Phase division and transition modeling based on the dominant phase identification for multiphase batch process quality prediction

2019 ◽  
Vol 42 (5) ◽  
pp. 1022-1036 ◽  
Author(s):  
Xiaochu Tang ◽  
Yuan Li
Keyword(s):  
Cumulative Effect ◽  
Batch Process ◽  
Batch Processes ◽  
Just In Time ◽  
Phase Identification ◽  
Quality Prediction ◽  
Time Model ◽  
Transition Modeling ◽  
Dominant Phase ◽  
Prediction Strategy

Batch processes are carried out from one steady phase to another one, which may have multiphase and transitions. Modeling in transitions besides in the steady phases should also be taken into consideration for quality prediction. In this paper, a quality prediction strategy is proposed for multiphase batch processes. First, a new repeatability factor is introduced to divide batch process into different steady phases and transitions. Then, the different local cumulative models that considered the cumulative effect of process variables on quality are established for steady phases and transitions. Compared with the reported modeling methods in transitions, a novel just-in-time model can be established based on the dominant phase identification. The proposed method can not only consider the dynamic characteristic in the transition but also improve the accuracy and the efficiency of transitional models. Finally, online quality prediction is performed by accumulating the prediction results from different phases and transitions. The effectiveness of the proposed method is demonstrated by penicillin fermentation process.

Online quality prediction based on the double cumulative model for multiphase batch processes with transitions

2021 ◽  
pp. 014233122110384
Author(s):  
Xiaochu Tang ◽  
Yuan Li
Keyword(s):  
Cumulative Effect ◽  
Batch Processes ◽  
Phase Identification ◽  
Quality Prediction ◽  
Competing Models ◽  
The Common ◽  
Simulation Results ◽  
Critical Phases ◽  
Dominant Phase ◽  
Cumulative Model

In multiphase batch processes, the final product quality prediction results depend on the common cumulative effects of all the critical phases with transitions on quality rather than a separated one. However, the common cumulative effect are rarely considered at the same time. To address this issue, a double cumulative model is proposed in this paper. The double model includes the internal cumulative model and the external cumulative model. On one hand, the critical local cumulative quality is introduced to isolate the local cumulative effect of different phases and transitions in the internal cumulative model. Especially, for transitions modeling, an average process trajectories method based on the dominant phase identification is developed to deal with the dynamic and uncertainty. On the other hand, the correlation of different cumulative models is extracted by constructing the external cumulative model. In this way, the cumulative effect of all the phases and transitions are considered simultaneously. The proposed method is applied to penicillin fermentation. The simulation results demonstrate the effectiveness and superiority of the proposed method over the competing models.

Online local modeling and prediction of batch process trajectories using just-in-time learning and LSTM neural network

2020 ◽  
Vol 20 (3) ◽  
pp. 715-726
Author(s):  
Feifan Shen ◽  
Jiaqi Zheng ◽  
Lingjian Ye ◽  
Nael El-Farra
Keyword(s):  
Neural Network ◽  
Batch Reactor ◽  
Batch Process ◽  
Batch Processes ◽  
Just In Time ◽  
Complex Data ◽  
Trajectory Prediction ◽  
Modeling And Prediction ◽  
Monitoring Applications ◽  
Trajectory Interpolation

This paper deals with the online sample trajectory prediction problem of batch processes considering complex data characteristics and batch-to-batch variations. Although some methods have been proposed to implement the trajectory interpolation problem for quality prediction and monitoring applications, the accuracy and reliability are not ensured due to data nonlinearity, dynamics and other complicated feature. To improve the data interpolation performance, an improved JITL-LSTM approach is designed in this work. Firstly, an improved trajectory-based JITL strategy is developed to extract similar local trajectories. Then the LSTM neural network is used on the basis of the extracted trajectories with a modified network structure. Therefore, trajectory prediction and interpolation can be achieved according to the local JITL-LSTM model at each time index. A simulated fed-batch reactor process is presented to demonstrate the effectiveness of the proposed method.

scholarly journals Slow Time-Varying Batch Process Quality Prediction Based on Batch Augmentation Analysis

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 512
Author(s):  
Luping Zhao ◽  
Xin Huang
Keyword(s):  
Prediction Method ◽  
Batch Process ◽  
Batch Processes ◽  
Data Matrix ◽  
Injection Molding Process ◽  
Quality Prediction ◽  
Time Varying ◽  
Slow Time ◽  
Process Data ◽  
Molding Process

In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.

scholarly journals Two-Dimensional, Two-Layer Quality Regression Model Based Batch Process Monitoring

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 43
Author(s):  
Luping Zhao ◽  
Xin Huang
Keyword(s):  
Regression Model ◽  
Layer Structure ◽  
Batch Process ◽  
Batch Processes ◽  
Quality Prediction ◽  
Two Dimensional ◽  
Batch Process Monitoring ◽  
Multi Phase ◽  
The Relationship ◽  
Multi Mode

In this paper, a two-dimensional, two-layer quality regression model is established to monitor multi-phase, multi-mode batch processes. Firstly, aiming at the multi-phase problem and the multi-mode problem simultaneously, the relations among modes and phases are captured through the analysis between process variables and quality variables by establishing a two-dimensional, two-layer regression partial least squares (PLS) model. The two-dimensional regression traces the intra-batch and inter-batch characteristics, while the two-layer structure establishes the relationship between the target process and historical modes and phases. Consequently, online monitoring is carried out for multi-phase, multi-mode batch processes based on quality prediction. In addition, the online quality prediction and monitoring results based on the proposed method and those based on the traditional phase mean PLS method are compared to prove the effectiveness of the proposed method.

scholarly journals Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1074
Author(s):  
Federico Zuecco ◽  
Matteo Cicciotti ◽  
Pierantonio Facco ◽  
Fabrizio Bezzo ◽  
Massimiliano Barolo
Keyword(s):  
Production Capacity ◽  
Batch Process ◽  
Batch Processes ◽  
Flow Diagram ◽  
Production Cycle ◽  
Multivariate Statistical ◽  
Process Flow Diagram ◽  
Root Cause ◽  
Industrial Batch ◽  
Specialty Chemical

Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.

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