A Novel Dynamic Just-in-Time Learning Framework for Modeling of Batch Processes

2020 ◽  
Vol 59 (43) ◽  
pp. 19334-19344
Author(s):  
Tanuja Joshi ◽  
Vishesh Goyal ◽  
Hariprasad Kodamana
Keyword(s):  
Batch Processes ◽  
Just In Time ◽  
Learning Framework

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

2020 ◽  
Vol 99 (1) ◽  
pp. 334-344 ◽  
Author(s):  
Jianlin Wang ◽  
Kepeng Qiu ◽  
Yongqi Guo ◽  
Rutong Wang ◽  
Xinjie Zhou
Keyword(s):  
Soft Sensor ◽  
Batch Processes ◽  
Just In Time ◽  
Sensor Development

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.

Just-in-Time-Learning based Extended Prediction Self-Adaptive Control for batch processes

2016 ◽  
Vol 43 ◽  
pp. 1-9 ◽  
Author(s):  
Qing-Lin Su ◽  
Martin Wijaya Hermanto ◽  
Richard D. Braatz ◽  
Min-Sen Chiu
Keyword(s):  
Adaptive Control ◽  
Batch Processes ◽  
Just In Time ◽  
Self Adaptive

A Framework for “Just-in-Time Learning” Decision Support in Organizations

2021 ◽  
pp. 449-466
Author(s):  
Mark Salisbury
Keyword(s):  
Decision Support ◽  
Expert Systems ◽  
The United States ◽  
Department Of Energy ◽  
Just In Time ◽  
United States Department ◽  
Discussion Section ◽  
States Department ◽  
Learning Framework ◽  
Provide Decision Support

This article describes an integrated “Just-in-Time Learning” framework for providing decision support in organizations. The framework emerges from years of work with the national laboratories and facilities that are under the direction of the United States Department of Energy. The article begins by describing expert systems technology and how it has been used to provide decision support in organizations. This is followed by a discussion of the strengths and weaknesses of expert systems technology for this purpose. Next, a “Just-in-Time Learning” framework is introduced where the theoretical foundation for the framework is described. Afterwards, the other aspects of the framework including the types of knowledge, learners it serves, and how the framework can be utilized for decision support are detailed. Finally, a discussion section summarizes how a Just-in-Time Learning Framework can achieve some of the strengths -- while overcoming some of the weaknesses -- of expert system technology for providing decision support in organizations.

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