Determination of Optimum Drying Temperature Profile by Iterative Learning Control (ILC) Method to Obtain a Desired Moisture Content in Tablets

  • Nahid Sanzida Department of Chemical Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000
Keywords: Batch process, Iterative learning control, LTV perturbation model, Moisture content, Tablet manufacturing

Abstract

The paper presents an industrial case study example to evaluate the performance of the linear time varying (LTV) perturbation model based iterative learning control (ILC) in a pilot scale batch system. The operating data based strategy applied here is based on utilizing the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs and thereby providing a convergent batch-to-batch improvement of the process performance indicator. The method was applied to determine the required drying temperature of Paracetamol granules to obtain desired moisture content at the end of the batch. After granulation operations, Paracetamol granules were dried in a fluid bed dryer in the pilot plant laboratory of GlaxoSmithKline Bangladesh Limited, Chittagong, Bangladesh. These results demonstrate the potential of the ILC approach for controlling batch processes without rigorous process models.

Chemical Engineering Research Bulletin 20(2018) 1-7

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Abstract
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PDF
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Published
2018-06-06
How to Cite
Sanzida, N. (2018). Determination of Optimum Drying Temperature Profile by Iterative Learning Control (ILC) Method to Obtain a Desired Moisture Content in Tablets. Chemical Engineering Research Bulletin, 20(1), 1-7. https://doi.org/10.3329/cerb.v20i1.36923
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Articles