Model based optimisation of highly automated industrial batch annealing operation

2006 ◽  
Vol 33 (4) ◽  
pp. 306-314 ◽  
Author(s):  
S. S. Sahay ◽  
K. Krishnan ◽  
M. Kulthe ◽  
A. Chodha ◽  
B. Bhattacharya ◽  
...  
Keyword(s):  
Model Based ◽  
Industrial Batch ◽  
Batch Annealing ◽  
Annealing Operation

An integrated batch annealing furnace simulator

2004 ◽  
Vol 120 ◽  
pp. 809-817
Author(s):  
S. S. Sahay
Keyword(s):  
Mechanical Properties ◽  
Control Strategy ◽  
Temporal Evolution ◽  
Furnace Productivity ◽  
Annealing Furnace ◽  
Industrial Batch ◽  
Batch Annealing ◽  
Annealing Operation ◽  
Prediction Capability

An integrated batch annealing furnace simulator with the capability of predicting spatial and temporal evolution of temperature, microstructure and mechanical properties of the coils during the batch annealing operation has been developed. The prediction capability of this integrated simulator has been extensively validated with data collected from several industrial batch annealing operations. In this article, the problems in controlling a batch annealing operation via conventional temperature based control strategy has been highlighted. These problems can be effectively resolved by using the integrated simulator. Furthermore, the utility of this simulator has been illustrated by a case study on optimization of coil dimensions for maximization of furnace productivity.

Neural Network Models for Industrial Batch Annealing Operation

2008 ◽  
Vol 23 (2) ◽  
pp. 203-208 ◽  
Author(s):  
Rajesh Mehta ◽  
Satyam S. Sahay ◽  
Amlan Datta ◽  
Aman Chodha
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Industrial Batch ◽  
Batch Annealing ◽  
Annealing Operation

Process Analytics, Modeling, and Optimization of an Industrial Batch Annealing Operation

2009 ◽  
Vol 24 (12) ◽  
pp. 1459-1466 ◽  
Author(s):  
Satyam S. Sahay ◽  
R. Mehta ◽  
S. Raghavan ◽  
R. Roshan ◽  
S. J. Dey
Keyword(s):  
Modeling And Optimization ◽  
Process Analytics ◽  
Industrial Batch ◽  
Batch Annealing ◽  
Annealing Operation

Application of a grey-box modelling approach for the online monitoring of batch production in the chemical industry

2020 ◽  
Vol 68 (7) ◽  
pp. 582-598
Author(s):  
Ala E. F. Bouaswaig ◽  
Keivan Rahimi-Adli ◽  
Matthias Roth ◽  
Alireza Hosseini ◽  
Hugo Vale ◽  
...  
Keyword(s):  
Batch Processes ◽  
Polymerization Process ◽  
Monitoring And Control ◽  
Continuous Processes ◽  
Model Based ◽  
Modeling Approach ◽  
Industrial Batch ◽  
Laboratory Reactor ◽  
Interest In Research ◽  
And Control

AbstractModel-based solutions for monitoring and control of chemical batch processes have been of interest in research for many decades. However, unlike in continuous processes, in which model-based tools such as Model Predictive Control (MPC) have become a standard in the industry, the reported use of models for batch processes, either for monitoring or control, is rather scarce. This limited use is attributed partly to the inherent complexity of the batch processes (e. g., dynamic, nonlinear, multipurpose) and partly to the lack of appropriate commercial tools in the past. In recent years, algorithms and commercial tools for model-based monitoring and control of batch processes have become more mature and in the era of Industry 4.0 and digitalization they are slowly but steadily gaining more interest in real-word batch applications. This contribution provides a practical example in this application field. Specifically, the use of a grey-box modeling approach, in which a multiway Projection to Latent Structure (PLS) model is combined with a first-principles model, to monitor the evolution of a batch polymerization process and predict in real-time the final batch quality is reported. The modeling approach is described, and the experimental results obtained from an industrial batch laboratory reactor are presented.

scholarly journals Model-based Design of Experiments for Polyether Production from Bio-based 1,3-Propanediol

2021 ◽  
Author(s):  
Anh-Duong Vo ◽  
Ali Shahmohammadi ◽  
Kimberley McAuley
Keyword(s):  
Design Of Experiments ◽  
Batch Reactor ◽  
Water Concentration ◽  
Operating Conditions ◽  
Parameter Estimates ◽  
Initial Water ◽  
Model Based ◽  
Decision Variables ◽  
Industrial Batch ◽  
High Concentrations

Sequential model-based design of experiments (MBDOE) is used to select operating conditions for new experiments in a batch-reactor that produces bio-based poly(trimethylene) ether glycol (PO3G). These Bayesian A-optimal experiments are designed to obtain improved estimates of the 70 fundamental-model parameter estimates, while accounting for the model structure and for data from eight previous industrial batch-reactor runs. Settings are selected for three decision variables: reactor temperature, initial catalyst level, and initial water concentration. If only one new experiment is conducted, it should be run at high temperature, with relatively high concentrations of catalyst and initial water. When two new runs are conducted, one should use an intermediate catalyst concentration. The effectiveness of the proposed MBDOE approach is tested using Monte-Carlo simulations, revealing that the selected experiments are superior compared to new experiments selected randomly from corners of the permissible design space.

Recrystallisation and Grain Growth in Batch Annealed Ti-IF Steels

2004 ◽  
Vol 467-470 ◽  
pp. 1099-1104 ◽  
Author(s):  
Hélène Petitgand ◽  
H. Réglé ◽  
Uwe Zimmermann
Keyword(s):  
Chemical Composition ◽  
Simple Model ◽  
Grain Growth ◽  
Growth Kinetics ◽  
Processing Parameters ◽  
Model Based ◽  
Batch Annealing ◽  
Grain Growth Kinetics ◽  
The Impact

In order to optimize the batch annealing cycles and increase the productivity of this process, the impact of the chemical composition and the processing parameters on the recrystallisation and grain growth kinetics were investigated on different Ti IF steels. A simple model based on an Avrami formulation has been developed for the prediction of the recrystallisation kinetics.

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