The recovery of zinc from metallurgical dust sludge is a crucial component of using solid waste as a resource in the metallurgical process, and deep eutectic solvent–ultrasonic synergistic enhanced leaching is an efficient method of doing so with excellent economic effects. The leaching rate of zinc is used as the value of response in this study, along with the four process conditions of leaching temperature, leaching time, liquid–solid ratio, and ultrasonic power. By building a regression model, the relationship between the various parameter components is investigated, and a strategy for optimization is then chosen and confirmed. The findings indicate that, for the parameters of temperature 40 °C, ultrasonic power 90 W, liquid–solid ratio 7:1 g/L, stirring speed 250 rpm, and leaching duration 80 min, the prediction value of the regression model of the zinc leaching rate is 98.47%. The average zinc leaching rate obtained by the 3 parallel verification experiments was 98.49%; the deviation from the regression model’s predicted value was 0.02%. This demonstrated that the experimental results were consistent with those predicted by the regression model, the experimental results were reliable and trustworthy, and the optimization scheme was reasonable and accurate. Compared with the conventional leaching method (leaching rate: 91.61%), the method under ultrasound increased the zinc leaching rate by 6.88%.

To improve the concentration performance of the concentrator in the iron ore beneficiation process for iron ore tailings, a coupled simulation analysis of the concentration process was conducted using the discrete element software EDEM (Engineering Discrete Element Method) and the finite element FLUENT software. The volume concentration at the bottom flow outlet of the concentrator was used as the evaluation index. The scraper rotation speed, feed rate, and feed concentration were considered as parameters. Response surface experiments were designed using the Box-Behnken module in Design Expert11 software, and numerical simulations were performed to obtain data. Based on the numerical simulation results, a prediction model was established using the backpropagation neural network (backpropagation neural network, BP-NN) and combined with the genetic algorithm (genetic algorithm, GA) for parameter optimization of the thickener’s concentration conditions. The results showed that with a scraper rotation speed of 9.7677 rpm, feed rate of 0.2037 m/s, and feed concentration of 6.5268%, the maximum outlet volume concentration reached approximately 62.00%. The predicted optimal working conditions were validated through physical tests and numerical simulations. The average outlet volume concentration in the physical tests was 60.712% (n = 10) (“n” is the number of experiments), with an error of only 2.077% compared to the predicted value. The middle outlet volume concentration in the numerical simulation experiments was 59.951% (n = 10), with an error of only 3.304% from the expected value. These results demonstrate the feasibility of using a genetic neural network for optimizing the EDEM–FLUENT simulation parameters of the thickener, providing valuable insights for the matching optimization of the thickener’s process parameters.