Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research

Nominated as an outstanding PhD thesis by Tsinghua University

Develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle

Proposes an effective process monitoring strategy to eliminate false alarms in industrial production

Presents a holistic framework for adaptive process monitoring system design

Offers dynamic quality prediction models with improved data utilization and accuracy for product quality control

Nominated as an outstanding PhD thesis by Tsinghua University Develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle Proposes an effective process monitoring strategy to eliminate false alarms in industrial production Presents a holistic framework for adaptive process monitoring system design Offers dynamic quality prediction models with improved data utilization and accuracy for product quality control

Inhalt
Introduction.- Concurrent monitoring of steady state and process dynamics with SFA.- Online monitoring and diagnosis of control performance with SFA and contribution plots.- Recursive SFA algorithm and adaptive monitoring system design.- Probabilistic SFR model and its applications in dynamic quality prediction.- Improved DPLS model with temporal smoothness and its applications in dynamic quality prediction.- Nonlinear and dynamic soft sensing model based on Bayesian framework.- Summary and open problems.