DSpace collection: 研究計畫

整治場址數位監測與高精度污染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2)

title: 整治場址數位監測與高精度污染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2) abstract: 本計畫以佶鼎科技股份有限公司廠址作為模場試驗場域，驗證與優化土壤及地下水污染整治技術。調查結果顯示，地下水中銅(Cu)與鎳(Ni)污染分布面積分別為507 及 811.2 平方公尺，另有多口監測井檢出鉛(Pb)濃度超標，顯示場址具多重重金屬污染特性。主要採用抽出處理法(Pump and Treat)控制污染，雖整體濃度呈下降趨勢，仍有局部監測井超標。為提升整治效能，輔以現地土壤淋洗法(In-situ SoilFlushing)，促進土壤中金屬脫附並抽取處理，並已完成模場試驗。場址含水層由多層砂質與黏土交互組成，具高度異質性， 7~8 公尺處之黏土層形成滯留屏障，使污染物分佈不均，抽出與灌注效率受限。此外，地下水流向受降雨與鄰近抽水行為影響，流場變異大，增加整治操作與成效評估之挑戰。因此，本模場計畫規劃共兩年研究期程，第一年提出技術需求與獲得研究成果，包括： (1)即時地下水位與基礎水質觀測以強化整治策略規劃； (2)評估含水層水力傳導係數空間分布以增進抽注作業效率； (3)運用光纖高解析監測技術評估垂直向導水特性及水流通量。為強化地質分層與材料分布之空間解析，提高藥劑注入與抽出過程效率。本年度將以第一年之觀測資料與試驗成果為基礎，導入長短期記憶網路(Long Short-Term Memory, LSTM)結合退火演算法(Simulated Annealing, SA)之深度學習架構，進行污染物濃度時空變化的預測與溯源分析。LSTM 模型能有效捕捉地下水污染傳輸中具非線性與時序依賴的特性，藉由大量歷史水位、水質與抽灌操作資料之訓練，預測未來污染濃度與趨勢；退火演算法則用於模型參數與初始權重之全域優化，以避免深度學習陷入局部極值，提升預測穩定度與泛化能力。此組合技術可同時進行「污染來源區反演」與「整治策略模擬」，透過比對模擬結果與監測資料差異，不斷迭代修正模型參數，最終建立污染源時空分布的最佳化反演結果。整體技術具備自動化訓練流程與高效參數搜尋能力，可有效提升多重污染(Cu、 Ni、 Pb)傳輸之預測精度，協助識別主要污染來源、評估整治成效，並提供後續決策支援。;This project designates the Gi Ding Technology Co., Ltd. industrial site as a pilot-scale test field to validate and optimize soil and groundwater contamination remediation technologies. Site investigations indicate that copper (Cu) and nickel (Ni) contamination in groundwater cover areas of approximately 507 m² and 811.2 m², respectively. In addition, multiple monitoring wells exhibit lead (Pb) concentrations exceeding regulatory standards, demonstrating the presence of multi-metal contamination at the site. Pump-and-treat remediation has been implemented as the primary control measure, and although overall contaminant concentrations show a declining trend, exceedances persist at several localized monitoring points. To enhance remediation efficiency, in-situ soil flushing has been employed as a supplementary technique to promote desorption of metals from the soil matrix followed by extraction and treatment of contaminated groundwater. Pilot-scale testing of this approach has been completed. The site aquifer system consists of interbedded sandy and clayey layers with pronounced heterogeneity. A clay layer at depths of approximately 7–8 m acts as a semi-confining barrier, resulting in uneven contaminant distribution and limiting the effectiveness of extraction and injection operations. Furthermore, groundwater flow directions are strongly influenced by precipitation events and nearby pumping activities, leading to highly variable flow fields and increased uncertainty in remediation operation and performance evaluation. Accordingly, the pilot study is structured over a two-year research period. In the first year, technical needs were identified and key outcomes were achieved, including: (1) implementation of real-time groundwater level and baseline water quality monitoring to strengthen remediation strategy development; (2)assessment of the spatial variability of aquifer hydraulic conductivity to improve the efficiency of pumping and injection operations; and (3) application of high-resolution fiber-optic sensing techniques to evaluate vertical hydraulic connectivity and groundwater fluxes. These efforts aim to refine the spatial resolution of stratigraphic layering and material distribution, thereby enhancing the effectiveness of chemical injection and extraction processes. Building upon the observational data and experimental results obtained in the first year, the second-year research will integrate a deep learning framework combining Long Short-Term Memory (LSTM) networks with a Simulated Annealing (SA) algorithm to predict the spatiotemporal evolution of contaminant concentrations and to conduct source identification analyses. LSTM models are well suited to capture the nonlinear behavior and temporal dependencies inherent in groundwater contaminant transport. Trained on extensive historical datasets of ground water levels, water quality measurements, and pumping–injection operations, the model will forecast future contaminant concentrations and trends. The simulated annealing algorithm will be employed for global optimization of model parameters and initial network weights, mitigating the risk of convergence to local minima and improving prediction robustness and generalization performance. This integrated approach enables simultaneous “contaminant source inversion” and “remediation strategy simulation.” By iteratively minimizing discrepancies between simulated outputs and observed monitoring data, model parameters are progressively refined to derive an optimized reconstruction of the spatiotemporal distribution of contamination sources. Overall, the proposed methodology features an automated training workflow and efficient parameter search capability, substantially enhancing predictive accuracy for multi-metal contaminant transport (Cu, Ni, Pb), supporting identification of primary contamination sources, evaluation of remediation effectiveness, and provision of robust decision support for subsequent site management.

裂縫網絡結構控制參數的表徵：基於滲流與裂縫長度的流道和誘發地震活動預測框架;Characterizing Fracture Network Structural Controls: a Predictive Framework for Flow Channeling and Induced Seismicity via Percolation and Fracture Length

title: 裂縫網絡結構控制參數的表徵：基於滲流與裂縫長度的流道和誘發地震活動預測框架;Characterizing Fracture Network Structural Controls: a Predictive Framework for Flow Channeling and Induced Seismicity via Percolation and Fracture Length abstract: 裂隙岩體中的地下水流動與溶質傳輸行為，是高階放射性廢棄物深地層處置、地熱能源開發及地質碳封存等國家重大工程之關鍵課題。然而，傳統離散裂隙網路（DFN）模擬多將裂隙視為靜態幾何，往往忽視了岩體在應力與水力耦合作用下，裂隙幾何型態（如長度、孔徑）的動態演化過程，以及岩石基質對整體流場的交互影響，導致滲透率與傳輸預測產生顯著偏差。為突破此限制，本計畫旨在開發一套創新的「混合域裂隙岩層水力-力學耦合數值模式」。研究核心整合岩石損傷演化理論與斷裂力學機制，並引入自適應非結構化網格細化技術。本研究將基於質量與動量守恆原理，建立考慮裂隙與基質交互作用之控制方程式，能量平衡理論模擬次臨界裂隙生長與活化行為，藉以精確捕捉裂隙在流體注入或地質應力改變過程中之動態邊界變化。在驗證與應用方面，計畫將透過合成案例分析，並與國際著名軟體進行交叉比對驗證。同時，將結合台灣離島結晶岩地區之現地調查參數，建立符合本土水文地質特性之模型。預期成果將產出一套具物理機制基礎之分析工具，能動態量化裂隙演化對水力參數之影響，為我國深地層處置安全評估與地質能源開發提供更具科學信賴度之決策依據。

探討台灣東北外海低重力異常成因;Investigation into the Causative Source of the Significant Gravity Anomaly Low Offshore Northeast Taiwan

title: 探討台灣東北外海低重力異常成因;Investigation into the Causative Source of the Significant Gravity Anomaly Low Offshore Northeast Taiwan abstract: 台灣位於兩個隱沒系統的交會處：東北部琉球隱沒帶，以及南部馬尼拉隱沒帶。在台灣東部及外海，菲律賓海板塊從覆蓋在歐亞板塊之上轉變為隱沒至歐亞板塊和琉球島弧之下，因此兩板塊間相互作用及其相關構造非常複雜。板塊間的相互作用產生了許多地震並在台灣陸地造成災害。對於台灣東部構造特性的了解及相關地震防災是很重要的工作。本計畫欲探討花蓮外海一個明顯而特殊的重力異常低區，其成因可能能為此構造複雜的區域提供ㄧ些想法。

整合型除水濃縮氣相層析系統開發;Development of an Integrated Dehydration and Concentration Gas Chromatography System

title: 整合型除水濃縮氣相層析系統開發;Development of an Integrated Dehydration and Concentration Gas Chromatography System abstract: 本計畫旨在開發一套高靈敏度且即時的揮發性有機化合物（VOCs）監測系統，結合創新除水技術與熱脫附預濃縮，並採用心切氣相層析分離方法，以提升空氣品質監測的準確度與效率。透過整合先進的氣體流路控制與雙重偵測器配置，系統可有效分析多種複雜成分，尤其針對工業區及交通樞紐等污染密集地區，此系統將有助於環境污染源快速辨識與即時管控，提升監測數據的科學性與應用價值，同時培育專業技術人才，促進產學合作與技術產業化。在社會影響方面將提升空氣污染監測的精準度與時效，有助於環境健康風險的及時評估與預警，保障民眾健康，促進公共衛生改善，透過有效監控污染源排放，促進政府環境政策的科學決策與落實，增強社會對環境保護的認知與參與。