Learning-based Soft Sensing and Predictions for Process Industries

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