Data-Knowledge Integration to improve the reliability of LCA projets in the Enterprise of the futur – i-DAVE
Data-Knowledge Integration to improve the reliability of LCA projets in the Enterprise of the futur
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Challenges and objectives
Despite the growing interest in life cycle assessment (LCA) and product carbon footprint (PCF) methods and tools, their application faces three challenges: data collection, the selection of relevant cost centers, and the robustness of reference databases. These challenges are very difficult because experts mastering the industrial process do not necessarily have the LCA/PCF culture. In addition, validating and aggregating all factors is a very complex activity because the data is collected from very heterogeneous sources, in varied business contexts and life phases. Current collection methods remain mostly manual, based on questionnaires, and dedicated tools are often disconnected from the company's overall digital chain. These issues are more critical in the case of long-life systems. Once the studies are completed, another challenge concerns the exploitation of the results for the prediction of carbon trajectories. The objective of the i-DAVE project is to propose a knowledge-based and AI-based framework, interoperable in a PLM approach, for the reliability of LCA/ECP studies. This is a dual decision-making aid: 1) Upstream of LCA studies to make the input data robust and configure the LCA/ECP study parameters. 2) Downstream for the exploitation of study results in the definition of the best action plans to reduce the ECP footprint.
A response to the above challenges is expected through three complementary solutions: 1) A knowledge base defined in the form of an ontology of the LCA/ECP domain and coupled with an inference engine, implementing business rules to meet specific assistance needs such as the choice of scope and environmental cost centers, etc. It also involves supporting process traceability and the characterization of systems in terms of LCA/ECP factors. 2) Intelligent connectors to ensure the interoperability of LCA/ECP tools with the different modules of the company's digital chain for extracting data from heterogeneous sources. The Product Lifecycle Management (PLM) approach will be used to ensure the cross-functional integration of all types of information systems and databases useful for LCA/ECP studies. 3) A tool for managing low-carbon strategies in companies coupled with innovative dashboards. This tool contains in particular algorithms for predicting future trajectories based on history, and classification algorithms to help choose the best operational alternatives generating the minimum carbon emission impact. This module is based on a behavioral model containing useful performance indicators and cause-effect relationships between process parameters and environmental impacts.
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Despite the growing interest in life cycle assessment (LCA) and product carbon footprint (PCF) issues, the application of these methods and tools is often facing three challenges that can disrupt their results: Data collection; Choice of "energy cost" centers; and definition of data repositories. These challenges are very difficult because the expert of industrial processes doesn’t have the LCA/ECP culture. Moreover, the validation and aggregation of all the factors is a very complex activity because the data are collected from very heterogeneous sources, in various contexts and life phases. These issues are even more critical when the target system has a long life cycle (SLLC) (i.e. trains, ships, large production systems, power plants, etc.).
The objective of the i-DAVE project is to propose an interoperable framework based on knowledge and AI, connecting PLM and LCA approaches for the reliability of studies dedicated to SLLC. The idea is to rely on :
- Knowledge management and engineering methods to build a generic LCA/PCF model. It will be used also to support process traceability and the formalization of expert rules for decision maiking along LCA/ECP studies.
- Product Lifecycle Management (PLM) approach for multi-sources data extraction, including information systems, sensors or other connected objects. Intelligent connectors will be developed to support the interoperability of LCA/PCF tools with the different modules of the company's digital chain.
- Big data and machine learning techniques for aggregating historical data into relevant KPIs and predicting environmental sustainability behaviors.
Project coordination
Emmanuel ROZIERE (Laboratoire des Sciences du Numérique de Nantes)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
Partnership
GREEN WAY Systems
CETIM
LS2N Laboratoire des Sciences du Numérique de Nantes
Toovalu TOOVALU
Laboratoire Roberval. Unité de recherche en mécanique acoustique et matériaux.
Help of the ANR 643,631 euros
Beginning and duration of the scientific project:
January 2024
- 42 Months
