The well-doublet production model has far-reaching implications for the sustainable utilization of geothermal resources. The position of the injection well in the geothermal production process is closely connected to the emergence of thermal breakthroughs and the production lifespan. Thus, it is necessary to optimize the well placement. However, traditional simulation and optimization approaches require a long time and have a high computing burden. In this paper, a surrogate model based on the back propagation neural network (BPNN) is trained to improve the drawbacks of previous approaches, and it is combined with the genetic algorithm (GA) to develop a simulation-optimization approach to find the optimal well placement of a well-doublet geothermal production system. To guarantee that the training data have appropriate physical significance, the TOUGH2 program is used for the hydro–thermal model development of the geothermal reservoir of the Minghuazhen Formation in Tianjin, China. A sensitivity analysis is used to select the series of samples used for training, which includes temperature and pressure variation, heat extraction rate (Wh), and economic cost. The results reveal that the surrogate model has excellent prediction accuracy and efficiency for physical processes, and the genetic algorithm optimization outcomes are consistent with predictions, which is of practical importance.

Whether groundwater Cr(VI) enrichment near landfills results from direct leachate input or geogenic mobilization remains unresolved. Groundwater and leachate hydrochemistry, a Cl⁻-based mixing model, PHREEQC inverse modeling, sediment Cr fractionation, and correlation analysis were used to investigate a loess-gully landfill in Northwest China. The site was divided into four groundwater environmental units along the flow path. Cr(VI) ranged from below detection to 0.170 mg/L, and 35% of samples exceeded the Class III limit (0.05 mg/L). At the near-source groundwater drainage outlet (II-GEU), Cr(VI) was below detection despite an 8.22% apparent leachate contribution. Downgradient, the contribution decreased to 5.12%, yet Cr(VI) reached 4.17 times the conservative-mixing estimate. Thus, direct leachate mixing cannot explain downstream enrichment. Near the landfill, organic-rich leachate favored reducing conditions and Cr(VI) reduction. Downgradient, increasingly oxidizing conditions limited Cr(VI) reduction, while SO₄²⁻ competed with chromate for mineral-surface adsorption sites, promoting Cr(VI) desorption and mobility. Leachate-induced mineral dissolution and cation exchange may further facilitate Cr(VI) migration by altering groundwater chemistry and mineral-surface properties. Landfill leachate therefore acts primarily as a geochemical trigger, inducing non-conservative transport and downstream release of geogenic Cr(VI), rather than as a direct Cr(VI) source. This mechanism has implications for monitoring and risk management in loess aquifers.