Learning-Based Predictions and Soft Sensing for Process Industries

Nyhet

Theory, Methodology and Applications

Häftad, Engelska, 2026

2 449 kr

Kommande

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

Covers the benefits and an explanation of recent developments in prediction and soft sensing systems
Unifies existing and emerging concepts concerning advanced prediction models/architectures
Provides a series of the latest results in, including but not limited to, supervised learning, semi-supervised learning, self-supervised learning, probabilistic learning

Produktinformation

Utgivningsdatum2026-07-01
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor350
FörlagElsevier Science
ISBN9780443367595

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Företagsekonomi inom Ekonomi och ledarskap
Affärsapplikationer inom Data och it

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. 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.

Section 1: Theory 1. Introduction of Prediction2. Theoretical Foundations of Paste-Filling System3. Foundation of Aluminium Electrolysis SystemSection 2: Methodology 4. Machine Learning Basics for Prediction & Soft SensingSection 3: Application 5. A Novel Supervised Soft Sensor Framework Based on Convolutional Laplacian Extreme Learning Machine: CNN-LapsELM6. A Novel Semi-Supervised Soft Sensor Framework Based on Stacked Auto-Encoder Wavelet Extreme Learning Machine: SAE-WELM7. A Novel Soft Sensor Based on Laplacian Hessian Semi-Supervised Hierarchical Extreme Learning Machine: LHSS-HELM8. A Self-Supervised Prediction Framework Based on Deep Long Short-Time Memory for Aluminum Electrolysis: SSDLSTM9. A Self-Supervised Prediction Framework Based on Convolutional Deep Long Short-Time Memory for Aluminum Temperature Application: CNN-SSDLSTM10. A Novel Probabilistic Prediction Framework Based on Bayesian Machine Learning: BLSTM11. Direct Data-Driven Quantile Regressor Forecaster for Underflow Concentration Soft Sensing: DDQRF12. A Novel Key-Quality Prediction Framework for Industrial Deep Cone Thickener: DualLSTM13. A Deeply-Efficient Long Short-Time Memory Framework for Underflow Concentration Prediction: DE-LSTM14. An Ensemble Prediction Method for Probabilistic Forecasting of Aluminium Electrolysis Process