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Gas–droplet contaminants such as HCl gas and hydrochloric droplets are typical pollutants in industrial indoor environments. They are harmful to workers. As the transportation modes of the gas and droplet phases are different, they cause different exposure characteristics for human beings. This study numerically investigated the transport of HCl gas and hydrochloric droplets emitted from a pickling tank to a human microenvironment. Meanwhile, inhalation and deposition of hydrochloric acid gas and hydrochloric acid droplets by humans were also studied, which were influenced by the droplet initial diameter (10–100 μm), air draught (0.1–0.5 m/s) and the distance between the source and the manikin (0.5–1.5 m). The results showed that the cases considering an initial droplet diameter of 50 μm could cause the largest droplet deposition on the manikin, which was influenced by the combination of droplet gravity and plume. The velocity of the air draught could significantly enhance the inhalation of HCl gas and droplet deposition on the manikin when the velocity was ≥ 0.3 m/s. Furthermore, when the distance was increased from 1.0 to 1.5 m, the thermal plume enhanced the inhalation of HCl gas.

Drilling costs of ultra-deep well is the significant part of development investment, and accurate prediction of drilling costs plays an important role in reasonable budgeting and overall control of development cost. In order to improve the prediction accuracy of ultra-deep well drilling costs, the item and the dominant factors of drilling costs in Tarim oilfield are analyzed. Then, those factors of drilling costs are separated into categorical variables and numerous variables. Finally, a BP neural network model with drilling costs as the output is established, and hyper-parameters (initial weights and bias) of the BP neural network is optimized by genetic algorithm (GA). Through training and validation of the model, a reliable prediction model of ultra-deep well drilling costs is achieved. The average relative error between prediction and actual values is 3.26%. Compared with other models, the root mean square error is reduced by 25.38%. The prediction results of the proposed model are reliable, and the model is efficient, which can provide supporting for the drilling costs control and budget planning of ultra-deep wells.