Effect of phosphogypsum on saline-alkalinity and aggregate stability of bauxite residue

A column experiment was conducted to investigate the effect of phosphogypsum (PG) on the saline- alkalinity, and aggregate stability of bauxite residue. Results showed that: with increasing leaching time, the concentrations of saline-alkali ions decreased while the concentration increased in bauxite residue leachate; compared with CK (control group) treatment, pH, electric conductivity (EC), exchangeable sodium percentage (ESP), sodium absorption ratio (SAR), and exchangeable Na⁺ content of bauxite residue were reduced following PG treatment; average particle sizes in aggregates following CK and PG treatments were determined to be 155 and 193 nm, respectively. SR-μCT test results also confirmed that bauxite residue following PG treatment acquired larger aggregates and larger pore diameter. These results indicate that the PG treatment could significantly modulate the saline-alkalinity, and simultaneously enhance aggregate stability of bauxite residue, which provides a facile approach to reclaim bauxite residue disposal areas.     

Influence of temperature on acoustic emission source location accuracy in underground structure

An acoustic emission (AE) experiment was carried out to explore the AE location accuracy influenced by temperature. A hollow hemispherical specimen was used to simulate common underground structures. In the process of heating with the flame, the pulse signal of constant frequency was stimulated as an AE source. Then AE signals received by each sensor were collected and used for comparing localization accuracy at different temperatures. Results show that location errors of AE keep the same phenomenon in the early and middle heating stages. In the later stage of heating, location errors of AE increase sharply due to the appearance of cracks. This provides some beneficial suggestions on decreasing location errors of structural cracks caused by temperature and improves the ability of underground structure disaster prevention and control.     

Evidence of fluid evolution of Baoshan Cu−Pb−Zn polymetallic deposit: Constraints from in-situ sulfur isotope and trace element compositions of pyrite

In-situ LA-ICP-MS and S isotopes of pyrite from the Baoshan Cu polymetallic deposit were conducted to investigate the ore-forming process and the enrichment mechanism of elements. Three generations of pyrite (Py I, Py II, and Py III) in the skarn-type ores and pyrite in the carbonate-hosted sulfide ores from central, western, and northern (C_Py, W_Py, and N_Py) mining districts are selected for comparison. Compared with Py I and Py III, the contents of most elements in Py II are apparently higher. The As and Se contents are high within a wide range and are decoupled in the growth band of the C_Py. The highest As, Se, and Pb contents were found in W_Py and N_Py. These results indicate the drastic changes in the temperature and fluid mixing during the mineralization. The occurrence of fluctuation and change in temperature and f(O₂) was triggered by intermittent pulses of magmatic-hydrothermal fluids, mixing with meteoric water, and water−rock interactions. The sulfur isotopes of all species of pyrite indicated the magmatic source. The change in the f(O₂) conditions caused slight differences in the sulfur isotope compositions. Consequently, a metallogenic model was proposed to explain the ore-forming processes.     

Corrosion behaviour of Mg-Gd-Y-Zn-Ag alloy components with different sizes after cooling

The corrosion behaviour of Mg-6Gd-3Y-1Zn-0.3Ag (wt.%) alloy components with different sizes after cooling was investigated. The alloys in the small components (SC) cooled fast, which were composed of α-Mg matrix and coarse long-period stacking ordered (LPSO) phases. The alloys in the large components (LC) cooled slowly, and there were thin lamellar LPSO phases precipitating inside the grains, except for α-Mg matrix and coarse LPSO phases. The hydrogen evolution test revealed that the corrosion rate of LC sample was higher than that of SC sample. Electrochemical impedance spectroscopy (EIS) test showed that the surface film on LC alloys provided worse protection. The corrosion morphologies indicated that the precipitation of the thin lamellar LPSO phases in LC sample caused severe micro-galvanic corrosion, which accelerated the rupture of the surface film.     

Atomic-level insights into the modification mechanism of Fe (III) ion on smithsonite (1 0 1) surface from DFT calculation

Fe(III) ion can strongly inhibit the sulphidation amine flotation of smithsonite. However, its modification mechanism on smithsonite surface is still obscure. In this work, a systematic study of the modification of Fe(III) ion on smithsonite (1 0 1) surface was performed using DFT calculation. The optimal number of H₂O ligands for Fe(III) ion hydrates in aqueous conditions was probed, and [Fe(OH)₂(H₂O)₄]⁺ and [Fe(OH)₄]^? were identified as the major modification species, then their adsorption and bonding mechanisms were further revealed by analyzing the frontier orbitals, density of state, Mulliken population, and electron density. The calculated adsorption structures were consistent with the former experiment, and we found the O site that bonded to the C atom on smithsonite surface was the most favorable position for [Fe(OH)₂(H₂O)₄]⁺ and [Fe(OH)₄]^? adsorptions. Besides, their adsorption mechanisms on smithsonite surface were principally due to the combined effect of FeO bond and hydrogen bonding. Simultaneously, hydrogen bonding greatly enhanced the stability of the adsorption structures. Moreover, the dominant orbital contribution for the bonding of FeO was primarily due to the orbital hybridization between Fe 3d and O 2p orbitals. This work can help in deeper understanding of the depression of Fe(III) ion on the sulphidation amine flotation of smithsonite.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

剪切解络-超滤分离金属关键指标预测与验证

根据剪切解络-超滤过程中金属离子的动态浓度关系和质量守恒定律,采用CodeBlocks17.12软件编写程序,建立了剪切解络-超滤工艺关键指标预测模型,实现了金属离子浓度、回收率、体积稀释倍数等指标的预测,并用实验加以验证。依次在转速800 r/min、1 400 r/min、3 000 r/min下回收模拟废水中43 μg/L的Co²⁺、995 μg/L的Ni²⁺和88 μg/L的La³⁺,3种金属的回收率均达99.8%以上时,超滤过程体积稀释倍数的预测值分别为7.0、7.5、5.75,实验值分别为7.5、7.25、6.25,可见预测值与实验值基本一致。本模型对研究方案制定和调整有实际指导意义。     

Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Nanograin–glass dual-phasic,elasto-flexible,fatigue-tolerant,and heat-insulating ceramic sponges at large scales

Ceramics are considered intrinsically brittle at room temperature, which is mainly attributed to the limited availability of crystallographic slips and pre-existing geometrical flaws. Moreover, the lack of flexibility has severely hindered many high-end applications of ceramic materials. Here, we produce ceramic sponges that are simultaneously ultra-light, elasto-flexible, thermally insulating, and can fully recover from large deformation with a near-zero Poisson's ratio. These spongy materials also possess superb fatigue resistance without the accumulation of damage or structural collapse for 10,000 large-scale compressive or buckling cycles. We demonstrate the exceptional flexibility is enabled by the elastic distortion of nanograin–glassy dual phase and the fiber bulking in open-cell three-dimensional structure. Moreover, these spongy materials possess superior temperature-invariant superelasticity from deep cryogenic temperatures (−196 °C) to high temperature (1500 °C). Our study not only developed mechanically reliable lightweight ceramics for numerous extreme applications, but also provided new theoretical insights into the origin of flexibility in polycrystalline ceramics.