Ultrasonic Energy Transfer Optimization in Sludge Dewatering: Volume-Dependent Tuning of Ultrasonic Duration and Intensity for Enhanced Cavitation Efficiency

Ultrasonic Energy Transfer Optimization in Sludge Dewatering: Volume-Dependent Tuning of Ultrasonic Duration and Intensity for Enhanced Cavitation Efficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ultrasonic Energy Transfer Optimization in Sludge Dewatering: Volume-Dependent Tuning of Ultrasonic Duration and Intensity for Enhanced Cavitation Efficiency Jianhao Chen, Yongzheng Qi, Junhao Zhang, Qingquan Bian, Silin Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8666028/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract High water content in sludge has consistently posed a significant challenge in sludge treatment processes. As an effective approach for sludge dewatering, ultrasound treatment induces cavitation effects, mechanical vibration, and thermal effects to rapidly disrupt sludge floc structures and release bound water, with sonication energy density and ultrasonic duration serving as key control parameters that ultimately determine dewatering efficiency. This study identified the optimal sonication energy densities and ultrasonic durations for sludge volumes of 500ml, 750ml, 1000ml, 1250ml, and 1500ml as 0.161W/ml, 0.165W/ml, 0.171W/ml, 0.177W/ml, 0.180W/ml and 31s, 33s, 34s, 37s, 41s, respectively. Comprehensive analyses including WC, SRF, viscosity, particle size distribution, SEM, FTIR, and NMR were conducted on sludge samples of different volumes under these optimal ultrasonic conditions. The results demonstrated that while ultrasound treatment significantly reduced WC compared to raw sludge across all volumes, the degree of reduction varied with sludge volume. As sludge volume increased, the required ultrasonic intensity and duration increased accordingly, with the improvement in water reduction showing an initial enhancement followed by a gradual decline. The study established optimal sonication energy densities and durations for different sludge volumes, investigated the variations in WC and specific resistance to filtration under optimal ultrasonic conditions, examined the relationship between sonication energy density, ultrasonic duration, and sludge volume, and provided mechanistic insights through microscopic and spectroscopic analyses. These findings offer valuable guidance for industrial-scale sludge treatment by identifying appropriate ultrasonic conditions to enhance sludge reduction efficiency for large-volume applications. Ultrasonic duration Sonication energy density FTIR SEM NMR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1.Introduction Sludge treatment represents a critical stage in wastewater processing, with its efficiency directly impacting environmental sustainability and resource recovery potential. In recent years, ultrasonic technology has gained widespread application in sludge pretreatment due to its high efficiency and environmental friendliness. This technology effectively disrupts sludge floc structures through cavitation effects, facilitating organic matter release and dewaterability enhancement [1–4] . However, the treatment efficacy is highly dependent on operational parameters, particularly ultrasonic duration and sonication energy density [5–7] , which collectively determine both sludge volume reduction efficiency and WC levels. Ultrasonic duration serves as a critical parameter regulating sludge disintegration efficiency and directly influences dewatering performance. The study conducted by Barati Rashvanlou [8] demonstrated that under ultrasonic treatment conditions with an energy input of 700 kJ/kg TS and a duration of 1minute, significant release of EPS-bound water was achieved along with enhanced sludge porosity, resulting in optimal sludge dewatering performance.Mao H [9] reported that 2 min of 30 kHz ultrasonic pretreatment reduced sludge WC from 93.03% to 86.95% after centrifugal dewatering, effectively halving the dewatered sludge cake volume. HUAN [10] found that short ultrasonic treatment durations yielded limited effects, with DD COD increasing by less than 4%, while prolonged sonication time significantly improved sludge dewaterability.Liyan Liu [11] demonstrated that ultrasound treatment at 45 W power for 45 minutes could effectively activate persulfate oxidation to enhance the dewaterability of drilling sludge.Wei Zhao [12] found that maximal cell lysis efficiency was achieved under the optimal conditions of 2.0 W/ml sonication energy density, 40 min ultrasonic duration, and pH 7.0, which simultaneously reduced sludge WC and enhanced sludge stabilization.Notably, optimal ultrasonic duration promotes the release of bound water from cellular structures, whereas excessive treatment causes cell wall rupture [13–18] .This leads to intracellular substance release and extracellular polymer redistribution, triggering sludge particle reaggregation and viscosity increase, ultimately impairing dewaterability. The studies collectively suggest a nonlinear relationship between ultrasonic duration and dewatering performance, where benefits peak before diminishing with overtreatment. The ultrasonic duration determines the cumulative energy effect, while the sonication energy density directly governs cavitation intensity. Yihua Zhao [19] demonstrated that high-power ultrasound can effectively disintegrate sludge within shorter timeframes, whereas low-power ultrasound requires prolonged exposure to achieve comparable disintegration. Under identical specific energy input conditions, the combination of lower power density with extended sonication time was found to yield superior disintegration efficiency compared to higher power density with shorter treatment duration.Ma D [20] experimentally determined that the optimal conditions for ultrasound-enhanced electro-osmotic dewatering were 0.255 W/cm² for 3.5 minutes, achieving a dewatering efficiency of 40.78%. Concurrently, Y Qi [21] identified an optimal sonication energy density range of 0.4 ~ 0.6 W/ml, beyond which cell fragment reaggregation occurred, adversely affecting dewatering performance. These findings collectively demonstrate that while moderate sonication energy densities enhance sludge dewaterability, excessive energy input may induce localized overheating and organic matter degradation, ultimately impairing dewatering efficiency [22–26] . While extensive research has been conducted worldwide on ultrasonic duration and energy density for sludge treatment, investigations into their coupling effects with sludge volume remain notably insufficient. This study examines the impact of optimal ultrasonic parameters (duration and energy density) on dewatering efficiency across different sludge volumes, employing both macroscopic performance evaluation and microscopic mechanistic analysis. The findings are expected to provide valuable references for selecting appropriate ultrasonic parameters in industrial-scale treatment of large-volume sludge. 2.Material and method 2.1 Characteristics of Experimental Sludge The sludge used in this experiment was activated sludge collected from the secondary sedimentation tank of a municipal wastewater treatment plant in Zhenjiang City. To ensure the accuracy and reliability of experimental data, the same batch of sludge was used throughout the study. The raw sludge exhibited the following characteristics: WC of 91.42%, bulk density of 1.034 g/cm³, SRF of 1.723×10¹² m/kg, and particle size distribution with d₁₀, d₅₀, and d₉₀ values of 13.981 µm, 51.231 µm, and 182.452 µm respectively, along with a mean particle size of 82.375 µm. 2.2 Experimental apparatus The main instruments and equipment used in this experiment included: an SM-900A ultrasonic cell disruptor for sludge treatment, an LT2200E laser particle size analyzer for granulometry analysis, a CS-101-2 electric thermostatic drying oven for water determination, an NDJ-8SN digital viscometer for viscosity measurement, a QH-240 dual sludge specific resistance measurement device for filtration characterization, a 650s Fourier transform infrared spectrometer (FTIR) for chemical composition analysis, an EM-30PLUS scanning electron microscope (SEM) for microstructural observation, a Himac CR21N high-speed refrigerated centrifuge for sample separation, and a PQ001 low-field nuclear magnetic resonance (NMR) analyzer for water distribution assessment. 2.3 Experimental Methods This experiment utilized an SM-900A ultrasonic cell disruptor. During the experiment, the horn tip was immersed approximately 5 cm into the sludge sample. The start time, ultrasonication duration, and power ratio were controlled via the touchscreen. The ultrasonic power and frequency were determined by the horn, with a rated frequency of 20 kHz and a rated power of 900 W. The ultrasonic conditioning's energy density was set according to the following formula: Energy density = (Rated power 900 W × Power ratio) / Sludge volume. The ultrasonication time was uniformly set to 30 s, with different energy densities (0.036, 0.072, 0.108, 0.18, and 0.252 W/ml) applied to 1000 ml sludge samples for ultrasonic cell disruption experiments. After treatment, the sludge was divided into three portions: one portion was subjected to particle size analysis using a laser particle size analyzer, another portion was tested for specific resistance to filtration (SRF) using a dual-sludge specific resistance measurement device, and the remaining portion was processed in a high-speed refrigerated centrifuge. Part of the centrifugally dewatered sludge cake was placed in an oven to determine its water content (WC), while another portion underwent nuclear magnetic resonance (NMR) analysis. The optimal energy density for sludge treatment under conditions of 20 kHz,30 s, and 1000ml was determined. Additionally, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed on the dried sludge obtained from the oven.Subsequently, the ultrasonic cell disruptor was set to the optimal energy density, and the disruption time was varied (5, 15, 30, 45, and 60 s). After each experiment, the same analyses as above were conducted to determine the optimal ultrasonication time for 1000 ml sludge samples. Under the same conditions of 20 kHz and 30 s, ultrasonic cell disruption experiments were conducted on sludge samples with varying volumes (500,750, 1250, and 1500ml). The same five energy density levels were applied to determine the optimal energy density for each volume.Subsequently, for each volume under the rated power and optimal energy density conditions, the cell disruption time was varied to identify the optimal ultrasonication duration for each volume at its respective optimal energy density.The same analytical methods as previously described were employed to evaluate the treatment effects.The optimal energy density and ultrasonication time were determined for each of the five sludge volumes (500, 750, 1000, 1250,and 1500ml). Under these optimized ultrasonic conditions, key parameters including WC, SRF, and particle size distribution were measured. Furthermore, microscopic mechanism analysis was conducted to investigate the treatment effects at the fundamental level. 2.4 Analytical Methods 2.4.1 Sludge Water Content (WC) A geotextile filter was placed at the bottom of the centrifuge sample bottles for filtration. The sludge was equally divided into four portions and added into the bottles. The bottles were then sealed with their top caps and weighed to ensure the mass difference between each sample bottle was less than 4 g. The centrifugal dewatering temperature was set at 25°C. After subjecting the sludge to centrifugation at 6000 r/min for 5 minutes, the separated filter cakes were collected. Each filter cake was further divided into three equal parts, and the water content of each part was measured separately. 2.4.2 Sludge Specific Resistance (SRF) A 200 ml sludge sample was precisely measured using a graduated cylinder and transferred into a Buchner funnel pre-lined with filter paper. Under unsealed conditions, constant-pressure vacuum filtration was conducted at 0.06 MPa. The filtrate volume was recorded at 10-second intervals until no further changes were observed, indicating filtration completion (evidenced by vacuum dissipation and filter cake cracking). The recorded data were processed to calculate the SRF based on Darcy's law. Due to the inherent complexity and potential experimental errors associated with SRF determination, multiple trials were performed to ensure reliability, and the final reported value represents the average of these repeated measurements. 2.4.3 Viscosity Analysis A 45 ml sludge sample was precisely introduced into the viscometer measurement cylinder. The dynamic viscosity was then determined by quantifying the rotational resistance encountered by the spindle during its controlled rotation within the fluid medium, in accordance with the torque-viscosity correlation principle. 2.4.4 Particle Size Analysis The particle size distribution of sludge filtrate was determined using a laser diffraction particle size analyzer. Prior to analysis, the sludge samples were diluted with deionized water to achieve a uniform concentration of 15 mg/L suspension. The obscuration level was maintained within the optimal 10%-20% range during measurements to ensure data reliability. Each sample underwent three consecutive measurement cycles, and the final reported values represent the arithmetic mean of these replicates. 2.4.5 Nuclear Magnetic Resonance (NMR) Analysis The dewatered sludge cake after centrifugation is placed in a mold for shaping. After measuring its volume, it is placed into a measuring test tube, which is then positioned in the measurement area of a nuclear magnetic resonance (NMR) instrument to determine the transverse relaxation time T2 and water distribution of the water in the sludge cake. 2.4.6 Fourier Transform Infrared Spectroscopy (FTIR) Analysis The dried sludge was ground into a powder, and the sample was then placed into a Fourier Transform Infrared Spectroscopy (FTIR) analyzer. The sample was irradiated with infrared light, causing vibrational transitions in the chemical bonds or functional groups of the molecules, which absorbed infrared light at specific frequencies, thereby generating an infrared spectrum. By analyzing the positions and intensities of the absorption peaks, changes in the internal structure of the sludge were investigated. 2.4.7 Scanning Electron Microscopy (SEM) Analysis The prepared dried sludge sample was placed under an electron microscope, and SEM (Scanning Electron Microscopy) was used to capture magnified microstructural images at 200× and 500× magnification. The microstructural morphology of the sludge was then observed and analyzed. 3.Results and discussion 3.1 The effect of different sludge volumes on sludge dewatering under optimal ultrasonic conditions 3.1.1 The influence of different sludge volumes on water content (WC) under optimal ultrasonic conditions This study conducted tests at 20 kHz for 30 s using five sludge volumes (500, 750, 1000, 1250, and 1500 ml), each subjected to five different ultrasonic energy densities (0.036, 0.072, 0.108, 0.18, and 0.252 W/ml). WC and SRF were measured, revealing the optimal energy densities for each volume as 0.161, 0.165, 0.171, 0.177, and 0.180 W/ml, respectively.Subsequently, each sludge volume was treated at its optimal energy density while varying ultrasonic duration (5, 15, 30, 45, and 60 s). WC and SRF measurements identified the optimal sonication times as 31, 33, 34, 37, and 41 s for the respective volumes.Finally, the combined effects of the optimal energy density and sonication time for each volume were investigated by measuring WC and SRF under these conditions. From Fig. 1 , it can be observed that as the volume of sludge treatment increases, the minimum WC of the sludge first decreases and then increases. Compared to the original sludge WC of 91.42%, it can be seen that under optimal ultrasonic conditions, sludge of different volumes can significantly reduce the WC. However, due to the varying volumes, the required optimal ultrasonic energy density and treatment time also differ, leading to different interactive effects. As a result, the minimum WC achieved under optimal ultrasonic conditions varies for different sludge volumes. The experiment measured that under the conditions of 37s, 0.177W/ml, and 1250ml, the sludge's minimum WC reached its lowest value of 74.85%. This indicates that as the volume increases, the number of flocs inside the sludge also increases, requiring higher optimal ultrasonic conditions to achieve the lowest WC. With the intensification of ultrasonic conditions, the floc structures within the sludge gradually break down, and cell walls rupture, releasing bound water and improving the overall dewatering performance of the sludge. Although the sludge WC is minimized under optimal ultrasonic conditions, the interaction between longer ultrasonic treatment times and higher energy densities as the volume increases causes some of the broken small particles (such as proteins and polysaccharides) to re-flocculate and form new small flocs. This slightly deteriorates the sludge dewatering performance, resulting in a higher WC compared to smaller volumes under optimal ultrasonic conditions. 3.1.2 The effect of different sludge volumes on specific resistance to filtration (SRF) under optimal ultrasonic conditions From Fig. 2 , it can be observed that the trend of sludge's specific resistance to SRF is similar to that of WC, both showing an initial decrease followed by an increase. Under the optimal ultrasonic energy density and treatment time, the SRF varies with different sludge volumes. At a volume of 1250 ml, the SRF reaches its lowest value. Since SRF directly affects sludge dewatering efficiency, this observation aligns with the WC trend shown in Fig. 1 . This suggests that for different sludge volumes, the optimal ultrasonic energy density and treatment time exert varying degrees of influence on the internal pore structure of the sludge. This variation may be attributed to the interaction between ultrasonic energy density and treatment time in sludge dewatering, which is volume-dependent. As the volume increases, the sludge contains more floc structures, requiring higher optimal ultrasonic energy density and longer treatment time-both of which positively enhance dewatering by disrupting the extracellular polymeric substances (EPS) and breaking cell walls to release bound water. However, when the volume continues to increase, the interaction between ultrasonic energy density and treatment time under optimal conditions leads to further cell destruction, releasing excessive proteins (PN) and polysaccharides (PS). This increases sludge viscosity, SRF, and WC,ultimately deteriorating dewatering performance. 3.1.3 The effect of different sludge volumes on viscosity under optimal ultrasonic conditions Figure 3 shows the effect of different sludge volumes on viscosity under optimal ultrasonic conditions. Viscosity refers to the internal frictional resistance exhibited by sludge during flow or deformation under force, serving as a key parameter characterizing sludge flow properties. As can be observed from the figure, with increasing volume and enhanced ultrasonic conditions, sludge viscosity shows a decreasing trend, reaching its minimum at 1250ml,which was a pattern consistent with the WC variation. However, as the volume continues to increase with further intensification of ultrasonic conditions, the interaction between ultrasonic energy density and treatment time leads to increased sludge viscosity, which conversely becomes detrimental to further dewatering. 3.1.4 The Effect of Different Sludge Volumes on Particle Size Distribution under Optimal Ultrasonic Conditions In soil mechanics, the coefficient of uniformity (C u ) reflects the distribution range of soil particle sizes, as defined by Eq. 3 − 1. A larger coefficient of uniformity indicates a wider distribution of internal particles and more non-uniform gradation. The coefficient of curvature (C c ), defined by Eq. 3 − 2, characterizes the overall shape of the particle size distribution curve. Figure 4 shows the particle size distribution curves under optimal conditions for different volumes. It can be observed from the figure that the sludge particle size is primarily distributed between 10 ~ 500 µm.As seen in Table 1 , under the optimal ultrasonic energy density and sonication time for different volumes, as the volume increases, the sludge's C u and C c first increase and then decrease, while the average particle size of the sludge first decreases and then increases.At a volume of 1250ml, the sludge's C u reaches a maximum of 5.941, indicating a more uneven gradation of internal particles. The C c peaks at 1.236, suggesting a more continuous particle size distribution and more stable internal water channels. The average particle size is the smallest at 69.685 µm, indicating a higher proportion of fine particles and stronger sludge dewaterability.This phenomenon may occur because, as the volume increases, the optimal ultrasonic energy density and sonication time required to achieve the best dewatering effect also increase. The enhanced interaction between these factors leads to more cell structures being unable to withstand the instantaneous high pressure generated by cavitation bubble collapse. Simultaneously, ultrasonic treatment breaks down more sludge floc structures, resulting in a looser sludge structure, increased coefficients of uniformity and curvature, a more continuous gradation, and a smaller particle size. However, in the case of 1500 ml, even though the ultrasonic energy density and sonication time are at their optimal levels, the sludge's C u and C c decrease, and the average particle size increases. This may be because, as the volume increases, the number of internal floc structures rises. Under the combined influence of ultrasonic energy density and sonication time, while some flocs in the 1500 ml sludge disintegrate, the newly formed small particles re-agglomerate under ultrasonic conditions to form larger structures, leading to an increase in the average sludge particle size. Table 1 Coefficients of uniformity and curvature under different ultrasonic energy densities at a volume of 1000 ml Processing conditions d 10 (µm) d 30 (µm) d 60 (µm) C u C c Mean particle size(µm) 31s,0.161W/ml,500ml 11.621 28.759 58.754 5.056 1.211 74.307 33s,0.165W/ml,750ml 10.907 27.887 58.413 5.356 1.221 73.706 34s,0.171W/ml,1000ml 9.949 26.334 56.595 5.689 1.232 73.161 37s,0.177W/ml,1250ml 9.066 24.568 53.857 5.941 1.236 69.685 41s,0.180W/ml,1500ml 10.878 27.988 59.283 5.450 1.215 78.357 3.2 Mechanism Analysis of the Influence of Different Sludge Volumes on Sludge Dewatering under Optimal Ultrasonic Conditions 3.2.1 Scanning Electron Microscopy (SEM) Analysis of Sludge Cake Figure 5 shows the SEM images of raw sludge at 200× and 500× magnification, respectively. From the images, it can be observed that the raw sludge contains a large amount of EPS adhering to the cell surfaces. These EPS cause the sludge to form flocs with a negatively charged surface, retaining water and providing a self-protective function to ensure microbial survival. Additionally, the structure is dense, with almost no visible pores, making sludge dewatering difficult. Figures 6 – 10 show the SEM images of sludge cakes obtained after dewatering under optimal ultrasonic conditions for different sludge volumes. Since each volume was treated under its respective optimal ultrasonic conditions, the dewatering effects were all at their best. Compared with Fig. 5 (SEM image of raw sludge), it can be observed that the treated sludge cakes all exhibited larger pores, which facilitated sludge dewatering. The internal floc structures of the sludge were extensively broken, and the overall sludge structure became looser, with a large number of cell walls rupturing to release bound water. Comparing the SEM images of the sludge cakes across the five different volumes reveals that as the volume increased, the internal floc structures of the sludge also increased, and the required optimal ultrasonic conditions for treatment intensified accordingly. With the rise in acoustic energy density and ultrasonic duration, more EPS structures within the sludge were disrupted, leading to a looser sludge structure. The number of pores gradually increased, and the size of the pores also expanded, further promoting water removal. Simultaneously, the rupture of numerous cell walls released bound water from the cells, enhancing the sludge's dewaterability. This explains the trends observed in Figs. 1 and 2 , where the WC and SRF of the sludge gradually decreased as the volume increased. However, as the volume continued to increase, the optimal ultrasonic conditions required to disrupt the sludge's EPS structures also intensified. Due to the interaction between acoustic energy density and ultrasonic duration, while a significant portion of the EPS structures was degraded, the rupture of cell walls released bound water, polysaccharides, and proteins. This caused some inorganic particles and microbial cell fragments to reaggregate, forming small floc aggregates and leading to the re-compaction of the sludge. As a result, surface pore channels closed, reducing dewatering efficiency. 3.2.2 FTIR analysis of sludge cake Figure 11 shows the FTIR spectra of sludge under optimal ultrasonic conditions for different volumes. Since sludge contains a large number of floc structures and most organic functional groups are present in the polysaccharides and proteins of EPS, the FTIR spectra can be used to observe changes in functional groups after ultrasonic conditioning. This helps evaluate the extent of floc structure disruption within the sludge. As can be seen from Fig. 11 , the sludge exhibits a broad absorption band around 3400 cm⁻¹, corresponding to the stretching vibration of O-H bonds, indicating the presence of bound water in the sludge. The absorption peaks near 1400 cm⁻¹ and 1630 cm⁻¹ are attributed to the stretching vibrations of N-H bonds and C-N bonds, respectively, associated with proteins. The absorption peak near 1120 cm⁻¹ corresponds to the stretching vibration of O-H or C-O-C bonds in carbohydrates. Meanwhile, the peak around 600 cm⁻¹ is assigned to the ring vibrations of C-C and C-O-H bonds in aromatic amino acids and nucleotides. Comparative analysis reveals that the absorption peaks of the raw sludge cake are significantly stronger than those of the treated groups, indicating the presence of abundant EPS and bound water in the untreated sludge.Examining the absorption peaks at 3400 cm⁻¹ across different sludge volumes shows that the peaks initially become flatter but then turn steeper again after 1250 ml. This suggests that as the volume increases, the optimal ultrasonic conditions required for sludge treatment intensify, leading to the breakdown of EPS structures and the release of bound water. Consequently, the O-H bond stretching vibration weakens, resulting in a flattened peak. However, with further increases in volume and ultrasonic intensity, small inorganic particles and microbial cell fragments reagglomerate under prolonged sonication and higher energy density, partially trapping bound water within the sludge cake. This explains the subsequent rise in peak intensity at 3400 cm⁻¹. The vibration peaks near 1400 cm⁻¹ and 1630 cm⁻¹, associated with proteins, exhibit a gradual flattening trend as volume increases. This is attributed to enhanced cell wall disruption under intensified ultrasonic conditions, releasing both bound water and intracellular organic matter (e.g., PN and PS), which are then removed with the freed water. However, at higher volumes and ultrasonic intensities, the re-flocculation of fine particles entraps some PN and PS substances, causing a rebound in the related vibration peaks. 3.2.3 Nuclear Magnetic Resonance (NMR) analysis of sludge Figure 12 shows the transverse relaxation time (T 2 ) distribution of water in sludge cakes under optimal ultrasonic conditions at different volumes. As can be seen from the figure, the ultrasonically conditioned sludge cakes exhibit only a single T 2 peak, which reflects the different binding states of water molecules in the sample. A shorter T 2 time indicates tighter binding between water and surrounding materials. The T 21 peak (0 ~ 10 ms) corresponds to strongly bound water, primarily located inside microbial cells or bound to proteins, making it the most difficult to remove. The T 22 peak (10 ~ 100 ms) represents weakly bound water, which is adsorbed by capillary action in flocs and requires greater mechanical pressure or flocculation for effective removal. The T 23 peak (> 100 ms) is attributed to free water. As shown in Fig. 12 , the T 2 spectrum of the sludge samples primarily exhibits two peaks (T 21 and T 22 ), indicating the presence of two distinct water states- strongly bound water and weakly bound water. Furthermore, the signal intensity of bound water at different relaxation times varies significantly under optimal ultrasonic conditions across different sludge volumes. The peak relaxation times and porosity characteristics for different volumes under their respective optimal ultrasonic conditions are summarized in Table 2 . As shown in Table 2 , after ultrasonic conditioning under optimal conditions for different volumes, the free water in the sludge was effectively removed.It can be observed that as the volume increases, the peak relaxation times of the internal water first increase and then decrease,while the sludge porosity follows a similar trend—initially increasing before decreasing. Table 2 Peak relaxation times and porosity of internal water in sludge at different volumes under optimal ultrasonic conditions Sample Name T 21 Max(ms) T 22 Max(ms) T 23 Max(ms) porosity(%) 31s,0.161W/ml,500ml 0.1979 9.7712 - 57.81 33s,0.165W/ml,750ml 0.2121 11.4895 - 58.14 34s,0.171W/ml,1000ml 0.2274 13.5099 - 60.13 37s,0.177W/ml,1250ml 0.2437 14.6497 - 60.35 41s,0.180W/ml,1500ml 0.2274 10.5956 - 59.23 Comparative analysis reveals that at the volume of 1250 ml, the T 21 peak relaxation time reaches its maximum value of 0.2437 ms, and the T 22 peak relaxation time also peaks at 14.6497 ms. Additionally, the porosity attains its highest value of 60.35%.This phenomenon occurs because, as the volume increases, the floc structures within the sludge multiply, and the required optimal ultrasonic duration and energy density also rise. Their synergistic effect leads to extensive floc disintegration, resulting in a looser sludge structure and increased porosity. The weakened binding between water and surrounding materials, along with reduced sludge particle size, facilitates water removal. As the volume continues to increase, the optimal ultrasonic time and acoustic energy density required for the sludge also rise. Their interaction leads to the restructuring of the sludge's internal framework. While a significant amount of EPS undergoes cracking, small particulate matter, along with PN, PS and other components, re-flocculates into clusters, clogging the pores. Additionally, this flocculation entraps water, causing the internal structure to become densely compacted again. Table 3 The relationship between internal surface relaxation rate and pore-throat distribution in sludge of different volumes under optimal ultrasonic conditions Surface relaxivity(µm/ms) Pore-throat size distribution(%) 31s,0.161W/ml,500ml 33s,0.165W/ml,750ml 34s,0.171W/ml,1000ml 37s,0.177W/ml,1250ml 41s,0.180W/ml,1500ml 0-0.1 3.1357 1.1728 0 0 0.5169 0.1–0.16 15.538 13.3022 0.7484 0.6826 14.1492 0.16–0.25 20.7393 20.9001 19.762 22.0944 23.1442 0.25–0.4 13.6091 15.2901 22.779 24.6124 15.7913 0.4–0.63 4.7878 7.4748 16.1804 12.9606 5.6284 0.63-1 0 0 0.6602 0 0 Table 3 shows the relationship between the internal surface relaxation rate and pore-throat distribution of sludge under optimal ultrasonic conditions at different volumes. The surface relaxation rate reflects the influence intensity of the pore surface on the relaxation of fluid hydrogen nuclei. From the table, it can be observed that under the conditions of 31s, 0.161W/ml, and 500ml, the pore-throat distribution is mainly concentrated in the range of 0.16 ~ 0.25µm, accounting for 20.7393%. Under the conditions of 33s, 0.165W/ml, and 750ml, the pore-throat distribution is also concentrated in the range of 0.16 ~ 0.25µm, accounting for 20.9001%. As the relaxation rate increases, the proportion of pore-throat distribution also increases. Furthermore, under the conditions of 34s, 0.171W/ml, 1000ml and 37s, 0.177W/ml, 1250ml, the pore-throat distributions are primarily concentrated in the range of 0.25 ~ 0.4µm, with the latter showing a higher proportion of 24.6124% in this surface relaxation rate range. As the volume continues to increase, under the conditions of 41s, 0.180W/ml, and 1500ml, the pore-throat distribution is concentrated in the range of 0.16 ~ 0.25µm, with a higher proportion of 23.1442%. This indicates that the internal pore-throat size decreases, leading to poorer sludge dewaterability compared to the 1250ml case. It can be concluded that as the volume increases, under the optimal ultrasonic conditions, the internal flocs of the sludge are broken, the structure becomes looser, and the pores enlarge, resulting in a gradual increase in the internal surface relaxation rate. However, at the 1500ml volume, due to the interaction between acoustic energy density and ultrasonic duration, the internal porosity decreases, the average particle size increases, and the overall surface relaxation rate decreases, leading to reduced sludge dewaterability compared to smaller volumes. 4.Conclusion Since the effectiveness of ultrasonic sludge treatment is closely related to ultrasonic duration and acoustic energy density, this experiment investigates the relationship between sludge volume and the optimal ultrasonic duration and acoustic energy density through macro- and micro-mechanism analyses. Under the optimal acoustic energy density and ultrasonic treatment time, sludge with different volumes exhibits varying effects on dewatering performance. For sludge volumes of (500, 750, 1000, 1250, 1500 ml), as the volume increases, the water content and specific resistance first decrease and then increase. Meanwhile, the uniformity coefficient and curvature coefficient under each volume show an initial increase followed by a decrease. The average particle size of the sludge also demonstrates a trend of first decreasing and then increasing. By comparing the SEM images, FTIR spectra, and NMR water distribution maps at different volumes, it can be observed that as the volume increases, the EPS inside the sludge increase, and the optimal ultrasonic energy density and treatment time required for sludge treatment also rise. The EPS structure within the sludge undergoes extensive breakdown, and the cell walls rupture. The sludge structure becomes looser, internal pores enlarge, and the particle size gradually decreases, releasing a significant amount of bound water, thereby improving sludge dewaterability.However, as the volume continues to increase, the optimal ultrasonic energy density and treatment time also increase. The interactive effects between these factors lead to the breakdown of the EPS structure while simultaneously causing small inorganic particles to reagglomerate with organic matter such as PN and PS released from ruptured cell walls, forming new small floc aggregates. This results in an increase in bound water content, a larger average sludge particle size, and clogged internal pores, narrowing the dewatering channels and negatively impacting sludge dewaterability. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors acknowledge the financial support from The Belt and Road Special Foundation of The National Key Laboratory of Water Disaster Prevention(2021491611)and the National Natural Science Foundation of China (52278355). References Szaja A, Szulżyk-Cieplak J, Łagód S, et al. Recent Developments in the Application of Ultrasonication in Pre-Treatment of Municipal Sewage Sludge[J]. Journal of Ecological Engineering, 2023, 24(12). Laganà F, Pullano S A, Angiulli G, et al. Optimized Analytical–Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use[J]. Algorithms, 2024, 17(12): 592. Zawieja I, Wolny L. Ultrasonic disintegration of sewage sludge to increase biogas generation[J]. Chemical and Biochemical Engineering Quarterly, 2013, 27(4): 491-497. Erden G, Filibeli A. Ultrasonic pre‐treatment of biological sludge: consequences for disintegration, anaerobic biodegradability, and filterability[J]. Journal of Chemical Technology & Biotechnology, 2010, 85(1): 145-150. Le N T, Julcour-Lebigue C, Barthe L, et al. Optimisation of sludge pretreatment by low frequency sonication under pressure[J]. Journal of Environmental Management, 2016, 165: 206-212. Tytła M. The effects of ultrasonic disintegration as a function of waste activated sludge characteristics and technical conditions of conducting the process—Comprehensive analysis[J].International Journal of Environmental Research and Public Health, 2018, 15(10): 2311. Şahinkaya S, Sevimli M F, Aygün A. Improving the sludge disintegration efficiency of sonication by combining with alkalization and thermal pre-treatment methods[J]. Water Science and Technology, 2012, 65(10): 1809-1816. Barati Rashvanlou R, Pasalari H, Moserzadeh A A, et al. A combined ultrasonic and chemical conditioning process for upgrading the sludge dewaterability[J]. International Journal of Environmental Analytical Chemistry, 2022, 102(7): Mao H, Chi Y, Wang F, et al. Effect of ultrasonic pre-treatment on dewaterability and water distribution in sewage sludge[J]. Waste and Biomass Valorization, 2018, 9: 247-253. HUAN L, YIYING J, MAHAR R B, et al. Effects of ultrasonic disintegration on sludge microbial activity and dewaterability[J].J Hazard Mater, 2009, 161(2): 1421-1426. Liu L, Yan H, Yang C, et al. Dewatering of drilling sludge by ultrasound assisted Fe (ii)-activated persulfate oxidation[J]. RSC advances, 2018, 8(52): 29756-29766. Zhao W, Zhan X, Liu W, et al. Research on ultrasonic treatment in the field of actual excess sludge treatment[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2021, 651(4): 042046. Le NT, Julcour-Lebigue C,Delmas H. An executive review ofsludge pretreatment by sonication[J]. Journal of Environmental Sciences,2015.37:139-153 Liu H, Wang X, Qin S, et al. Comprehensive role of thermal combined ultrasonic pre-treatment in sewage sludge disposal[J]. Science of the Total Environment, 2021, 789: 147862. Golbabaei Kootenaei F, Mehrdadi N, Nabi Bidhendi G, et al. Improvement of sludge dewatering by ultrasonic pretreatment[J]. International Journal of Environmental Research, 2022, 16(4): 50. Xu X, Cao D, Wang Z, et al. Study on ultrasonic treatment for municipal sludge[J]. Ultrasonics sonochemistry, 2019, 57: 29-37. Siddiqui M I, Rameez H, Farooqi I H, et al. Recent advancement in commercial and other sustainable techniques for energy and material recovery from sewage sludge[J]. Water, 2023, 15(5): 948. Zhou C H, Ling Y, Zeng M, et al. Influence of microwave andultrasound on sludge dewaterability[J].Advanced MaterialsResearch,2014.955:2074-2079 Zhao Y H, Zhang B, Tao J, et al. Optimization of energy consumption of the ultrasonic pretreatment on sludge disintegration[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 592(1): 012198. Ma D, Lin S, Cui C, et al. Application of weak ultrasonic treatment on sludge electro-osmosis dewatering[J]. Environmental technology, 2018, 39(10): 1340-1349. Qi Y, Chen J, Xu H, et al. Optimizing sludge dewatering efficiency with ultrasonic Treatment: Insights into Parameters, Effects, and microstructural changes[J]. Ultrasonics Sonochemistry, 2024, 102: 106736. Lin F, Li J, Liu M, et al. New insights into the effect of extracellular polymeric substance on the sludge dewaterability based on interaction energy and viscoelastic acoustic response analysis[J]. Chemosphere, 2020, 261: 127929. Cai M Q, Hu J Q, Wells G, et al. Understanding mechanisms of synergy between acidification and ultrasound treatments for activated sludge dewatering: from bench to pilot–scale investigation[J]. Environmental science & technology, 2018, 52(7): 4313-4323. Yin X, Han P, Lu X, et al. A review on the dewaterability of bio-sludge and ultrasound pretreatment[J]. Ultrasonics Sonochemistry, 2004, 11(6): 337-348. Xu H, He P, Yu G, et al. Effect of ultrasonic pretreatment on anaerobic digestion and its sludge dewaterability[J]. Journal of Environmental Sciences, 2011, 23(9): 1472-1478. Zhang G, Zhang P, Chen Y. Ultrasonic enhancement of industrial sludge settling ability and dewatering ability[J]. Tsinghua Science & Technology, 2006, 11(3): 374-378. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 24 Jan, 2026 First submitted to journal 21 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8666028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587863563,"identity":"7001658b-9bae-4e63-bed3-47ea634505c2","order_by":0,"name":"Jianhao Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACNv7mgw8+VPyX45d/fPBBQkUNYS18EseSDWecYTaWbEhLNnhw5hhhLXIMOWbCvG3MiQYHcswkH7YwE+EwhmNpjDPOsCUwHDhjVpHYwMbA396dgF8Lc/MxoF948hgb28puJO6QYZA4c3YDIVvSgX6RKGZmZt52I/EMG4OBRC4hLTlm0rxtBoltbAxmBYltzERrSUjs4WExYyBOCySQDxhLSLAlSyScOcZD0C/y/eCoPCBnf4P54McfFTVy/O29+LVgAB7SlI+CUTAKRsEowAoA0sJNLb0+skcAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-7259-2718","institution":"Jiangsu University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jianhao","middleName":"","lastName":"Chen","suffix":""},{"id":587863564,"identity":"45ba2068-b58b-4da7-bff6-c9911b450a5d","order_by":1,"name":"Yongzheng Qi","email":"","orcid":"https://orcid.org/0000-0001-8395-4552","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yongzheng","middleName":"","lastName":"Qi","suffix":""},{"id":587863565,"identity":"8c51109a-896d-489a-ae70-1c71e0775503","order_by":2,"name":"Junhao Zhang","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Zhang","suffix":""},{"id":587863566,"identity":"635e48dd-3255-40bb-92c2-25ecd7ae8f66","order_by":3,"name":"Qingquan Bian","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qingquan","middleName":"","lastName":"Bian","suffix":""},{"id":587863567,"identity":"46f0f288-2d11-4f97-9943-f3dfe4726e19","order_by":4,"name":"Silin Wu","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Silin","middleName":"","lastName":"Wu","suffix":""},{"id":587863568,"identity":"886ee096-bfc0-4580-b69d-58bfe1a137b2","order_by":5,"name":"Liyan Wang","email":"","orcid":"","institution":"Jiangsu University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Liyan","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-01-22 06:35:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8666028/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8666028/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102536562,"identity":"fd435b60-9253-4902-9d42-97594d28f245","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19060,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of different sludge volumes on WC under optimal ultrasonic conditions\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/1308632a183f824328715a95.png"},{"id":102746209,"identity":"b6d70091-6418-409f-8f97-b0538b358efa","added_by":"auto","created_at":"2026-02-16 08:56:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17113,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of different sludge volumes on SRF under optimal ultrasonic conditions\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/3da9b21be00eeeca9076d446.png"},{"id":102536564,"identity":"8d5b3dfa-6dec-4529-b799-95c202798d9d","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19261,"visible":true,"origin":"","legend":"\u003cp\u003eThe Influence of Different Sludge Volumes on Viscosity under Optimal Ultrasonic Conditions\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/601ba7e25cd3b81c7f3000cb.png"},{"id":102536563,"identity":"745ff97e-5856-46a5-8c30-1b7b87115712","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47793,"visible":true,"origin":"","legend":"\u003cp\u003eParticle size distribution under optimal conditions for different volumes\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/0fc83465d6921de5c5aad621.jpeg"},{"id":102536570,"identity":"78fa9261-aa7e-41e3-bec7-7382634c83a6","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":982962,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of raw sludge\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/3ff8ac741089c97943c89118.jpeg"},{"id":102746531,"identity":"195e9ed6-9e42-43b1-88fd-72a926ddee9e","added_by":"auto","created_at":"2026-02-16 08:58:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":457236,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of sludge (500 ml) under 31s and 0.161 W/ml ultrasonic treatment\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/5987e690584733664e7ea1c7.png"},{"id":102536565,"identity":"311fb91a-7aef-4027-be7a-842a3ed7ed7e","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":452316,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of sludge (750 ml) under 33s and 0.165 W/ml ultrasonic treatment\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/b5990962de18ae424a186eea.png"},{"id":102536573,"identity":"1c97ad7e-954e-4c07-8244-af7e4cfd8413","added_by":"auto","created_at":"2026-02-12 17:34:11","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":460144,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of sludge (1000 ml) under 34s and 0.171 W/ml ultrasonic treatment\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/f75e731eb3c6c325b76ca066.png"},{"id":102749951,"identity":"a20ec0f2-fc4d-455c-bb8b-db04faad38e3","added_by":"auto","created_at":"2026-02-16 09:16:02","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":473460,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of sludge (1250 ml) under 37s and 0.177 W/ml ultrasonic treatment\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/dc9ae24e1a8e10d10236b85a.png"},{"id":102536569,"identity":"256c8a51-df73-4b0e-bca4-92ff5bc4e848","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":509298,"visible":true,"origin":"","legend":"\u003cp\u003eSEM image of sludge (1500 ml) under 41s and 0.180 W/ml ultrasonic treatment\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/4523daf49d680a1cb0e443d8.png"},{"id":102746444,"identity":"1a3fe029-fc0a-44e5-881d-bb16432cdf8d","added_by":"auto","created_at":"2026-02-16 08:57:44","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":48220,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra of sludge under optimal ultrasonic conditions at different volumes\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/a7111609a6ee28e7d53f5413.jpeg"},{"id":102536567,"identity":"f5cfcca8-1256-49d4-a718-aee6112b5bc5","added_by":"auto","created_at":"2026-02-12 17:34:10","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":51690,"visible":true,"origin":"","legend":"\u003cp\u003eTransverse relaxation time (T\u003csub\u003e2\u003c/sub\u003e) distribution of water in sludge under optimal ultrasonic conditions at different volumes\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/535355211e777f061483473e.png"},{"id":104808933,"identity":"0f0df1ef-b97c-4bac-bc37-25ed9f4d6ef3","added_by":"auto","created_at":"2026-03-17 12:44:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4937315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8666028/v1/2abab42c-88d5-492a-9547-59fb08dd8964.pdf"}],"financialInterests":"","formattedTitle":"Ultrasonic Energy Transfer Optimization in Sludge Dewatering: Volume-Dependent Tuning of Ultrasonic Duration and Intensity for Enhanced Cavitation Efficiency","fulltext":[{"header":"1.Introduction","content":"\u003cp\u003eSludge treatment represents a critical stage in wastewater processing, with its efficiency directly impacting environmental sustainability and resource recovery potential. In recent years, ultrasonic technology has gained widespread application in sludge pretreatment due to its high efficiency and environmental friendliness. This technology effectively disrupts sludge floc structures through cavitation effects, facilitating organic matter release and dewaterability enhancement \u003csup\u003e[1\u0026ndash;4]\u003c/sup\u003e. However, the treatment efficacy is highly dependent on operational parameters, particularly ultrasonic duration and sonication energy density \u003csup\u003e[5\u0026ndash;7]\u003c/sup\u003e, which collectively determine both sludge volume reduction efficiency and WC levels.\u003c/p\u003e \u003cp\u003eUltrasonic duration serves as a critical parameter regulating sludge disintegration efficiency and directly influences dewatering performance. The study conducted by Barati Rashvanlou\u003csup\u003e[8]\u003c/sup\u003edemonstrated that under ultrasonic treatment conditions with an energy input of 700 kJ/kg TS and a duration of 1minute, significant release of EPS-bound water was achieved along with enhanced sludge porosity, resulting in optimal sludge dewatering performance.Mao H\u003csup\u003e[9]\u003c/sup\u003ereported that 2 min of 30 kHz ultrasonic pretreatment reduced sludge WC from 93.03% to 86.95% after centrifugal dewatering, effectively halving the dewatered sludge cake volume. HUAN \u003csup\u003e[10]\u003c/sup\u003efound that short ultrasonic treatment durations yielded limited effects, with DD\u003csub\u003eCOD\u003c/sub\u003e increasing by less than 4%, while prolonged sonication time significantly improved sludge dewaterability.Liyan Liu\u003csup\u003e[11]\u003c/sup\u003edemonstrated that ultrasound treatment at 45 W power for 45 minutes could effectively activate persulfate oxidation to enhance the dewaterability of drilling sludge.Wei Zhao\u003csup\u003e[12]\u003c/sup\u003efound that maximal cell lysis efficiency was achieved under the optimal conditions of 2.0 W/ml sonication energy density, 40 min ultrasonic duration, and pH 7.0, which simultaneously reduced sludge WC and enhanced sludge stabilization.Notably, optimal ultrasonic duration promotes the release of bound water from cellular structures, whereas excessive treatment causes cell wall rupture\u003csup\u003e[13\u0026ndash;18]\u003c/sup\u003e.This leads to intracellular substance release and extracellular polymer redistribution, triggering sludge particle reaggregation and viscosity increase, ultimately impairing dewaterability. The studies collectively suggest a nonlinear relationship between ultrasonic duration and dewatering performance, where benefits peak before diminishing with overtreatment.\u003c/p\u003e \u003cp\u003eThe ultrasonic duration determines the cumulative energy effect, while the sonication energy density directly governs cavitation intensity. Yihua Zhao\u003csup\u003e[19]\u003c/sup\u003e demonstrated that high-power ultrasound can effectively disintegrate sludge within shorter timeframes, whereas low-power ultrasound requires prolonged exposure to achieve comparable disintegration. Under identical specific energy input conditions, the combination of lower power density with extended sonication time was found to yield superior disintegration efficiency compared to higher power density with shorter treatment duration.Ma D\u003csup\u003e[20]\u003c/sup\u003eexperimentally determined that the optimal conditions for ultrasound-enhanced electro-osmotic dewatering were 0.255 W/cm\u0026sup2; for 3.5 minutes, achieving a dewatering efficiency of 40.78%. Concurrently, Y Qi\u003csup\u003e[21]\u003c/sup\u003eidentified an optimal sonication energy density range of 0.4\u0026thinsp;~\u0026thinsp;0.6 W/ml, beyond which cell fragment reaggregation occurred, adversely affecting dewatering performance. These findings collectively demonstrate that while moderate sonication energy densities enhance sludge dewaterability, excessive energy input may induce localized overheating and organic matter degradation, ultimately impairing dewatering efficiency\u003csup\u003e[22\u0026ndash;26]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile extensive research has been conducted worldwide on ultrasonic duration and energy density for sludge treatment, investigations into their coupling effects with sludge volume remain notably insufficient. This study examines the impact of optimal ultrasonic parameters (duration and energy density) on dewatering efficiency across different sludge volumes, employing both macroscopic performance evaluation and microscopic mechanistic analysis. The findings are expected to provide valuable references for selecting appropriate ultrasonic parameters in industrial-scale treatment of large-volume sludge.\u003c/p\u003e"},{"header":"2.Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Characteristics of Experimental Sludge\u003c/h2\u003e \u003cp\u003eThe sludge used in this experiment was activated sludge collected from the secondary sedimentation tank of a municipal wastewater treatment plant in Zhenjiang City. To ensure the accuracy and reliability of experimental data, the same batch of sludge was used throughout the study. The raw sludge exhibited the following characteristics: WC of 91.42%, bulk density of 1.034 g/cm\u0026sup3;, SRF of 1.723\u0026times;10\u0026sup1;\u0026sup2; m/kg, and particle size distribution with d₁₀, d₅₀, and d₉₀ values of 13.981 \u0026micro;m, 51.231 \u0026micro;m, and 182.452 \u0026micro;m respectively, along with a mean particle size of 82.375 \u0026micro;m.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Experimental apparatus\u003c/h2\u003e \u003cp\u003eThe main instruments and equipment used in this experiment included: an SM-900A ultrasonic cell disruptor for sludge treatment, an LT2200E laser particle size analyzer for granulometry analysis, a CS-101-2 electric thermostatic drying oven for water determination, an NDJ-8SN digital viscometer for viscosity measurement, a QH-240 dual sludge specific resistance measurement device for filtration characterization, a 650s Fourier transform infrared spectrometer (FTIR) for chemical composition analysis, an EM-30PLUS scanning electron microscope (SEM) for microstructural observation, a Himac CR21N high-speed refrigerated centrifuge for sample separation, and a PQ001 low-field nuclear magnetic resonance (NMR) analyzer for water distribution assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental Methods\u003c/h2\u003e \u003cp\u003eThis experiment utilized an SM-900A ultrasonic cell disruptor. During the experiment, the horn tip was immersed approximately 5 cm into the sludge sample. The start time, ultrasonication duration, and power ratio were controlled via the touchscreen. The ultrasonic power and frequency were determined by the horn, with a rated frequency of 20 kHz and a rated power of 900 W. The ultrasonic conditioning's energy density was set according to the following formula: Energy density = (Rated power 900 W \u0026times; Power ratio) / Sludge volume.\u003c/p\u003e \u003cp\u003eThe ultrasonication time was uniformly set to 30 s, with different energy densities (0.036, 0.072, 0.108, 0.18, and 0.252 W/ml) applied to 1000 ml sludge samples for ultrasonic cell disruption experiments. After treatment, the sludge was divided into three portions: one portion was subjected to particle size analysis using a laser particle size analyzer, another portion was tested for specific resistance to filtration (SRF) using a dual-sludge specific resistance measurement device, and the remaining portion was processed in a high-speed refrigerated centrifuge. Part of the centrifugally dewatered sludge cake was placed in an oven to determine its water content (WC), while another portion underwent nuclear magnetic resonance (NMR) analysis. The optimal energy density for sludge treatment under conditions of 20 kHz,30 s, and 1000ml was determined. Additionally, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were performed on the dried sludge obtained from the oven.Subsequently, the ultrasonic cell disruptor was set to the optimal energy density, and the disruption time was varied (5, 15, 30, 45, and 60 s). After each experiment, the same analyses as above were conducted to determine the optimal ultrasonication time for 1000 ml sludge samples.\u003c/p\u003e \u003cp\u003eUnder the same conditions of 20 kHz and 30 s, ultrasonic cell disruption experiments were conducted on sludge samples with varying volumes (500,750, 1250, and 1500ml). The same five energy density levels were applied to determine the optimal energy density for each volume.Subsequently, for each volume under the rated power and optimal energy density conditions, the cell disruption time was varied to identify the optimal ultrasonication duration for each volume at its respective optimal energy density.The same analytical methods as previously described were employed to evaluate the treatment effects.The optimal energy density and ultrasonication time were determined for each of the five sludge volumes (500, 750, 1000, 1250,and 1500ml). Under these optimized ultrasonic conditions, key parameters including WC, SRF, and particle size distribution were measured. Furthermore, microscopic mechanism analysis was conducted to investigate the treatment effects at the fundamental level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analytical Methods\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Sludge Water Content (WC)\u003c/h2\u003e \u003cp\u003eA geotextile filter was placed at the bottom of the centrifuge sample bottles for filtration. The sludge was equally divided into four portions and added into the bottles. The bottles were then sealed with their top caps and weighed to ensure the mass difference between each sample bottle was less than 4 g. The centrifugal dewatering temperature was set at 25\u0026deg;C. After subjecting the sludge to centrifugation at 6000 r/min for 5 minutes, the separated filter cakes were collected. Each filter cake was further divided into three equal parts, and the water content of each part was measured separately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Sludge Specific Resistance (SRF)\u003c/h2\u003e \u003cp\u003eA 200 ml sludge sample was precisely measured using a graduated cylinder and transferred into a Buchner funnel pre-lined with filter paper. Under unsealed conditions, constant-pressure vacuum filtration was conducted at 0.06 MPa. The filtrate volume was recorded at 10-second intervals until no further changes were observed, indicating filtration completion (evidenced by vacuum dissipation and filter cake cracking). The recorded data were processed to calculate the SRF based on Darcy's law. Due to the inherent complexity and potential experimental errors associated with SRF determination, multiple trials were performed to ensure reliability, and the final reported value represents the average of these repeated measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Viscosity Analysis\u003c/h2\u003e \u003cp\u003eA 45 ml sludge sample was precisely introduced into the viscometer measurement cylinder. The dynamic viscosity was then determined by quantifying the rotational resistance encountered by the spindle during its controlled rotation within the fluid medium, in accordance with the torque-viscosity correlation principle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Particle Size Analysis\u003c/h2\u003e \u003cp\u003eThe particle size distribution of sludge filtrate was determined using a laser diffraction particle size analyzer. Prior to analysis, the sludge samples were diluted with deionized water to achieve a uniform concentration of 15 mg/L suspension. The obscuration level was maintained within the optimal 10%-20% range during measurements to ensure data reliability. Each sample underwent three consecutive measurement cycles, and the final reported values represent the arithmetic mean of these replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5 Nuclear Magnetic Resonance (NMR) Analysis\u003c/h2\u003e \u003cp\u003eThe dewatered sludge cake after centrifugation is placed in a mold for shaping. After measuring its volume, it is placed into a measuring test tube, which is then positioned in the measurement area of a nuclear magnetic resonance (NMR) instrument to determine the transverse relaxation time T2 and water distribution of the water in the sludge cake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6 Fourier Transform Infrared Spectroscopy (FTIR) Analysis\u003c/h2\u003e \u003cp\u003eThe dried sludge was ground into a powder, and the sample was then placed into a Fourier Transform Infrared Spectroscopy (FTIR) analyzer. The sample was irradiated with infrared light, causing vibrational transitions in the chemical bonds or functional groups of the molecules, which absorbed infrared light at specific frequencies, thereby generating an infrared spectrum. By analyzing the positions and intensities of the absorption peaks, changes in the internal structure of the sludge were investigated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.4.7 Scanning Electron Microscopy (SEM) Analysis\u003c/h2\u003e \u003cp\u003eThe prepared dried sludge sample was placed under an electron microscope, and SEM (Scanning Electron Microscopy) was used to capture magnified microstructural images at 200\u0026times; and 500\u0026times; magnification. The microstructural morphology of the sludge was then observed and analyzed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3.Results and discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The effect of different sludge volumes on sludge dewatering under optimal ultrasonic conditions\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 The influence of different sludge volumes on water content (WC) under optimal ultrasonic conditions\u003c/h2\u003e \u003cp\u003eThis study conducted tests at 20 kHz for 30 s using five sludge volumes (500, 750, 1000, 1250, and 1500 ml), each subjected to five different ultrasonic energy densities (0.036, 0.072, 0.108, 0.18, and 0.252 W/ml). WC and SRF were measured, revealing the optimal energy densities for each volume as 0.161, 0.165, 0.171, 0.177, and 0.180 W/ml, respectively.Subsequently, each sludge volume was treated at its optimal energy density while varying ultrasonic duration (5, 15, 30, 45, and 60 s). WC and SRF measurements identified the optimal sonication times as 31, 33, 34, 37, and 41 s for the respective volumes.Finally, the combined effects of the optimal energy density and sonication time for each volume were investigated by measuring WC and SRF under these conditions.\u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be observed that as the volume of sludge treatment increases, the minimum WC of the sludge first decreases and then increases. Compared to the original sludge WC of 91.42%, it can be seen that under optimal ultrasonic conditions, sludge of different volumes can significantly reduce the WC. However, due to the varying volumes, the required optimal ultrasonic energy density and treatment time also differ, leading to different interactive effects. As a result, the minimum WC achieved under optimal ultrasonic conditions varies for different sludge volumes. The experiment measured that under the conditions of 37s, 0.177W/ml, and 1250ml, the sludge's minimum WC reached its lowest value of 74.85%. This indicates that as the volume increases, the number of flocs inside the sludge also increases, requiring higher optimal ultrasonic conditions to achieve the lowest WC. With the intensification of ultrasonic conditions, the floc structures within the sludge gradually break down, and cell walls rupture, releasing bound water and improving the overall dewatering performance of the sludge.\u003c/p\u003e \u003cp\u003eAlthough the sludge WC is minimized under optimal ultrasonic conditions, the interaction between longer ultrasonic treatment times and higher energy densities as the volume increases causes some of the broken small particles (such as proteins and polysaccharides) to re-flocculate and form new small flocs. This slightly deteriorates the sludge dewatering performance, resulting in a higher WC compared to smaller volumes under optimal ultrasonic conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e3.1.2 The effect of different sludge volumes on specific resistance to filtration (SRF) under optimal ultrasonic conditions\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be observed that the trend of sludge's specific resistance to SRF is similar to that of WC, both showing an initial decrease followed by an increase. Under the optimal ultrasonic energy density and treatment time, the SRF varies with different sludge volumes. At a volume of 1250 ml, the SRF reaches its lowest value. Since SRF directly affects sludge dewatering efficiency, this observation aligns with the WC trend shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis suggests that for different sludge volumes, the optimal ultrasonic energy density and treatment time exert varying degrees of influence on the internal pore structure of the sludge. This variation may be attributed to the interaction between ultrasonic energy density and treatment time in sludge dewatering, which is volume-dependent. As the volume increases, the sludge contains more floc structures, requiring higher optimal ultrasonic energy density and longer treatment time-both of which positively enhance dewatering by disrupting the extracellular polymeric substances (EPS) and breaking cell walls to release bound water.\u003c/p\u003e \u003cp\u003eHowever, when the volume continues to increase, the interaction between ultrasonic energy density and treatment time under optimal conditions leads to further cell destruction, releasing excessive proteins (PN) and polysaccharides (PS). This increases sludge viscosity, SRF, and WC,ultimately deteriorating dewatering performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 The effect of different sludge volumes on viscosity under optimal ultrasonic conditions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the effect of different sludge volumes on viscosity under optimal ultrasonic conditions. Viscosity refers to the internal frictional resistance exhibited by sludge during flow or deformation under force, serving as a key parameter characterizing sludge flow properties. As can be observed from the figure, with increasing volume and enhanced ultrasonic conditions, sludge viscosity shows a decreasing trend, reaching its minimum at 1250ml,which was a pattern consistent with the WC variation. However, as the volume continues to increase with further intensification of ultrasonic conditions, the interaction between ultrasonic energy density and treatment time leads to increased sludge viscosity, which conversely becomes detrimental to further dewatering.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 The Effect of Different Sludge Volumes on Particle Size Distribution under Optimal Ultrasonic Conditions\u003c/h2\u003e \u003cp\u003eIn soil mechanics, the coefficient of uniformity (C\u003csub\u003eu\u003c/sub\u003e) reflects the distribution range of soil particle sizes, as defined by Eq.\u0026nbsp;3\u0026thinsp;\u0026minus;\u0026thinsp;1. A larger coefficient of uniformity indicates a wider distribution of internal particles and more non-uniform gradation. The coefficient of curvature (C\u003csub\u003ec\u003c/sub\u003e), defined by Eq.\u0026nbsp;3\u0026thinsp;\u0026minus;\u0026thinsp;2, characterizes the overall shape of the particle size distribution curve.\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"526\" height=\"169\"\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the particle size distribution curves under optimal conditions for different volumes. It can be observed from the figure that the sludge particle size is primarily distributed between 10\u0026thinsp;~\u0026thinsp;500 \u0026micro;m.As seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, under the optimal ultrasonic energy density and sonication time for different volumes, as the volume increases, the sludge's C\u003csub\u003eu\u003c/sub\u003e and C\u003csub\u003ec\u003c/sub\u003e first increase and then decrease, while the average particle size of the sludge first decreases and then increases.At a volume of 1250ml, the sludge's C\u003csub\u003eu\u003c/sub\u003e reaches a maximum of 5.941, indicating a more uneven gradation of internal particles. The C\u003csub\u003ec\u003c/sub\u003e peaks at 1.236, suggesting a more continuous particle size distribution and more stable internal water channels. The average particle size is the smallest at 69.685 \u0026micro;m, indicating a higher proportion of fine particles and stronger sludge dewaterability.This phenomenon may occur because, as the volume increases, the optimal ultrasonic energy density and sonication time required to achieve the best dewatering effect also increase. The enhanced interaction between these factors leads to more cell structures being unable to withstand the instantaneous high pressure generated by cavitation bubble collapse. Simultaneously, ultrasonic treatment breaks down more sludge floc structures, resulting in a looser sludge structure, increased coefficients of uniformity and curvature, a more continuous gradation, and a smaller particle size.\u003c/p\u003e \u003cp\u003eHowever, in the case of 1500 ml, even though the ultrasonic energy density and sonication time are at their optimal levels, the sludge's C\u003csub\u003eu\u003c/sub\u003e and C\u003csub\u003ec\u003c/sub\u003e decrease, and the average particle size increases. This may be because, as the volume increases, the number of internal floc structures rises. Under the combined influence of ultrasonic energy density and sonication time, while some flocs in the 1500 ml sludge disintegrate, the newly formed small particles re-agglomerate under ultrasonic conditions to form larger structures, leading to an increase in the average sludge particle size.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients of uniformity and curvature under different ultrasonic energy densities at a volume of 1000 ml\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ed\u003csub\u003e10\u003c/sub\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed\u003csub\u003e30\u003c/sub\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003csub\u003e60\u003c/sub\u003e(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC\u003csub\u003eu\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eC\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean particle size(\u0026micro;m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31s,0.161W/ml,500ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e74.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33s,0.165W/ml,750ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34s,0.171W/ml,1000ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37s,0.177W/ml,1250ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e69.685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41s,0.180W/ml,1500ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e78.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e3.2 Mechanism Analysis of the Influence of Different Sludge Volumes on Sludge Dewatering under Optimal Ultrasonic Conditions\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Scanning Electron Microscopy (SEM) Analysis of Sludge Cake\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the SEM images of raw sludge at 200\u0026times; and 500\u0026times; magnification, respectively. From the images, it can be observed that the raw sludge contains a large amount of EPS adhering to the cell surfaces. These EPS cause the sludge to form flocs with a negatively charged surface, retaining water and providing a self-protective function to ensure microbial survival. Additionally, the structure is dense, with almost no visible pores, making sludge dewatering difficult.\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show the SEM images of sludge cakes obtained after dewatering under optimal ultrasonic conditions for different sludge volumes. Since each volume was treated under its respective optimal ultrasonic conditions, the dewatering effects were all at their best. Compared with Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (SEM image of raw sludge), it can be observed that the treated sludge cakes all exhibited larger pores, which facilitated sludge dewatering. The internal floc structures of the sludge were extensively broken, and the overall sludge structure became looser, with a large number of cell walls rupturing to release bound water.\u003c/p\u003e \u003cp\u003eComparing the SEM images of the sludge cakes across the five different volumes reveals that as the volume increased, the internal floc structures of the sludge also increased, and the required optimal ultrasonic conditions for treatment intensified accordingly. With the rise in acoustic energy density and ultrasonic duration, more EPS structures within the sludge were disrupted, leading to a looser sludge structure. The number of pores gradually increased, and the size of the pores also expanded, further promoting water removal. Simultaneously, the rupture of numerous cell walls released bound water from the cells, enhancing the sludge's dewaterability. This explains the trends observed in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, where the WC and SRF of the sludge gradually decreased as the volume increased.\u003c/p\u003e \u003cp\u003eHowever, as the volume continued to increase, the optimal ultrasonic conditions required to disrupt the sludge's EPS structures also intensified. Due to the interaction between acoustic energy density and ultrasonic duration, while a significant portion of the EPS structures was degraded, the rupture of cell walls released bound water, polysaccharides, and proteins. This caused some inorganic particles and microbial cell fragments to reaggregate, forming small floc aggregates and leading to the re-compaction of the sludge. As a result, surface pore channels closed, reducing dewatering efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 FTIR analysis of sludge cake\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the FTIR spectra of sludge under optimal ultrasonic conditions for different volumes. Since sludge contains a large number of floc structures and most organic functional groups are present in the polysaccharides and proteins of EPS, the FTIR spectra can be used to observe changes in functional groups after ultrasonic conditioning. This helps evaluate the extent of floc structure disruption within the sludge.\u003c/p\u003e \u003cp\u003eAs can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, the sludge exhibits a broad absorption band around 3400 cm⁻\u0026sup1;, corresponding to the stretching vibration of O-H bonds, indicating the presence of bound water in the sludge. The absorption peaks near 1400 cm⁻\u0026sup1; and 1630 cm⁻\u0026sup1; are attributed to the stretching vibrations of N-H bonds and C-N bonds, respectively, associated with proteins. The absorption peak near 1120 cm⁻\u0026sup1; corresponds to the stretching vibration of O-H or C-O-C bonds in carbohydrates. Meanwhile, the peak around 600 cm⁻\u0026sup1; is assigned to the ring vibrations of C-C and C-O-H bonds in aromatic amino acids and nucleotides.\u003c/p\u003e \u003cp\u003eComparative analysis reveals that the absorption peaks of the raw sludge cake are significantly stronger than those of the treated groups, indicating the presence of abundant EPS and bound water in the untreated sludge.Examining the absorption peaks at 3400 cm⁻\u0026sup1; across different sludge volumes shows that the peaks initially become flatter but then turn steeper again after 1250 ml. This suggests that as the volume increases, the optimal ultrasonic conditions required for sludge treatment intensify, leading to the breakdown of EPS structures and the release of bound water. Consequently, the O-H bond stretching vibration weakens, resulting in a flattened peak. However, with further increases in volume and ultrasonic intensity, small inorganic particles and microbial cell fragments reagglomerate under prolonged sonication and higher energy density, partially trapping bound water within the sludge cake. This explains the subsequent rise in peak intensity at 3400 cm⁻\u0026sup1;.\u003c/p\u003e \u003cp\u003eThe vibration peaks near 1400 cm⁻\u0026sup1; and 1630 cm⁻\u0026sup1;, associated with proteins, exhibit a gradual flattening trend as volume increases. This is attributed to enhanced cell wall disruption under intensified ultrasonic conditions, releasing both bound water and intracellular organic matter (e.g., PN and PS), which are then removed with the freed water. However, at higher volumes and ultrasonic intensities, the re-flocculation of fine particles entraps some PN and PS substances, causing a rebound in the related vibration peaks.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Nuclear Magnetic Resonance (NMR) analysis of sludge\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e shows the transverse relaxation time (T\u003csub\u003e2\u003c/sub\u003e) distribution of water in sludge cakes under optimal ultrasonic conditions at different volumes. As can be seen from the figure, the ultrasonically conditioned sludge cakes exhibit only a single T\u003csub\u003e2\u003c/sub\u003e peak, which reflects the different binding states of water molecules in the sample. A shorter T\u003csub\u003e2\u003c/sub\u003e time indicates tighter binding between water and surrounding materials.\u003c/p\u003e \u003cp\u003eThe T\u003csub\u003e21\u003c/sub\u003e peak (0\u0026thinsp;~\u0026thinsp;10 ms) corresponds to strongly bound water, primarily located inside microbial cells or bound to proteins, making it the most difficult to remove. The T\u003csub\u003e22\u003c/sub\u003e peak (10\u0026thinsp;~\u0026thinsp;100 ms) represents weakly bound water, which is adsorbed by capillary action in flocs and requires greater mechanical pressure or flocculation for effective removal. The T\u003csub\u003e23\u003c/sub\u003e peak (\u0026gt;\u0026thinsp;100 ms) is attributed to free water.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e, the T\u003csub\u003e2\u003c/sub\u003e spectrum of the sludge samples primarily exhibits two peaks (T\u003csub\u003e21\u003c/sub\u003e and T\u003csub\u003e22\u003c/sub\u003e), indicating the presence of two distinct water states- strongly bound water and weakly bound water. Furthermore, the signal intensity of bound water at different relaxation times varies significantly under optimal ultrasonic conditions across different sludge volumes. The peak relaxation times and porosity characteristics for different volumes under their respective optimal ultrasonic conditions are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, after ultrasonic conditioning under optimal conditions for different volumes, the free water in the sludge was effectively removed.It can be observed that as the volume increases, the peak relaxation times of the internal water first increase and then decrease,while the sludge porosity follows a similar trend\u0026mdash;initially increasing before decreasing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePeak relaxation times and porosity of internal water in sludge at different volumes under optimal ultrasonic conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003csub\u003e21\u003c/sub\u003e Max(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e22\u003c/sub\u003e Max(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003csub\u003e23\u003c/sub\u003e Max(ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eporosity(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31s,0.161W/ml,500ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.7712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33s,0.165W/ml,750ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34s,0.171W/ml,1000ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37s,0.177W/ml,1250ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.6497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41s,0.180W/ml,1500ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.5956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e59.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparative analysis reveals that at the volume of 1250 ml, the T\u003csub\u003e21\u003c/sub\u003e peak relaxation time reaches its maximum value of 0.2437 ms, and the T\u003csub\u003e22\u003c/sub\u003e peak relaxation time also peaks at 14.6497 ms. Additionally, the porosity attains its highest value of 60.35%.This phenomenon occurs because, as the volume increases, the floc structures within the sludge multiply, and the required optimal ultrasonic duration and energy density also rise. Their synergistic effect leads to extensive floc disintegration, resulting in a looser sludge structure and increased porosity. The weakened binding between water and surrounding materials, along with reduced sludge particle size, facilitates water removal.\u003c/p\u003e \u003cp\u003eAs the volume continues to increase, the optimal ultrasonic time and acoustic energy density required for the sludge also rise. Their interaction leads to the restructuring of the sludge's internal framework. While a significant amount of EPS undergoes cracking, small particulate matter, along with PN, PS and other components, re-flocculates into clusters, clogging the pores. Additionally, this flocculation entraps water, causing the internal structure to become densely compacted again.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relationship between internal surface relaxation rate and pore-throat distribution in sludge of different volumes under optimal ultrasonic conditions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurface relaxivity(\u0026micro;m/ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePore-throat size distribution(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31s,0.161W/ml,500ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33s,0.165W/ml,750ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34s,0.171W/ml,1000ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37s,0.177W/ml,1250ml\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41s,0.180W/ml,1500ml\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.1\u0026ndash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.1492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.16\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.7393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.1442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.25\u0026ndash;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.6091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.6124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.7913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.4\u0026ndash;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.1804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.9606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.63-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the relationship between the internal surface relaxation rate and pore-throat distribution of sludge under optimal ultrasonic conditions at different volumes. The surface relaxation rate reflects the influence intensity of the pore surface on the relaxation of fluid hydrogen nuclei. From the table, it can be observed that under the conditions of 31s, 0.161W/ml, and 500ml, the pore-throat distribution is mainly concentrated in the range of 0.16\u0026thinsp;~\u0026thinsp;0.25\u0026micro;m, accounting for 20.7393%. Under the conditions of 33s, 0.165W/ml, and 750ml, the pore-throat distribution is also concentrated in the range of 0.16\u0026thinsp;~\u0026thinsp;0.25\u0026micro;m, accounting for 20.9001%. As the relaxation rate increases, the proportion of pore-throat distribution also increases.\u003c/p\u003e \u003cp\u003eFurthermore, under the conditions of 34s, 0.171W/ml, 1000ml and 37s, 0.177W/ml, 1250ml, the pore-throat distributions are primarily concentrated in the range of 0.25\u0026thinsp;~\u0026thinsp;0.4\u0026micro;m, with the latter showing a higher proportion of 24.6124% in this surface relaxation rate range. As the volume continues to increase, under the conditions of 41s, 0.180W/ml, and 1500ml, the pore-throat distribution is concentrated in the range of 0.16\u0026thinsp;~\u0026thinsp;0.25\u0026micro;m, with a higher proportion of 23.1442%. This indicates that the internal pore-throat size decreases, leading to poorer sludge dewaterability compared to the 1250ml case.\u003c/p\u003e \u003cp\u003eIt can be concluded that as the volume increases, under the optimal ultrasonic conditions, the internal flocs of the sludge are broken, the structure becomes looser, and the pores enlarge, resulting in a gradual increase in the internal surface relaxation rate. However, at the 1500ml volume, due to the interaction between acoustic energy density and ultrasonic duration, the internal porosity decreases, the average particle size increases, and the overall surface relaxation rate decreases, leading to reduced sludge dewaterability compared to smaller volumes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4.Conclusion","content":"\u003cp\u003eSince the effectiveness of ultrasonic sludge treatment is closely related to ultrasonic duration and acoustic energy density, this experiment investigates the relationship between sludge volume and the optimal ultrasonic duration and acoustic energy density through macro- and micro-mechanism analyses.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUnder the optimal acoustic energy density and ultrasonic treatment time, sludge with different volumes exhibits varying effects on dewatering performance. For sludge volumes of (500, 750, 1000, 1250, 1500 ml), as the volume increases, the water content and specific resistance first decrease and then increase. Meanwhile, the uniformity coefficient and curvature coefficient under each volume show an initial increase followed by a decrease. The average particle size of the sludge also demonstrates a trend of first decreasing and then increasing.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBy comparing the SEM images, FTIR spectra, and NMR water distribution maps at different volumes, it can be observed that as the volume increases, the EPS inside the sludge increase, and the optimal ultrasonic energy density and treatment time required for sludge treatment also rise. The EPS structure within the sludge undergoes extensive breakdown, and the cell walls rupture. The sludge structure becomes looser, internal pores enlarge, and the particle size gradually decreases, releasing a significant amount of bound water, thereby improving sludge dewaterability.However, as the volume continues to increase, the optimal ultrasonic energy density and treatment time also increase. The interactive effects between these factors lead to the breakdown of the EPS structure while simultaneously causing small inorganic particles to reagglomerate with organic matter such as PN and PS released from ruptured cell walls, forming new small floc aggregates. This results in an increase in bound water content, a larger average sludge particle size, and clogged internal pores, narrowing the dewatering channels and negatively impacting sludge dewaterability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the financial support from The Belt and Road Special Foundation of The National Key Laboratory of Water Disaster Prevention(2021491611)and the National Natural Science Foundation of China (52278355).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSzaja A, Szulżyk-Cieplak J, Łag\u0026oacute;d S, et al. Recent Developments in the Application of Ultrasonication in Pre-Treatment of Municipal Sewage Sludge[J]. Journal of Ecological Engineering, 2023, 24(12).\u003c/li\u003e\n\u003cli\u003eLagan\u0026agrave; F, Pullano S A, Angiulli G, et al. Optimized Analytical\u0026ndash;Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use[J]. Algorithms, 2024, 17(12): 592.\u003c/li\u003e\n\u003cli\u003eZawieja I, Wolny L. Ultrasonic disintegration of sewage sludge to increase biogas generation[J]. Chemical and Biochemical Engineering Quarterly, 2013, 27(4): 491-497.\u003c/li\u003e\n\u003cli\u003eErden G, Filibeli A. Ultrasonic pre‐treatment of biological sludge: consequences for disintegration, anaerobic biodegradability, and filterability[J]. Journal of Chemical Technology \u0026amp; Biotechnology, 2010, 85(1): 145-150.\u003c/li\u003e\n\u003cli\u003eLe N T, Julcour-Lebigue C, Barthe L, et al. Optimisation of sludge pretreatment by low frequency sonication under pressure[J]. Journal of Environmental Management, 2016, 165: 206-212.\u003c/li\u003e\n\u003cli\u003eTytła M. The effects of ultrasonic disintegration as a function of waste activated sludge characteristics and technical conditions of conducting the process\u0026mdash;Comprehensive analysis[J].International Journal of Environmental Research and Public Health, 2018, 15(10): 2311.\u003c/li\u003e\n\u003cli\u003eŞahinkaya S, Sevimli M F, Ayg\u0026uuml;n A. Improving the sludge disintegration efficiency of sonication by combining with alkalization and thermal pre-treatment methods[J]. Water Science and Technology, 2012, 65(10): 1809-1816.\u003c/li\u003e\n\u003cli\u003eBarati Rashvanlou R, Pasalari H, Moserzadeh A A, et al. A combined ultrasonic and chemical conditioning process for upgrading the sludge dewaterability[J]. International Journal of Environmental Analytical Chemistry, 2022, 102(7): \u003c/li\u003e\n\u003cli\u003eMao H, Chi Y, Wang F, et al. Effect of ultrasonic pre-treatment on dewaterability and water distribution in sewage sludge[J]. Waste and Biomass Valorization, 2018, 9: 247-253.\u003c/li\u003e\n\u003cli\u003eHUAN L, YIYING J, MAHAR R B, et al. Effects of ultrasonic disintegration on sludge microbial activity and dewaterability[J].J Hazard Mater, 2009, 161(2): 1421-1426.\u003c/li\u003e\n\u003cli\u003eLiu L, Yan H, Yang C, et al. Dewatering of drilling sludge by ultrasound assisted Fe (ii)-activated persulfate oxidation[J]. RSC advances, 2018, 8(52): 29756-29766.\u003c/li\u003e\n\u003cli\u003eZhao W, Zhan X, Liu W, et al. Research on ultrasonic treatment in the field of actual excess sludge treatment[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2021, 651(4): 042046.\u003c/li\u003e\n\u003cli\u003eLe NT, Julcour-Lebigue C,Delmas H. An executive review ofsludge pretreatment by sonication[J]. Journal of Environmental Sciences,2015.37:139-153\u003c/li\u003e\n\u003cli\u003eLiu H, Wang X, Qin S, et al. Comprehensive role of thermal combined ultrasonic pre-treatment in sewage sludge disposal[J]. Science of the Total Environment, 2021, 789: 147862.\u003c/li\u003e\n\u003cli\u003eGolbabaei Kootenaei F, Mehrdadi N, Nabi Bidhendi G, et al. Improvement of sludge dewatering by ultrasonic pretreatment[J]. International Journal of Environmental Research, 2022, 16(4): 50.\u003c/li\u003e\n\u003cli\u003eXu X, Cao D, Wang Z, et al. Study on ultrasonic treatment for municipal sludge[J]. Ultrasonics sonochemistry, 2019, 57: 29-37.\u003c/li\u003e\n\u003cli\u003eSiddiqui M I, Rameez H, Farooqi I H, et al. Recent advancement in commercial and other sustainable techniques for energy and material recovery from sewage sludge[J]. Water, 2023, 15(5): 948.\u003c/li\u003e\n\u003cli\u003eZhou C H, Ling Y, Zeng M, et al. Influence of microwave andultrasound on sludge dewaterability[J].Advanced MaterialsResearch,2014.955:2074-2079\u003c/li\u003e\n\u003cli\u003eZhao Y H, Zhang B, Tao J, et al. Optimization of energy consumption of the ultrasonic pretreatment on sludge disintegration[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2019, 592(1): 012198.\u003c/li\u003e\n\u003cli\u003eMa D, Lin S, Cui C, et al. Application of weak ultrasonic treatment on sludge electro-osmosis dewatering[J]. Environmental technology, 2018, 39(10): 1340-1349.\u003c/li\u003e\n\u003cli\u003eQi Y, Chen J, Xu H, et al. Optimizing sludge dewatering efficiency with ultrasonic Treatment: Insights into Parameters, Effects, and microstructural changes[J]. Ultrasonics Sonochemistry, 2024, 102: 106736.\u003c/li\u003e\n\u003cli\u003eLin F, Li J, Liu M, et al. New insights into the effect of extracellular polymeric substance on the sludge dewaterability based on interaction energy and viscoelastic acoustic response analysis[J]. Chemosphere, 2020, 261: 127929.\u003c/li\u003e\n\u003cli\u003eCai M Q, Hu J Q, Wells G, et al. Understanding mechanisms of synergy between acidification and ultrasound treatments for activated sludge dewatering: from bench to pilot\u0026ndash;scale investigation[J]. Environmental science \u0026amp; technology, 2018, 52(7): 4313-4323.\u003c/li\u003e\n\u003cli\u003eYin X, Han P, Lu X, et al. A review on the dewaterability of bio-sludge and ultrasound pretreatment[J]. Ultrasonics Sonochemistry, 2004, 11(6): 337-348.\u003c/li\u003e\n\u003cli\u003eXu H, He P, Yu G, et al. Effect of ultrasonic pretreatment on anaerobic digestion and its sludge dewaterability[J]. Journal of Environmental Sciences, 2011, 23(9): 1472-1478.\u003c/li\u003e\n\u003cli\u003eZhang G, Zhang P, Chen Y. Ultrasonic enhancement of industrial sludge settling ability and dewatering ability[J]. Tsinghua Science \u0026amp; Technology, 2006, 11(3): 374-378.\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"korean-journal-of-chemical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kjce","sideBox":"Learn more about [Korean Journal of Chemical Engineering](http://link.springer.com/journal/11814)","snPcode":"11814","submissionUrl":"https://www.editorialmanager.com/kjce/default2.aspx","title":"Korean Journal of Chemical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Subscription","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ultrasonic duration, Sonication energy density, FTIR, SEM, NMR","lastPublishedDoi":"10.21203/rs.3.rs-8666028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8666028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh water content in sludge has consistently posed a significant challenge in sludge treatment processes. As an effective approach for sludge dewatering, ultrasound treatment induces cavitation effects, mechanical vibration, and thermal effects to rapidly disrupt sludge floc structures and release bound water, with sonication energy density and ultrasonic duration serving as key control parameters that ultimately determine dewatering efficiency. This study identified the optimal sonication energy densities and ultrasonic durations for sludge volumes of 500ml, 750ml, 1000ml, 1250ml, and 1500ml as 0.161W/ml, 0.165W/ml, 0.171W/ml, 0.177W/ml, 0.180W/ml and 31s, 33s, 34s, 37s, 41s, respectively. Comprehensive analyses including WC, SRF, viscosity, particle size distribution, SEM, FTIR, and NMR were conducted on sludge samples of different volumes under these optimal ultrasonic conditions. The results demonstrated that while ultrasound treatment significantly reduced WC compared to raw sludge across all volumes, the degree of reduction varied with sludge volume. As sludge volume increased, the required ultrasonic intensity and duration increased accordingly, with the improvement in water reduction showing an initial enhancement followed by a gradual decline. The study established optimal sonication energy densities and durations for different sludge volumes, investigated the variations in WC and specific resistance to filtration under optimal ultrasonic conditions, examined the relationship between sonication energy density, ultrasonic duration, and sludge volume, and provided mechanistic insights through microscopic and spectroscopic analyses. These findings offer valuable guidance for industrial-scale sludge treatment by identifying appropriate ultrasonic conditions to enhance sludge reduction efficiency for large-volume applications.\u003c/p\u003e","manuscriptTitle":"Ultrasonic Energy Transfer Optimization in Sludge Dewatering: Volume-Dependent Tuning of Ultrasonic Duration and Intensity for Enhanced Cavitation Efficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 17:34:05","doi":"10.21203/rs.3.rs-8666028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-02-09T06:12:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T00:01:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-24T15:13:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Korean Journal of Chemical Engineering","date":"2026-01-22T01:34:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"korean-journal-of-chemical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kjce","sideBox":"Learn more about [Korean Journal of Chemical Engineering](http://link.springer.com/journal/11814)","snPcode":"11814","submissionUrl":"https://www.editorialmanager.com/kjce/default2.aspx","title":"Korean Journal of Chemical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Subscription","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d7d0dfd9-72bc-4495-9f67-1a78095e526a","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-12T17:34:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 17:34:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8666028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8666028","identity":"rs-8666028","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}