An Experimental and Computational Approach to Development of Mixing Processes for Miscible Liquids in Unbaffled Tanks.

PURPOSE: Mixing of liquids is a critical unit operation in the biopharmaceutical drug product manufacturing. It commonly consists of mixing miscible liquids to dilute bulk drug substance (DS) or pool multiple lots of drug substance. In the past, at-scale mixing studies have been conducted to determine the mixing parameters, namely mixing speed, and mixing time. At-scale studies have historically been utilized to overcome the challenges associated with geometric dissimilarity of mixing systems found when scaling up. In addition, such studies are quite costly, as they often use actual DS to overcome a lack of representativeness associated with simple salt trace models often employed. As a result, there is a significant need for alternative cost-effective methods that can predict mixing parameters with close agreement to actual experiments and operations. METHOD: At-scale mixing experiments were conducted using full-sized tanks and surrogate solutions. Several computational fluid dynamic (CFD) simulation methods were conducted and compared with the experiments to determine the most reliable computational techniques. RESULTS: The experiments demonstrate that surrogate solutions can be used reliably to determine mixing parameters in at-scale studies instead of the valuable drug products. Studying different CFD methods also showed that transient simulations that use a large eddy simulation (LES) viscous model and a sliding mesh can correctly predict the mixing parameters. CONCLUSION: Results of this study establish a practical and reliable methodology to perform mixing studies for miscible liquids with different kinematic viscosities. The methods discussed herein greatly reduce the routine mixing study costs in the biopharmaceutical industry and increase efficiency and accuracy of the results, allowing proactive scale-up of mixing operations.

An Ultrasound Tomography Method for Monitoring CO2 Capture Process Involving Stirring and CaCO3 Precipitation.

In this work, an ultrasound computed tomography (USCT) system was employed to investigate the fast-kinetic reactive crystallization process of calcium carbonate. USCT measurements and reconstruction provided key insights into the bulk particle distribution inside the stirred tank reactor and could be used to estimate the settling rate and settling time of the particles. To establish the utility of the USCT system for dynamical crystallization processes, first, the experimental imaging tasks were carried out with the stirred solid beads, as well as the feeding and stirring of the CaCO3 crystals. The feeding region, the mixing process, and the particles settling time could be detected from USCT data. Reactive crystallization experiments for CO2 capture were then conducted. Moreover, there was further potential for quantitative characterization of the suspension density in this process. USCT-based reconstructions were investigated for several experimental scenarios and operating conditions. This study demonstrates a real-time monitoring and fault detection application of USCT for reactive crystallization processes. As a robust noninvasive and nonintrusive tool, real-time signal analysis and reconstruction can be beneficial in the development of monitoring and control systems with real-world applications for crystallization processes. A diverse range of experimental studies shown here demonstrate the versatility of the USCT system in process application, hoping to unlock the commercial and industrial utility of the USCT devices.

Streamlining of the Powder Mixing Process based on a Segregation Test.

In direct compression of tablets, it is crucial to maintain content uniformity within acceptable margins, especially in formulations with low drug loading. To assure it, complex and multistep mixing processes are utilized in the industry. In this study, we suggest the use of a simple segregation test to evaluate mixing process performance and mixture segregation to produce tablets having satisfying content uniformity while keeping the process as simple and low cost as possible. Eventually, the formulation propensity to segregation can be evaluated using process analytical technology (PAT) to adjust the mixing process parameters to changing source drug properties. In this study, that approach was examined on a model drug with a broad batch-to-batch variability in particle size and shape. Excipients were chosen so that the resulting blend composition mimicked some marketed formulations. For each drug batch, two formulation blends were prepared through different preparation processes (one simple and one complex) and subsequently subjected to segregation tests. From those, segregation coefficients were obtained to compare segregation tendencies and homogeneity robustness between the drug batches and the blend preparation methods. The inter-particulate interactions were substantially influenced by the drug particle morphology and size and resulted in different segregation behavior. Based on these findings, a simple segregation test proved to be a useful tool for determining the suitability of different batches of the model drug to be used in a certain formulation. Moreover, for a particular batch A, the test revealed a potential for mixing process simplification and therefore process intensification and cost reduction.

[Research on feedforward control method of mixing process for compound Danshen Dripping Pills based on quality by design(QbD)].

The mixing process is one of the key operation units for solid preparation of traditional Chinese medicine. The physical properties such as particle size, density and viscosity of the mixture are key factors that need to be controlled, which will directly affect the performance of the preparation molding process and product quality. Subsequent dripping process performance and appearance qua-lity of dripping pills will be affected by dynamic viscosity of materials in the mixing process. Based on this, with mixing process of compound Danshen dripping pills as the object, a feedforward control method for the dripping pill mixing process was established based on the concept of quality by design(QbD). Firstly, critical quality attribute(CQA)-dynamic viscosity, critical material attributes(CMAs)-the moisture content of compound Danshen extract, average molecular weight of polyethylene glycol 6000 and critical process parameter(CPP)-mixing temperature were identified through the analysis of properties for multiple batches of the raw materials and excipients as well as technological mechanism. Then the Box-Behnken experimental design was used to establish the regression model among CMA, CPP and CMA(R■=0.972 0, RMSE =16.24) to obtain the design space. Finally, through the verification of three batches within the design space, the mixing process temperature was adjusted according to the properties of the raw materials and exci-pients to achieve accurate control of the dynamic viscosity attribute. The relative deviation between the actual dynamic viscosity value and the target value was less than 3.0 %. The feedforward control of the mixing process of compound Danshen dripping pills was rea-lized in this study, which can contribute to improving quality consistency of the mixing process intermediates, simultaneously provide a reference for the research on the process quality control of other Chinese medicine dripping pills.

Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes.

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.

Advanced CFD modelling of air and recycled flue gas staging in a waste wood-fired grate boiler for higher combustion efficiency and greater environmental benefits.

Grate-fired boilers are commonly used to burn biomass/wastes for heat and power production. In spite of the recent breakthrough in integration of advanced secondary air systems in grate boilers, grate-firing technology needs to be advanced for higher efficiency and lower emissions. In this paper, innovative staging of combustion air and recycled flue gas in a 13 MWth waste wood-fired grate boiler is comprehensively studied based on a numerical model that has been previously validated. In particular, the effects of the jet momentum, position and orientation of the combustion air and recycled flue gas streams on in-furnace mixing, combustion and pollutant emissions from the boiler are examined. It is found that the optimized air and recycled flue gas jets remarkably enhance mixing and heat transfer, result in a more uniform temperature and velocity distribution, extend the residence time of the combustibles in the hot zone and improve burnout in the boiler. Optimizing the air and recycled flue gas jet configuration can reduce carbon monoxide emission from the boiler by up to 86%, from the current 41.0 ppm to 5.7 ppm. The findings of this study can serve as useful guidelines for novel design and optimization of the combustion air supply and flue gas recycling for grate boilers of this type.

Development of pea protein-based bioplastics with antimicrobial properties.

BACKGROUND: In the present work, bioplastics from renewable polymers were studied in order to reduce the huge generation of plastic wastes, causing an environmental problem that continues owing to the increasing demand for plastic products. RESULTS: Bioplastics with much better antimicrobial properties, in particular against Gram-positive bacteria, were obtained with the addition of nisin to the initial protein/plasticizer mixture. However, the addition of nisin produces more rigid but less deformable bioplastics (higher Young's modulus but lower strain at break). CONCLUSION: The results obtained are useful to demonstrate the antimicrobial properties of pea protein-based bioplastics by adding nisin and make them suitable as potential candidates to replace conventional plastics in food packaging. © 2016 Society of Chemical Industry.

Hydrodynamics and dissolved organic matter components shaped the fate of dissolved heavy metals in an intensely anthropogenically disturbed estuary.

Anthropogenic activities and natural erosion caused abundant influx of heavy metals (HMs) and organic matter (OM) into estuaries characterized by the dynamic environments governed by tidal action and river flow. Similarities and differences in the fate of HM and OM as well as the influences of OM on HMs remain incomplete in estuaries with seasonal human activity and hydrodynamic force. To address this gap, dissolved HMs (dHMs) and fluorescence dissolved OM (FDOM) were investigated in the Pearl River Estuary, a highly seasonally anthropogenic and dynamic estuary. It aimed to elucidate the effects of hydrodynamic conditions and DOM on the seasonal fate of dHMs via the multivariate statistical methods. Our findings indicated dHMs and FDOM exhibited consistently higher levels in the upper estuarine and coastal waters in both seasons, predominantly controlled by the terrestrial/anthropogenic discharge. In the wet season, dHMs and humic-like substances (HULIS) were positively correlated, showing that dHMs readily combined with HULIS. This association led to a synchronous decrease offshore along the axis of the estuary and the transport following the river plume in the surface affected by the salt wedge. Contrarily, dHMs were prone to complex with protein-like components impacted by the hydrodynamics during the dry season. Principal component analysis (PCA) results revealed the terrestrial/anthropogenic inputs and the fresh-seawater mixing process were the most crucial factors responsible for the fate of dHM in wet and dry seasons, respectively, with DOM identified as a secondary but significant influencing factor in both seasons. This study holds significance in providing valuable insights into the migration, transformation, the ultimate fate of dHMs in anthropogenically influenced estuaries, as well as the intricate dynamics governing coastal ecosystems.

Influence of NR/MWCNT Blending on Rotor Metal Friction and Wear during Mixing Process.

Mixing involves blending raw rubber or masticated rubber with additives using a rubber mixer, which is the most critical process in rubber production. The internal mixer, as the most important mixing equipment, experiences rotor wear during prolonged operation, affecting the gap between the mixer rotor and the chamber wall. This wear reduces mixing effectiveness, weakens filler dispersion, and ultimately impacts rubber performance. In recent years, as research on multi-walled carbon nanotubes (MWCNTs) and nanomaterials has deepened, their broad application prospects have become increasingly apparent. The objective of the present study is to understand and quantify rotor wear in rubber blends during the mixing process as influenced by multi-walled carbon nanotubes. This study found that with the increase in MWCNT content, the proportion of abrasive wear rises, while the proportion of corrosive wear decreases, leading to reduced overall wear. Compared to rubber without MWCNTs, the Payne effect decreased by 6.78%, 9.57%, 13.03%, 20.48%, and 26.06% with the addition of 1 phr, 3 phr, 5 phr, 7 phr, and 9 phr of MWCNTs, respectively. The friction coefficients between the rubber and metal increased by 6.31%, 8.57%, 25.43%, 39.31%, and 47.61%, while the metal wear rate decreased by 9.08%, 10.73%, 13.41%, 17.46%, and 25%. Conversely, the friction coefficients were reduced by 19.39%, 22.42%, 33.94%, 66.06%, and 76.36%.

The Mechanical Performance and Reaction Mechanism of Slag-Based Organic-Inorganic Composite Geopolymers.

A series of organic-inorganic composite geopolymer paste samples were prepared with slag-based geopolymer and three types of hydrophilic organic polymers, i.e., PVA, PAA, and CPAM, by ordinary molding and pressure-mixing processes. The reaction mechanism between slag-based geopolymer and organic polymers was studied by FT-IR, NMR, and SEM techniques. The experimental results showed that the slag-based geopolymer with the addition of 3% PVA presented the highest 28-day flexural strength of 19.0 MPa by means of a pressure-mixing process and drying curing conditions (80 °C, 24 h) compared with the geopolymers incorporating PAA and CPAM. A more homogeneous dispersion morphology was also observed by BSE and SEM for the 3% PVA-incorporated slag-based geopolymer. The FT-IR testing results confirmed the formation of a C-O-Si (Al) bond between PVA and the slag-based geopolymer. The deconvolution of the Q3 and Q2(1Al) species obtained by 29Si NMR testing manifested the addition of PVA and increased the length of the silicon backbone chain in the geopolymer. These findings confirmed that a composite geopolymer with high toughness can be produced based on the interpenetrating network structure formed between organic polymers and inorganic geopolymer.

Experimental and Numerical Study of Computer Vision-Based Real-Time Monitoring of Polymeric Particle Mixing Process in Rotary Drum.

In the drum mixing of particulate polymers, segregation may occur. By measuring the mixing status in real time, it is possible to implement corrective measures to prevent separation and improve the efficiency of the process. This study aims to develop and validate a real-time vision system designed to monitor the mixing process of polymeric particles in a rotary drum mixer, employing a novel centroid-based model for determining the mixing index. The proposed centroid-based model is capable of addressing the radial particle segregation issue without the need for extra image-processing procedures like image subdivision or pixel randomization. This innovative approach greatly improves computational efficiency by processing over 68 image frames per second. The new processing method is 2.8 times faster than the gray-level co-occurrence matrix method and 21.6 times faster than the Lacey index approach. This significantly improves real-time monitoring capabilities and enables real-time image processing using only affordable single-board computers and webcams. The proposed vision-based system for monitoring rotary drum mixing has undergone validation via cross-validation using discrete element method simulations, ensuring its accuracy and reliability.

The distinct phases of fresh-seawater mixing intricately regulate the nitrogen transformation processes in a high run-off estuary: Insight from multi-isotopes and microbial function analysis.

Excessive anthropogenic nitrogen inputs lead to the accumulation of nitrogen, and significantly impact the nitrogen transformation processes in estuaries. However, the governing of nitrogen during its transport from terrestrial to estuary under the influence of diverse human activities and hydrodynamic environments, particularly in the fresh-seawater mixing zone, remains insufficient researched and lack of basis. To address this gap, we employed multi-isotopes, including δ15N-NO3-, δ18O-NO3-, δ15N-NH4+, and δ15N-PN, as well as microbial function analysis, to investigate the nitrogen transformation processes in the Pearl River Estuary (PRE), a highly anthropogenic and terrestrial estuary. Principle component analysis (PCA) confirmed that the PRE could clearly partitioned into three zone, e.g., terrestrial area (T zone), mixing area (M zone) and seawater area (S zone), in terms of nitrogen transportation and transformation processes. The δ15N-NO3- (3.38±0.60‰) and δ18O-NO3- (6.35±2.45‰) results in the inner estuary (T area) indicate that NO3-attributed to the domestic sewage and groundwater discharge in the river outlets lead to a higher nitrification rate in the outlets of the Pearl River than in the reaching and seawater intrusion areas, although nitrate is rapidly diluted by seawater after entering the estuary. The transformation of nitrogen in the T zone was under significant nitrogen fixation (0.61 ± 0.22 %) and nitrification processes (0.0043 ± 0.0032 %) (presumably driven by Exiguobacterium sp. (14.1 %) and Cyanobium_PCC-6307 (8.1 %)). In contrast, relatively low δ15N-NO3- (6.83 ± 1.24‰) and high δ18O-NO3- (22.13±6.01‰) imply that atmospheric deposition has increased its contribution to seawater nitrate and denitrification (0.53±0.13 %) was enhanced by phytoplankton/bacterial (such as Psychrobacter sp. and Rhodococcus) in the S zone. The assimilation of NH4 results from the ammonification of NO3- reduces δ15N-NH4+ (5.36 ± 1.49‰) and is then absorbed by particulate nitrogen (PN). The retention of nitrogen when fresh-seawater mixing enhances the elevation of δ15N-NH4+ (8.19 ± 2.19‰) and assimilation of NH4+, leading to an increase in PN and δ15N-PN (6.91 ± 1.52‰) from biological biomass (mainly Psychrobacter sp. and Rhodococcus). The results of this research demonstrate a clear and comprehensive characterization of the nitrogen transformation process in an anthropogenic dominated estuary, highlighting its importance for regulating the nitrogen dissipation in the fresh-seawater mixing process in estuarine ecosystems.

A concise summary of powder processing methodologies for flow enhancement.

The knowledge of powder properties has been highlighted since the 19th century since most formulations focus on solid dosage forms, and powder flow is essential for various manufacturing operations. A poor powder flow may generate problems in the manufacturing processes and cause the plant's malfunction. Hence these problems should be studied and rectified beforehand by various powder flow techniques to improve and enhance powder flowability. The powder's physical properties can be determined using compendial and non-compendial methods. The non-compendial practices generally describe the powder response under the stress and shear experienced during their processing. The primary interest of the current report is to summarize the flow problems and enlist the techniques to eliminate the issues associated with the powder's flow properties, thereby increasing plant output and minimizing the production process inconvenience with excellent efficiency. In this review, we discuss powder flow and its measurement techniques and mainly focus on various approaches to improve the cohesive powder flow property.

Effect of Adding Pyrolysis Carbon Black (CBp) on Soft Friction and Metal Wear during Mixing.

In the cracking process of waste tires, pyrolysis carbon black (CBp), as a solid product, accounts for about 35% of the total tire rubber content. Here, the treated CBp has been gradually applied to the tire formula to improve the recycling efficiency of waste tires. This study elucidated the influence of adding CBp during the tire mixing process on soft friction and metal wear. Compared with industrial carbon black (I-CB), the friction coefficient of CBp was smaller at different mixing stages, and the ripple caused by adhesion friction was not evident. After the modified CBp (M-CBp) was obtained by implementing the surface activation of common CBp (C-CBp), the friction coefficient between M-CBp and metal increased by 10%, while the filler dispersion and comprehensive mechanical properties showed an upward trend. The wear rate of metal was higher than that observed after adding I-CB during the same mixing mode; thus, it was necessary to strengthen the wear resistance of the inner-wall surface of the mixing chamber. The -OH group on the M-CBp surface can also participate in the silane coupling reaction and aggravate the metal wear of the mixer chamber wall. Through a comparison of results, the mixing friction coefficient can reflect the strength of filler-rubber interaction, which in turn can preliminarily represent the dispersion effect and comprehensive properties, reveal the reason behind the poor performance of CBp, and highlight the need for modification from the perspective of tribology.

Mechanical properties of bio-cementation materials in pre-precipitation mixing process.

Urease-producing bacteria (UPB) could be used to cement loose sand particles. The UPB would produce free ammonia and carbon dioxide during the process of hydrolyzing urea, and part of the free ammonia would be discharged into the air to cause certain pollution to the atmospheric environment. The carbon dioxide could react with alkaline oxide to form carbonates and improved the strength in GGBS comparing with medium containing different concentrations of urea. By adding hydrogen phosphate ions and magnesium salts, free ammonia could be converted into environmentally friendly magnesium ammonium phosphate. The mixture of biological magnesium ammonium phosphate and basic magnesium carbonate could be synthesized through the bio-mineralization process. Through the pre-precipitation mixing process, the loose sand particles could be cemented into a whole. Scanning electron microscopy (SEM) images of the sand column showed that the mixture of biological magnesium ammonium phosphate and basic magnesium carbonate could better fill in the pores of sand grains. In the pre-precipitation mixing process, the optimal standing time and dosage of the bio-cement slurry prepared by the bio-mineralization method were 6 h and 30%, respectively. The average interface bonding force between CJ2 and glass slide was 2.12 N.

Deep Semi-Supervised Just-in-Time Learning Based Soft Sensor for Mooney Viscosity Estimation in Industrial Rubber Mixing Process.

Soft sensor technology has become an effective tool to enable real-time estimations of key quality variables in industrial rubber-mixing processes, which facilitates efficient monitoring and a control of rubber manufacturing. However, it remains a challenging issue to develop high-performance soft sensors due to improper feature selection/extraction and insufficiency of labeled data. Thus, a deep semi-supervised just-in-time learning-based Gaussian process regression (DSSJITGPR) is developed for Mooney viscosity estimation. It integrates just-in-time learning, semi-supervised learning, and deep learning into a unified modeling framework. In the offline stage, the latent feature information behind the historical process data is extracted through a stacked autoencoder. Then, an evolutionary pseudo-labeling estimation approach is applied to extend the labeled modeling database, where high-confidence pseudo-labeled data are obtained by solving an explicit pseudo-labeling optimization problem. In the online stage, when the query sample arrives, a semi-supervised JITGPR model is built from the enlarged modeling database to achieve Mooney viscosity estimation. Compared with traditional Mooney-viscosity soft sensor methods, DSSJITGPR shows significant advantages in extracting latent features and handling label scarcity, thus delivering superior prediction performance. The effectiveness and superiority of DSSJITGPR has been verified through the Mooney viscosity prediction results from an industrial rubber-mixing process.

An Experimental Study on the Dielectric Properties of Rubber Materials.

According to specific formulas, the mixing of rubber samples occurs by two methods: open mixing and internal mixing. The effects of frequency, mixing process, carbon black (CB) content, zinc oxide (ZnO) content, and stearic acid (SA) content on the dielectric properties of rubber materials were studied. The results showed that the effects of the mixing process on the dielectric properties of the rubber samples cannot be ignored, and the appropriate mixing process should be selected when preparing the required rubber materials. The dielectric constant and loss factor of the rubber samples vary depending on the frequency. The dielectric constant had a peak and valley value, while the loss factor only had a peak. The dielectric constant and loss factor of rubber samples were significantly affected by the content of CB, ZnO, and SA. The peak frequency decreased with the increase in CB content, however, the dielectric constant increased with an increase in CB content. The higher the ZnO content, the lower the peak frequency. In addition, the dielectric constant and loss factor increased with an increase in ZnO content. The higher the SA content, the greater the peak frequency. In addition, the dielectric constant and loss factor decreased with an increase in SA content. It is hoped that the experimental results obtained can provide guidance for the study of the dielectric properties, microwave absorption properties, and microwave heating characteristics of rubber polymers.

Mineral Addition and Mixing Methods Effect on Recycled Aggregate Concrete.

This paper presents influence of treatment and mixing methods on recycled aggregate concretes (RAC) designed regarding various techniques. Absolute Volume Method (AVM) according to TS 802, Equivalent Mortar Volume Method (EMV), silica fume (SF) as a mineral addition were considered in the design of concretes. In total, four groups of concretes were produced in the laboratory: (1) natural aggregate concrete (NAC) designed with AVM as control concrete, (2) RAC designed with AVM as control RAC, (3) RAC with SF as a mineral addition designed with AVM as treated RAC and (4) RAC designed with EMV as treated RAC. The tests were performed at 28th days and the statistical analysis were made on the test results. According to the results, EMV and SF increased the compressive strength of concretes and this resulted an increase in the strength class of concrete. A significant statistical difference between the concretes were determined. According to multiple comparison analysis, it was found that especially there was a significant relationship among NAC, RAC and RAC-EMV. In addition, it was recommended that EMV and AVM with 5% SF could be used in the design of RAC rather than AVM only to achieve the target strength class C30/37.

Virtual Experiments of Particle Mixing Process with the SPH-DEM Model.

Particle mixing process is critical for the design and quality control of concrete and composite production. This paper develops an algorithm to simulate the high-shear mixing process of a granular flow containing a high proportion of solid particles mixed in a liquid. DEM is employed to simulate solid particle interactions; whereas SPH is implemented to simulate the liquid particles. The two-way coupling force between SPH and DEM particles is used to evaluate the solid-liquid interaction of a multi-phase flow. Using Darcy's Law, this paper evaluates the coupling force as a function of local mixture porosity. After the model is verified by two benchmark case studies, i.e., a solid particle moving in a liquid and fluid flowing through a porous medium, this method is applied to a high shear mixing problem of two types of solid particles mixed in a viscous liquid by a four-bladed mixer. A homogeneity metric is introduced to characterize the mixing quality of the particulate mixture. The virtual experiments with the present algorithm show that adding more liquid or increasing liquid viscosity slows down the mixing process for a high solid load mix. Although the solid particles can be mixed well eventually, the liquid distribution is not homogeneous, especially when the viscosity of liquid is low. The present SPH-DEM model is versatile and suitable for virtual experiments of particle mixing process with different blades, solid particle densities and sizes, and liquid binders, and thus can expedite the design and development of concrete materials and particulate composites.

Visualization of mass transfer in mixing processes in high solid anaerobic digestion using Laser Induced Fluorescence (LIF) technique.

High solid anaerobic digestion (HSAD) is a promising technology for the treatment of organic waste. Mixing process in HSAD is quite difficult with long mixing time, poor homogenization, significant short-circuiting and stagnant zones. However, the mass transfer in mixing process in HSAD has not been visualized due to the lack of a proper method. In this study, a novel approach for experimentally quantifying the mass transfer in HSAD's mixing process was proposed in a mixing tank equipped with multistage impellers by means of the Laser Induced Fluorescence (LIF) technique. Flow field was investigated for better illustrating the mass transfer, thus Particle Image Velocimetry (PIV) and Computational Fluid Dynamics (CFD) technique were conducted for flow field measurement. The obtained results revealed that the feedstock tended to accumulate around the impeller in the HSAD system, especially near the 1st stage. The tracer diffused rapidly around the 1st impeller in t = 10 s, but the diffusion around the 2nd impeller was quite tough even after 4 h 08 min 23 s. And prolonging mixing time could not significantly improve the flow pattern along with the mixing. In this study, new insight was thrown into the visualization of the mass transfer in mixing process within a HSAD reactor. The visualization of mass transfer in the mixing process in HSAD could offer reference information to the study of the mixing process of HSAD even in full-scale.