Predictive Quality Modeling for Ultra-Short-Pulse Laser Structuring utilizing Machine Learning

Leyendecker, Lars; Zuric, Milena; Nazar, Muhammad Atique; Johannes, Karl; Schmitt, Robert

doi:10.1016/j.procir.2023.03.047

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Designing and implementing soft sensors involves various processes, such as data preprocessing, algorithm-based model construction, ensemble modeling, and model validation [ 40 , 41 , 42 ]. Researchers have proposed hybrid models that combine AI techniques such as fuzzy logic systems and neural networks to handle complexity and enhance predictive power [ 43 , 44 ]. Additionally, one largely unexplored domain within the soft sensor field is the incorporation of automated model selection and hyperparameter tuning techniques, such as AutoML.…”

Section: State Of the Artmentioning

confidence: 99%

Soft Sensing of LPG Processes Using Deep Learning

Sifakis,

Sarantinoudis,

Tsinarakis

et al. 2023

Sensors

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This study investigates the integration of soft sensors and deep learning in the oil-refinery industry to improve monitoring efficiency and predictive accuracy in complex industrial processes, particularly de-ethanization and debutanization. Soft sensor models were developed to estimate critical variables such as the C2 and C5 contents in liquefied petroleum gas (LPG) after distillation and the energy consumption of distillation columns. The refinery’s LPG purification process relies on periodic sampling and laboratory analysis to maintain product specifications. The models were tested using data from actual refinery operations, addressing challenges such as scalability and handling dirty data. Two deep learning models, an artificial neural network (ANN) soft sensor model and an ensemble random forest regressor (RFR) model, were developed. This study emphasizes model interpretability and the potential for real-time updating or online learning. The study also proposes a comprehensive, iterative solution for predicting and optimizing component concentrations within a dual-column distillation system, highlighting its high applicability and potential for replication in similar industrial scenarios.

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