Comparison of different machine learning algorithms to estimate liquid level for bioreactor management

Yu, Sung Il; Rhee, Chaeyoung; Shin, Seung Gu

doi:10.4491/eer.2022.037

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Subsequent to this, a simple linear regression analysis was conducted based on the centroid values derived from the K-mean clustering process. Linear regression is the most common approach for modeling numeric data and can be adapted to almost all types of data [41].…”

Section: Unsupervised and Supervised Learning In Machine Learningmentioning

confidence: 99%

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation

Lee,

Kim

et al. 2024

Processes

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Assessing oxidation–reduction potential (ORP) is of paramount importance in the efficient management of wastewater within both chemical and biological treatment processes. However, despite its critical role, insufficient information exists about how reactive chemical species generated by cold plasma (CP) in chemical treatment are associated with ORP and air flow rate. Therefore, we aim to identify the correlation between ORP and the removal of organic pollutants when using CP treatment. Additionally, we introduce a machine-learning-based operation to predict removal efficiency in the CP process. Results reveal a significant correlation of over 0.9 between real-time ORP and total organic carbon (TOC), which underscores the efficacy of ORP as a key parameter. This approach made it possible to control OH radical generation by regulating the air flow rate of the CP. This study posits that smart management facilitated by machine learning has the potential to enhance the economic viability of CP feasibility while maintaining overall treatment performance.

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Section: Unsupervised and Supervised Learning In Machine Learningmentioning

confidence: 99%

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation

Lee,

Kim

et al. 2024

Processes

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“…Limited use in the presence of large datasets, questionable model interpretability and lack of uncertainty disclosure associated with prediction hamper further dissemination. Has been gradually replaced in several settings by other methods, e.g., artificial neural networks and random forests, as these also provide more accurate predictions [2,[118][119][120].…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

Machine Learning: A Suitable Method for Biocatalysis

Sampaio

Fernandes

2023

Catalysts

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Biocatalysis is currently a workhorse used to produce a wide array of compounds, from bulk to fine chemicals, in a green and sustainable manner. The success of biocatalysis is largely thanks to an enlargement of the feasible chemical reaction toolbox. This materialized due to major advances in enzyme screening tools and methods, together with high-throughput laboratory techniques for biocatalyst optimization through enzyme engineering. Therefore, enzyme-related knowledge has significantly increased. To handle the large number of data now available, computational approaches have been gaining relevance in biocatalysis, among them machine learning methods (MLMs). MLMs use data and algorithms to learn and improve from experience automatically. This review intends to briefly highlight the contribution of biocatalysis within biochemical engineering and bioprocesses and to present the key aspects of MLMs currently used within the scope of biocatalysis and related fields, mostly with readers non-skilled in MLMs in mind. Accordingly, a brief overview and the basic concepts underlying MLMs are presented. This is complemented with the basic steps to build a machine learning model and followed by insights into the types of algorithms used to intelligently analyse data, identify patterns and develop realistic applications in biochemical engineering and bioprocesses. Notwithstanding, and given the scope of this review, some recent illustrative examples of MLMs in protein engineering, enzyme production, biocatalyst formulation and enzyme screening are provided, and future developments are suggested. Overall, it is envisaged that the present review will provide insights into MLMs and how these are major assets for more efficient biocatalysis.

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“…The tests concluded that random forest was the best algorithm producing more consistent results with low error rate and more than 90% accuracy in the prediction of n-caproate and n-caprylate ( Liu et al., 2022a ). To predict the accuracy of real-time liquid level four ML algorithms, multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), and support vector machine (SVM) with radial basis kernel were analyzed and found that ANN and RF models performed well ( Yu et al., 2022 ).…”

Section: Ai-based ML Algorithms In Recombinant Protein Productionmentioning

confidence: 99%

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Parthiban,

Vijeesh,

Gayathri

et al. 2023

Front. Plant Sci.

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Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

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Comparison of different machine learning algorithms to estimate liquid level for bioreactor management

Cited by 11 publications

References 31 publications

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation

Smart Strategic Management for the Cold Plasma Process Using ORP Monitoring and Total Organic Carbon Correlation

Machine Learning: A Suitable Method for Biocatalysis

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

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