Journal ArticleDOI
Dimensions and Analysis of Uncertainty in Industrial Modeling Process
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This article is published in Journal of Chemical Engineering of Japan.The article was published on 2018-07-20. It has received 12 citations till now. The article focuses on the topics: Uncertainty quantification & Steelmaking.read more
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Journal ArticleDOI
Gray-box Soft Sensors in Process Industry: Current Practice, and Future Prospects in Era of Big Data
TL;DR: In this work, various design aspects of theGB models are discussed followed by their application in the process industry and the changes in the data-driven part of the GB models in the context of enormous amount of process data collected in Industry 4.0 are elaborated.
Journal ArticleDOI
An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition
TL;DR: In this paper, a multi-output artificial neural networks (ANN) model was devised to cope with variations (uncertainty) in crude composition, which is an extended version of another method of cut-point optimization based on hybridization of Taguchi and genetic algorithm.
Journal ArticleDOI
Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process
TL;DR: In this article, a framework of data-based soft sensors was developed using ensemble learning method, i.e., boosting, for prediction of composition, quantity, and quality of product, namely fatty acid methyl esters (FAME), in biodiesel production process from vegetable oil.
Journal ArticleDOI
Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty
TL;DR: A data-driven, i.e., ensemble, model of the entrained flow gasification process was developed to predict conversion efficiency and reliability and a non-intrusive polynomial chaos expansion method was used that predicts probability distribution of the conversion efficiency.
Proceedings ArticleDOI
Virtual sensing of catalytic naphtha reforming process under uncertain feed conditions
TL;DR: In this study, a soft sensor is developed through the ensemble learning method, i.e., boosting, for prediction of RON value of the naphtha reforming process and the boosted model outperformed the ANN model.