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The unit specializes in evaluating, optimizing, controlling and automating industry processes. This includes process modeling and simulation through the development of dynamic models, digital twins and other techniques, as well as advanced process control and optimization, using techniques such as control predictive based on models, scheduling or neural networks. In addition, it comprehensively evaluates industrial processes, addressing techno-economic viability, eco-efficiency and risk and resilience analysis. The unit promotes sustainability and energy efficiency as fundamental pillars for the digital transition of the industry.
Process modeling and simulation: development of dynamic, gray box, hybrid or reduced models; simulators for personnel training; digital twins for decision support; parameter estimation; data reconciliation.
Advanced process control: control systems; model-based predictive control (MPC); Economy MPC and hybrid MPC.
Optimal process operation: real-time optimization; coordination of continuous and batch processes; scheduling; optimization with uncertainty; stochastic optimization in two stages.
Process supervision: resource use efficiency indicators (REIs/KPIs); plant-wide data reconciliation; estimation of unmeasured variables; controller supervision; fault detection and diagnosis methods.
Industrial Computing: development of control systems (DCS, MPC) and functionalities with industrial applications; OPC-based systems for communications between processes and systems; implementation of large-scale systems with real-time databases (Osisoft PI); Hardware in the Loop (HIL) systems.
Artificial neural networks
Development of sustainability indicators in the urban water cycle.
Water-energy-carbon nexus in the urban water cycle.
Analysis of economic viability including valuation of environmental externalities.
Evaluation of the technical, economic and environmental efficiency in the provision of drinking water and wastewater treatment.
Assessment of risk and resilience in the urban water cycle in the face of natural threats.
de la Fuente Tagarro, C., Martín-González, D., De Lucas, A., Bordel, S., Santos-Beneit, F. (2024) Current Knowledge on CRISPR Strategies Against Antimicrobial-Resistant Bacteria. Antibiotics. https://doi.org/10.3390/antibiotics13121141
Maziotis, A., Sala-Garrido, R., Mocholi-Arce, M., Molinos-Senante, M. (2024) Understanding water-energy-carbon nexus in English and Welsh water industry by assessing eco-productivity of water companies. npj Clean Water. https://doi.org/10.1038/s41545-024-00420-8
Maziotis, A., Sala-Garrido, R., Mocholi-Arce, M., Molinos-Senante, M. (2024) Evaluating carbon performance in the water industry: A longitudinal analysis of England and Wales. Results in Engineering. https://doi.org/10.1016/j.rineng.2024.103580
Ruiz Palomar, C., García Álvaro, A., Muñoz, R., Repáraz, C., Ortega, M.F., de Godos, I. (2024) Pre-Commercial Demonstration of a Photosynthetic Upgrading Plant: Investment and Operating Cost Analysis. Processes. https://doi.org/10.3390/pr12122794
Jo, Y., Hoyos, E.G., Blanco, S., Kim, S.-H., Muñoz, R. (2024) Assessing nitrous oxide emissions from algal-bacterial photobioreactors devoted to biogas upgrading and digestate treatment. Chemosphere. https://doi.org/10.1016/j.chemosphere.2024.142528
