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Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic
Vinícius V Santana1,2, Márcio A F Martins2, José M Loureiro1
1Laboratory of Separation and Reaction Engineering, Associate Laboratory LSRE-LCM, Department of Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
Deep recurrent neural networks (DRNNs) enable efficient model-based optimization for simulated moving bed reactors (SMBRs). This approach effectively characterizes the feasible operation region, ensuring optimal performance for complex chemical processes.
Area of Science:
- Chemical Engineering
- Process Optimization
- Artificial Intelligence in Chemical Processes
Background:
- Model-based optimization of simulated moving bed reactors (SMBRs) is computationally intensive.
- Surrogate models, particularly artificial neural networks (ANNs), are increasingly used for complex process modeling.
- ANNs have been applied to simulated moving bed (SMB) units but not extensively to reactive SMBs (SMBRs).
Purpose of the Study:
- To develop and evaluate deep recurrent neural networks (DRNNs) for optimizing simulated moving bed reactors (SMBRs).
- To establish a consistent method for assessing optimality using surrogate models in SMBR optimization.
- To characterize the feasible operation region of SMBRs using DRNNs.
Main Methods:
- Optimization of SMBRs using deep recurrent neural networks (DRNNs).
- Characterization of the feasible operation region by recycling data points from a metaheuristic technique for optimality assessment.
- Utilizing ANNs for modeling the SMBR unit.
Main Results:
- DRNNs demonstrate capacity for modeling and optimizing SMBRs.
- The proposed method successfully characterizes the feasible operation region for SMBRs.
- The DRNN-based optimization approach meets optimality criteria for complex SMBR processes.
Conclusions:
- Deep recurrent neural networks (DRNNs) provide an effective solution for computationally demanding SMBR optimization.
- The integration of DRNNs with metaheuristic techniques offers a robust method for optimality assessment and feasible region characterization.
- This study advances the application of ANNs in reactive separation processes, paving the way for more efficient chemical process design.
