HI IBERIA INGENIERIA Y PROYECTOS S.L.

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Pic legend 1: DONES-FLUX

Pic legend 2: SEDA

Pic legend 3: SM

Pic legend 4: e-PROA

General information

Company name

HI IBERIA INGENIERIA Y PROYECTOS S.L.
Adress

C/ Juan Hurtado de Mendoza, 14
Web

https://www.hi-iberia.es/
Turnover

3.80 million EUR in year 2023

Employees

108 in year 2023
SME

YES
Contact Info:

Phone

+34914585119
Email

dones@hi-iberia.es

Activity and Skills

At HI Iberia (HIB), we bring over two decades of expertise in AI development tailored to meet the rigorous demands of Big Science facilities. Our competencies are anchored in deploying advanced AI, machine learning, and deep learning techniques to enhance operational efficiency, predictive modeling, and real-time data analytics in complex scientific environments.
Capabilities for Big Science Facilities

1. Advanced Data Analytics and Machine Learning: HIB applies AI for predictive maintenance and fault detection in large-scale scientific equipment, ensuring uptime and efficiency. Projects like MAPRE and DONES-FLUX showcase their expertise in handling complex data streams and optimizing critical infrastructure, including particle accelerators.
2.AI-Driven Materials Discovery: HIB accelerates material discovery with AI, using deep learning and reinforcement learning. Projects like SMART MATERIAL are essential for rapid material innovation in fusion reactors and accelerator technologies.
3. Simulation and Predictive Modeling: HIB uses AI techniques like Deep Learning Surrogate Models (DLSMs) and Fourier Neural Operators (FNOs) to enhance predictive modeling for fusion facilities, improving efficiency and accuracy in physical processes while reducing computational costs.
4. Computer Vision and Image Analysis: HIB has developed real-time computer vision solutions for monitoring and analyzing visual data, as demonstrated in the LIFEonLive project, which are crucial for anomaly detection in Big Science facilities.
5. AI for Energy Optimization: HIB optimizes renewable energy integration and energy flow within fusion research facilities. Projects like ENIGMA and DONES-FLUX focus on maximizing energy efficiency and reducing costs in large-scale scientific environments.
6. Intelligent Infrastructure Management: HIB creates AI-based models for managing and optimizing infrastructure in complex scientific environments, as demonstrated in DONES-MAGIA and DONES-FLUX, enhancing reliability and maintainability.
7. Natural Language Processing (NLP) and Conversational AI: HIB develops NLP and conversational AI tools for better human-machine interaction, assisting researchers and operators in managing complex datasets in Big Science environments.
8. Cybersecurity and Edge Computing: HIB integrates cutting-edge cybersecurity and edge computing solutions to ensure data security and low-latency processing, vital for the secure and efficient operation of scientific facilities, especially under extreme conditions like those in IFMIF-DONES.

Contracts for Big Science facilities

[IFMIF-DONES] VATIAC (Accelerator Systems Integrated Technology Validator) (2025 - 2027)

The CDTI has awarded to the HACES-DONES the Pre-commercial Public Procurement contract (PPP) for the development of the technological validator for high intensity hadron accelerator systems of the IFMIF-DONES, VATIAC (Accelerator Systems Integrated Technology Validator). The scope of the VATIAC project includes the design, manufacture, assembly and testing of a validator, consisting of: high energy transmission line (HEBT), medium energy transmission line (MEBT), radio frequency system (RFPS), test bench or DPLATE, auxiliary systems, central control system (ICS) . The common thread of this integration will be the homogeneity and compatibility of the control, regulation, supervision and data acquisition and diagnostics systems, which will make it possible, in the future, to use data processing and machine learning methods to optimize the different processes that take place in the plant and improve its availability through the use of predictive maintenance tools.

Relevant R&D projects

[CDTI - TRANSMISIONES 2024] Circularity in the wind farms reboosting by Reuse, Recycling, ecodesign strategies and development of new Recyclable and repairable materials (R3POWER) (2025)

The wind energy sector needs circular disruptive changes to dynamize its economy and reduce its environmental impact. In this sense, cross-cutting artificial intelligence (AI) technologies through the development of predictive algorithms appear as a potential solution to accelerate industrial implementations of novel technologies. The R3POWER project aims to make a differential leap in the present context, presenting an integral, reliable and solid initiative in “integral circular repowering” of wind farms, applying 3 joint strategies that maximize its circularity around the use of waste - reuse, recycling and revaluation (zero waste) - and around its prevention - new easily recyclable and repairable materials - all catalyzed by transversal technologies such as AI, and pursuing the low environmental and economic impact of each of the strategies.

[MINCOTUR PERTE NAVAL 2022] Development of Green Propulsion Systems for Maritime Ships (Spectrum (SISPROVE)) (2024 - 2025)

Ship power plants are complex systems with several sub-systems interacting with each other in different sailing conditions. For each navigation condition, consumers and generators must be in balance to ensure the correct operation of the ship. A proper management of this balance would allow optimizing consumptions and reducing possible emissions occurring during the operation of the ship. HI Iberia will participate with the creation of a digital platform called SPECTRUM, based on the interconnection of data through AI neural networks for the training of intelligent agents with the capacity to act as controllers for a subsystem of batteries and generators of the ship's electric power plant, to design and validate an optimized and self-adaptive solution, capable of intelligently dealing with the variability, exigency and demands of the operating scenarios presented on the vessel. https://www.hi-iberia.es/artificial-intelligence/spectrum

[European Defence Fund (EDF) 2021] Hybrid Energy Grid and Propulsion System (HEGAPS) (2024 - 2025)

The HEGAPS project, financed by European defense funds (EDF 2021), seeks to optimize the definition processes of future hybrid systems for warships. Both in the definition of the generation and storage systems, as well as the control systems themselves, the project aims to optimize the definition of future hybrid systems for warships. HI-Iberia is targeting the following two challenges: Development of a configurable virtual network to optimize the integration of the different systems to be installed in the power plant using AI techniques. Development of AI-based control software for the power plant to maximize the capabilities of the power plant. This controller will allow real-time decision making on the future physical plant.

[CDTI - TRANSMISIONES 2023] New nanomaterials driven by artificial intelligence and industrial biotechnology (NanomatIA) (2024)

Searching the vast combinatorial space of materials is highly time-consuming and expensive, and a new, accelerated way of locating suitable materials for a target performance becomes necessary. In this context, the infusion of AI in materials design and discovery presents an unprecedented approach. The objective is to accelerate the design and discovery of new materials in the energy storage sector (LIBs), through AI strategies focused on two main axes to achieve advanced properties as set by the market: nano and biotechnology. The project will build a specific architecture of AI agents to discover and/or predict in an automated way new configurations in electrochemical recipes for the main components of a cell with nanomaterials obtained by conventional and biotechnological processes, to achieve cells with advanced and sustainable functionalities. https://nanomatia.hi-iberia.es

[CDTI - TRANSMISIONES 2023] Application Of Advanced Digital Systems For Optimizing Monitoring, Operation And Integration Of Floating Offshore Wind Energy (OPTIMAR) (2024)

OPTIMAR aims to optimize the operation, maintenance and grid integration of offshore wind farms by increasing asset availability and optimizing annual energy production (AEP) through non-intrusive monitoring, predictive fault algorithm design and active management in secure data spaces by applying AI in the context of cybersecurity. More specifically, OPTIMAR will research on new intelligent and non-intrusive sensing solutions and advanced algorithm design hosted in secure sovereign data space from acoustic, lidar, machine vision and vibration information. Hi-Iberia's objectives range from the overall design of the ICT architecture required for this type of floating offshore wind farms, to the resolution of different use cases. In particular, the early detection of structural failures using artificial intelligence techniques and the optimization of operation and maintenance strategies will be the focus of the project's efforts. https://optimar.org.es/en/

[MINCOTUR PERTE NAVAL 2022] RESEARCH ON TECHNOLOGIES FOR TWINNING AND DIGITAL EXPLOITATION OF CONNECTED IOT ASSETS IN THE NAVAL SECTOR (KIMIKO) (2023 - 2025)

Marine corrosion is a crucial challenge in the marine industry, significantly impacting the durability and safety of ships and their components. Both ship hulls and internal piping are exposed to constant degradation due to aggressive environments. This problem not only leads to high maintenance and repair costs, but can also jeopardize the structural integrity of vessels, thus compromising operational safety. Model corrosion on ships, covering both hull and internal piping due to exposure to the marine environment. Develop new materials that are more resistant to corrosion in pipelines and create innovative coatings for the hull of ships Objectives: Prediction of environmental data by time of year and location Dynamic corrosion prediction Simulation of the environment at the atomic level Generation of new steels more resistant to corrosion Generation of more corrosion-resistant coatings Experimental validation https://www.hi-iberia.es/artificial-intelligence/kimiko

[CDTI - MISIONES 2022] Industrial technology research project aimed at optimizing the efficiency of a large scientific fusion facility, such as IFMIF-DONES. (DONES-FLUX) (2022 - 2024)

Industrial technology research project aimed at optimizing the efficiency of a large scientific fusion facility, such as IFMIF-DONES. The project's main objective is to optimize the different flows within IFMIF-DONES, eliminating inefficiencies and reducing risk in the facility's operation and maintenance. Grid power flow: An intelligent system with buffering capacity and intelligent demand management will be developed to control the power flow. Deuteron flow: Control will be optimized through extraction systems, radiofrequency cavities and AI strategies, improving efficiency and reducing maintenance. Lithium flow: Through the development of optical systems to measure lithium curtain parameters and argon pressure in the target chamber, together with predictive assembly and control. Neutron flux: Implementation of a predictive control system to optimize neutron flux characteristics in the irradiation zone, enhancing operational safety and efficiency. https://dones-flux.hi-iberia.es/

[CDTI - MISIONES 2022] Integrated digital platform for Operation and Maintenance of floating wind farms (ePROA) (2022 - 2024)

Advanced technologies for the Operation and Maintenance (O&M) of floating wind farms. Its focus is on improving the functionality, sustainability and circular economy of these facilities, thus contributing to the modernization of the Spanish shipbuilding sector. One of e-PROA's key achievements is the design of an advanced Digital Twin for floating wind facilities. The integration of surrogate models with deep learning, including PiNNs (Physics informed Neural Networks) and NOs (Neural Operators), has allowed to significantly improve the accuracy and speed in the estimation of the lifetime of the structures, anomaly detection and failure prediction. The developed Digital Twin focuses on several critical components of floating wind farms, such as mooring lines, tower and float. For each of these components, specific surrogate models have been generated to facilitate load monitoring, fatigue estimation and anomaly detection. https://e-proa.hi-iberia.es/en/home/

[I+D DGAM Ministerio de Defensa] Predictive On-board Maintenance (MAPRE) (2022 - 2024)

The MAPRE project has focused on developing an advanced predictive maintenance platform, based on Artificial Intelligence, for the Spanish Navy ships. The project has integrated two main components: A Predictor Ashore (PET) and a Predictor On Board (PAB). The PET has been designed to process data and train AI models, while the PAB, equipped with previously trained models, processes data in real time to assess the condition of the assets and predict their remaining useful life. This strategy enables predictive maintenance to be planned in advance, improving operational efficiency and reducing downtime. The MAPRE platform, with its innovative combination of AI, real-time monitoring, and Airflow architecture on Kubernetes, has achieved a significant advance in predictive maintenance for the Spanish Navy, optimizing resources and improving the operational efficiency of its ships. https://www.hi-iberia.es/artificial-intelligence/mapre?category=infrast…

[Red.ES] Collaborative intelligence for sustainable cities (Green) (2022 - 2024)

The GREEN project is an artificial intelligence platform that seeks to develop an industrial solution to exploit IoT data by combining technologies such as Federated Learning, Blockchain and Smart Contract. Its main objective is to optimize and accelerate electric vehicle charging service transactions in a decentralized, secure and transparent way. The platform allows to generate a predictive model of the energy demand required in charging stations (RE). In this way, charging station managers (RMS) can negotiate a price adjusted to their needs with the Electricity Grid Provider (ERP), without compromising customer data and maintaining fair competition. https://green.hi-iberia.es/en/home-eng/

[ISR12205 INARIM I+D DGAM Ministerio de Defensa] Maduration of the SEDA Technology Demonstrator (MADS) (2022 - 2024)

The MADS project seeks to improve the SEDA technology demonstrator to provide research and software development services in the field of Artificial Intelligence systems. New satellite data sources and AI techniques will be incorporated to improve automatic processing and expand the capabilities of the SEDA system. It also focuses on additional technological objectives such as the implementation of a REST API and the definition of interfaces for the use of already trained AI algorithms in other operational contexts. With the integration of Edge Computing technology, it will be possible to carry out data processing and analysis tasks at the place where they are generated, which will optimize decision making and improve real-time information management. SEDA will be fully parameterizable so that users can configure the location to be monitored, the area to be covered and the type of actions, and allow the configuration of model retraining actions by personnel https://seda.hi-iberia.es/en/

[Grid 2030 RED.es] ElectrIc Grid AI (ENIGMA) (2019 - 2022)

The integration of emerging renewable energies into the electricity system requires the optimization of resources to address the problem of frequency stabilization. HI-Iberia in collaboration with the companies Prysma and Ingelectus proposes a paradigm shift in the control of the electricity system through the use of artificial intelligence methodologies. The same Reinforcement Learning techniques that have been successful in defeating the world champions in chess or Go are generalized to the field of power system control. ENIGMA proposes the design of intelligent agents capable of acting as controllers in power generation plants. Replacing the current control system with a more optimized solution capable of adapting intelligently to the variability of scenarios that arise in the power grid. https://www.hi-iberia.es/artificial-intelligence/enigma

[COINCIDENTE 2018] SatEllite Data AI (SEDA) (2019 - 2022)

With the exponential increase in the number of satellites and their derived data, it is essential to have a tool to manage this information in order to make it useful. The potential of Artificial Intelligence (AI) is combined here with data processing and advances in the field of Data Fusion for the automatic analysis of satellite information. This platform is based on a set of AI engines that allow, on the one hand, the collection, fusion and analysis of satellite data (mainly radar images) from private channels and open sources and, on the other hand, the automatic detection in the images (Remote Sensing) derived from them, of “anomalies” or objects whose identification is key when making decisions that may compromise security both nationally and internationally. https://seda.hi-iberia.es/en/

[ITEA3 Call4 cluster EUREKA] SeCuRe and Agile connected Things (SCRATCh) (2018 - 2022)

HI-Iberia was the National Coordinator and the leader of package 1 where the Requirements and Architecture of the platform are defined. In addition, HI-Iberia's goal was to improve our methodology of work in the implementation of platforms, for this one of its main tasks in SCRATCh wasthe development of methodologies for the implementation of platforms that consider security and agility throughout the development cycle and the deployment of the same taking advantage of the proposed DevOps approach. As an additional application of the developed methodologies, through the SCRATCh project HI-Iberia expanded its technology and application of the data fusion module from different heterogeneous sources in the smart grid area.

[PENTA programme] MUlti-level Secur Ity for Critical Services (MuSiC) (2017 - 2020)

Multi-level Security for Critical Services is a PENTA project working on the provision of scalable and certification-oriented solutions for embedded systems with ARM architectures and with mixed levels of security and criticality. A reference architecture with two separate execution levels (secure and non-secure) on the same hardware is proposed and applications will be developed to demonstrate the validity of the solution in various domains. The European project is led by ST Microelectronics France and has a duration of 36 months. In the project, HI iberia is working on the development of tools for runtime adaptability of secure embedded scene and people recognition solutions with links to evolutions of the LIFEonLive product.

[CDTI - MISIONES] Industrial research on strategic materials for cost-optimized, high energy density lithium-ion batteries in sustainable electro-mobility (LiON-HD) (2015 - 2020)

Main objective: significantly improving the energy density of lithium-ion batteries, reducing their costs and increasing their sustainability. HI-Iberia focused on the development of AI models capable of proposing cathode materials that meet the desired characteristics. Within the framework of the project, Reinforcement Learning and generative models capable of generating materials with chosen properties were combined. This approach was based on the interaction between a generative model, which proposed materials, and a predictive one, which inferred the properties and feasibility of the suggested materials. These two models allowed a targeted and efficient search in the materials search space, with ICMM experimentally testing the suggested materials. This comprehensive approach, combining advanced AI algorithms and state-of-the-art experimental techniques, enabled a massive and intelligent search for disruptive materials. https://www.hi-iberia.es/artificial-intelligence/lion

[FP7-SECURITY] Advanced pRotection of critical buildinGs by Overall anticipating System (ARGOS) (2014 - 2015)

ARGOS aims to design and develop an innovative early warning security system for critical infrastructure protection, which is able to anticipate potential threats by improving the effectiveness of current security systems in preventing physical intrusions by expanding the “security zone” beyond the infrastructure perimeter, and to react by sharing information in a timely manner.