The large‐scale and resourceful utilization of solid waste is one of the important ways of sustainable development. The big data brings hope for further development in all walks of life, because huge amounts of data insist on the principle of “turning waste into treasure”. The steel big data has been taken as the research object in this paper. Firstly, a big data collection and storage system has been set up based on the Hadoop platform. Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. Then, the alternatives for resource recovery are obtained according to the predicted composition of the steel slag. And considering the three aspects of economic feasibility, resource suitability, and environmental acceptance, the comprehensive evaluation system based on AHP is established to realize the recommendation of the optimal resource approach. Finally, taking a steel plant in Hebei as an example, the alternatives according to the prediction of the composition of steel slag are blast furnace iron‐making, recycling waste steel, and cement admixture. The comprehensive evaluation values of the three resources are 0.48, 0.57, and 0.76, respectively, and the optimized resource of the steel slag produced by the steel plant is used as the cement admixture.

The resource and large-scale utilization of waste ceramic materials, magnesium slag, and coal gangue are one of the important ways for the sustainable development in metallurgy, coal, and other related enterprises. In this paper, waste ceramic materials were used as aggregates; coal gangue and magnesium slag were used as mixed binder; and the all solid-waste-based permeable bricks with excellent performance were prepared by forming pressure at 5 MPa. The mechanical properties and water permeability of the all-solid-waste-based permeable bricks were evaluated. The results proved that the porous channel of permeable brick is mainly composed of waste ceramic materials with a particle size of 2–3 mm. Pore structures below 200 μm were mainly composed of fine aggregate and mixed binder. Using 60% coarse aggregate, 20% fine aggregate, 10% coal gangue, and 10% magnesium slag as raw materials, the all-solid-waste-based permeable bricks were obtained by pressing at 6 MPa and sintering at 1200 °C, which exhibited the best performance, and its water permeability, compressive strength, and apparent porosity were 1.56 × 10⁻² cm/s, 35.45 MPa, and 13.15%, respectively. Excellent water permeability, compressive strength, and apparent porosity of the all solid-waste-based permeable bricks were ascribed to the high content of connecting open pores, and closely adhesive force were ascribed to the porous microstructure constructed by the grading of waste ceramic materials and the tight conjoined points of the liquid phases in coal gangue and magnesium slag at a high sintering temperature.

The effective removal of uranium from an aqueous solution is a highly valuable process for the environment and health. In this study, we developed a facile and rapid method to synthesize hierarchically porous Cu-BTC (RT-Cu-BTC) using a cooperative template strategy. The as-synthesized RT-Cu-BTC exhibited hierarchically porous structure and excellent thermostability, as revealed by X-ray powder diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Compared with conventional metal–organic frameworks (MOFs) and zeolites, the obtained RT-Cu-BTC exhibited enhanced adsorption capacity (839.7 mg·g⁻¹) and high removal efficiency (99.8%) in the capture of uranium (VI) from aqueous solutions. Furthermore, the conditions such as adsorbent dose, contact time, and temperature in adsorption of uranium (VI) by RT-Cu-BTC were investigated in detail. The thermodynamics data demonstrated the spontaneous and endothermic nature of the uranium (VI) adsorption process. The Langmuir isotherm and pseudo-second-order models could better reflect the adsorption process of uranium (VI) onto RT-Cu-BTC. In addition, the as-synthesized RT-Cu-BTC showed excellent stability in removing uranium (VI) from an aqueous solution. This work provides a facile and rapid approach for fabricating hierarchically porous MOFs to realize a highly efficient removal of uranium (VI) from aqueous systems.