{"title":"建立不同收获期 Miscanthus x Giganteus 的产量模型并估算其能量特性","authors":"Ivan Brandić, N. Voća, J. Leto, N. Bilandzija","doi":"10.3390/agriengineering6010026","DOIUrl":null,"url":null,"abstract":"This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.","PeriodicalId":505370,"journal":{"name":"AgriEngineering","volume":"26 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods\",\"authors\":\"Ivan Brandić, N. Voća, J. Leto, N. Bilandzija\",\"doi\":\"10.3390/agriengineering6010026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.\",\"PeriodicalId\":505370,\"journal\":{\"name\":\"AgriEngineering\",\"volume\":\"26 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AgriEngineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/agriengineering6010026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AgriEngineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/agriengineering6010026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究旨在利用人工神经网络（ANN）估算木薯（MxG）的产量和能量特性，其中考虑到了种植年份、地点和收获时间等因素。该研究在两个不同的地理区域进行，历时三年，采用 ANN 回归模型估算 MxG 的低发热值（LHV）和产量。模型显示出较高的预测准确性，低发热值的 R2 值为 0.85，产量的 R2 值为 0.95，相应的 RMSE 值为 0.13 和 2.22。发现植株高度和芽数之间存在影响产量的显着相关性。此外，对 ANN 模型的敏感性分析表明，分类和连续输入变量对预测结果都有影响。这些结果凸显了 MxG 作为可持续生物质能源的作用，并为优化生物质生产、影响能源政策以及促进可再生能源和全球能源可持续发展提供了启示。
Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods
This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.
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