Learning-Based Predictions and Soft Sensing for Process Industries

Theory, Methodology and Applications
Erscheinungsjahr 2026
ISBN: 978-0-443-36759-5
Verlag: Elsevier Science

Buch, Englisch, 350 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Learning-Based Predictions and Soft Sensing for Process Industries covers prediction and soft sensing in industrial processes subject to specific challenges with AI-empowered learning algorithms. With the aid of a data-driven modeling strategy, the book explores the problems of industrial prediction and soft sensing, and formulates a series of learning-based theory, methodology and applications. The book introduces the basics of the prediction and soft sensing backgrounds, including the different categories of prediction theory. Secondly, the book looks at the foundations of machine learning methodologies, which covers supervised learning prediction, semi-supervised and self-supervised prediction. Finally, the book examines some novel learning-based models/architectures

Autoren/Hrsg.

Karimi, Hamid Reza

Lei, Yongxiang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung

Inhaltsverzeichnis

Section 1: Theory

1. Introduction of Prediction
2. Theoretical Foundations of Paste-Filling System
3. Foundation of Aluminium Electrolysis System

Section 2: Methodology

4. Machine Learning Basics for Prediction & Soft Sensing

Section 3: Application

5. A Novel Supervised Soft Sensor Framework Based on Convolutional Laplacian Extreme Learning Machine: CNN-LapsELM
6. A Novel Semi-Supervised Soft Sensor Framework Based on Stacked Auto-Encoder Wavelet Extreme Learning Machine: SAE-WELM
7. A Novel Soft Sensor Based on Laplacian Hessian Semi-Supervised Hierarchical Extreme Learning Machine: LHSS-HELM
8. A Self-Supervised Prediction Framework Based on Deep Long Short-Time Memory for Aluminum Electrolysis: SSDLSTM
9. A Self-Supervised Prediction Framework Based on Convolutional Deep Long Short-Time Memory for Aluminum Temperature Application: CNN-SSDLSTM
10. A Novel Probabilistic Prediction Framework Based on Bayesian Machine Learning: BLSTM
11. Direct Data-Driven Quantile Regressor Forecaster for Underflow Concentration Soft Sensing: DDQRF
12. A Novel Key-Quality Prediction Framework for Industrial Deep Cone Thickener: DualLSTM
13. A Deeply-Efficient Long Short-Time Memory Framework for Underflow Concentration Prediction: DE-LSTM
14. An Ensemble Prediction Method for Probabilistic Forecasting of Aluminium Electrolysis Process

Über Autor(innen)

Lei, Yongxiang
Dr Yongxiang Lei received the B.Sc. Degree in Automation from the University of South China in 2017 and an M.Sc. in control engineering from Central South University, Changsha, China in 2020. In 2024, received his Ph.D. degree in Mechanical Engineering from Politecnico di Milano. Dr Lei's research interests are in the areas of machine learning, prediction & control, industrial process modeling, simulation and application, soft sensing.

Karimi, Hamid Reza
Dr. Karimi received the B.Sc. (First Hons.) degree in power systems from the Sharif University of Technology, Tehran, Iran, in 1998, and the M.Sc. and Ph.D. (First Hons.) degrees in control systems engineering from the University of Tehran, Tehran, in 2001 and 2005, respectively. His research interests are in the areas of control systems/theory, mechatronics, networked control systems, intelligent control systems, signal processing, vibration control, ground vehicles, structural control, wind turbine control and cutting processes. He is an Editorial Board Member for some international journals and several Technical Committee. Prof. Karimi has been presented a number of national and international awards, including Alexander-von-Humboldt Research Fellowship Award (in Germany), JSPS Research Award (in Japan), DAAD Research Award (in Germany), August-Wilhelm-Scheer Award (in Germany) and been invited as visiting professor at a number of universities in Germany, France, Italy, Poland, Spain, China, Korea, Japan, India.