-based models that operate across unit, plant, and system scales and remain valid outside historical operating conditions, enabling scenario analysis, optimisation, and control design.

My foundational expertise includes modelling of energy-intensive unit operations, particularly industrial drying (timber, foods, biomass, wastewater sludge), and separation and extraction processes, including enzyme-assisted nutraceutical recovery (e.g. anthocyanins). These applications provide validated sub-models for heat and mass transfer, kinetics, and energy demand within larger digital-twin frameworks.

Current research focuses on plant-wide and system-level digital twins for energy, water, and carbon optimisation, supported by hybrid modelling approaches that combine mechanistic simulation with data-informed calibration and explainable AI. A key research strand investigates informative and human-centred digital twins, exploring how adaptive visualisation, immersive environments, and role-specific interfaces improve understanding, trust, and adoption of advanced modelling tools in both industry and education.