Photocatalytic degradation technology is the most promising method for removing formaldehyde. The instruments of detecting the concentration of formaldehyde are almost long analysis cycle, complex operations, expensive, and unable to realize the real-time and on-line test. Besides, they are difficult to integrate with the degradation devices,which could make it to be more practical equipment. In this paper,a photocatalytic combination unit was designed, which consisted of degradation and detection parts, and a new method to predict formaldehyde concentration was presented. The metal oxide semiconductor ( MOS) gas sensor array was applied to assess the degradation rate. Experimental data of degradation formaldehyde with different concentrations by P25 were extracted and the quantitative analysis method of Back Propagation Artificial Neural Network ( BP-ANN ) was used, which realized the prediction of the formaldehyde concentration. And the maximum error between prediction results and actual results was only 4. 33%.%在治理甲醛的方法中,光催化降解技术最具发展前途.而研究过程中测定甲醛浓度的仪器大多分析周期长,操作较繁琐,价格昂贵,无法实现实时在线测试,同时很难与降解装置进行集成,成为更为实用的测试仪器.本文设计一套光催化降解与检测相融合的装置,并提出了一种预测甲醛浓度的新方法.采用金属氧化物半导体(MOS)气敏传感器阵列来评定光催化材料降解甲醛的效率.通过提取10次P25降解不同浓度甲醛的实验数据,采用BP-ANN的定量识别方法进行数据分析,可实现对新样本中甲醛浓度的预测,且预测结果与实际结果间的最大误差仅为4.33％.
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