查看更多>>摘要：The high lithium content in spent lithium ion batteries (LIBs) and a large number of spent LIBs generation shift the target of lithium recovery to solid waste. Adsorption separation is of great application value for lithium recovery. There is still a lack of a lithium adsorption material with excellent properties. Herein, an environmental hydrolytic polymerization to prepare lithium ion-imprinted membranes (LIIMs) with high adsorption capacity and selectivity is presented. The LIIMs can reach adsorption equilibrium in 40 min, showing fast adsorption kinetics. The optimal adsorption capacity of the LIIMs for Li~+ was 132.00 mg g~(-1) after immersing in a 300 mg L~(-1) LiCl solution based on ample binding sites and a strong affinity force. Langmuir isothermal adsorption results proved that the imprinting sites on LIIMs were homogeneous. The selective separation factors (α) of Li~+ to Mg~(2+), K~+, Ca~(2+), Na~+ were 6.80, 17.00, 21.30, and 24.60, respectively, which implies the superior selectivity LIIMs toward Li~+. The adsorption capacity of LIIMs remained about 97 % after six cycles. Due to the suitable cavity, abundant rebinding sites, and strong affinity, the LIIMs have a good selective adsorption ability to Li~+. Therefore, the LIIMs would have potential applications for separating Li~+ from spent LIBs.
查看更多>>摘要：An orthogonal composite approach is proposed to study the multielement synergistic modified TiO2 photocatalytic films. The relationship between different preparation factors and photocatalytic evaluation indexes including photo-degradation activities, antibacterial properties, light transmittance and hydrophilicity, is revealed by the correlation and difference analysis. The increase of F content improves the photocatalytic and antibacterial activities. While the increase of Fe content and calcination temperature are not conducive to the transmittance. Additionally, reduced graphene oxide (rGO) is beneficial to improve the antibacterial properties, but it is not conducive to the self-cleaning performance. The interaction between different factors cannot be ignored in the design of multifunctional TiO2 films from Pareto analysis. The optimal state shows that the glass coated with multifunctional F_((9%))-N_((1%))-TiO2/rGO_((0.5%))-300 °C film exhibits the highest photo-degradation (59.8%) and antibacterial activities (87.1%) with superior transmittance (84.1%) and hydrophilicity (7.0°) under visible-light irradiation condition.
查看更多>>摘要：Deep learning methods have become the mainstream research direction in the field of chemical process fault detection and diagnosis, which have great application and research value. However, the existing deep fault diagnosis methods are faced with challenges such as missing data, high-dimensional redundancy and difficulty in fault feature mining, which limits their application in industry. Based on this, a fault diagnosis method for complex chemical process based on multi-model fusion is proposed. This approach avoids over-fitting the model due to excessive redundant data by introducing a FunkSVD matrix decomposition model to augment the missing data without changing the data distribution and then inputting an extreme gradient boosting tree model to learn key features. Finally, the model memory and generalization capability are improved by training a very deep factor decomposer diagnostic model to extract and fuse linear, low-order interaction and high-order interaction features in an all-round way to adaptively establish the correlation between fault features and fault conditions. To validate the model effectiveness, extensive experiments were conducted on the Tennessee Eastman Process dataset and Fluidized Catalytic Cracker fractionation unit dataset, and the results showed that the proposed method has significant performance advantages over existing diagnostic methods in terms of precision and recall metrics.
查看更多>>摘要：In the present work, filtration performance and fouling behavior of four poly(vinylidene difluoride) (PVDF) membranes with different composition and molecular weight cut-off (MWCO) were investigated in a lab-scale submerged membrane bioreactor (MBR) system, treating real pharmaceutical wastewater. Poly(ethylene oxide)-block-poly(methyl methacrylate) (PEO-b-PMMA) diblock copolymer or FeCl2 or the combination of PEO-b-PMMA and FeCl2 were used as special additives during membrane formation. A 30-day filtration experiment was performed using four membranes in the same aeration tank simultaneously, and filtration performance of the membranes was investigated over the entire filtration period. Fouling parameters were calculated for all membranes and the TMP-step method was used to determine the critical flux of the membranes. Formed cake layers onto the surface of the membranes at the end of each filtration run were collected and extracellular polymeric substances (EPSs), excitation and emission matrix (EEM) fluorescence spectroscopy, and Fourier transform infrared (FTIR) spectroscopy analyses were performed for the cake layers. Collected cake layers after EPSs extraction were dried and their masses were measured to estimate anti-biofilm formation potential of the membranes. Obtained results revealed that incorporation of the PEO-b-PMMA diblock copolymer into the PVDF membrane accompany with FeCl2 salt which tailors the MWCO and prevents the increment of the pore size improves membrane performance and reduces its fouling propensity, in a way that membrane with smaller MWCO containing copolymer revealed lower flux decline, higher critical flux, higher flux recovery ratio (FRR) and lower biofilm mass per area. Moreover, EPS and EEM analyses revealed that membrane surface chemistry has considerable effect on the composition of the cake layers. Finally, chemical oxygen demand (COD) removal efficiency proved that MWCO does not have obvious effect on effluents' quality. The present study confirms the importance of the surface chemistry and MWCO on fouling behavior of the membranes in the MBR system and reveals that the addition of the PEO-b-PMMA diblock copolymer to the PVDF membrane improves anti-fouling property of the membrane considerably.
查看更多>>摘要：This study proposes a multi-objective mathematical programming model to design an oil supply chain network at three levels: upstream, midstream and downstream. It includes oil wells, gas injection wells, production units, refineries, gas injection centers, distribution centers, export and import terminals. The proposed model optimizes all three economic, social and environmental goals at the same time. Drilling injection and production wells, transportation costs, production costs, gas injection center costs, as well as fixed and annual costs of refineries and distribution centers are all minimized in the economic dimension. The environmental dimension of sustainability reduces greenhouse gas emissions, while the social dimension of sustainability increases job opportunities. The optimization of associated petroleum gas injection operations into the reservoir is also taken into account in this study. The proposed model considers the uncertainty of critical parameters and employs a robust possibilistic hybrid approach to deal with it, as well as an interactive fuzzy approach to solve it. A case study in Iran is used to evaluate the suggested model's performance. In comparison to the single-objective economic model, the results of solving the model show that taking sustainability into account has reduced greenhouse gas emissions and increased job opportunities. The crude oil extraction potential increases in each period when gas injection optimization and reservoir pressure are taken into account. Furthermore, the cost of the supply chain grows as the amount of uncertainty in the suggested model rises. As a result, the uncertainty of system parameters must be considered.
查看更多>>摘要：In designing a gas-liquid bubble column, an accurate estimation of the gas hold-up is important. A generalized Machine learning-based data-driven methodology is presented to predict the gas hold-up, with the industrial-scale application for designing a bubble column. The predictions by machine learning methods such as support vector regression (SVR), random forest (RF), extra trees (EXT), and artificial neural network (ANN) have been compared. The methodology adopted for the prediction of gas hold-up with the help of independent parameters such as column diameter, column height, sparger design, sparger location, percentage free area, superficial gas, liquid velocity, pressure, temperature, the density of gas & liquid, viscosity of gas & liquid, surface tension has been presented. An extensive set of experimental data (4042 data points) has been extracted from the literature covering various design and operating parameters. The performance of the machine learning methods has been compared using mean absolute percentage error (MAPE), mean square error (MSE), and determination coefficient/prediction accuracy (R-square). Based on these statistical parameters, with 97% of prediction accuracy (R-square), MSE = 0.00031, and MAPE = 7.9, the performance of the extra tree is found to be most suitable for the prediction of the gas hold-up.
NSTL