Learning-based Soft Sensing and Predictions for Process Industries
Theory, Methodology and Applications
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Product details:
- Publisher Academic Press
- Date of Publication 1 October 2026
- ISBN 9780443367595
- Binding Paperback
- No. of pages242 pages
- Size 229x152 mm
- Weight 450 g
- Language English 700
Categories
Long description:
Learning-Based Soft Sensing and Predictions for Process Industries: Theory, Methodology and Applications covers prediction and soft sensing in industrial processes that are 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, methodologies, and applications. The book introduces the basics of prediction and soft sensing backgrounds, including different categories of prediction theory. Secondly, covers the foundations of machine learning methodologies, including supervised learning prediction, semi-supervised, and self-supervised prediction. Finally, the book examines 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 surrounding 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
Table of Contents:
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
