Work

Data-driven models for a salt production process towards an Industry 4.0 evolution

Year: 2023

Type: conference-paper

Abstract: This work develops a predictive model of the production process of brine salt in an Italian industrial site. The methodology uses dimensionality reduction via standard statistical techniques and one year of production data has been acquired via direct connection to the plant control system. A code developed in Python analyze each plant, screen the raw data, and regress the models via principal component regression (PCR) and partial least squares (PLS). Results show good reliability for the prediction of the evaporative plant while the depuration model still needs refinements to be performed.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.10045328

Authors: Vaccari, Marco, Manzoni, Davide, Locatelli, Marco, Stella, Silvio Rudi, Lenzi, Maurizio, Pannocchia, Gabriele

Institution: University of Pisa

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: European Commission