懸浮固體為環保法規所規範的出流標準之一，故針對污水處理系統而言，如何對處理單元進行有效率的監測與控制乃為一大重要課題，懸浮固體的去除，目前多依靠沉澱池進行重力沉澱的方式去除，故懸浮固體的沉澱特性及水力停留時間的設定將對沉澱處理單元的效率有決定性的影響，因此，本研究針對初沉池以多點連續掃描吸收光譜建立一套水中懸浮固體沉澱特性的量測方法。先利用主成分分析篩選出主吸收波長並結合多元線性迴歸，建立水中懸浮固體濃度推估公式，其推估結果相對誤差大多低於20%，進一步透過懸浮固體濃度加上連續掃描吸收光譜資訊，後以類神經網路建立懸浮固體沉澱特性預測模式，其驗證結果顯示，結合2個掃描點所建立之預測模式可以有效的縮短耗費時間，可以連續掃描3分鐘的光譜資訊建立的預測模式，其驗證結果可繪製出懸浮固體濃度隨時間沉澱的沉澱特性曲線，進而獲得水力停留時間，本研究所發展之量測技術具有穩定度與可靠度，未來可以提供實廠及時水質監測之技術，以提升沉澱處理單元之效率。;Suspended solids is one of the standard of environmental regulations. For wastewater treatment, how to effectively monitor and control is an important issue. Currently, the removal of suspended solids relies on the sedimentation tank, using gravity settling to remove. Therefore, the settling characteristics of suspended solids and the hydraulic retention time will play a decisive role on the efficiency of sedimentation tank. In this study, to establish a method for measuring the settling characteristics of suspended solids, using multi-point continuous scanning absorption spectrum. Firstly, using Principal Component Analysis (PCA) to pick out the main absorption wavelength and combining multiple linear regression to establish a formula for estimating the concentration of suspended solids. The most of relative error of the estimation results are less than 20%, and then, the prediction model of settling characteristics of suspended solids established by artificial neural network (ANN). According to the results shown, the prediction model combining the two scanning points can effectively shorten the time-consuming. The verification result draw the settling characteristics curve of the suspended solids concentration precipitated with time, and obtain the hydraulic retention time. This measurement method has stability and reliability, and can provide the technology of real-time water quality monitoring in the future. To improve the efficiency of the sedimentation tank.
