Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

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机器学习在发电燃烧中的应用综述

尽管世界正在转向使用更多可再生能源，但燃烧系统仍将在全球能源的近期发展中发挥重要作用。为了走一条通往未来的可持续道路并减少全球变暖的影响，提高燃烧过程的效率和性能并将其排放降至最低是很重要的。机器学习技术是一种具有成本效益的解决方案，可通过建模、预测、预测、优化、故障检测和过程控制来提高燃烧系统的可持续性。本研究的目的是对机器学习技术在与发电相关的不同燃烧过程中的应用的研究现状进行回顾和讨论。根据燃烧过程的类型，机器学习技术的应用分为三大类：（1）煤炭和天然气发电厂，（2）生物质燃烧，以及（3）碳捕获系统。本研究讨论了机器学习在燃烧领域的潜在好处和挑战，并为未来的研究提供了一些研究方向。总体而言，所进行的审查表明，机器学习技术可以在将燃烧系统转向低排放过程方面发挥重要作用，同时提高运行灵活性并降低运行成本。本研究讨论了机器学习在燃烧领域的潜在好处和挑战，并为未来的研究提供了一些研究方向。总体而言，所进行的审查表明，机器学习技术可以在将燃烧系统转向低排放过程方面发挥重要作用，同时提高运行灵活性并降低运行成本。本研究讨论了机器学习在燃烧领域的潜在好处和挑战，并为未来的研究提供了一些研究方向。总体而言，所进行的审查表明，机器学习技术可以在将燃烧系统转向低排放过程方面发挥重要作用，同时提高运行灵活性并降低运行成本。