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›› 2014, Vol. 14 ›› Issue (3): 433-438.

• 反应与分离 • 上一篇    下一篇

室内环境中细木工板甲醛释放预测模型

张浩 朱庆明 刘秀玉 刘影   

  1. 安徽工业大学建筑工程学院 安徽工业大学建筑工程学院 安徽工业大学建筑工程学院 安徽工业大学建筑工程学院
  • 收稿日期:2014-03-03 修回日期:2014-04-10 出版日期:2014-06-20 发布日期:2014-06-20
  • 通讯作者: 张浩

Predictive Model for Formaldehyde Emission from Block Board in Indoor Environment

ZHANG Hao ZHU Qing-ming LIU Xiu-yu LIU Ying   

  1. School of Civil Engineering and Architecture, Anhui University of Technology School of Civil Engineering and Architecture, Anhui University of Technology School of Civil Engineering and Architecture, Anhui University of Technology School of Civil Engineering and Architecture, Anhui University of Technology
  • Received:2014-03-03 Revised:2014-04-10 Online:2014-06-20 Published:2014-06-20
  • Contact: ZHANG Hao

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被引次数

5

摘要： 采用环境测试舱模拟室内环境，测定其中的细木工板的甲醛释放浓度，考察环境温度和相对湿度对其释放的影响；分析细木工板中甲醛气体扩散机理，并进行实际室内环境中细木工板释放甲醛实验，与模拟室内环境对比；最后运用灰色预测模型和神经网络模型建立灰色神经网络模型，对实际室内环境中细木工板甲醛释放规律进行预测. 结果表明，随环境温度和相对湿度升高，板材释放的甲醛浓度增加，且温度对甲醛释放活跃期影响更显著，低温和低湿度时板材中甲醛释放更易达到稳定；细木工板释放甲醛浓度与室内外温差呈正相关性，热压渗风作用对室内细木工板释放甲醛浓度的变化有重要影响；灰色神经网络模型的预测与实验数据吻合较好，平均绝对误差为-0.0007 mg/m3，相对误差为0.208%~5.981%.

关键词: 甲醛, 释放, 细木工板, 室内环境, 灰色神经网络, 预测

Abstract: Environment testing chamber was used to simulate indoor environment, the emission concentration of formaldehyde from block board in the chamber at different environmental temperatures and relative humidities measured, and the emission mechanism analyzed. The experiment of formaldehyde emission from block board in actual indoor environment was carried out and compared with that in the chamber. Grey neural network model was established by gray prediction and neural network models to forecast the emission concentration in actual indoor environment. The results indicated that with the rise of temperature and relative humidity, the emission concentration of formaldehyde increased, and temperature has more significant effect in the active period of emission. The emission concentration was easier to reach stable state at low temperature and relative humidity and proportional to the change of temperature difference between indoor and outdoor, hot-pressing seeping wind affected the emission concentration. The predicted data by grey neural network model agreed with the experimental data, their average absolute error was -0.0007 mg/m3, and relative error 0.208%~5.981%.

Key words: formaldehyde, emission, block board, indoor environment, grey neural network, prediction

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