Rapid detection method for dehydration performance of anaerobic digestion product

Abstract

The invention provides a rapid detection method for the dehydration performance of anaerobic digestion residues, and belongs to the technical field of biomass waste dehydration performance detection. The method specifically comprises the following steps: collecting full-wave-band infrared spectrum data of anaerobic digests in a wavelength range of 400-4000 cm <-1 >; smoothing the obtained infrared spectrum data by using infrared spectrum analysis software; according to the obtained data, selecting infrared water spectrum data related to O-H bending vibration in a wavelength range of 1550-1750cm <-1 >, performing Gaussian deconvolution and peak-dividing fitting by using peak-dividing fitting software, and decomposing into 6 sub-peaks, namely p1-p6; and calculating the area ratio of each sub-peak, representing the dehydration performance of the anaerobic digestion product by using the ratio sigma p1-2 / sigma p3-6 of the sum of the area ratios of the sub-peak 1-2 and the sub-peak 3-6, and representing that the dehydration is difficult when the value is higher. The infrared detection method disclosed by the invention has the advantages of cleanness, high efficiency, low dosage, low cost, batch detection and the like.

Description

Technical Field

[0001] This invention relates to the technical field of biomass waste dehydration performance testing, and in particular to a rapid testing method for the dehydration performance of anaerobic digesters. Background Art

[0002] In studies of deep dewatering of biomass waste, dewatering performance is a crucial indicator, often characterized by capillary absorption time (CST) and specific filtration resistance (SRF). However, these methods suffer from low detection efficiency and poor reproducibility. Especially for anaerobic digestion residues, CST testing of a single wet raw sample can take tens of minutes, while testing of dry samples takes hours, rendering CST meaningless. SRF, calculated from multiple variables, involves complex procedures; the detection and recording of each independent variable can affect the results, and a single sample consumption can reach 100 ml. Therefore, neither CST nor SRF can achieve efficient, batch detection of the dewatering performance of anaerobic digests. Thus, developing an analytical technique with low sample dosage, high efficiency, and good reproducibility is imperative.

[0003] Confocal Raman microscopy imaging (CRM) has been widely used in the study of hydrogen bond states, particularly in research on the water binding tightness in plant and animal tissues such as the stratum corneum of skin, apples, and potatoes. However, in practical applications, it has been found that CRM is not suitable for organic waste samples with high water content and poor structure. During spectral acquisition, the sample rapidly separates in the capillary, severely affecting the representativeness of the detection results. More importantly, biomass wastes such as anaerobic digesters and sludge have complex compositions, rich in humic substances, protein residues, and microbial metabolic coenzymes, whose own fluorescence emission can directly mask the Raman signal peaks. This strong fluorescence interference cannot be eliminated by selecting long-wavelength lasers. Therefore, there is an urgent need to develop a novel analytical method that is unaffected by external interference and can accurately detect the dehydration performance of organic matter. Summary of the Invention

[0004] To address the above technical problems, this invention provides a rapid detection method for the dehydration performance of anaerobic digesters. This invention offers an accurate, efficient, and sample-saving analytical method for detecting the dehydration performance of anaerobic digesters and other biomass wastes.

[0005] The purpose of this invention is to provide a rapid detection method for the dehydration performance of anaerobic digests, comprising the following steps:

[0006] (1) Collect anaerobic digests at 400-4000 cm³ -1Full-band infrared spectral data across the wavelength range;

[0007] (2) Use infrared spectroscopy analysis software to smooth the infrared spectral data obtained in step (1);

[0008] (3) Based on the data obtained in step (2), select 1550-1750cm. -1 Infrared water spectral data related to OH bending vibration in the wavelength range were decomposed into 6 sub-peaks (p1-p6) using Gaussian deconvolution and peak fitting software.

[0009] (4) Based on the sub-peaks p1-p6 obtained in step (3), calculate the area ratio of each sub-peak (the ratio of the area of ​​each sub-peak to the total area of ​​the six sub-peaks), and use the ratio ∑p1-2 / ∑p3-6, which is the sum of the area ratios of sub-peaks 1-2 and sub-peaks 3-6, to characterize the dehydration performance of the anaerobic digest. The higher the value, the more difficult it is to dehydrate. Here, ∑p1-2 / ∑p3-6 refers to the ratio of the sum of the area ratios of sub-peaks 1 and 2 to the sum of the area ratios of the four sub-peaks 3, 4, 5, and 6.

[0010] In some embodiments of the present invention, in step (1), the anaerobic digest is collected at 400-4000 cm⁻¹. -1 Infrared spectral data for the wavelength range were obtained using a Fourier transform infrared spectrometer (FTIR).

[0011] In some embodiments of the present invention, attenuated total reflectance mode (ATR-FTIR) of Fourier transform infrared spectroscopy (FTIR) is employed. During sampling, the anaerobic digest sample simply needs to be tightly covered by its own surface tension on the ATR crystal sampling window.

[0012] In some embodiments of the present invention, in step (2), the smoothing process is based on the premise that no characteristic peak information is lost or changed.

[0013] In some embodiments of the present invention, in step (3), when using peak fitting software to perform Gaussian deconvolution on the characteristic peaks of the infrared water spectrum, the Resp Fn Width term parameter should be adjusted until the data points are evenly distributed on both sides of the curve.

[0014] In some embodiments of the present invention, in step (3), the peak fitting, the fitting parameter Iteration should reach 7, and the goodness of fit R. 2 It should be greater than 0.99.

[0015] The technical solution of the present invention has the following advantages compared with the prior art:

[0016] FTIR detection offers several advantages: short sampling time (only a few minutes required) and good reproducibility; low sample dosage (only a few microliters needed to cover the ATR crystal window); and cleaner operation compared to CST and SRF methods. Furthermore, it eliminates the need for consumables such as filtration devices and filter paper, reducing costs. These characteristics enable FTIR detection for efficient, high-volume analysis of the dehydration performance of anaerobic digests. Attached Figure Description

[0017] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings, wherein...

[0018] Figure 1 FTIR water spectra of two anaerobic digests (1550-1750 cm⁻¹) -1 The peak separation results are shown, taking AD-LG-1 and AD-LG-3 as examples.

[0019] Figure 2 The graph shows the correlation between the area ratio of sub-peaks in the FTIR water spectrum of anaerobic digests and CST. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0021] This embodiment provides a rapid method for detecting the dehydration performance of anaerobic digests, as detailed below:

[0022] Table 1 Sample Information

[0023]

[0024]

[0025] The anaerobic digestion residues came from three anaerobic digestion plants. A total of five original samples were collected, numbered 1-5. Samples 6-15 were samples that had undergone ultrasonic pretreatment under different operating conditions. Detailed information is shown in Table 1. Sample 3 (AD-LG-3) was dry anaerobic digestion residue, containing many solid impurities and exhibiting poor homogeneity. It should be passed through an 18-mesh sieve for homogenization before ultrasonic treatment and index testing.

[0026] The FTIR test used a Spectrum Two infrared spectrometer (PE2) from PerkinElmer, USA. The CST verification test used a Triton 304M CST analyzer from Triton, UK. The CST filter paper was also manufactured by Triton (approximately 10 RMB / sheet).

[0027] First, a Fourier transform infrared spectrometer was used to scan the sample in attenuated total reflectance mode (ATR-FTIR) to obtain samples at 400-4000 cm⁻¹. -1 Full-band infrared spectral data within the wavelength range. For sample loading, use a 1mL pipette, adjusting the volume to measure a few microliters of sample. The pipette tip should be removed by approximately 0.5cm to ensure the sample concentration remains unchanged. There is no need to lower the pressure column; allow the sample to completely cover the ATR crystal window using its own surface tension.

[0028] The obtained infrared spectral data is appropriately smoothed using the FTIR's built-in spectral analysis software or OMNIC software. This embodiment uses OMNIC software, ensuring no characteristic peak information is lost or altered. The "Automatic Smoothing" option in the "Data Processing" module should be selected. For the smoothed data, the range of 1550-1750 cm⁻¹ should be selected. -1 In the infrared water band related to OH bending vibration within the wavelength range, baseline correction, Gaussian deconvolution, and peak fitting were performed using peak fitting software. This embodiment uses PeakFit software. During deconvolution, the parameters of the Resp, Fn, and Width terms should be adjusted until the data points are evenly distributed on both sides of the curve. During peak fitting, it is manually adjusted to 6 sub-peaks, and iterative fitting is performed continuously until the Iteration term parameter reaches 7 and the goodness-of-fit R0 is achieved. 2 >0.99.

[0029] In fact, the assumption that FTIR characteristic peaks possess superposition information and can be resolved into multiple sub-peaks is supported by evidence; this technique has been applied to the detection of protein secondary structure in sludge dry matter. FTIR spectra are located in the 1600-1700 cm⁻¹ range. -1 The characteristic peak at a certain frequency can be decomposed into six sub-peaks, with the center located at 1630-1640 cm⁻¹. -1 1640-1645cm -1 and 1648-1657cm -1 The sub-peaks at the locations are closely related to three protein structures: β-sheet, random coil, and α-helix.

[0030] In this embodiment, peak separation was performed using wet anaerobic digester AD-LG-1 and dry anaerobic digester AD-LG-3 from anaerobic digester plant A as examples. The results are as follows: Figure 1 As shown in the figure. Statistical analysis of the peak results for all samples revealed that the wet anaerobic digest had a higher water content, with its sub-peaks located at 1586.4, 1586.4, 1612.2, 1634.3, 1657.9, 1681.6, and 1707 cm⁻¹. -1Nearby, the sub-peaks of the dry anaerobic digests showed a certain degree of blue shift, with their centers located at 1601.0, 1622.2, 1641.5, 1661.4, 1682.3, and 1704.9 cm⁻¹, respectively. -1 Nearby. The area ratio of the sub-peaks in the FTIR water spectrum of the samples is shown in Table 2.

[0031] contrast Figure 1 In (a) and (b), a significant difference in the peak area ratio of sub-peak 2 (p2) and sub-peak 4 (p4) can be observed, suggesting that p2 and p4 may be related to the dehydration performance of anaerobic digests.

[0032] The accuracy of this method was verified by correlating the FTIR water spectrum sub-peak information with the CST detection results. Given the difficulty in dehydrating anaerobic digests, and the fact that the samples contained dry anaerobic digests with low water content, which are even more difficult to dehydrate, the samples were uniformly diluted 10-fold before CST detection. 3 mL of sample was taken using a 5 mL pipette and diluted to 30 mL, with approximately 0.5 cm of the pipette tip removed. However, even after dilution, the CST value of the dry anaerobic digests still exceeded the instrument's reading range. Therefore, the time it took for the instrument to beep twice when the two sets of electrodes sensed moisture was recorded, and the CST value was calculated using the time difference.

[0033] In previous studies, 1550-1800cm -1 The infrared region in the frequency range of 3200-3800 cm⁻¹ is thought to be associated with the bending vibrations of OH bonds. -1 The mid-infrared region of the frequency range is considered to be related to the stretching vibrations of OH bonds, 5400-5800 cm⁻¹. -1 The near-infrared region in the frequency range is considered to be associated with water molecules, and all three characteristic bands have been used for moisture detection. However, the 5400-5800 cm⁻¹ frequency range... -1 The frequency exceeded the scanning range of the PE 2 infrared spectrometer used in this embodiment, and only the 1550-1750 cm⁻¹ range was detected. -1 The characteristic band sub-peak information of the frequency range is correlated with CST, and its shape is a complete characteristic peak.

[0034] According to Pearson correlation analysis, when 1550-1750cm -1 When the FTIR water spectrum in the frequency range was decomposed into 6 sub-peaks, the correlation coefficients between the area proportions of p2 and p4 and CST were 0.993 and -0.991, respectively. The R-squared value of the goodness-of-fit test was... 2All values ​​were greater than 0.98, indicating strong positive and negative correlations, respectively. Furthermore, the summation and ratio of sub-peak area proportions, namely ∑p1-2, ∑p3-6, and ∑p1-2 / ∑p3-6, had correlation coefficients with CST of 0.993, -0.991, and 0.992, respectively. This suggests that the ∑p1-2 / ∑p3-6 index can characterize the dehydration performance of anaerobic digests; a larger value indicates that the anaerobic digests are more difficult to dehydrate.

[0035] Plot the pairwise correlation between sub-peak information and CST using ∑p2, ∑p4, and ∑p1-2 / ∑p3-6 as the x-axis and CST as the y-axis, as shown below. Figure 2 As shown in the figure, there is a linear regression relationship between CST and ∑p1-2 / ∑p3-6, with y = 16709.679x - 5750.207. However, the two variables in the figure are not strictly positively correlated, or expressed as R... 2 The result of not reaching 0.99 may be related to the CST detection error caused by the 10-fold dilution of the sample. Dry anaerobic digest is quite viscous, and during dilution, a small amount of sample may remain on the inner wall of the pipette tip, causing measurement errors. However, infrared detection is more accurate and has good reproducibility; the dehydration performance can be judged by comparing the ∑p1-2 / ∑p3-6 values ​​between samples.

[0036] The CST test results show that even after a 10-fold dilution, the CST values ​​of wet anaerobic digests are mainly concentrated in the 100-400s range, requiring several minutes to over ten minutes. The detection time should be increased from the CST value to the time it takes for water to reach the inner electrode. The detection time for dry anaerobic digests exceeds one hour. It can be said that CST detection efficiency is very low, making it difficult to perform batch testing. In contrast, FTIR scanning only takes a few minutes (<3 minutes), and it is cleaner and requires a lower sample dose compared to CST detection, making it a promising new approach for detecting the dehydration performance of anaerobic digests.

[0037] Table 2. FTIR water spectra of the samples (1550-1750 cm⁻¹) -1 Sub-peak area ratio

[0038]

[0039]

[0040] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A rapid detection method for the dehydration performance of anaerobic digests, characterized in that, Includes the following steps: (1) Collect anaerobic digests at 400-4000 cm³ -1 Full-band infrared spectral data across the wavelength range; (2) Use infrared spectroscopy analysis software to smooth the infrared spectral data obtained in step (1); (3) Based on the data obtained in step (2), select 1550-1750cm. -1 Infrared water spectral data related to OH bending vibration in the wavelength range were decomposed into 6 sub-peaks (p1-p6) using Gaussian deconvolution and peak fitting software. (4) Based on the sub-peaks p1-p6 obtained in step (3), calculate the area ratio of each sub-peak, and use the ratio of the sum of the area ratios of sub-peaks 1-2 and sub-peaks 3-6, ∑p1-2 / ∑p3-6, to characterize the dehydration performance of the anaerobic digest. The higher the value, the more difficult it is to dehydrate.

2. The rapid detection method for the dehydration performance of anaerobic digests according to claim 1, characterized in that, In step (1), anaerobic digests are collected at 400-4000 cm³. -1 The wavelength was determined using a Fourier transform infrared spectrometer (FTIR).

3. The rapid detection method for the dehydration performance of anaerobic digests according to claim 2, characterized in that, The attenuated total reflection mode (ATR-FTIR) of a Fourier transform infrared spectrometer (FTIR) was used.

4. The rapid detection method for the dehydration performance of anaerobic digests according to claim 1, characterized in that, In step (2), the smoothing process is based on the premise that no characteristic peak information is lost or changed.

5. The rapid detection method for the dehydration performance of anaerobic digests according to claim 1, characterized in that, In step (3), Gaussian deconvolution is performed on the characteristic peaks of the infrared water spectrum using peak fitting software. The Resp FnWidth parameter should be adjusted until the data points are evenly distributed on both sides of the curve.

6. The rapid detection method for the dehydration performance of anaerobic digests according to claim 1, characterized in that, In step (3), the peak fitting should have a fitting parameter Iteration of 7 and a goodness of fit R. 2 It should be greater than 0.99.

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