Practical Production Process Research Articles

Flow, Heat‐Transfer, and Mixing Behaviors of Scrap Steel in a Refining Ladle with Bottom Blowing

The flow, heat‐transfer, and mixing behaviors of steel scraps with different amounts, positions, and sizes added from the top of the bottom‐blown 300 t ladle are numerically investigated and compared through a coupled model. In the results, it is shown that it takes 59 s to mix the temperature of molten steel after adding scrap steel at the position of x = 0 mm, y = 553.5 mm, and z = 3375 mm. The further the addition position is from the axial position of the permeable brick, the shorter the mixing time of the speed and temperature of molten steel. For the scrap amount of 0.5, 1.5, and 2.5 t, the mixing time of molten steel temperature is 44, 78, and 47 s, correspondingly, which exhibits a pattern of initial increase followed by decrease, with an ≈8 K decline in molten steel temperature for every additional 1.0 t of scrap. When considering the scrap size of 10, 30, and 50 mm, the average temperature mixing time of molten steel is 44, 61, and 45 s, respectively. In this research, theoretical guidance can be can be provided for the addition of scrap in ladle during practical production processes.

Toward understandable semi-supervised learning fault diagnosis of chemical processes based on long short-term memory ladder autoencoder (LSTM-LAE) and self-attention (SA)

Fault diagnosis and localization play vital role in chemical process monitoring. Finding the root cause of fault accurately and timely is the key to avoid serious accidents and ensure process safety. Unfortunately, most deep learning-based models only involves in predicting fault state and interpretability is still not fully explored. Besides, lack of labeled samples in practical situations makes supervised learning difficult to implement. To circumvent the obstacle, semi-supervised learning based on long short-term memory ladder autoencoder is combined with self-attention mechanism, which is intended to establish interpretable model by explicitly clarifying the corresponding relationship between abnormal variables and faults. Using Tennessee Eastman process and practical high-purity carbonate production process as benchmark, fault diagnosis and identification performance of proposed LSTM-LAE-SA is validated, and interpretability analysis is performed to demonstrate its capabilities in abnormal variable locations. The developed understandable model could improve operator's trust and industrial application in fault diagnosis system.

Saline–Alkali Soil Reclamation Contributes to Soil Health Improvement in China

Soil salinization is a significant threat to soil health, especially to the agricultural ecosystem; it reduces vegetation biomass, destroys ecosystem diversity, and limits land use efficiency. This area of investigation has garnered extensive attention in China, especially in the arid and semi-arid areas, totaling 7.66 × 106 ha. A variety of theoretical research and technology developments have contributed to soil water and salt regulation and the screening of salt-tolerant varieties to improve nutrient utilization efficiency and microbial control and reduce ecological problems due to saline-based obstacles. These techniques can be classified into physical treatments, chemical treatments, biological treatments, and combined treatments; these different measures are all aimed at primarily solving saline–alkali stress. In general, the improvement and utilization of saline–alkali soil contribute to soil health improvement, concentrating on high-quality development, food security, ecological security, cultivated land protection, and agricultural upgrading. However, the risks of various technologies in the practical production process should be highlighted; green and healthy measures are still expected to be applied to saline–alkali land.

Adaptive Temporal-Spatial Pyramid Variational Autoencoder Model for Multirate Dynamic Chemical Process Soft Sensing Application.

Data-driven soft sensors play an important role in practical processes and have been widely applied. They provide real-time prediction of quality variables and then guide production and improve product quality. In practical chemical production processes, nonlinear dynamic multirate data is widespread and challenging to model. This paper innovatively proposes a temporal-spatial pyramid variational autoencoder (TS-PVAE) model for the nonlinear temporal-spatial feature pyramid extraction from multirate data. This structure not only selectively utilizes multirate data but also handles complex nonlinear time-series data. Based on this, integrated with just-in-time (JIT) learning, an adaptive TS-PVAE (ATS-PVAE) model is developed. In this model, historical data are used for real-time fine-tuning of the model, leading to the development of an adaptive model. Finally, the proposed models are validated by an industrial case of a methanation furnace, demonstrating a superior estimation performance.

Effect of frother on dynamic detachment of rising bubble-coarse particle aggregates at gas-liquid interface

In the flotation process, the bubble-coarse particle aggregates easily undergo detachment at the gas-liquid interface, resulting in low mineral recovery. This paper tested the variation rule of surface tension with a concentration in the solution of four alcohols frother, namely, n-pentanol (NPA), methyl isobutyl carbinol (MIBC), 2-octanol, and polypropylene glycol (PPG). The effects of frothers on the motion characteristics of rising bubble-coarse particle aggregates and the dynamic detachment of aggregates at the gas-liquid interface were investigated separately using a high-speed dynamic camera system and a dynamic detachment device. The results showed that the solution surface tension decreased and then tended to be stabilized with the increase of frother concentration. Under the same concentration, the stronger the surface activity of the frother, the smaller the rising velocity of the bubble-coarse particle aggregates, and the more stable the motion state of the aggregates, with a smaller velocity fluctuation. The lower rising velocity reduced the particle detachment probability when aggregates collided with the gas-liquid interface. The merger time of the bubble-coarse particle aggregates was extended with the improved surface activity of the frother, increasing by more than 1 s compared to the solution without the frother. The study will contribute to effectively reduce the dynamic detachment probability of bubbles-coarse particle aggregates at the gas-liquid interface by selecting the type and concentration of frothers, meanwhile optimizing the reagent regime to enhance the coarse particle flotation recovery in the practical production process.

Design of a yarn feeder based on a piezoelectric actuator

Yarn feeding is a crucial step in various stages of the textile industry, and plays an important role in ensuring the stable operation of knitting machines. In response to the working environment of yarn feeders and the periodic intermittent yarn usage in loop forming systems, this paper proposes a new type of piezoelectric-actuated yarn feeder with the characteristics of small size, low energy consumption, switchable yarn feeding modes, and self-locking. The working principle of the new yarn feeder is systematically analyzed and simulated, and the performance of the yarn feeder is experimentally investigated and compared with theoretical analysis. The relationship curves between yarn feeding speed and driving frequency, as well as yarn feeding speed and driving voltage peak-to-peak value, are drawn, and the stability segmentation line of the yarn feeder is defined. The yarn feeding accuracy of the proposed yarn feeder is verified, and the displacement-time relationship curves of yarn under different conditions are plotted to explore the yarn feeding characteristics. In summary, the proposed piezoelectric-actuated yarn feeder has good performance and can meet the needs of practical production processes. It provides a valuable reference for the combination of piezoelectric driving and the textile industry.

Deep Mixed Domain Generalization Network for Intelligent Fault Diagnosis Under Unseen Conditions

Emerging intelligent fault diagnosis models based on domain adaptation can resolve domain shift problems produced by different working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address this challenge, a deep mixed domain generalization network (DMDGN) is proposed for intelligent fault diagnosis. In this novel model, data augmentation is applied to both class and domain spaces, adversarial learning is employed to introduce adversarial perturbations, and a domain-based discrepancy metric is used to balance intra-domain and inter-domain distances. The model can effectively learn more domain-invariant and discriminative features from multiple source domains to perform different generalization tasks for different working loads and machines. The feasibility of the DMDGN model is verified on two public datasets and one private dataset collected from practical production processes. Empirical results show that the DMDGN model outperforms several state-of-the-art models.

Influence of particle size of initial oxides on the dielectric properties of ilmenite-type Zn0.7Mg0.3TiO3 ceramics

The core of this research is to elucidate the effect of the particle size of the starting oxide on the dielectric properties of Zn0.7Mg0.3TiO3 ceramics. The study was carried out using a conventional solid-state reaction route. The microstructure optimization of Zn0.7Mg0.3TiO3 ceramics was achieved by introducing nanoscale particles. The distortion of octahedral [TiO6] and the effect of crystal structure led to significant changes in the dielectric properties. Stability analysis shows that usage of nanoparticles decreases the temperature stability of Zn0.7Mg0.3TiO3 ceramics. This work will offer a well-defined guideline for the selection of raw materials to prepare ceramics in the practical production process.

Feature attention gated context aggregation network for single image dehazing and its application on unmanned aerial vehicle images

AbstractSingle‐image dehazing is a highly challenging ill‐posed task in the field of computer vision. To address this, a new image dehazing model with feature attention, named feature attention gated context aggregation network (FAGCA‐Net), is proposed to tackle the issues of incomplete or over‐dehazing caused by the original model's inability to handle non‐uniform haze density distributions. A feature attention module that combines channel attention and spatial attention is introduced. Additionally, the authors propose a new extended attention convolutional block, which not only addresses the grid artefacts caused by the extended convolution but also provides added flexibility in handling different types of feature information. At the same time, in addition to the input image itself, incorporating the dark channel and edge channel of the image as the final input of the model is helpful for the model learning process. To demonstrate the robustness of the new model, it is applied to two completely different dehazing datasets, and it achieves significant dehazing performance improvement over the original model. Finally, to verify the effectiveness of the model in practical production processes, the authors apply it as an image preprocessing step to a set of UAV (Unmanned Aerial Vehicle) images of foreign objects. The result shows that the UAV images after being processed by FAGCA‐Net for haze removal have a better impact on subsequent usage.

Influence mechanism of Fe(II/III) doping on the adsorption of methylamine salts on kaolinite surfaces elucidated through DFT calculations

To investigate the effect of Fe(II/III) doping on the microscopic mechanism of methylamine salt hydrophobic modifiers on kaolinite surfaces, the adsorption characteristics of various methylamine cations on Fe(II/III)-doped kaolinite (denoted as Fe(II/III)-Kao) surfaces are calculated by density functional theory (DFT). The simulation results, including the Fukui index, adsorption energy, Mulliken bond populations, and charge analysis, reveal that Fe(II/III) doping enhances the surface activity of kaolinite, with significant effects observed in proximity to the doping site. Consequently, Fe(II/III) doping strengthens the interaction between methylamine cations and kaolinite surfaces. The adsorption mechanism of various methylamine cations on Fe(II/III)-Kao surfaces is attributed to a combination of hydrogen bonding and electrostatic attraction, with electrostatic attraction dominating the adsorption process. By comparing the adsorption energy of methylamine cations and alkyl carbon chains on Fe(II/III)-Kao surfaces, it becomes evident that the alkyl carbon chain has minimal impact on the adsorption of the hydrophobic modifier on Fe(II/III)-Kao surfaces. This finding confirms that the ammonium salt hydrophobic modifier primarily adsorbs onto Fe(II/III)-Kao surfaces through its polar head groups. The research results establish a theoretical foundation for further investigations on fine clay particle interface control and hydrophobic modifier design, ultimately guiding practical production processes.

Analysis of parametric instability of cigarettes based on computational fluid dynamics methods

In actual production tests, there are large fluctuations in the ventilation rate and smoking resistance of cigarettes. In this paper, fluctuations in ventilation rate and smoking resistance are attributed to variations in the number of ventilation holes in the joint paper and the porosity of each component. The effects of small changes in porosity of each component were analyzed by using computational fluid dynamics methods. The results showed that the difference in the effective number of ventilation holes caused by the gluing process had little effect on the ventilation rate and smoking resistance of cigarettes in the practical production process. The smoking resistance is mainly affected by the structural parameters of filter tow and cut tobacco section in the length direction of cigarettes, the porosity of cigarette paper and packaging paper is positively related to the cigarette ventilation rate, the porosity of cut tobacco and the porosity of filter tow are negatively related to the cigarette ventilation rate, and the influence of shaped paper is the smallest. The results can provide technical and theoretical basis for the optimization of cigarette process parameters and the stabilization of ventilation rate.

On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)

Since tool wear during machining process imposes a significant limitation on production quality as well as efficiency, on-line tool wear monitoring (TWM) is one of considerably crucial issues for intelligent manufacturing system. However, there is still a lack of universal on-line TWM techniques appropriate for the practical production process with variable machining parameters. Therefore, this study proposes a highly condition-adaptive method for tool wear state monitoring and remaining useful life (RUL) estimation under time-varying operation conditions. Multi-domain features are first extracted from vibration, acoustic emission and sound signals, and subsequently fused by the kernel principal component analysis (KPCA) to highlight discriminative information correlated to tool wear. A condition-adaptive hidden semi-Markov model (CAHSMM) is established to describe the evolution of tool wear, in which the accelerated failure time model (AFTM) embedded with Taylor tool life equation is utilized to determine the state transition probabilities that are strongly dependent on cutting parameters and duration time. Moreover, to extrapolate the Gaussian mixture models (GMMs) used to represent the observation probability beyond the range where they were trained, a Moment matching based on-line unsupervised transfer learning (OUTL) method is developed. After obtaining the state transition and observation probabilities, a modified forward algorithm is eventually implemented so as to assess tool wear online. Both the dataset from milling experiments of Ti6Al4V and PHM 2010 are employed for verifying the effectiveness of the proposed method. The results show that the presented method enables to reliably evaluate wear state and RUL of the cutter even under time-switching operation parameters, which demonstrates its promising potential for industrial applications.

Development of eco-friendly chitosan-g-polyacrylamide preformed particle gel for conformance control in high-temperature and high-salinity reservoirs

Oil and gas extraction has become challenging nowadays due to the accompanying amount of excess produced water that results in poor recoverability of hydrocarbon, besides other environmental and economic isues. A recent and efficient technology for conformance control is the injection of preformed particle gels (PPGs), which results in a more practical production process. Nevertheless, existing treatments fail in high-temperature reservoirs, are extremely sensitive to salinity, and are hazardous. The characteristics of the designed PPG, such as mechanical strength and thermal durability, is mainly depend on their crosslinking method. Polysaccharides-based gels prepared by physical crosslinking are weaker than the ones crosslinked by strong covalent bonding. This paper uses one of the polysaccharides and proposes an environmentally friendly PPG for water shutoff applications in reservoirs of high temperature (≤130 °C) and high salinity (200,000 ppm), named chitosan grafted polyacrylamide crosslinked with N, N′-methylene bisacrylamide, synthesized chemically by microwave assisted method. The PPG's chemical compositions, grafting and crosslinking mechanism have been investigated by FTIR spectroscopy and SEM techniques. Swelling kinetics, swelling capacity, and mechanical strength measurements were conducted in different conditions to evaluate the influence of the reservoir conditions, such as salinity, temperature, and pH, on the PPG stability. TGA experiments were also performed to examine the thermal stability. Results have shown that the grafting method has produced a PPG with improved mechanical strength, thermal durability, and salt insensitivity. These results are consistent with the testing observations, where the swelling capacities and the storage modulusof Cs/PAMBA samples, with different MBA content, in deionized water were 2.72–11.64 g/g and 4272.1–22,687 Pa, respectively, while they are 2.52–13.82 g/g and 3699.6–22,910, respectively, in saline solution of TDS 67.2976 g/L. The PPGs are thermally stable and resist temperatures up to 130 °C. Besides being eco-friendly, the Cs/PAMBA showed good long-term thermal stability in high-temperature and high-salinity environments.

The Contribution Teaching Factory On Practical Learning Process, Production Process And Vocational School Quality

The purpose of the research is; (1) to describe teaching factory implementation; (2) to describe contribution of teaching factory on practical learning; (3) to describe contribution of teaching factory on production process; (4) to describe contribution of teaching factory on vocational school quality. This research employed qualitative method with case study approach. The research was done in SMK Muhammadiyah1 Sukoharjo, and the research subject is headmaster, teachers, and students. The data collection technique is taken by observation, interview and documentation with qualitative analysis data followed data reduction, data display and conclusion drawing. The result of the research shows that; (1) teaching factory implementation runs well and the teaching factory in the school is more modern and it has become the technopark as reference of other vocational schools’ teaching factory; (2) teaching factory learning contributes significantly on practical learning; (3) teaching factory learning contributes production process; (4) and teaching factory learning contributes vocational school quality in SMK Muhammadiyah 1 Sukoharjo.

Solving the Two-Crane Scheduling Problem in the Pre-Steelmaking Process

This research is motivated by the practical pre-steelmaking stage in large iron and steel companies, which have steady and heavy demands for the steelmaking production process. Our problem studied the pre-steelmaking stage, which consists of two steps that are needed in each convertor before the steelmaking process. During each step, a necessary transportation must be operated by a crane. In contrast to the classical two-machine flowshop problem during which both machines are fixed, these transporting operations are performed by two mounted, removeable cranes. Our problem is scheduling two-crane operations for the sake of minimizing the last convertors’ completion time (makespan); that is, the last finish time among the total operation of the two cranes is minimized. This study was concerned with resolving the interference between two cranes by determining the sequence of loading operations and how each crane avoids the other in order to let it complete its next operation first. A mixed integer linear programming (MILP) model was developed to represent the problem, and we further present the computational complexity of the problem. The result implies that our problem is very difficult to solve, and it is computationally challenging to solve the model. A special case is provided, which can be optimally solved in polynomial time. Furthermore, an evolutionary algorithm cuckoo search (CS) algorithm was attempted to obtain near-optimal solutions for medium- and large-scale problems. Finally, the efficiency and effectiveness of our methods were validated by numerical results in both simulated instances as well as real data from a practical production process.

A Three-Step Framework for Multimodal Industrial Process Monitoring Based on DLAN, TSQTA, and FSBN

The process monitoring method for industrial production can technically achieve early warning of abnormal situations and help operators make timely and reliable response decisions. Because practical industrial processes have multimodal operating conditions, the data distributions of process variables are different. The different data distributions may cause the fault detection model to be invalid. In addition, the fault diagnosis model cannot find the correct root cause variable of system failure by only identifying abnormal variables. There are correlations between the trend states of the process variables. If we do not consider these correlations, this may result in an incorrect fault root cause. Therefore, multimodal industrial process monitoring is a tough issue. In this paper, we propose a three-step framework for multimodal industrial process monitoring. The framework aims for multimodal industrial processes to detect the faulty status timely and then find the correct root variable that causes the failure. We present deep local adaptive network (DLAN), two-stage qualitative trend analysis (TSQTA), and five-state Bayesian network (FSBN) to implement fault detection, identification, and diagnosis step by step. This framework can detect the system failure timely, identify abnormal variables, and find the root cause variable and the fault propagation path. The case studies on the Tennessee Eastman simulation and a practical chlorobenzene production process are provided to verify the effectiveness of the proposed framework in multimodal industrial process monitoring.

Zirconium Phosphate Assisted Phosphoric Acid Co-Catalyzed Hydrolysis of Lignocellulose for Enhanced Extraction of Nanocellulose.

The high mechanical strength, large specific surface area, favorable biocompatibility, and degradability of nanocellulose (CNC) enable it to be a potential alternative to petroleum-based materials. However, the traditional preparation of CNCs requires a large amount of strong acid, which poses a serious challenge to equipment maintenance, waste liquid recycling, and economics. In this study, a solid and easily recoverable zirconium phosphate (ZrP) was used to assist in the phosphoric acid co-catalyzed hydrolysis of lignocellulose for extracting CNCs. Due to the presence of acidic phosphate groups, ZrP has a strong active center with a high catalytic activity. With the assistance of ZrP, the amount of phosphoric acid used in the reaction is significantly reduced, improving the equipment's durability and economic efficiency. The effects of the process conditions investigated were the phosphate acid concentration, reaction temperature, and reaction time on the yield of CNCs. The Box-Behnken design (BBD) method from the response surface methodology (RSM) was applied to investigate and optimize the preparation conditions. The optimized pre-treatment conditions were 49.27% phosphoric acid concentration, 65.38 °C reaction temperature, and 5 h reaction time with a maximal cellulose yield (48.33%). The obtained CNCs show a granular shape with a length of 40~50 nm and a diameter of 20~30 nm, while its high zeta potential (-24.5 mV) make CNCs present a stable dispersion in aqueous media. Moreover, CNCs have a high crystallinity of 78.70% within the crystal type of cellulose Ⅰ. As such, this study may pioneer the horizon for developing a green method for the efficient preparation of CNC, and it is of great significance for CNCs practical production process.

Industrial Vision: Rectifying Millimeter-Level Edge Deviation in Industrial Internet of Things With Camera-Based Edge Device

Nowadays, to realize the intelligent manufacturing in Industrial Internet of Things (IIoT) scenarios, novel approaches in computer vision are in great demand to tackle the new challenges in IIoT environment. These approaches, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Industrial Vision</i> , are expected to offer customized solutions for intelligent manufacturing in an accurate, time efficient and robust manner. In this paper, we propose a novel approach to industrial vision, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> , to rectify the edge deviation automatically for Irradiated Cross-linked Polyethylene Foam (IXPE) production with millimeter-level accuracy. IXPE has been one of the most commonly used materials in industry. During the production process of IXPE sheets, their edges need keep aligned strictly, otherwise, they could quickly get out of the border of the rolling plate and cause the huge economic loss. We deploy a commercial camera with mobile edge node in front of the IXPE sheet to continuously detect and rectify the edge deviation. Particularly, to handle the complex production environment when extracting the edge of IXPE sheet, we deploy a pair of reference bars with high-contrast colors to efficiently differentiate the sheet edge from the background. Then, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bi-direction Edge Tracking method</i> to perform the edge detection from both vertical and horizontal aspects. To realize the rectification using mobile edge nodes with limited computing resources, we reduce the cost of computation by extracting the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Minimized Region of Interest</i> , i.e., the edge area overlapped with the higher contrast reference bar on both sides. We further design a negative feedback control system with multi-stage feedback regulation mechanism, keeping the edge deviation within <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">millimeter-level</i> . We implemented <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> on the ARM64 platform and performed evaluation in the practical IXPE production process. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Edge-Eye</i> achieves the average accuracy of 5 mm for the edge deviation rectification, with the average latency of 200 ms for edge deviation detection. During the process of 20-month real deployment for 36 production lines, 66 manpower per day (90% of the overall manpower) has been saved, and the utilization rate of IXPE material increases from 87% to 94%.

G-C3N4Photocatalysts: Utilizing Electron–Hole Pairs for Boosted Redox Capability in Water Splitting

Since the first discovery of solar-driven water splitting catalyzed by TiO2semiconductors, extensive research works have been devoted over the decades. Currently, the design of a photocatalyst with dual redox potential is of prominent interest to fully utilize both photogenerated electrons and holes in the redox reactions. Among all, the coproduction of H2and O2from water using metal-free carbon nitride (g-C3N4) has been viewed as a rising star in this field. However, the hole-mediated oxidation reaction is commonly recognized as the rate-determining step, which drastically leads to poor overall water splitting efficiency. On top of that, rapid recombination and undesirable back reaction appeared as one of the challenging parts in overall water splitting. In this review, the up-to-date advances in modified g-C3N4-based photocatalysts toward efficient overall water splitting are summarized, which are mainly classified into structural and defect engineering, single-atom catalysis, cocatalyst loading, and heterojunction construction. This review also addresses the underlying idea and concept to tackle the aforementioned problem with the use of emerging modification strategies, hence serving as the guiding star for future research. Despite the outstanding breakthrough thus far, critical recommendations related to g-C3N4photocatalytic systems are prospected to pave the way toward the implementation in the practical energy production process.

Polyvinyl alcohol formaldehyde three-dimensional composite sponges with hierarchical pore structure for W/O emulsion separation

The emulsion separation of oily wastewater is always a challenge topic in both fundamental research and practical application. In this paper, polyvinyl alcohol formaldehyde composite sponges (PVF/PVF) with hierarchical pore structure were established by physical absorption/chemical crosslinking and hydrophobic modification via introducing hydrophobic long alkyl chain. As a result, pore size was adjusted from 74.8 µm of pristine PVF to less than 20 µm of hydrophobic PVF, the water contact angle was 120° for final as-prepared sponges, i.e. size sieving and surface wettability could meet the need of emulsion separation. As-prepared sponges could separate W/O (water-in-oil) emulsion efficiently only under the action of gravity, with a maximum flux of 2.9 × 105 L m-2h−1 bar−1 and separation efficiency nearly 100 %. More importantly, the as-prepared sponges displayed excellent reusability, which could be recovered by simply washing. These composite sponges, which were obtained by a simple method and low cost, could be easily scaled up in practical production process, providing an outstanding candidate material for efficient W/O emulsion separation.