The XPM project aims to integrate explanations into Artificial Intelligence (AI) solutions within the area of Predictive Maintenance (PM). Real-world applications of PM are increasingly complex, with intricate interactions of many components. AI solutions are a very popular technique in this domain, and especially the black-box models based on deep learning approaches are showing very promising results in terms of predictive accuracy and capability of modelling complex systems. However, the decisions made by these black-box models are often difficult for human experts to understand – and therefore to act upon. The complete repair plan and maintenance actions that must be performed based on the detected symptoms of damage and wear often require complex reasoning and planning process, involving many actors and balancing different priorities. It is not realistic to expect this complete solution to be created automatically – there is too much context that needs to be taken into account. Therefore, operators, technicians and managers require insights to understand what is happening, why it is happening, and how to react. Today’s mostly black-box AI does not provide these insights, nor does it support experts in making maintenance decisions based on the deviations it detects. The effectiveness of the PM system depends much less on the accuracy of the alarms the AI raises than on the relevancy of the actions operators perform based on these alarms. In the XPM project, we will develop several different types of explanations (anything from visual analytics through prototypical examples to deductive argumentative systems) and demonstrate their usefulness in four selected case studies: electric vehicles, metro trains, steel plant and wind farms. In each of them, we will demonstrate how the right explanations of decisions made by AI systems lead to better results across several dimensions, including identifying the component or part of the process where the problem has occurred; understanding the severity and future consequences of detected deviations; choosing the optimal repair and maintenance plan from several alternatives created based on different priorities; and understanding the reasons why the problem has occurred in the first place as a way to improve system design for the future.

The DEVULCAIN project aims at studying recycling of vulcanized rubber waste from shoe soles. To date, no efficient and environmentally friendly industrial recycling solution exists. All used footwear is directed to energy recovery centers or landfills, contaminating air, water and soil. One of "green" solutions to recycle these rubbers is devulcanization (break of the sulfur bridges). Thus, the material regains its initial structure, giving secondary materials of industrial interest. Although several devulcanization techniques exist, it remains limited to a laboratory scale and the mechanisms involved are not yet understood. DEVULCAIN proposes to study and develop a continuous devulcanization process by twin screw extrusion, which could be used at industrial scale. The phenomena involved in this process will be analyzed and its parameters will be optimized. To facilitate this task, the thermal, mechanical and chemical effects will be evaluated separately using two laboratory pilots adapted or designed for the needs of the project. Model materials consisting of a single type of rubber or rubber mixtures will be studied. The technology developed on the pilot will be adapted for continuous use on extruders. At the end of the project, the potential of devulcanized materials will be evaluated for recycling. This project involves three complementary partners. Two academic partners, the laboratory LaMé - INSA CVL, coordinator of the project, has acquired solid skills in the field of rubber recycling, and IMT Nord Europe - CMP has a strong experience in processing of polymers. An industrial partner, the company REVIVAL, which develops solutions for recycling used shoe materials.

Within the context of energy shift towards a decrease in the contribution of fossil fuels, the development of new stationary energy storage systems is mandatory. Indeed, the intrinsic intermittent and variable nature of renewable energy sources, such as windmill and photovoltaic, require energy storage. Redox-flow batteries, allowing a decoupling of energy and power, are well adapted to such requirements. As a matter of fact, this technology presents advantages as compared to Li-ion systems presently under development for such applications, in particular for security and recyclability issues. However, the most advanced redox-flow batteries (Vanadium redox-flow batteries, studied since the 80’s) remain expensive with limitations in terms of stability and capacities. The present project aims at developing a full redox-flow battery, based on the flow of redox-mediators based aqueous solutions (pH around 7), using sodium insertion materials immobilized in the battery tanks. The use of these insertion materials will allow an increase in the energy density of these systems, and thus to potentially reduce their size. These materials will be free of toxic or expensive metallic element. To perform these research studies, we created a multidisciplinary team which will allow to break the technological locks related to the development of such innovative and performing systems. The project partners will pursue in particular the study and development of a pilot battery so as to demonstrate the potentialities of this approach for electrochemical energy storage at large scale (coupling with windmill and photovoltaic systems), with storage time of the order of a dozen hours.

Agriculture with the use of fertilizers is known to emit volatile organic compounds (VOC) that have the potential in forming secondary organic aerosols (SOA) through their reactions with atmospheric photo-oxidants. In a societal context that encourages the recycling of Organic Waste Products (OWP) in agriculture, the comparative effects of mineral fertilizers and OWP on these emissions are of particular interest. The objective of this project is to elucidate the mechanisms of production and degradation of VOC emitted by different OWP and to quantify the potential for SOA formation. It will address this fundamental problem in atmospheric science by combining laboratory experiments and field measurements. It also aims to model this SOA formation in order to assess the environmental impacts in an agricultural landscape and provide recommendations to preserve air quality following the use of organic and mineral fertilizers.

The APPRENTIS project concerns the safety of industrial or port areas presenting risks (e.g., fire, explosion or toxic leakage). The operational objective is to provide a decision support software tool to plan monitoring and rescue patrols. This tool will minimize the cost of the patrols carried out by mobile agents (as drones or automated vehicles) by optimizing physical and financial resources based on the analysis of data flows. The questions we would like to answer are as follows: • During monitoring, how many mobile agents are required to perform a given set of measurements at given positions? What sensors should each of these agents equip? How to define the patrols of each of the agents in order to meet the overall monitoring requirements? • In the event of an incident, how to use these same monitoring agents to quickly obtain relevant information on the incident, the damage and any victims? How to transport and distribute rescue supplies with the help of intervention agents? Finally, how can we jointly and effectively use monitoring and intervention agents? The originality of the method proposed to solve these problems is based on modeling aspects and a resolution methodology that are derived from discrete event systems (DESs) and artificial intelligence (AI). This dual approach is motivated by the exponential complexity of the problem which appears when the problems of configuration and planning of the patrols of each of the agents are combined, the latter depending on the evaluated configuration. The expected result of the APPRENTIS project is a demonstrator that can serve as a basis for the development of a software devoted to the configuration of the monitoring and intervention patrols from a catalog of equipment, the description of the infrastructure, and the patrol specifications. We target, in particular, 3 types of audiences: 1) Companies with SEVESO classified sites (156 sites in Hauts de France region, 86 sites in Normandy region, 99 sites in the PACA region and 94 sites in the Ile de France region) which are called upon to strengthen the monitoring of their installations; 2) Organizations and local authorities in charge of crisis intervention (SDIS, urban communities, associations as ORMES); 3) Economic interest groups which are concerned with the production, transport or storage of products at risk (Ports of Le Havre, Rouen and Paris - HAROPA, Grand Port Maritime de Marseille - GPMM). The consortium of partners (ULHN - GREAH - EA 3220, AMU - LIS - UMR 7020, IMTLD, USPN - LURPA - EA 1385) was formed on the basis of the partners’ experience in the risk management and in the implementation and use of DES and AI tools. ULHN in Normandy region and IMTLD in Hauts de France region are located in the two regions targeted by the call RA-SIOMRI. AMU and USPN are located in two large and densely populated cities for which the potential impacts of industrial incidents are particularly serious. Finally, the city of Marseille offers similarities with the city of Le Havre through its port activity, an additional vector of risks due to the storage of hazardous materials, and through its concentration of SEVESO industrial sites near residential areas (Fos-sur-Mer near Marseille and Tancarville near Le Havre). The longer-term challenge we initiate here is to coordinate the means of monitoring and intervention in an automated way by combining predictive and decision-making models, and using model-based methods as well as database-based methods.