Risk Assessment for a Twin-Screw Granulation Process Using a Supervised Physics-Constrained Auto-encoder and Support Vector Machine Framework

Sampat, Chaitanya; Ramachandran, Rohit

doi:10.1007/s11095-022-03313-y

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…The limits of the constraints were determined by a combination of the design space used for the experiments as well as the assessing the risk of the model using a framework developed in Sampat and Ramachandran. 32 The value of the objective function was determined from the predictions of the developed LSTM models and heat balance equations. The objective function was not a continuous function; thus, an optimization algorithm that did not require the calculation of the gradient had to be chosen.…”

Section: Optimization Frameworkmentioning

confidence: 99%

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network

Sampat,

Ramachandran

2023

ACS Eng. Au

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Traditional pharmaceutical manufacturing processes for solid oral dosage forms can be inefficient and have been known to produce a large amount of undesired product. With the progressing trend of achieving carbon neutrality, there is an impetus to increase the energy efficiency of these manufacturing processes while maintaining the critical quality attributes of the product. One of the important steps in downstream pharmaceutical manufacturing is wet granulation, and within that, twin screw granulation (TSG) is a popular continuous manufacturing technique. In this study, the energy efficiency of the TSG process was maximized by combining a long-term memory (LSTM) model with an optimization algorithm. The LSTM model was trained on time-series process data obtained from the TSG experimental runs. The optimization process, with the objective of maximizing energy efficiency, was performed using a stochastic optimization algorithm, and constraints were enforced on the process parameter design space. Experimental runs at the optimal process parameters were conducted on the TSG equipment with updates occurring at predefined intervals depending on the optimization scenarios. The purpose of these experimental runs was to validate the capability of increasing the overall process energy efficiency when operating at the optimized process parameters. A maximum increase of 27% was obtained between two tested optimization scenarios while maintaining the yield of the granules at the end of the twin-screw granulation process.

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Section: Optimization Frameworkmentioning

confidence: 99%

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network

Sampat,

Ramachandran

2023

ACS Eng. Au

Self Cite

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Autoencoder-based inverse design and surrogate-based optimization of an integrated wet granulation manufacturing process

Dan,

Ramachandran

2024

International Journal of Pharmaceutics: X

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Multi-physical predictions in electro-osmotic micromixer by auto-encoder physics-informed neural networks

Chang,

Huai,

Liu

et al. 2023

Physics of Fluids

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Electro-osmotic micromixers (EMMs) are used for manipulating microfluidics because of the advantages on electro-osmosis mechanisms. The intricate interdependence between various fields in the EMM model presents a challenge for traditional numerical methods. In this paper, the flow parameters and electric potential are predicted based on the solute concentration by utilizing the physics-informed neural networks (PINNs) method. The unknown spatiotemporal dependent fields are derived from a deep neural network trained by minimizing the loss function integrating data of scalar field and corresponding governing equations. Moreover, the auto-encoder structure is developed to improve the performance of PINNs in the EMM. The comparisons between the results of auto-encoder PINNs and previous PINNs show a reduction in relative errors for transverse and longitudinal velocities from 83.35% and 84.24% to 9.88% and 12.29%, respectively, in regions with large-gradient velocities. Furthermore, our results demonstrate that the proposed method is robust to noise in the scalar concentration.

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Risk Assessment for a Twin-Screw Granulation Process Using a Supervised Physics-Constrained Auto-encoder and Support Vector Machine Framework

Cited by 3 publications

References 49 publications

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network

Optimizing Energy Efficiency of a Twin-Screw Granulation Process in Real-Time Using a Long Short-Term Memory (LSTM) Network

Autoencoder-based inverse design and surrogate-based optimization of an integrated wet granulation manufacturing process

Multi-physical predictions in electro-osmotic micromixer by auto-encoder physics-informed neural networks

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