Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals

Nikita, Saxena; Tiwari, Arvind; Sonawat, Deepak; Kodamana, Hariprasad; Rathore, Anurag S.

doi:10.1016/j.ces.2020.116171

Cited by 43 publications

(25 citation statements)

References 36 publications

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“…The trained ANN was then used to optimize the input energy consumption to fit a continuous multiphase flow process over the long-term. Nikita et al proposed a novel reinforcement learning-based method for optimization of the process flow rate in order to reach the maximum yield for continuous processing of biopharmaceuticals. Overall, most of the above studies mainly applied the pure ML models to optimize a single parameter of flow and transport processes while most often the maximization of multiphase flow and device performance needs to optimize multiple parameters simultaneously.…”

Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

116

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Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep learning and physics-informed ML, is a valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental and computational methods. ML can provide flexible techniques to facilitate the conceptual development of new robust predictive models for multiphase flows and reactors by finding hidden pattern/information/mechanism in a data set. Due to such emergence, we thereby comprehensively survey, explore, analyze, and discuss key advancements of recent ML applications to hydrodynamics, heat and mass transfer, and reactions in single-phase and multiphase flow systems from different aspects: (1) development of multiphase closure models of drag force, turbulence stresses and heat/mass transfer to improve the accuracy and efficiency of typical CFD simulations; (2) image reconstruction, regime identification, key parameter predictions, and optimization of multiphase flow and transport fields; (3) reaction kinetics modeling (e.g., predictions of reaction networks, kinetic parameters, and species production) and reaction condition optimization. These sections also discuss and analyze the key advantages and weakness of ML for solving the problems in the domain of multiphase flows and reactors. Finally, we summarize the under-solving challenges and opportunities in order to identify future directions that would be useful for the research community. Future development and study of multiphase flows and reactors are envisaged to be accelerated by ML and data science.

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Section: Current Status and Challengesmentioning

confidence: 99%

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Zhu

Chen

Ouyang

et al. 2022

Ind. Eng. Chem. Res.

116

View full text Add to dashboard Cite

show abstract

“…Nikita et al described a reinforcement ML algorithm where they formulated a maximization problem using cation exchange chromatography for separation of charge variants by optimization of the process flowrate. 75 Mechanistic models such as general rate models were shown to predict elution peaks in ion-exchange process chromatography. 76 The proposed model can be used to predict the separation of charge variants, allowing optimization and control of preparative scale chromatography.…”

Section: Future Perspectivesmentioning

confidence: 99%

Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing

Walsh

Myint

Nguyen-Khuong

et al. 2022

mAbs

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Ensuring consistent high yields and product quality are key challenges in biomanufacturing. Even minor deviations in critical process parameters (CPPs) such as media and feed compositions can significantly affect product critical quality attributes (CQAs). To identify CPPs and their interdependencies with product yield and CQAs, design of experiments, and multivariate statistical approaches are typically used in industry. Although these models can predict the effect of CPPs on product yield, there is room to improve CQA prediction performance by capturing the complex relationships in high-dimensional data. In this regard, machine learning (ML) approaches offer immense potential in handling non-linear datasets and thus are able to identify new CPPs that could effectively predict the CQAs. ML techniques can also be synergized with mechanistic models as a ‘hybrid ML’ or ‘white box ML’ to identify how CPPs affect the product yield and quality mechanistically, thus enabling rational design and control of the bioprocess. In this review, we describe the role of statistical modeling in Quality by Design (QbD) for biomanufacturing, and provide a generic outline on how relevant ML can be used to meaningfully analyze bioprocessing datasets. We then offer our perspectives on how relevant use of ML can accelerate the implementation of systematic QbD within the biopharma 4.0 paradigm.

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“…Kensert et al 151 applied a double deep-Q network with two feedforward NNs to the problem of selecting the scouting runs to improve chromatographic retention models, demonstrating the method on an example in which the fraction of acetonitrile must be selected to maximise the information gained from the run. Additionally, Nikita et al 152 utilised a form of RL to select the flow rate in cation exchange chromatography, demonstrating that this is more effective than a simple trial and error selection method.…”

Section: Applications Of Reinforcement Learning In Chemistrymentioning

confidence: 99%

A review of reinforcement learning in chemistry

et al. 2022

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Reinforcement learning based optimization of process chromatography for continuous processing of biopharmaceuticals

Cited by 43 publications

References 36 publications

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Harnessing the potential of machine learning for advancing “Quality by Design” in biomanufacturing

A review of reinforcement learning in chemistry

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