A thermal cracking furnace is an important equipment in the petrochemical industry that is typically used for breaking long hydrocarbons into short chains and producing coke as a byproduct. Deposition of the generated coke increases the temperature at the outside coil wall, necessitating regular furnace maintenance to prevent coil failure. Therefore, this study proposed a machine learning approach with a posteriori-based feature to predict the service life of the furnace to runtime failure. The proposed approach consists of a two-level machine learning model, which aims to improve prediction accuracy and reduce feature sensitivity. The label is classified as a week-range label, which can be categorized by classification criteria into three classes: weekly, bi-weekly, and quarter-weekly. The first-level model is utilized to extract sensor features into the posterior probability class label score. These scores are then processed and sorted into moving windows to generate features for the second-level model. The results showed that the proposed model could extract process variation and identify service needs, which improved classification accuracy by 23.94 % and 17.67 % for the clean and coke-contaminated datasets compared to the conventional classification model, respectively. Additionally, the most general class range (quarter-weekly) provided the best performance compared to the bi-weekly and weekly classes. Therefore, the model has the potential for the prediction of the furnace service life under pseudo-steady state conditions, where coking evolves gradually over time.
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