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| 玉米与小麦根茬燃烧主要影响因素及特性分析 |
| Key factors and characteristics of corn and wheat root stubble burning |
| 投稿时间：2025-10-20 修订日期：2026-01-14 |
| DOI： |
| 中文关键词: 根茬 含土率；燃尽率 响应面法 农业废弃物利用 |
| 英文关键词: stubble soil content burnout rate response surface methodology utilization of agricultural waste |
| 基金项目:河南农业大学科学技术创新基金(2024CXZX010)Project supportedScience and Technology Innovation Fund of Henan Agricultural University(2024CXZX010) |
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| 中文摘要: |
| 【目的】为揭示农作物根茬燃烧过程的特点和主要影响因素,推动其清洁高效利用,【方法】本文以河南玉米和小麦根茬为对象,开展燃烧实验并基于 Design-Expert 软件建立多因素响应回归模型进行分析。【结果】实验结果显示,随着含水率和含土率的升高,综合燃烧指数明显下降。进一步分析表明,含水率、含土率及过量空气系数均对燃尽率具有显著影响,其中含水率和含土率是主要因素,而过量空气系数对燃尽率的作用呈现先升后降的规律。优化分析显示：玉米根茬在含水率11.58%、含土率16.56%,过量空气系数为1.79时,预测燃尽率为92.9%,实测值为92.8%；小麦根茬在含水率15.17%、含土率10.23%、过量空气系数1.59时,预测燃尽率为89.6%,实测值达到90.3%。二者均与模型预测结果高度一致,验证了模型的可靠性。【结论】本研究通过优化燃烧参数,减少根茬不完全燃烧带来的污染物排放,为农业废弃物清洁利用与区域环境治理提供技术支撑。 |
| 英文摘要: |
| To reveal the characteristics and main influencing factors of the burning process of crop residues and promote their clean and efficient utilization, this study took the corn and wheat residues from Henan Province as the research objects, conducts combustion experiments, and establishes a multi-factor response regression model based on the Design-Expert software for analysis. The experimental results showed that the comprehensive combustion index decreases significantly with the increase of moisture content and soil content. Further analysis indicated that moisture content, soil content and excess air coefficient all have significant effects on the burnout rate, among which moisture content and soil content were the main factors, while the effect of excess air coefficient on the burnout rate showed a pattern of first increasing and then decreasing. Optimization analysis showed that for corn residues, when the moisture content was 11.58%, the soil content was 16.56%, and the excess air coefficient was 1.79, the predicted burnout rate was 92.9%, and the measured value was 92.8%; for wheat residues, when the moisture content was 15.17%, the soil content is 10.23%, and the excess air coefficient was 1.59, the predicted burnout rate was 89.6%, and the measured value was 90.3%. Both were highly consistent with the model prediction results, verifying the reliability of the model. This study optimizes the combustion parameters to reduce the pollutant emissions caused by incomplete combustion of residues, providing technical support for the clean utilization of agricultural wastes and regional environmental governance. |
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