The global industry has suffered deep changes in the last years because of the successful development and integration of new technologies. Industry 4.0 has emerged as a new standard for achieving efficiency and improving processes. Among the technologies used in Industry 4.0, Internet of Things applied to industry (IIoT) enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimize overall production value. Because of its importance, in this project, a methodology for extracting, analyzing and using the data gathered by IIoT devices is proposed in order to extract meaningful information and to predict industrial key metrics with Artificial Intelligence. In addition, for the complete validation of the proposed methodology, a practical implementation of all the mentioned aspects is carried out by developing a study of the industrial process in the wastewater treatment field using the data collected by an Industrial Internet of Things infrastructure and modelling key time series metrics, such as total organic carbon (TOC) and carbon removal performance (CRP) by using Machine Learning models XGBOOST Regressor, Multi-Layer Perceptron (MLP) Regressor and Support Vector Regressor (SVR) to implement a dashboard with an operational panel and a decision-making panel that helps anticipate possible deviations in the performance of the industrial process
