将生物质转化为可持续的生物氢:深入分析

IF 4.9
Md. Merajul Islam and Amina Nafees
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引用次数: 0

摘要

氢被认为是在未来实现零温室气体排放的过程中最有效的替代燃料之一。目前,它仍然主要来自不可再生能源,如化石燃料。不幸的是,关于我们对这些枯竭资源的依赖,一个重要的担忧是它们对我们环境的深刻不利影响。我们认为,拟议的生物质到可持续氢战略的发展是生产可持续战略氢源的一个有吸引力的机会。为了实现生物氢的大规模商业应用,必须优化一系列操作参数。在这种情况下,机器学习对于实现这些结果以及物理化学,生物和电化学方法至关重要。这些先进的技术使研究人员能够优化过程,预测结果,提高实验效率。通过将机器学习与传统方法相结合,科学家可以发现以前无法获得的见解。本文综述了从生物质中制备生物氢的热化学、生物和电化学方法的最新进展。先进的方法和热化学过程,如热等离子体,对于气化材料、建模过程、处理污水污泥以及通过捕获和利用二氧化碳来提高氢气产量至关重要。这些方法在提高生物氢生产的效率和可持续性方面显示出巨大的希望。通过利用创新技术,研究人员旨在优化转化过程,提高氢的整体产量,为更清洁的能源解决方案做出贡献。它强调了机器学习在操作分析中的应用,强调了其捕捉操作和性能因素之间复杂关系的能力。作者对生物氢的应用、障碍和可持续性进行了彻底的研究。作者概述了未来的前景和挑战。这些发现提供了对生物氢研究现状及其未来潜力的全面了解。通过阐明这些见解,作者为该领域正在进行的论述贡献了宝贵的知识。最后,作者阐述了他们的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Transforming biomass into sustainable biohydrogen: an in-depth analysis

Transforming biomass into sustainable biohydrogen: an in-depth analysis

Hydrogen is considered one of the most effective alternative fuels in the journey toward achieving zero greenhouse gases in the future. Currently, it remains predominantly sourced from non-renewable energy resources, such as fossil fuels. Unfortunately, a significant concern regarding our dependence on these exhausted resources is their profound adverse effects on our environment. We view the development of a proposed biomass-to-sustainable hydrogen strategy as an attractive opportunity to produce a sustainable strategic hydrogen source. To achieve large-scale commercial adoption of biohydrogen, it is essential to optimize a range of operating parameters. In this context, machine learning is essential for achieving such results alongside physicochemical, biological, and electrochemical methods. These advanced techniques enable researchers to optimize processes, predict outcomes, and enhance the efficiency of experiments. By integrating machine learning with traditional methods, scientists can uncover insights that were previously unattainable. This review explores the recent advancements in thermochemical, biological, and electrochemical methods for generating biohydrogen from biomass. Advanced methodologies and thermochemical processes, like thermal plasma, are crucial for gasifying materials, modelling processes, treating sewage sludge, and enhancing hydrogen production by capturing and using CO2. These methods have shown significant promise in increasing the efficiency and sustainability of biohydrogen production. By leveraging innovative techniques, researchers aim to optimize the conversion processes and enhance the overall yield of hydrogen, contributing to cleaner energy solutions. It highlights the use of machine learning in operational analysis, emphasizing its ability to capture complex relationships between operational and performance factors. The authors have thoroughly examined the applications, obstacles, and sustainability of biohydrogen. The authors have outlined the forthcoming perspectives and challenges. These findings provide a comprehensive understanding of the current state of biohydrogen research and its future potential. By articulating these insights, the authors contribute valuable knowledge to the ongoing discourse in this field. Ultimately, the authors have articulated their findings.

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