DATAMINE4.0 is an IF for Career Development for Natasa Sarafijanovic-Djukic, PhD in Computer/Communication Sciences obtained from EPFL, Switzerland, a researcher in statistical modelling originally from Serbia, whereby she will work in IRIS, a Spanish R&D and advanced engineering SME. The project will study the modeling and analysis of data coming from complex industrial and organizational applications, in order to identify hidden causistic relationships in order to calibrate production and organizational control systems. It is highly multidisciplinary combining data science, statistics, artificial intelligence, telecommunications, specific engineering and organizational domains and information technology processing competences. In line with new paradigms such as Industry 4.0 and the Internet of Things. Although machine learning paradigms have been in existence for several decades, their penetration in the industrial and public organization setting has been limited. The goal is to build calibration and optimization models using advanced statistical and machine learning methods and other artificial intelligence inspired algorithms, for applications in which existing methods have been unsatisfactory. This will have a big impact on productivity and repeatability of winning formulas for key industrial applications such as customized production, public sector applications (e.g. optimization of urban mobility), and food processing. This is in line with findings published by the European Union. The developed data mining solutions will greatly enhance the current performance of the selected applications, allowing them a competitive advantage over current methods. The work will be done in an applied R&D environment in line with the objective for Dr. Natasa Sarafijanovic-Djukic’s career continuation. An engineering approach will be ideally completed with 7 formal scientific trainings, including two academic secondments, each of 2 months.

In response to growing industry demand for rapid, non-destructive techniques for foreign body detection and identification in food products, IRIS (an advanced engineering company from Spain that specialises in the integration of real-time monitoring solutions in the industry) will accelerate the development of an affordable, rapid and accurate hyperspectral imaging (HSI) system for the on-line detection of foreign bodies and chemical composition analysis in modern food processing plants. The HYPERA® system is based on a novel and disruptive combination of Shortwave Infrared Hyperspectral Imaging (SWIR-HSI) in reflectance with Near Infrared Multispectral Imaging (NIR-MSI) in reflectance/transmittance and chemometric software. HYPERA offers outstanding performance for the detection of foreign bodies in foodstuffs by enabling: a) penetration (enabled by the NIR); b) composition differentiation (enabled by the SWIR); and c) resolving the image distortion caused by scattering (enabled by the chemometrics); d) compact and low-cost design (enabled by the extended infrared spectral bands to obtain more chemical information from the sample using novel detectors), e) high inspection speeds and versatility (enabled by combining a flexible hyperspectral imager design with integrated chemometric tools). The HYPERA® system, which will be suitable for multiple application purposes and object, will equip food processors to prevent contaminated food entering the supply chain, thereby protecting the brands as well as the consumer.

AI-CUBE seeks to enhance the understanding of different digital technologies related to artificial intelligence (AI) and big data (BD) applied in process industries for all the SPIRE industrial sectors. At the start of the project in September 2020 there were eight SPIRE industrial sectors considered, namely: cement, ceramics, chemicals, engineering, minerals and ores, non-ferrous metals, steel and water. At the start of 2021, also pulp & paper and refineries were added – which were then considered and integrated into the list of sectors being analysed through the project. Therefore, a close collaboration with industry is mandatory to achieve in-depth insights into possible application areas of AI for processes, technology, sensor applicability and assessment of their level of penetration. The overall project approach is based on the development of a 3-dimensional conceptual matrix based on: 1) AI and BD technologies 2) Application areas (activities and industrial processes) 3) SPIRE sectors AI-CUBE’s main goal is to define a roadmap in AI and the use of BD for the process industry and their maturity level across the industrial sectors, including guidelines for implementation. Industrial stakeholders and associations will validate the consolidated roadmap ensuring solution feasibility and benefits for the European industrial community. A crosslinked vision over process industry sectors shall facilitate cooperation and boost technologies deployment at their full potential. An in-depth consultation with industry (association, representatives, companies) will provide an overview of current AI and BD algorithms application, identifying exploitable synergies among sectors. A deep study of the application areas in planning and operations within other industrial sectors facilitates a gap analysis, propitiating knowledge sharing among processes and sectors. A Multi-Actor Multi-Criteria analysis will obtain a widely supported and consensus-based action plan for industrial consultation. This will allow the inclusion of a broad stakeholder community representing the main industry actors throughout all the SPIRE sectors, with which the project consortium has strong connections that will support sector integration and stakeholders’ engagement.

MoniTank offers a commercial continuous advanced structural health monitoring system (SHM) for underground storage tanks (USTs) based on acoustic emission and guided wave technology, risk based inspection techniques, IoT and advance signal processing and data acquisition system. The product will be able to identify structural integrity faults with 99% accuracy of detecting structural problems in USTs before they lead to rupture or failure. Consortium including INT NDT, IRIS, TWI, BIC and Flotek will avail the business opportunity provided by market of 132k potential customers (filling stations in EU) and foreseen maintenance services investments in storage systems, resulting in growing NDT and maintenance needs and lack of robust autonomous continous SHM systems for USTs in oil and gas production and distribution industries market at present by offering MoniTank product. Limitations of current monitoring systems performance, their non compliance with legislation/standards requirement of detection accuracy and reliance on experienced workforce for inspection and maintenance create a differentiation strategic advantage for successful market replication of MoniTank. Our product will be able to provide continuous monitoring and will use intelligent algorithms to predict potential structural failure. The technologies integrated and finalised during this project will consist of: sensors; processing capabilities (MCU processor hardware); AE sensors, transmission data; batteries; and data wireless transmission and cloud computing, therefore presenting the target customers with a system for risk based inspection criterion for USTs. Our vision is to grow our business by €24million in gross sales with a return on investment of about 15:1 in 5 years post project commercialisation. With business growth, we will create 112 new jobs. It is our strong belief that the Fast Track to Innovation Pilot is the ideal financial instrument for us to accelerate commercialization of MoniTank.

Increasing industrial uptake of project findings is at the heart of project SPRING. It is the essential building block for ensuring greater impact of SPIRE projects and therefore progress towards the SPIRE roadmap goals of increased resource and energy efficiency in the EU process industries. Instead of focusing on a small cluster of projects, SPRING has been developed to provide the mechanism to enhance the impact of all SPIRE projects. Project SPRING’s objective is to increase progress towards the SPIRE goals and enhance return on investment in projects by addressing the needs of those who make the decisions to adopt process innovations in industry and barriers to their adoption. It will do this by providing guidance to project participants, decision makers in industry and broader SPIRE stakeholders, enabling them collectively to: 1. Improve the articulation of the value of project exploitable outputs 2. Improve the articulation of industry needs and barriers-to-uptake of exploitable outputs 3. Improve the mapping of project value to industry needs 4. Identify policy gaps and recommendations to improve project impact To address these objectives, the project will deliver six sets of exploitable outputs: A – Guidance on best practice of how to measure progress, impact and success of SPIRE projects. B – Frameworks for getting different levels of project results to the right audience through the spire2030.eu portal C – A model for mapping project outputs to industry needs, through thematic, interactive industry workshops, expert input and technology scanning methods. D – Guidance for understanding business barriers to uptake, including best practice for enabling good decision making when evaluating project outputs. E – A package of training and network groups to upskill SPIRE project participants F – Identification of policy gaps and future SPIRE needs