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Industrial Chemistry
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Innovative Process Enhancement through Machine Learning: An Analysis of Biohydrogen Generation from Waste Materials

Yuanpeng Dong*
Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Sweden
*Corresponding Author : Yuanpeng Dong, Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Sweden, Email: yuanpengdong@gmail.com

Received Date: Sep 02, 2024 / Accepted Date: Sep 30, 2024 / Published Date: Sep 30, 2024

Abstract

Biohydrogen production from waste materials presents a sustainable solution for clean energy, but optimizing this process remains challenging due to the complexity of biological systems and variable operational conditions. This paper explores the integration of machine learning (ML) techniques to enhance biohydrogen production processes. By employing ML algorithms for data analysis, predictive modeling, and real-time process control, significant improvements in yield and efficiency can be achieved. The study reviews various applications of ML in optimizing fermentation, substrate selection, and operational conditions, demonstrating how these advanced methods can overcome traditional limitations. Despite the promise of ML, challenges such as data quality, system complexity, and scalability need to be addressed. This analysis highlights the transformative potential of ML in advancing biohydrogen production and offers insights into future research directions for achieving more efficient and sustainable energy solutions.

Citation: Yuanpeng D (2024) Innovative Process Enhancement through Machine Learning: An Analysis of Biohydrogen Generation from Waste Materials. Ind Chem, 10: 302.

Copyright: © 2024 Yuanpeng D. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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