EC Microbiology

Research Article Volume 21 Issue 1 - 2025

Advanced RTD Prediction and Optimization in Three-Phase Bubble Column Reactors: Leveraging Deep Learning for Enhanced Industrial Efficiency

Goddindla Sreenivasulu1*, R Ramakoteswara Rao1, B Sarath Babu1, P Akhila Swathantra1 and Asadi Srinivasulu2

1Department of Chemical Engineering, Sri Venkateswara University, Tirupati, India

2Department of crcCARE, Newcastle, University of Newcastle, Australia

*Corresponding Author: Goddindla Sreenivasulu, Department of Chemical Engineering, Sri Venkateswara University, Tirupati, India.
Received: December 02, 2024; Published: January 06, 2025



This research aims to improve industrial efficiency by focusing on bubble column reactors, which are critical to many industrial processes. However, predicting residence time distribution (RTD) and optimizing reactor performance is challenging due to the complex interplay of factors like gas and liquid flow rates, particle size, reactor pressure, and temperature. To tackle these challenges, a Convolutional Neural Network (CNN) model is employed to analyze and optimize RTD in these reactors. The dataset incorporates key operational variables such as gas flow rate, liquid flow rate, mass transfer coefficient, CO2 uptake, energy output, and flow velocity. The CNN model was trained for 50 epochs and tested on a validation set. The results showed that the model reached 100% accuracy during training but only achieved 66.67% validation accuracy, with a final validation loss of 0.8111. This suggests that the model overfitted the training data, performing well on the training set but struggling to generalize, as reflected in the steady validation accuracy. The training process, however, was highly efficient, completing in 12.4 seconds. The study highlights the promise of deep learning for optimizing RTD in complex reactors. Nevertheless, the research recommends implementing techniques such as regularization and early stopping to mitigate overfitting and improve generalization. The findings lay the groundwork for future exploration, focusing on integrating real-time sensor data and employing more advanced neural network architectures to further enhance reactor performance.

 Keywords: Bubble Column Reactors; Residence Time Distribution (RTD); Three-Phase Reactors; Deep Learning; Convolutional Neural Networks (CNN); Process Optimization; Industrial Efficiency and Reactor Performance

  1. Mosavi A., et al. “Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning”. Engineering Applications of Computational Fluid Mechanics1 (2019): 482-492.
  2. Shamshirband S., et al. “Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants”. Engineering Applications of Computational Fluid Mechanics 1 (2020): 367-378.
  3. Gandhi AB., et al. “Development of unified correlations for volumetric mass-transfer coefficient and effective interfacial area in bubble column reactors for various gas-liquid systems using support vector regression”. Industrial and Engineering Chemistry Research9 (2009): 4216-4236.
  4. Shamshirband S., et al. “Prediction of flow characteristics in the bubble column reactor by the artificial pheromone-based communication of biological ants”. arXiv (2020).
  5. Babanezhad M., et al. “High-performance hybrid modeling chemical reactors using differential evolution based fuzzy inference system”. Scientific Reports1 (2020): 21304.
  6. Liu X., et al. “Refining computer tomography data with super-resolution networks to increase the accuracy of respiratory flow simulations”. Future Generation Computer Systems 159 (2024): 474-488.
  7. Achour S and Hosni Z. “ML-driven models for predicting CO2 uptake in metal-organic frameworks (MOFs)”. Canadian Journal of Chemical Engineering (2024).
  8. Hassanian R., et al. “Optimizing wind energy production: Leveraging deep learning models informed with on-site data and assessing scalability through HPC”. Acta Polytechnica Hungarica9 (2024).
  9. Pata J., et al. “Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors”. Communications Physics1 (2024): 124.
  10. Garcia Amboage JP., et al. “Model performance prediction for hyperparameter optimization of deep learning models using high performance computing and quantum annealing”. EPJ Web of Conferences 295 (2024): 12005.
  11. Hassanian R., et al. “Deep learning model capable of simulating the inertial particle path in particle-laden turbulent flow”. In 5th International Conference on Numerical Methods in Multiphase Flows (ICNMMF-5: Reykjavik, Iceland (2024).
  12. Demou AD and Savva N. “Hybrid AI-analytical modeling of droplet dynamics on inclined heterogeneous surfaces”. Mathematics8 (2024): 1188.
  13. Hassanian R., et al. “Wind velocity and forced heat transfer model for photovoltaic module”. Fluids1 (2024): 17.
  14. Rüttgers M., et al. “Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning”. Computers in Biology and Medicine 173 (2024): 108383.
  15. Pata J., et al. “Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors”. Communications Physics1 (2024): 124.
  16. Hassanian R., et al. “Turbulent flow prediction-simulation: Strained flow with initial isotropic condition using a GRU model trained by an experimental Lagrangian framework, with emphasis on hyperparameter optimization”. Fluids 4 (2024): 84.
  17. Higashida A., et al. “Robustness evaluation of large-scale machine learning-based reduced order models for reproducing flow fields”. Future Generation Computer Systems 159 (2024): 243-254.

Goddindla Sreenivasulu., et al. “Advanced RTD Prediction and Optimization in Three-Phase Bubble Column Reactors: Leveraging Deep Learning for Enhanced Industrial Efficiency”. EC Microbiology  21.1 (2025): 01-14.