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Reinforcement Learning for Batch Bioprocess Optimization

arXiv 1904.07292 ↗First posted 15 Apr 2019math.OC cs.SY eess.SYcited by 28

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Abstract (arXiv)

Bioprocesses have received a lot of attention to produce clean and sustainable alternatives to fossil-based materials. However, they are generally difficult to optimize due to their unsteady-state operation modes and stochastic behaviours. Furthermore, biological systems are highly complex, therefore plant-model mismatch is often present. To address the aforementioned challenges we propose a Reinforcement learning based optimization strategy for batch processes. In this work, we applied the Policy Gradient method from batch-to-batch to update a control policy parametrized by a recurrent neural network. We assume that a preliminary process model is available, which is exploited to obtain a preliminary optimal control policy. Subsequently, this policy is updatedbased on measurements from thetrueplant. The capabilities of our proposed approach were tested on three case studies (one of which is nonsmooth) using a more complex process model for thetruesystemembedded with adequate process disturbance. Lastly, we discussed the advantages and disadvantages of this strategy compared against current existing approaches such as nonlinear model predictive control.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 1; 2024: 7; 2025: 8; 2026: 1.

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  1. 2021Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuningcited by 24
  2. 2022Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approachcited by 12
  3. 2021Safe Chance Constrained Reinforcement Learning for Batch Process Controlcited by 11
  4. 2024Control-Informed Reinforcement Learning for Chemical Processescited by 9
  5. 2024Machine learning for industrial sensing and control: A survey and practical perspectivecited by 8
  6. 2021A Meta-Reinforcement Learning Approach to Process Controlcited by 5
  7. 2021Safe Real-Time Optimization using Multi-Fidelity Gaussian Processescited by 5
  8. 2023Surrogate Empowered Sim2Real Transfer of Deep Reinforcement Learning for ORC Superheat Controlcited by 5
  9. 2021Policy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processescited by 4
  10. 2022When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Developmentcited by 4
  11. 2022Machine learning in bioprocess development: From promise to practicecited by 4
  12. 2025Reinforcement learning for efficient and robust multi-setpoint and multi-trajectory tracking in bioprocessescited by 4
16 more
  1. 2025Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Reviewcited by 4
  2. 2024Human-Algorithm Collaborative Bayesian Optimization for Engineering Systemscited by 3
  3. 2022Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learningcited by 2
  4. 2024Enhancing reinforcement learning for population setpoint tracking in co-culturescited by 2
  5. 2025Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)cited by 2
  6. 2025Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineeringcited by 2
  7. 2022Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Datacited by 1
  8. 2024Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimationcited by 1
  9. 2025Reinforcement learning for robust dynamic metabolic controlcited by 1
  10. 2025Reinforcement learning meets bioprocess control through behaviour cloning: Real-world deployment in an industrial photobioreactorcited by 1
  11. 2022TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process controlcited by 0
  12. 2024Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectivescited by 0
  13. 2024Sensitivity Analysis on Policy-Augmented Graphical Hybrid Models with Shapley Value Estimationcited by 0
  14. 2025Multi-Mode Process Control Using Multi-Task Inverse Reinforcement Learningcited by 0
  15. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Reviewcited by 0
  16. 2026Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modelingcited by 0