Agent-based models minimizing carbon usage in flexible and efficient future integrated steelworks

Informations projet

AgiFlex

N° de convention de subvention: 101138813

DOI

10.3030/101138813

Date de signature de la CE 24 Novembre 2023

Date de début 1 Decembre 2023

Date de fin 30 Novembre 2028

Financé au titre de

• Digital, Industry and Space

Coût total

€ 5 203 182,50

Contribution de l’UE

€ 4 691 795,00

Coordonné par

ABO AKADEMI
Finland

Periodic Reporting for period 1 - AgiFlex (Agent-based models minimizing carbon usage in flexible and efficient future integrated steelworks)

Période du rapport: 2023-12-01 au 2026-05-31

Résumé du contexte et des objectifs généraux du projet

The steel industry is undergoing a gradual transition towards more sustainable production while continuing to meet the demand for high-quality steel products. Traditional steelmaking processes remain a significant source of CO2 emissions but new production technologies, increased use of renewable electricity, and hydrogen-based processes are creating opportunities to reduce environmental impacts. However, the transition presents considerable challenges, as steel producers must make long-term operational and investment decisions under uncertain future conditions, including changes in energy availability, energy prices, carbon costs, and market requirements.

Future steelworks are expected to include combinations of established and emerging production routes. As these technologies interact within the same industrial site, managing material flows, energy systems, process gases, and emissions becomes increasingly complex. There is thus a need for decision-making tools both for planning long-term investments and operation under fluctuating market conditions during the transition to lower-carbon production.

The AgiFlex project addresses this need by developing a digital modelling and optimisation platform for integrated steelworks. The project combines digital twins of current and future production processes with agent-based optimisation methods to analyse plant-wide interactions and support decision-making. This platform can thus help identifying operating strategies and development pathways that reduce CO2 emissions, improve energy efficiency, lower fossil fuel consumption, and make more effective use of available resources, including process gases and hydrogen.

A central aspect of the developed tools is the possibility to flexibly evaluate different plant configurations rather than relying on predefined production flows. This allows exploration of different technology combinations and operating conditions, also in intermediate transition stages where conventional blast furnace-based production and newer direct reduction routes may operate simultaneously.

Overall objectives of the project are thus to develop the multi-agent framework, create digital twins for current and future steelmaking processes, analyse alternative production and energy scenarios, identify optimized decarbonisation pathways, and validate the approach through industrial demonstrations. The project also seeks to support strategic decisions concerning process integration, renewable energy use, hydrogen deployment, carbon capture technologies, and circular material flows.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

Initially, AgiFlex established the technical foundations of the planned platform by developing the core multi-agent system architecture, defining plausible optimisation methods, and creating the first set of digital twins representing process units in integrated steelworks.
The system architecture is a modular multi-agent framework integrating digital twins, optimisation algorithms, communication layers, and user interfaces. The architecture separates process simulation from decision-making functions and enables scalable development towards distributed optimisation and decision support. Initial versions of the framework have been implemented and tested, while centralised optimisation solutions have been established as benchmarks for future distributed approaches.

First versions of digital twins were developed for key process units including coke plants, sinter plants, blast furnaces, direct reduction shaft furnaces, basic oxygen furnaces, electric arc furnaces, secondary metallurgy, gas handling systems, power plants and hydrogen production units. Depending on data availability and process maturity, both first-principles and data-driven modelling approaches were applied. In addition, machine-learning-based forecasting models using Deep Echo State Networks were developed for renewable electricity generation and green hydrogen prices, showing promising predictive performance compared with conventional forecasting approaches.

Representative industrial scenarios for the future transitions were established for the two demonstration sites, covering intermediate stages combining blast furnace and direct reduction production routes. Where initial data was available, process unit models were adapted to units at the industrial sites, and simplified system simulations were conducted to test the models and connections.

Initial web-based human-machine interfaces were also developed, and preparations for industrial implementation have begun through validation activities using industrial datasets, establishment of remote-access environments, and procurement of demonstration hardware.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

The initial work has established the building blocks for the system in development, and main results for the project are expected as the implementation at the two demonstration sites proceeds.