硝酸盐污染地下水原位碳源注入生物修复优化方案研究

Optimization of In-Situ Carbon Injection Bioremediation of Nitrate Contaminated Groundwater

以北京某场地生活垃圾填埋场硝酸盐污染地下水为研究对象,针对原位生物修复技术,建立生物耦合Monod(RT3D)溶质运移模型。利用遗传算法(GA)和模拟退火法(SA)对地下水硝酸盐污染羽治理区开展了碳源(乙醇)注入备选井方案和原位生物修复系统进行模拟优化研究。优化结果表明,在满足硝酸盐氮浓度降低到10mg·L-1的目标下,GA和SA两种优化方案均优化确定相同的3口碳源注入井,比原设计节省15口井。100d后硝酸盐氮生物降解总量去除率分别为90.78%和84.51%。碳源注入井的布置方式呈三角点布置,且上游点的碳源注入量大于其他两点注入量。原位生物修复两种优化算法对比表明SA优化系统治理成本比GA节省1.46%,且收敛性强,波动性小,但计算时间较长。

In-situ carbon injection is a promising and economically effective technique for enhanced biodenitrification of groundwater con-taminated by nitrate. Optimizing carbon injection can improve remediation efficiency. In this paper, a landfill site in Beijing was selected to perform an optimization study. A biological coupling Monod solute transport model（RT3D）based on in situ bioremediation technology was established. Genetic Algorithm（GA）and Simulated Annealing（SA）were used to optimize the layout of potential carbon（ethanol）injection wells in the control areas of groundwater nitrate plumes. The results show that, at 10 mg＆#183;L-1 of target nitrate concentration in the studied area, both GA and SA methods obtained the same three carbon injection wells, 15 wells fewer than the originally designed ones. These three injection wells should be arranged in triangular position. More carbon source should be injected in the upstream well than in two downstream wells. After 100 d of injection, the total removal of nitrate nitrogen was 90.78% and 84.51% for GA and SA, respectively. Compared to GA, SA cost 1.46% less, with stronger convergence and smaller variability but longer computing time.