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…mehr
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
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.
Inhaltsangabe
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
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
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