應用生物共生演算法推估地下水污染物釋放歷史與污染源位置

Applying Symbiotic Organisms Search to Identify the Release History and Source Location of Groundwater Contamination

林郁晨(Yu-Chen Lin)

指導教授 ： 葉弘德

國立交通大學/工學院/環境工程系所/碩士(2019年)

摘要

鑑定地下水污染源是擬定污染整治工作前不可或缺的步驟，然而透過逆推的方式推算污染源位置與釋放歷史，往往需要耗費大量的計算時間。本研究結合生物共生搜索演算法(Symbiotic Organisms Search, SOS)與AT123D的污染物傳輸的解析模式，來推算地下水污染源釋放歷程以及污染源位置。SOS演算法將可行解群模擬成生物之間共生的關係，使生物朝適合的方向移動，淘汰不適合的生物。AT123D透過解析解模擬污染物在地下水的分布，並且考慮各種污染物於地下水內的傳輸現象，如延散、吸附、衰變、生物降解等。經由SOS演算法逆推地下水污染物於一維、二維及三維傳輸問題中的污染源位置的座標與釋放方程式中的未知參數，包含污染物釋放強度、釋放時間與釋放寬度。除原始的SOS演算法外，本研究加入序的最佳化法(Ordinal Optimization Algorithm, OO algorithm)，用以提高搜尋效率，並於多維傳輸問題中設置多口採樣井，提高污染源座標鑑別結果的準確度。基於上述理想假設的結果，本研究進一步探討量測誤差，包含污染物濃度的採樣誤差及污染物傳輸參數的不確定性，以及生物降解作用對於釋放歷史與污染源位置座標推估造成的影響。最後，整合所有分析結果，針對本研究使用的方法提出一套識別地下水污染物的準則，其中包含適當的採樣時間間隔與範圍及決定相關參數的方法與步驟。

關鍵字

地下水污染物 ； 鑑別污染源 ； 污染歷史推估 ； 逆問題 ； 最佳化方法 ； 共生生物搜索演算法 ； 序的最佳化

並列摘要

Identifying the source of groundwater contamination is imperative before setting up cleanup strategies. The contaminant source identification however requires a great amount of computation time in the inverse calculation. This study develops an efficient approach to identify the contamination source location and the recover the release history based on coupling Symbiotic Organisms Search (SOS) algorithm and contaminant transport model AT123D. The SOS algorithm is a powerful optimization tool for the inverse problems by means of imitating the symbiotic relationship to move the organisms toward the current optimal organism and eliminate the worse ones. AT123D is an analytical computer package that considers various transport processes such as advection, dispersion, adsorption, decay, and degeneration. It provides a very useful tool for predicting groundwater pollution concentrations. Groundwater contamination in one-, two-, and three-dimensional transports are discussed in this study. The design variables such as coordinates of a source and unknown parameters of the release function including the release time, release strength, and release width are estimated by the SOS algorithm. Moreover, the SOS algorithm is combined with the OO algorithm to promote the computational efficiency of the iterative process. Multiple sampling wells are adopted for flow with 2- or 3-dimensional transport to improve the accuracy of source information. The measurement error in sampling concentration data, uncertainties in transport parameters, and the effect of biodegradation are also considered. Finally, this study provides a guideline for identifying the source location and reconstructing the release history of groundwater contamination. The guideline includes the suitable sampling time period, reliable sampling time interval, and the method and process for estimating the related parameters.

並列關鍵字

Groundwater contamination ； source location identification ； release history recovery ； inverse problem ； optimization approach ； Symbiotic Organisms Search ； Ordinal Optimization Algorithm

參考文獻

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