当前位置: X-MOL 学术Mine Water Environ. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Evaluation of Process Oriented Water Quality Predictions for Pit Lakes
Mine Water and the Environment ( IF 2.8 ) Pub Date : 2020-08-04 , DOI: 10.1007/s10230-020-00705-7
Ina Hildebrandt , Kai-Uwe Ulrich , Adrian Horn , Lutz Weber , Klaus Häfner , Claus Nitsche

Reliable prediction of water quality is essential to meet the official targets set for effluent composition and usage of pit lakes, and to identify the appropriate remediation technology. For this purpose, a complex conceptual model was established and tested on two pit lakes in central Germany with highly different acid loads. To assess the reliability of the predictions, we compared monitoring data from the past 8–10 years with previous water quality predictions. In the case of Lake Schladitz, with low acid load and sufficient buffering capacity, a simple setup with few model elements appeared adequate, and no readjustment of model settings was necessary. For six (pseudo-) conservative ions, the total average deviation between measured and predicted values was − 9%, as opposed to − 2.7% during calibration. For the repeatedly conditioned Lake Bockwitz, a model parameterized with more elements and specific process parameters determined from field and laboratory investigations proved adequate for calculating the technical alkalinity demand. During calibration, the total average deviation between measured and modelled conservative ions was +0.2%; it reached +4.5% during the prediction evaluation period. With continued reacidification of the lake, the predicted concentrations of dissolved Fe and Al increasingly deviated from lake measurements. Deviations diminished after the solubility constants of schwertmannite and hydrobasaluminite were included in the thermodynamic database. The developed conceptual workflow offers a tool to improve water quality predictions for other lake settings.

中文翻译:

坑湖的面向过程的水质预测评估

可靠的水质预测对于满足官方为污水成分和坑湖使用设定的目标以及确定适当的修复技术至关重要。为此,我们建立了一个复杂的概念模型,并在德国中部酸负荷差异很大的两个坑湖上进行了测试。为了评估预测的可靠性,我们将过去 8-10 年的监测数据与之前的水质预测进行了比较。在施拉迪茨湖的情况下,酸负荷低且缓冲能力充足,模型元素很少的简单设置似乎就足够了,不需要重新调整模型设置。对于六个(伪)保守离子,测量值和预测值之间的总平均偏差为 - 9%,而校准期间为 - 2.7%。对于反复调节的博克维茨湖,通过现场和实验室调查确定的包含更多元素和特定工艺参数的参数化模型证明足以计算技术碱度需求。在校准过程中,测量的和模拟的保守离子之间的总平均偏差为 +0.2%;在预测评估期间达到+4.5%。随着湖泊的持续再酸化,溶解的铁和铝的预测浓度越来越偏离湖泊测量值。在热力学数据库中包括施维特曼石和水铝石的溶解度常数后,偏差减小了。开发的概念工作流程提供了一种工具,可以改进其他湖泊环境的水质预测。从现场和实验室调查中确定的包含更多元素和特定工艺参数的参数化模型证明足以计算技术碱度需求。在校准过程中,测量的和模拟的保守离子之间的总平均偏差为 +0.2%;在预测评估期间达到+4.5%。随着湖泊的持续再酸化,溶解的铁和铝的预测浓度越来越偏离湖泊测量值。在热力学数据库中包括施维特曼石和水铝石的溶解度常数后,偏差减小了。开发的概念工作流程提供了一种工具,可以改进其他湖泊环境的水质预测。从现场和实验室调查中确定的包含更多元素和特定工艺参数的参数化模型证明足以计算技术碱度需求。在校准过程中,测量的和模拟的保守离子之间的总平均偏差为 +0.2%;在预测评估期间达到+4.5%。随着湖泊的持续再酸化,溶解的铁和铝的预测浓度越来越偏离湖泊测量值。在热力学数据库中包括施维特曼石和水铝石的溶解度常数后,偏差减小了。开发的概念工作流程提供了一种工具,可以改进其他湖泊环境的水质预测。
更新日期:2020-08-04
down
wechat
bug