## 3 Methodological Procedures
#### 4.2.1 Case Study: Ceramic Plates in Vacuum Filters  
As a case study, an industrial iron ore filtration facility located in Brazil with 14 ceramic filters was analyzed. Figure 6 shows a picture of a new ceramic plate and a ceramic filter undergoing periodic replacement of the entire 180-plate set. \begin{table}
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**Stage** & \multicolumn{1}{c}{**Description**} \\ \hline Business Understanding & Ceramic plates are the main components of a ceramic filter and correspond to approximately 40\% of the operating cost of an industrial filtration plant, in addition to having a direct effect on the production capacity and quality of the final product (moisture content). These are the reasons to optimize the useful life of these components, accurately identifying the optimal time to carry out plate set renewals. \\ \hline Data Acquisition and Data Understanding & Based on the business understanding phase and facility’s database, a selection of 12 sensors is made. According to process specialists, these variables can influence or be influenced by the change in the performance of ceramic plates, and this, alongside reliability models obtained by the failure and replacement data, can be combined to predict the remaining useful life of these components. The sensor reading data are obtained from the PIMS—Plant Information Management System (Osisoft AVEVA PI Vision/PI Datalink®, ver. 2023 SP1)—and the ceramic plate failure and replacement data are exported from the MES—Manufacturing Enterprise System (Osisoft AVEVA Production Management®, ver. 2020 U1). \\ \hline Data Preparation & After identifying the main process variables and obtaining the failure data records, they undergo treatment to remove outliers and missing data. In addition, different data sources are combined into one, making it possible to relate the useful life to the process variables. To prepare the data, electronic spreadsheets in Microsoft Excel® (MS Office 365 version) are used. \\ \hline Identification of Health Condition Indicators & In this stage, statistical descriptive analyses are carried out to achieve a correct and broad understanding of data that characterizes normal operation, and to determine which are the best indicators for anomalies in these readings over time. With a condition indicator, a time trigger is established in which the reliability of the plate sets reaches 70\%. Statistical analysis and modeling are performed using Minitab® software (version 19). \\ \hline Modeling & Modeling consists of identifying the best models of survival/the reliability of failure and plate replacement data and identifying the best machine learning models that may be able to use sensor readings as predictors of reliability and, ultimately, the remaining useful life of these components. Model creation uses only a fraction of the partitioned data, called the training set. \\ \cline{2-2}  & In the evaluation of the models, the main statistical metrics are used to evaluate the significance and predictive capacity. In this step, partitioned validation data is used as input into the model for evaluation. \\ \hline Model Deployment and Integration & After being tested and validated, the model can be released in a production environment in software/systems, being fed back whenever new data becomes available. \\ \hline \hline \end{tabular}
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Table 1: Description of the steps of the methodology used. Figure 5: Methodology applied in research. Ceramic plate failure data and ceramic filter sensor reading data were collected to predict the remaining useful life through an analysis of the influence of reliability and process variables on component performance over time. The Larox(r)CC-144, (Lappenranta, Finland) ceramic filters use 180 ceramic plates with 2 \(\upmu\)m pores. Note: Larox is now part of Metso: Outotec company (Espoo, Finland).