Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions

2003 ◽  
Vol 17 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Theodora Kourti
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Data Modeling ◽  
Batch Processes ◽  
Statistical Process ◽  
Dynamic Data ◽  
Start Ups

scholarly journals Synchronization-Free Multivariate Statistical Process Control for Online Monitoring of Batch Process Evolution

2022 ◽  
Vol 1 ◽  
Author(s):  
Rodrigo Rocha de Oliveira ◽  
Anna de Juan
Keyword(s):  
Process Control ◽  
Real Time ◽  
Statistical Process Control ◽  
Batch Process ◽  
Operating Conditions ◽  
Batch Processes ◽  
Multivariate Statistical Process Control ◽  
Real Time Monitoring ◽  
Multivariate Statistical ◽  
Statistical Process

Synchronization of variable trajectories from batch process data is a delicate operation that can induce artifacts in the definition of multivariate statistical process control (MSPC) models for real-time monitoring of batch processes. The current paper introduces a new synchronization-free approach for online batch MSPC. This approach is based on the use of local MSPC models that cover a normal operating conditions (NOC) trajectory defined from principal component analysis (PCA) modeling of non-synchronized historical batches. The rationale behind is that, although non-synchronized NOC batches are used, an overall NOC trajectory with a consistent evolution pattern can be described, even if batch-to-batch natural delays and differences between process starting and end points exist. Afterwards, the local MSPC models are used to monitor the evolution of new batches and derive the related MSPC chart. During the real-time monitoring of a new batch, this strategy allows testing whether every new observation is following or not the NOC trajectory. For a NOC observation, an additional indication of the batch process progress is provided based on the identification of the local MSPC model that provides the lowest residuals. When an observation deviates from the NOC behavior, contribution plots based on the projection of the observation to the best local MSPC model identified in the last NOC observation are used to diagnose the variables related to the fault. This methodology is illustrated using two real examples of NIR-monitored batch processes: a fluidized bed drying process and a batch distillation of gasoline blends with ethanol.

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