Virtual assistant using generative AI applied to iron ore concentration
Tiago Caixeta Nunes; Rodrigo Martins Gomes; Felipe Novaes Caldas; Ednei Rodrigues Rocha; Eric Guimarães Vieira; Marcelo Pereira de Castro Alves
Abstract
The use of Artificial Intelligence (AI) in the competitive mining market has attracted increasing interest in recent years, as these technologies play a significant role in data interpretation, modernization of production processes, and efficient use of mineral reserves. Among the various AI approaches, Generative Artificial Intelligence (Gen-AI) stands out as one of the most promising and disruptive. This paper aims to present a virtual assistant application based on Gen-AI, capable of providing answers to user questions using natural language about operational aspects, based on several data sources to assist in decision-making that influences the performance of iron ore concentration. The assistant considers information security infrastructure, uses language models (LLMs) and Retrieval-Augmented Generation (RAG) techniques to access plant databases and documents, while a multi-agent flow centralizes application information with production data, historical data, and technical documentation.
Keywords
Referências
1 Chandramohan R, Pyle M. Can AI save the mining industry? In: Proceedings of the XXXI International Mineral Processing Congress IMPC; 2024; Englewood. Englewood: SME; 2024. p. 1071-1802.
2 Fosso Wamba S, Bawack RE, Guthrie C, Queiroz MM, Carillo KDA. Are we preparing for a good AI society? A bibliometric review and research agenda. Technological Forecasting and Social Change. 2021;164:120482. https://doi.org/10.1016/j.techfore.2020.120482.
3 Fontoura L, Nascimento DLM, Neto JV, Caiado RGG. Energy Gen-AI technology framework: a perspective of energy efficiency and business ethics in operation management. Technology in Society. 2025;81:102847. https://doi.org/10.1016/j.techsoc.2025.102847.
4 Liao W, Lu X, Fei Y, Gu Y, Huang Y. Generative AI design for building structures. Automation in Construction. 2024;157:105187. https://doi.org/10.1016/j.autcon.2023.105187.
5 Jackson I, Ivanov D, Dolgui A, Namdar J. Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation. International Journal of Production Research. 2024;62(17):6120–6145. https://doi.org/10.1080/00207543.2024.2309309.
6 Ma X, Huo Y. Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology in Society. 2023;75:102362. https://doi.org/10.1016/j.techsoc.2023.102362.
7 Kumar A, Shankar A, Hollebeek LD, Behl AH, Lim WM. Generative artificial intelligence (GenAI) revolution: a deep dive into GenAI adoption. Journal of Business Research. 2025;189:115160. https://doi.org/10.1016/j. jbusres.2024.115160.
8 Kecht C, Egger A, Kratsch W, Roglinger M. Quantifying chatbots’ ability to learn business processes. Information Systems. 2023;113:102176. https://doi.org/10.1016/j.is.2023.102176.
9 Patel D, Sahu CK, Rai R. Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing. International Journal of Production Research. 2024;62(3):1041-1071. https://doi.org/10.1080/00207543.2023.2262050.
10 Raiaan MAK, Mukta MDSH, Fatema K, Fahad NM, Sakib S, Mim MMJ, et al. A review on large language models: architectures, applications, taxonomies, open issues and challenges. IEEE Access: Practical Innovations, Open Solutions. 2024;12:26839-26874. https://doi.org/10.1109/ACCESS.2024.3365742.
Submetido em:
24/10/2025
Aceito em:
19/03/2026
Facebook
Google+
Twitter
LinkedIn
Mendeley
StumbleUpon
CiteULike
Reddit
Email