Research Project

L-BEST: Supervision and fault-tolerant control of smart infrastructures based on advanced learning and optimization

Type

National Project

Start Date

01/09/2021

End Date

31/08/2024

Project Code

PID2020-115905RB-C21

Project illustration

Staff

Project Description

Project PID2020-115905RB-C21 funded by MCIN/ AEI /10.13039/501100011033

Smart infrastructures (SIs), such as water and energy networks, buildings, among others, are most important in modern society because of the services they provide and the essential resources they manage. In SIs, safety and efficiency must be taken into account in all steps of their life cycle, including design, operation and validation of the resulting performance.

L-BEST considers research in supervision and fault-tolerant control of SIs by means of two related methodologies: advanced learning and optimization. The use of advanced learning from data is supported by the fact that SIs include sensors and smart-grid technology providing a continuous flow of operational data. First-principles models alone may not capture the complex behaviours of these SIs, so that a combination with learning from operational data is proposed. Optimization-based control is concerned with computing strategies which improve specific performance indicators, such as those related to efficiency and safety. Advanced optimization and learning are proposed for this type of problems in large-scale and complex SIs.


The specific goals of L-BEST are grouped in five complementary axes:

(i) Designing learning-based approaches for monitoring, forecasting and control of SIs taking into account interaction of multiple sensors and actuators as well as uncertainty.

(ii) Developing reconfiguration methodologies for fault-tolerant control schemes applied to complex SIs.

(iii) Designing distributed control techniques for SIs based on advanced learning methods and evolutionary game theory.

(iv) Contributing to ontologies definition for platform development in order to provide a flexible solution capable to cover different nature of SIs.

(v) Implementing demonstrators for the design and implementation of the proposed learning-based supervision approaches and analyse the economic, social and environmental impact of the demonstrator results.


The research team has a strong background in two key areas: methodology developments towards optimization-based control of large-scale SIs (especially energy and water infrastructures, e.g., drinking water and sewer networks, canals and rivers, electric networks, clusters of microgrids); and fault diagnosis and fault-tolerant control towards the supervision of complex industrial systems.
The team also includes two researchers from Cetaqua Water Technology Centre, a foundation of the water company Aguas de Barcelona, CSIC and UPC, and a reference centre in the application of scientific knowledge to sustainable management of water and environmental systems. This collaboration is essential for the orientation of research towards real industrial and market needs, as well as for maximizing the impact and knowledge transfer towards industry. Realistic demonstration case studies will be provided by Cetaqua and industrial partners (EPO). Specifically, the case studies in smart water networks will be related to: monitoring for leak detection and localization in water networks, monitoring water quality, water and energy efficiency in water networks and network reconfiguration after fault, such as bursts or contamination events.


The expected impact of L-BEST has several aspects; namely: a scientific impact for the automatic control and SI management communities, dissemination in top journals, communication actions to more general public and, most importantly, technology transfer to the water industry.

Project Publications

Journal Publications

  • P. Irofti, L. Romero, F. Stoican and V. Puig. Learning dictionaries from physical-based interpolation for water network leak localization. IEEE Transactions on Control Systems Technology, 32(3): 755-766, 2024.

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  • C. Trapiello, L. Romero, J. Meseguer, V. Puig, G. Cembrano, B. Joseph, M. Sarrias, D. Saporta and M. Minoves. Automatic network response methodology for failure recovery or bursts in drinking water networks. Journal of Water Resources Planning and Management, 149(1): 04022073, 2023.

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  • J.L. Svensen, C. Sun, G. Cembrano and V. Puig. Model predictive control of urban drainage systems considering uncertainty. IEEE Transactions on Control Systems Technology, 31(6): 2968-2975, 2023.

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  • L. Romero, D. Alves, J. Blesa, G. Cembrano, V. Puig and E. Duviella. Leak detection and localization in water distribution networks: review and perspective. Annual Reviews in Control, 55: 392-419, 2023.

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  • Y.O. Eldigair, C. Kunusch and C. Ocampo-Martínez. Optimization-based thermal control strategy for auxiliary cooling circuits in fuel cell vehicles. IEEE Transactions on Transportation Electrification, 9(2): 2734-2743, 2023.

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  • A. Soldevila, J. Blesa, S. Tornil-Sin, R.M. Fernandez-Cantí and V. Puig. Incremental upgrading sensor placement methodology: application to the leak localization in water networks. Computers and Chemical Engineering, 2022(158): 107642, 2022.

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  • F. Arqué, C. Uribe and C. Ocampo-Martínez. Approximate Wasserstein attraction flows for dynamic mass transport over networks. Automatica, 143: 110432, 2022.

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  • J.P. Martínez, C. Ocampo-Martínez and N. Quijano. On distributed Nash equilibrium seeking in a class of contractive population games. IEEE Control Systems Letters, 6: 2972-2977, 2022.

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  • L. Romero, D. Alves, J. Blesa, G. Cembrano, V. Puig and E. Duviella. Leak localization in water distribution networks using data-driven and model-based approaches. Journal of Water Resources Planning and Management, 148(5): 04022016, 2022.

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  • K.M. Balla, C. Schou, J. Bendtsen, C. Ocampo-Martínez and C. Kallesoe. A nonlinear predictive control approach for urban drainage networks using data-driven models and moving horizon estimation. IEEE Transactions on Control Systems Technology, 30(5): 2147–2162, 2022.

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  • D. Alves, J. Blesa, E. Duviella and L. Rajaoarisoa. Robust data-driven leak localization in water distribution networks using pressure measurements and topological information. Sensors, 21(22): 7551, 2021.

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Conference Publications

  • L. Romero, G. Cembrano and V. Puig. Leak learning for graph-based state interpolation in water distribution networks, 2024 European Control Conference, 2024, Stockholm (Sweden), pp. 110-115.

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  • L. Romero, P. Irofti, F. Stoican and V. Puig. Nodal hydraulic head estimation through Unscented Kalman Filter for data-driven leak localization in water networks, 12th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, 2024, Ferrara, Vol 58 of IFAC Papers Online, pp. 67-72.

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  • X. Fang, J. Blesa and V. Puig. Fault diagnosis using interval data-driven LPV observers and structural analysis, 12th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, 2024, Ferrara, Vol 58 of IFAC Papers Online, pp. 25-30.

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  • X. Fang, J. Blesa and V. Puig. Fault prognosis approach using data-driven structurally generated residuals, 32nd Mediterranean Conference on Control and Automation, 2024, Chania - Crete, Greece, pp. 531-536.

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  • L. Gómez, I. Santos-Ruiz, J. Blesa, V. Puig and F.R. Lopez. Leak localization in an urban water distribution network using a LSTM deep neural network, 12th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, 2024, Ferrara, Vol 58 of IFAC Papers Online, pp. 79-84.

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  • J.P. Martínez, C. Ocampo-Martínez, N. Quijano and A. Ingimundarson. Microalgae production and maintenance optimization via mixed-integer model predictive control, 22nd IFAC World Congress, 2023, Yokohama, pp. 11100-11105.

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  • L. Romero, J. Blesa, G. Cembrano and V. Puig. A comparison between model-based and data-driven leak localization methods, 22nd IFAC World Congress, 2023, Yokohama, pp. 737-742.

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  • X. Fang, J. Blesa and V. Puig. Fault detection using data-driven LPV state estimation based on structural analysis and ANFIS, 2023 European Control Conference, 2023, Bucharest (Romania), pp. 1-6.

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  • L. Romero, G. Cembrano, V. Puig and J. Blesa. Model-free sensor placement for water distribution networks using genetic algorithms and clustering, 2022 IFAC Workshop on Control Methods for Water Resource Systems, 2022, Milan, Italy, Vol 33 of IFAC-PapersOnLine, pp. 54-59.

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  • D. Alves, J. Blesa, E. Duviella and L. Rajaoarisoa. Leak detection in water distribution networks based on water demand analysis, 11th IFAC Symposium on Fault Detection Supervision and Safety for Technical Processes, 2022, Pafos, Cyprus, Vol 55 of IFAC Papers Online, pp. 679-684.

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  • D. Alves, J. Blesa, E. Duviella and L. Rajaoarisoa. Multi-leak detection and isolation in water distribution networks, 2022 International Joint Conference on Water Distribution Systems Analysis (WDSA) & Computing And Control In The Water Industry (CCWI), 2022, Valencia.

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  • D. Alves, J. Blesa, E. Duviella and L. Rajaoarisoa. Topological analysis of water distribution networks for optimal leak localization, 2022 International Conference on Hydroinformatics, 2022, Bucharest, Romania, Vol 1136 of IOP Conference Series: Earth and Environmental Science, pp. 012043.

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  • P. Irofti, L. Romero, F. Stoican and V. Puig. Data-driven leak localization in water distribution networks via dictionary learning and graph-based interpolation, 6th IEEE Conference on Control Technology and Applications, 2022, Milan, Italy, pp. 1265-1270.

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  • D. Alves, J. Blesa, E. Duviella and L. Rajaoarisoa. Data-driven leak localization in WDN using pressure sensor and hydraulic information, 2nd IFAC Workshop on Integrated Assessment Modelling for Environmental Systems, 2022, Tarbes, France, Vol 55 of IFAC-PapersOnLine, pp. 96-101.

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  • F. Arqué, C. Uribe and C. Ocampo-Martínez. Application of Wasserstein attraction ows for optimal transport in network systems, 60th IEEE Conference on Decision and Control, 2021, Austin, TX, USA (Virtual), pp. 4058-4063.

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