Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Lim, Seung Ji; Son, Moon; Ki, Seo Jin; Suh, Sang-Ik; Chung, Jaeshik

doi:10.1016/j.biortech.2022.128518

Cited by 16 publications

(2 citation statements)

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“…This is paramount to warrant validated ranges of critical quality attributes of bioproducts, which determine the value ranges that must be met for the bioproduct to be released. Among the demerits of MLMs, the requirement of large datasets for proper model training, the need for high computational power and the complexity of the set up and concomitant risk of faulty design may be suggested as the major shortcomings [2,16,[95][96][97].…”

Section: A Brief Overview Of ML Algorithmsmentioning

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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Section: A Brief Overview Of ML Algorithmsmentioning

confidence: 99%

Machine Learning: A Suitable Method for Biocatalysis

Sampaio

Fernandes

2023

Catalysts

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“…Their ability to mimic the behavior of more complex systems is transforming how engineers approach process design, analysis, and optimization. The advancement and implementation of data-driven surrogate models in PSE have been thoroughly recognized. − Moreover, data-driven surrogate models represent a significant improvement in accuracy and performance in the modeling of complex processes, including those that were not previously able to be addressed. The abilities of these surrogate models have led to more precise results, which in turn improves decision-making and benefits sustainability.…”

Section: Introductionmentioning

confidence: 99%

Process Systems Engineering Tools for Optimization of Trained Machine Learning Models: Comparative and Perspective

López-Flores,

Ramírez-Márquez,

Ponce-Ortega

2024

Ind. Eng. Chem. Res.

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This article studies the relevance of innovative Process Systems Engineering (PSE) tools that can reformulate trained machine learning models that are driven by advances in computational technologies, showcasing a pivotal transformation in chemical engineering methodologies. The article also delves into how trained machine learning models are reformulated and optimized to refine engineering decisions as it provides a novel analysis of tools to develop machine learning models by reformulating them, and optimizing them in PSE, thus highlighting their significance and applications. It offers a comprehensive comparison of several cutting-edge tools, including JANOS, MeLOn, ENTMOOT, reluMIP, OptiCL, Gurobi Machine Learning, OMLT, and PySCIPOpt-ML, highlighting their distinct abilities for performance and decision-making. Furthermore, challenges related to the explicit formulation of the main machine learning models are discussed. Guidance is provided to select the appropriate tool according to users' requirements. Additionally, a comparative study of the tools is presented using a case study to analyze and compare the size and type of formulations, the optimal solution, and the computation times.

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