Process Parameters Research Articles

Achieving a High Thermally Conductive One Micron AlN Deposition by High Power Impulse Magnetron Sputtering plus Kick.

High-power impulse magnetron sputtering (HiPIMS) plus kick is a physical vapor deposition method that employs bipolar microsecond-scale voltage pulsing to precisely control the ion energy during sputter deposition. HiPIMS plus kick for AlN deposition is difficult since nitride deposition is challenged by low surface diffusion and high susceptibility to ion damage. In this current study, a systematic examination of the process parameters of HiPIMS plus kick was conducted. Under optimized main negative pulsing conditions, this study documented that a 25 V positive kick biasing for AlN deposition is ideal for optimizing a high quality film, as shown by X-ray diffraction and transmission electron microscopy as well as optimal thermal conductivity while increasing high speed deposition (25 nm/min) and obtaining ultrasmooth surfaces (rms roughness = 0.5 nm). HiPIMS plus kick was employed to deposit a single-texture 1 μm AlN film with a 7.4° rocking curve, indicating well oriented grains, which correlated with high thermal conductivity (121 W/m·K). The data are consistent with the optimal kick voltage enabling enhanced surface diffusion due to ion-substrate collisions without damaging the AlN grains.

Synergy between Fe-Co oxide catalysts supported over HZSM-5 for direct conversion of methane to methanol

The direct conversion of methane to methanol over iron and cobalt oxide based composite catalysts supported over HZSM-5 was carried out in a fixed-bed micro reactor under changing process parameters and catalyst compositions. The prepared catalysts were well characterized by various techniques including N2 adsorption desorption, X-ray diffraction (XRD), Fourier transformed infrared spectroscopy (FTIR), pyridine FTIR, X-ray photoelectron spectroscopy (XPS), UV–vis spectroscopy and temperature programmed reduction (H2-TPR). The characterization results revealed the presence of various reducible iron and cobalt oxide species depending on the Fe/Co molar ratio and total metal oxide loading. Moreover, the XPS and UV–vis spectroscopic results also confirmed the variation of Fe2+/Fe3+ and Co2+/Co3+ species depending on the Fe-Co molar ratio which had strong influence on the performance of the composite catalysts. It was found that the cobalt species favours the formaldehyde formation whereas the iron species promotes the formic acid formation in their respective pure supported oxide catalysts. However, the synergistic effect of iron and cobalt oxide in equimolar ratio in composite catalysts appeared as effective for achieving highest yield of methanol. The methanol selectivity was also influenced by the alteration of various studied process parameters. The higher reaction temperature favoured the overoxidation of methanol to CO2 whereas the methanol selectivity increased with increasing the methane to oxygen ratio. The lattice to adsorbed oxygen ratio also had strong influence on the activity of composite catalysts and methanol selectivity. The highest methanol yield of 1.87% was achieved at the methane conversion of 42.81 % with the 20Fe1Co1Z catalyst at 873 K reaction temperature with CH4/O2 ratio of 2:1 and WHSV of 1030 ml hr−1 gcat−1.

Manufacturing motor core lamination from thin non-oriented silicon steel sheet direct by pulsed laser cutting using multi-quality optimized process parameters

Manufacturing motor core lamination from thin non-oriented silicon steel sheet direct by pulsed laser cutting using multi-quality optimized process parameters

Model-Based input energy control for reproducible AISI 316L laser deposited tracks

In the Directed Energy Deposition (DED) process, when the mass flow of metal particles is relatively high, the thickness of the layers increases, leading to a more productive process. The higher the mass flow is, the more difficult it becomes to get a stable melt pool. The accumulation of residual heat in the previously consolidated material constitutes a thermal input affecting the balance at the laser-material interaction zone. An accurate control of the temperature of the laser-material interaction zone is critical to maintain the dynamic viscosity of the liquid metal within the narrow margin in which it can be managed in a stable way. The present work introduces a thermal model in which domains with tunable properties are considered to reproduce the growing of the manufactured sample. Concurrently, a virtual closed-loop PID regulator has been implemented in order to calculate suitable values of laser power to compensate the heat accumulated in the material, offering the results from the model as input process parameters to be directly applied in the real process. The levels of laser power proposed by the model have been experimentally applied, leading to a stable process capable of carrying out in reality the desired component.

Optimization methods in powder metallurgy for enhancing the mechanical properties: A systematic literature review

Abstract Powder metallurgy offers several advantages over traditional manufacturing methods, such as improved material utilization and waste reduction. The powder metallurgy process involves four primary steps, metal powder preparation, mixing, compaction, and sintering, followed by secondary operations such as forging, rolling, and extrusion. However, existing studies have predominantly focused on refining the primary processes, neglecting secondary processes that could further enhance mechanical properties. A systematic literature review following the Preferred Reporting Items for Systematic Review and Meta-Analysis method identified twenty-three relevant articles over a ten-year period, highlighting three main optimization methods, that are, the Taguchi method, Taguchi-based Grey Relational Analysis, and Response Surface Methodology. These methods effectively reduce experimental trials and optimize key parameters like compaction pressure and weight percentage, which significantly influence mechanical properties. The review emphasizes the key powder metallurgy process parameters that have a significant impact on mechanical properties, becoming the primary targets for optimization. It offers a focused examination of which process parameters require thorough investigation for optimization and the suitable optimization method to achieve optimal outcomes, whether for single or multiple responses, thereby enhancing the manufacturing process. However, inconsistencies in parameter significance across studies indicate the material-dependent nature of powder metallurgy materials. The review also identifies a gap in the literature regarding secondary process optimization and comparative analyses of optimization methods. Future research should explore optimizing secondary processes in powder metallurgy, conduct comparative studies of different optimization methods, and develop novel approaches for optimization to further enhance mechanical properties and process efficiency.

On Performance Enhancement of the STED Process Using Modified Tool Electrode

Abstract Shaped tube electrolytic drilling (STED) is an electrochemical machining variant that employs a tubular tool electrode to produce holes with a high aspect ratio on hard-to-machine materials. The tubular electrodes with a diameter below 1 mm produce undesired residue (center-peak) at the machining surface that hampers the machining rate. Therefore, this study attempts to improve the electrolyte flow and enhance the electrochemical dissolution through tool modification. The performance enhancement of the STED process in terms of material removal rate and average diametral overcut has been explored. The issues related to the limitations in material removal in the STED process are brought forth with the technique to resolve those difficulties. The experiments were conducted with the modified tubular tool, and the length of slits on the tool was selected based on the simulation insights and pilot study. The effects of input process parameters (applied voltage, tool feed rate, electrolyte concentration, and tool slit length) on the output responses obtained from the STED process are elaborated. Holes with diameters in the range 0.89-0.97 mm and 12 mm depth were fabricated. Further optimization of the process parameters in the design space is also presented to obtain sustainable process performance.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Crystallisation Dynamics in Large-Scale Extrusion Additive Manufacturing: An Analysis with and without Temperature Modification

Large-Scale Material Extrusion (LS-MEX) is increasingly being used in small-scale production and prototyping due to its ability to create components in new temporal and spatial dimensions. However, the use of this manufacturing process poses microscopic and macroscopic challenges not encountered in previous small-scale production systems. These challenges arise primarily from the prolonged retention of heat in the material, which leads to insufficient strength in the extruded strands at the macrostructural level. As a result, the component can collapse, a phenomenon known as ‘slumping’. Thermal energy also influences microstructural changes, such as crystallisation kinetics, which affect properties such as the strength and stiffness of the final product. The duration and dynamics of thermal energy are influenced by manufacturing parameters and the possible use of additional peripheral equipment, which affects component quality. In this study, the influence of thermal energy on structural processes through simulations of polyamide 6 with 40% carbon fibres (PA6 wt.%40 CF) is investigated. The results show that by adjusting the process parameters and using modification units, the thermal profile of the material can be accurately controlled, which allows the microstructural processes to be precisely controlled. This leads to the targeted modification of the macroscopic material properties. The focus of this work is on the combination of numerical simulations of the LS-MEX process with semi-empirical methods for the analysis of crystallisation processes. The application of the Nakamura model, which is used throughout similar investigations, allows a detailed description and prediction of the crystallisation kinetics during the manufacturing process. The study shows that the absolute degree of crystallisation can be determined with simplified assumptions using a combination of thermal simulations and semi-empirical approaches. It was found that the absolute degree of crystallisation increases from the outer interface of the strand to the print bed across the cross-section. This can be attributed to the specific thermal boundary conditions and the resulting temperature profiles at different points.

Data–Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

Study of CNC skew rolling of hollow rail axles with a mandrel

The manuscript presents a new manufacturing technology for the production of hollow railway axle forgings. The manufacturing technology analyzed is based on three-roll skew rolling (TRSR) using a computer numerically controlled (CNC) rolling mill. The study focused on comparing the rolling capability of hollow products without and with a mandrel calibrating the hole of the forging. The influence of tube billet size on the rolling process was also analyzed. FE analysis and experimental studies were carried out. An analysis of the accuracy of the internal hole of the forging depending on the adopted parameters of the rolling process was carried out. Based on numerical simulations, the state of strain and the flow pattern of the material and temperature distribution during rolling were determined. The force parameters of the rolling process of hollow forgings were also analyzed. Based on the research, a two-stage rolling technology for rolling hollow railway axle forgings was proposed. The results obtained indicate the suitability of using a calibrating mandrel to improve hole accuracy in hollow forgings rolled from a tubular billet. The gap c between the bore diameter of the forging and the diameter of the mandrel was measured. Increasing the tubular billet dimensions from Ø42.4 × 10 mm to Ø48.3 × 12.5 mm reduced the gap c by 49.8%. Rolling the billet Ø51 × 14.2 mm in two passes compared to rolling in one pass reduced the gap c by 45.5%.

An insight into viscosity and conductivity in the formulation of co-axial electrospun Carica papaya leaf extract

Objective: This research aimed to investigate the application of the coaxial electrospun method for the production of natural extracts (papaya leaf extract) fibre films. This was achieved through utilising different polymers and with a focus on the conductivity and the viscosity of polymer solutions as critical parameters to generate successful fibres.Significance: Electrospinning is a promising trending manufacturing method for incorporating thermolabile herbal extracts using coaxial electrospun features. However, the complexity of the electrospinning process and the feasibility of the product required precise scrutiny.Methods: The electrospinning solution parameters (conductivity and viscosity) were evaluated by employing various ratios of Eudragit L100 (EL100) and Eudragit L100-55 (EL100-55) pre-spinning polymeric blend solutions. The electrospinning process and ambient parameters were optimised. Following that, the in-silico physicochemical properties of phytochemical marker, rutin, were illustrated using SwissADME web tool. Both freeze-dried Carica papaya leaf extract and its produced films were characterised using Scanning Electron Microscopy (SEM), Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR), polarised light microscopy, and X-ray Powder Diffraction (XRPD).Results: The optimal values of conductivity (≈40–44 × 10−4 S/m) and viscosity (≈32–42 × 10−3 Pa·s) were determined for producing evenly distributed and small fibre diameters in SEM images. These parameters significance was highlighted in acquiring and maintaining adequate tangential stress for fibre elongation, which would consequently affect the morphology and diameter of the fibres formed.Conclusion: In conclusion, the solution, process, and ambient parameters are significant in developing natural extracts into films via electrospinning technology, and this includes the promising Carica papaya leaf extract films produced by coaxial electrospinning.

Laser-beam powder bed fusion of magnetostrictive Fe81Ga19 alloys: parameter optimization, microstructural evolution and magnetostrictive properties

Magnetostrictive Fe-Ga alloys, featuring with good machinability, high Curie temperature, and high permeability, have received increasing attention in fields such as actuators, implants, and energy harvesting. Unfortunately, bulk polycrystalline Fe-Ga alloys usually suffer poor magnetostrictive strains compromised by the randomness of grain structure and the intricate phase constitution. The current study was centered on the fabrication of bulk polycrystalline Fe81Ga19 alloys with tailored grain morphology and phase arrangement utilizing laser-beam powder bed fusion (LPBF) technology. Particular emphasis was laid on investigating the repercussions of LPBF process parameters on the microstructure and magnetostrictive performance. The findings illustrated a non-linear interplay between laser power and the relative density of laser powder bed fusion-fabricated (LPBFed) Fe81Ga19 alloys, marked by an initial augmentation followed by a subsequent decrement. Similarly, a consistent trend was observed for the LPBFed alloys at varying scan speeds. In particular, the LPBFed Fe81Ga19 alloys exhibited a highest density at optimized process parameters (laser power set at 120 W paired with a scan speed of 100 mm s−1) due to suitable laser energy input during LPBF process. It was experimentally shown that elongated columnar grains and disorder A2 phase structures were obtained within the alloys attibutes to the high temperature gradient and rapid cooling kinetics intrinsic to LPBF, contributing to a desirable magnetostrictive strain of ∼87 ppm for bulk polycrystalline Fe81Ga19 alloys. Moreover, a good dynamic magnetostrictive response of the LPBFed alloys was confirmed by the near-synchronous variations between magnetostrictive behavior and alternating magnetic fields. It can be derived from these findings that LPBF process may be a promising method to prepare bulk magnetostrictive Fe-Ga alloys for versatile applications.

An effective and comprehensive optimization strategy for preparing Ginkgo biloba leaf extract enriched in shikimic acid by macroporous resin column chromatography.

Previously reported preparation methods of Ginkgo biloba leaf extract (EGBL) have mainly focused on the enrichment of flavonoid glycosides (FG) and terpene trilactones (TT), which led to the underutilization of G. biloba leaves (GBL). To make full use of GBL, in this study, a comprehensive optimization strategy for preparing EGBL by macroporous resin column chromatography was proposed and applied to enrich FG, TT, and shikimic acid (SA) from GBL. Initially, the static adsorption and desorption were executed to select suitable resin. Then, the influences of solution pH were investigated by the static and dynamic adsorption. Subsequently, eight process parameters were systematically investigated via a definitive screening design (DSD). After verification experiments, scale-up enrichment was carried out, investigating the feasibility of the developed strategy for application on an industrial scale. It was found that XDA1 was the most appropriate adsorbent for the preparation of EGBL at solution pH 2.0. Furthermore, based on the constraints of the desired quality attributes, the optimized ranges of operating parameters were successfully acquired, and the verification experiments demonstrated the accuracy and reliability of using DSD to investigate the chromatography process for the preparation of EGBL. Finally, magnified experiments were successfully performed, obtaining the EGBL containing 26.54% FG, 8.96% TT, and 10.70% SA, which reached the SA level of EGB761, an international standard EGBL. The present study not only provided an efficient and convenient approach for the preparation of EGBL enriched in SA but also accelerated efforts to high-value utilization of GBL.

Optimizing FDM process parameters: predictive insights through taguchi, regression, and neural networks

Fused deposition modelling (FDM) is a popular additive manufacturing process used for rapid prototyping and the production of complex geometries. Despite its popularity, FDM’s susceptibility to variations in numerous process parameters can significantly impact the quality, design, functionality, and mechanical properties of 3D printed parts. This study explores thirteen FDM process parameters and their influence on the mechanical properties of polylactic acid (PLA) polymer, encompassing surface roughness, warpage, tensile and bending strength, elongation at break, deformation, and microhardness. The optimum parameters were identified alongside key contributors by applying the Taguchi method, signal-to-noise ratios, and analysis of variances (ANOVA). Notably, specific FDM parameters significantly affect the surface profile, with layer thickness contributing 32.65% and fan speed contributing 8.59% to the observed variations. Similarly, warping values show notable influence from nozzle temperature (29.53%), wall thickness (16.74%), layer thickness (16.56%), and retraction distance (12.80%). Tensile strength is primarily determined by wall thickness (31.83%), followed by infill percentage (26.73%) and infill pattern (16.18%). Elongation at break predominantly correlates with wall thickness (44.82%), with a supplementary contribution from nozzle temperature (10.90%). Microhardness lacks a dominant parameter. Bending strength variations primarily arise from layer thickness (38%), wall thickness (37.6%), and infill percentage (9.17%). Deformation tendencies are influenced by layer thickness (19.20%), print speed (11.37%), wall thickness, and fan speed (10.9% each). The optimized dataset of FDM process parameters was then employed in two prediction models: multiple-regression and artificial neural network (ANN). Evaluation based on the correlation coefficient (R2) and root mean squared error (RMSE) indicates that the ANN model outperforms the multiple-regression approach. The results indicate that precise control of FDM parameters, coupled with ANN predictions, facilitates the fabrication of 3D printed parts with the desired mechanical characteristics.

A comprehensive review of parametric optimization of electrical discharge machining processes using multi-criteria decision-making techniques

Optimization of electrical discharge machining (EDM) processes is a critical issue due to complex material removal mechanism, presence of multiple input parameters and responses (outputs) and interactions among them and varying interest of different stakeholders with respect to relative importance assigned to the considered responses. Multi-criteria decision making (MCDM) techniques have become potent tools in solving parametric optimization problems of the EDM processes. In this paper, more than 130 research articles from SCOPUS database published during 2013–22 are reviewed extracting information with respect to experimental design plans employed, materials machined, dielectrics used, process parameters and responses considered and MCDM tools applied along with their integration with other mathematical techniques. A detailed analysis of those reviewed articles reveals that the past researchers have mostly preferred Taguchi’s L9 orthogonal array as the experimental design plan; EDM oil as the dielectric fluid; medium and high carbon steels as the work materials; peak current and pulse-on time as the input parameters; material removal rate, tool wear rate and surface roughness as the responses; and grey relational analysis as the MCDM tool during conducting and optimizing EDM operations. This review paper would act as a data repository to the future researchers in understanding the stochastic behaviour of EDM processes and providing guidance in setting the tentative operating levels of varying input parameters along with achievable response values. The extracted dataset can be treated as an input to any of the machine learning algorithms for subsequent development of appropriate prediction models. This review also outlines potential future research avenues, emphasizing advancements in EDM technology and the integration of innovative multi-criteria decision-making tools.

Comparative numerical analysis between butt and lap joints of Mg alloy friction stir welding with considering tilted tool effect

Magnesium (Mg) alloy, as an important lightweight structural metal, can be effectively joined by the friction stir welding (FSW) method. Understanding FSW temperature and material flow coupling process in butt and lap welding configurations has important theoretical significance. In this study, the tilted tool induced incomplete contact distance is calculated by process parameters and slip rate, and the deflection of the contact interface is related to the tool torque. The non-uniform boundary conditions are considered. The numerical model is proposed and experimentally validated by using the measured temperatures and thermo-mechanically affected zone (TMAZ) profiles. The comparative numerical analysis between butt and lap welding with three levels of process parameters is further conducted. The peak temperature and material flow velocity along the specified circumferences in FSBW are more than 10 K and 4 mm/s higher than these in FSLW, respectively. When the rotation rate increases from 600 rpm to 800 rpm, the peak temperature and material flow velocity along the specified circumferences increase about 20 K and 9 mm/s, respectively. When the welding speed decreases from 150 mm/min to 30 mm/min, the temperature and material flow velocity fluctuations along the specified circumference decreases about 25 K and 14 mm/s, respectively.

Effect and mechanism of solution flow rate during interfacial polymerization on morphology and performance of hollow fiber membranes

Interfacial polymerization (IP) using m-phenylenediamine and trimesoyl chloride (TMC) is an essential method for producing thin-film composite (TFC) membranes. Nevertheless, IP is highly sensitive to changes in process parameters, especially the TMC solutions flow rate during inside-out TFC hollow fibers (HF) production. Research on the impact of flow conditions (i.e., laminar vs. turbulent flows) of these organic solutions on the final membrane characteristics and corresponding mechanism is still lacking. This research tested different TMC flow rates (0.1–2 mL/min) to produce inside-out TFC HF. With a TMC flow rate increase, (i) the leaf-like structures of the polyamide (PA) layer decreased in size, (ii) the thickness of the PA layer slightly increased, and (iii) the pure water permeability decreased. However, the elemental composition, chemical bonds, and NaCl rejection remained stable. Different fluid regimes are considered to affect the performance of HF membranes by affecting the heat dissipation process during IP reaction. A TMC flow rate in the laminar flow state (i.e., low flow rates) is recommended for synthesizing HFs. The current research provides new insights into the IP reaction mechanism of HF membrane, thus offering valuable guidance to optimize and produce membrane products with better performance.

The influence of laser texturing on the tribological behavior of titanium alloy Ti6Al4V in medical applications

This paper analyzes the tribological behavior of the Ti6Al4V ELI alloy subjected to laser texturization for medical purposes. Laser texturing enables one to observe specific patterns of the material surface at established depths. Microtexturing of the samples was performed using a 355 nm picosecond laser. The influence of the microtexturing process (depending on the process parameters) on the geometric parameters of the proposed laser texturing pattern was evaluated. Selected samples were subjected to tribological testing using the ball-on-plate technique in dry and lubricant-sliding methods (in Ringer solution). The wear properties were evaluated by comparing the coefficient friction, wear volumes, and wear ratio. A scanning electron microscope characterized the morphologies of the wear scar and the wear mechanism. The experimental results show that the surface texturing and the changes in microgrooves can reduce wear. The results indicate, that samples after laser texturing were characterized by 15% higher microhardness, compared to those in the initial state. It was found, a 26% reduction in friction coefficient and 29% in the wear volume compared to the smooth, untextured surface samples under lubricated conditions. The decrease in value of the coefficient friction and wear volume for the samples after the laser texturing process is an effect of synergistic of entrapped wear debris in micro-grooves and increased hardness for samples after laser textured.

Optimization of Ammonia Nitrogen Removal and Recovery from Raw Liquid Dairy Manure Using Vacuum Thermal Stripping and Acid Absorption Process: A Modeling Approach Using Response Surface Methodology

Dairy manure adds a substantial amount of nitrogen to wastewater due to its high levels of associated nutrients. Removal and recovery of ammonia nitrogen (NH3-N) from raw liquid dairy manure (RLDM) is greatly valued. This study was focused on the vacuum thermal stripping–acid absorption (VTS-AA) process for NH3-N from RLDM, followed by modeling and optimization. Using the response surface methodology (RSM)-based central composite design (CCD) approach, the critical operational parameters of the vacuum thermal stripping process, including temperature (50–70 °C), pH (9–11), vacuum pressure (35–55 kPa), and treatment time (60–90 min), were optimized. With the specified parameters set at temperature 69.9 °C, pH 10.5, vacuum pressure 53.5 kPa, and treatment time 64.2 min, the NH3-N removal efficiency attained was 98.58 ± 1.05%, aligning closely with the model prediction. Furthermore, the recovered ammonium sulfate ((NH4)2SO4) closely matched their commercial counterparts, confirming the effectiveness of the VTS-AA process in recovering NH3-N from RLDM. The distinct advantage of the employed technology lies in the concurrent energy demand reduction achieved by introducing a vacuum system. These findings contribute valuable insights into the practical implementation of the VTS-AA process for treating raw dairy manure, particularly in large-scale operational contexts.

Study on thermal runaway propagation inhibition of battery module by flame-retardant phase change material combined with aerogel felt

Phase change materials (PCMs) are susceptible to fire and may accelerate heat transfer when thermal runaway propagation (TRP) in lithium-ion battery (LIB) modules, requiring the design and safe use of insulation structures with excellent flame-retardant properties. In this work, the sandwich structure composed of flame-retardant phase change material (FRPCM) combined with aerogel felt (AEGF) is used to study the inhibitory effect on TRP of pouch battery modules (BMs). The structural materials are applied between cells, and the changes of temperature, voltage, mass and other parameters in the TRP process are analyzed by changing the thickness of FRPCMs and AEGFs, and the changes of heat flux of materials were also qualitatively analyzed. The results show that the increase of FRPCM and AEGF thickness can increase the interval time of TRP and reduce the overall thermal risk of the BMs. Compared to Test-1, the increase in TRP time between cells 1 and 2 in Test-2 to Test-5 ranged from 52.7% to 358.7%, and the longest thermal runaway interval reached 844 s. Both the structural materials in Test-6 and Test-7 block the TRP. Combining the thermal conductivity of PCMs and the thermal insulation properties of AEGFs, this work can effectively conduct heat to the environment and block the TRP when applied to BMs, which provides a valuable reference for efficient inhibition and blocking of TRP.