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A survey of Artificial Neural Network-based Prediction Models for Thermal Properties of Biomass

机译:基于人工神经网络的生物质热特性预测模型研究

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The global community has supported the need for sustainable and renewable energy due to environmental concerns from the greenhouse gas emission. Biomass stands as one of the most abundant and predictable sources of renewable energy. Therefore, to explore the maximum potential of biomass, a detailed understanding of its embedded potential is needed. However, most experimental procedures require equipment that is highly sophisticated and expensive. The advancement of knowledge in artificial intelligence and blockchain technology is unlocking new potential prediction accuracy for biomass thermal properties. Artificial Neural Network (ANN) is proving to be a vital tool that can enhance the research development in biomass energy prediction. This review highlights the stages in ANN modeling and the application of ANN in Biomass thermal value prediction. It identifies the research gaps in the current status of research on ANN as related to biomass and the direction for further study.
机译:由于来自温室气体排放的环境问题,国际社会支持对可持续和可再生能源的需求。生物质是最丰富和可预测的可再生能源之一。因此,为了探索生物质的最大潜力,需要对其内在潜力的详细了解。但是,大多数实验程序都需要高度复杂且昂贵的设备。人工智能和区块链技术知识的进步为生物质热特性释放了新的潜在预测准确性。人工神经网络(ANN)被证明是可以增强生物质能预测研究进展的重要工具。这篇综述重点介绍了人工神经网络建模的各个阶段以及人工神经网络在生物质热值预测中的应用。它确定了与生物量有关的人工神经网络研究现状中的研究差距以及进一步研究的方向。

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