data processing through ETL workflows, and the development of predictive models for geochemical analysis. The key differentiator lies in the end-to-end automation of the data pipeline and its direct integration with predictive models within a real operational environment. See Figure 1. Figure 1 – Data Transformation Process Using the CRISP-DM Methodology. Source: IBM (2021). During the business understanding phase, geochemical and hydrochemical objectives were defined, associated with the assessment of acid rock drainage (ARD) potential and metal leaching in mining materials. The main challenges identified included the management of large data volumes, heterogeneity of data sources, and prolonged manual processing times. This stage enabled the establishment of functional requirements for the technological solution, as well as key performance indicators (KPIs) for its evaluation. The data understanding and collection phase involved the integration of 15 years of historical hydrochemical data derived from laboratory tests associated with geochemical materials exposed to environmental conditions (barrel tests). The datasets underwent preliminary exploration, including the identification of temporal patterns, outliers, inconsistencies, and data gaps, thereby laying the foundation for robust analysis. In the data preparation phase, automated Extract, Transform, and Load (ETL) processes were implemented to ensure data quality, consistency, and standardization. The integration of automation agents (bots) enables the direct ingestion of laboratory reports in multiple formats (CSV, XLS, databases, among others), eliminating human transcription errors and ensuring full traceability from sample origin to the final predictive model.
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