Ahmet Coşgun, Burcu Oral, M. Erdem Günay, Ramazan Yıldırım
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引用次数: 0
Abstract
Biochar production from biomass sources is a highly complex, multistep process that depends on several factors, including feedstock composition (e.g., type of biomass, particle size) and operating conditions (e.g., reaction temperature, pressure, residence time). However, the optimal set of variables for producing the maximum amount of biochar with the required characteristics can be determined by using machine learning (ML). In light of this, the purpose of this paper is to examine ML applications in biochar processes for the production of sustainable fuels. First, recent developments in the field are summarized, and then, a detailed review of ML applications in biochar production is presented. Following that, a bibliometric analysis is done to illustrate the major trends and construct a comprehensive perspective for future studies. It is found that biochar yield is the most common target variable for ML applications in biochar production. It is then concluded that ML can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties of biochar which can be later used for decision-making, resource allocation, and fuel production.
从生物质来源生产生物炭是一个非常复杂的多步骤过程,取决于多个因素,包括原料成分(如生物质类型、颗粒大小)和操作条件(如反应温度、压力、停留时间)。不过,利用机器学习(ML)可以确定一组最佳变量,以生产出具有所需特性的最大量生物炭。有鉴于此,本文旨在研究 ML 在生物炭工艺中的应用,以生产可持续燃料。首先,总结了该领域的最新发展,然后详细回顾了 ML 在生物炭生产中的应用。随后,进行了文献计量分析,以说明主要趋势并为未来研究构建一个全面的视角。研究发现,生物炭产量是生物炭生产中应用 ML 的最常见目标变量。最后得出的结论是,ML 可以帮助发现隐藏的模式,并进行准确预测,以确定变量组合,从而获得所需的生物炭特性,随后可用于决策、资源分配和燃料生产。
期刊介绍:
BioEnergy Research fills a void in the rapidly growing area of feedstock biology research related to biomass, biofuels, and bioenergy. The journal publishes a wide range of articles, including peer-reviewed scientific research, reviews, perspectives and commentary, industry news, and government policy updates. Its coverage brings together a uniquely broad combination of disciplines with a common focus on feedstock biology and science, related to biomass, biofeedstock, and bioenergy production.