Evaluation of the applicability of gasification coarse slag as a fine aggregate in controlled low-strength material: preparation, performance, and environmental effect.

Gasification slag (GS) is rich in SiO2, Al2O3, and Fe2O3, and has excellent particle size gradation, which has the potential to be employed as an aggregate in the field of controlled low-strength material (CLSM). Nevertheless, the large-scale application of GS as the fine aggregate for the preparation of CLSM has been scarcely investigated. In the present work, the applicability of replacing part of coal gangue (CG) with gasification coarse slag (GCS) as fine aggregate for the preparation of CLSM was investigated. The results revealed that using GCS as a fine aggregate improved the flowability of CLSM, and increasing the GCS content from 0 to 50 wt% improved the flowability from 250.0 to 280.0 mm. The 28-day compressive strength of all CLSM conformed to the requirements of ACI Committee 229. Compared to the Blank group, the 7- and 28-day compressive strength of the CLSM increased by 23.07% and 26.80%, respectively, at a GCS content of 50 wt%. The increase in compressive strength was mainly due to the pore-filling and hydration-promoting effect of the GCS, which made the structure denser. The dense structure reduced the expansion rate, absorption, and porosity rate of CLSM and increased the wet density. The optimal process parameter was the addition of 10 wt% of GCS. The results of heavy metal ion leaching showed that the optimal sample GS10 leached all heavy metal ions in much less than the limit values of GB 8978-1996 and GB 5085.3-2007. The results will provide new ideas and technical approaches for the large-scale application of GCS as the fine aggregate in CLSM.

Efficient adsorption of Cu2+ and Cd2+ from groundwater by MgO-modified sludge biochar in single and binary systems.

In this study, MgO-modified sludge biochar (1MBC) prepared from sewage sludge was successfully used as an efficient adsorbent to remove heavy metals from groundwater. The adsorption performance and mechanism of 1MBC on Cu2+ and Cd2+ were investigated in single and binary systems, and the contribution of different mechanisms was quantified. Adsorption kinetics and isotherms analysis revealed that the adsorption processes of Cu2+ and Cd2+ by 1MBC followed the pseudo-second-order kinetic and Langmuir isotherm model in both systems, indicating that Cu2+ and Cd2+ were mainly controlled by chemisorption, and their theoretical maximum adsorption capacities were 240.36 and 219.06 mg·g-1, respectively. The results of the binary system showed that due to the competitive adsorption, the adsorption capacity of 1MBC for both heavy metals was lower than that of the single system, and the selective adsorption of Cu2+ was higher. The influencing variable experiments revealed that the adsorption of Cu2+ and Cd2+ by 1MBC had a wide pH adaption range and strong anti-interference ability to coexisting organics and ions. The adsorption mechanisms involved ion exchange (Cu: 47.39%, Cd: 53.17%), mineral precipitation (Cu: 35.31%, Cd: 24.18%), functional group complexation (Cu: 10.44%, Cd: 14.53%), and other possible mechanisms (Cu: 6.87%, Cd: 8.12%). Furthermore, 1MBC demonstrated excellent regeneration potential after five cycle times. Overall, the results have significant reference value for the practical application of removing heavy metals.

Variational Hybrid Monte Carlo for Efficient Multi-Modal Data Sampling.

The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much more efficiently than random-walk proposals, but may suffer from high autocorrelation. In this paper, we propose Langevin Hamiltonian Monte Carlo (LHMC) to reduce the autocorrelation of the samples. Probabilistic inference involving multi-modal distributions is very difficult for dynamics-based MCMC samplers, which is easily trapped in the mode far away from other modes. To tackle this issue, we further propose a variational hybrid Monte Carlo (VHMC) which uses a variational distribution to explore the phase space and find new modes, and it is capable of sampling from multi-modal distributions effectively. A formal proof is provided that shows that the proposed method can converge to target distributions. Both synthetic and real datasets are used to evaluate its properties and performance. The experimental results verify the theory and show superior performance in multi-modal sampling.