Fuzzy Phase Partition and Hybrid Modeling Based Quality Prediction and Process Monitoring Methods for Multiphase Batch Processes

2016 ◽  
Vol 55 (14) ◽  
pp. 4045-4058 ◽  
Author(s):  
Lijia Luo ◽  
Shiyi Bao ◽  
Jianfeng Mao ◽  
Di Tang ◽  
Zengliang Gao
Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1321
Author(s):  
Luping Zhao ◽  
Xin Huang ◽  
Hao Yu

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.


Minerals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 683
Author(s):  
Chris Aldrich ◽  
Xiu Liu

Froth image analysis has been considered widely in the identification of operational regimes in flotation circuits, the characterisation of froths in terms of bubble size distributions, froth stability and local froth velocity patterns, or as a basis for the development of inferential online sensors for chemical species in the froth. Relatively few studies have considered flotation froth image analysis in unsupervised process monitoring applications. In this study, it is shown that froth image analysis can be combined with traditional multivariate statistical process monitoring methods for reliable monitoring of industrial platinum metal group flotation plants. This can be accomplished with well-established methods of multivariate image analysis, such as the Haralick feature set derived from grey level co-occurrence matrices and local binary patterns that were considered in this investigation.


Author(s):  
Chiara Di Francescomarino ◽  
Chiara Ghidini ◽  
Fabrizio Maria Maggi ◽  
Fredrik Milani

2014 ◽  
Vol 971-973 ◽  
pp. 1481-1484
Author(s):  
Ke He Wu ◽  
Long Chen ◽  
Yi Li

In order to ensure safe and stable running of applications, this paper analyses the limitation of traditional process-monitoring methods, and then designs a new real-time process monitor method based on Mandatory Running Control (MRC) technology. This method not only can monitor the processes, but also can control them from system kernel level to improve the reliability and safety of applications, so as to ensure the security and stability of information system.


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