Determination of lead content in oilseed rape leaves in silicon-free and silicon environments based on deep transfer learning and fluorescence hyperspectral imaging.

The ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon-free and silicon environments. Further, the transfer stacked convolution auto-encoder (T-SCAE) algorithm was proposed based on the stacked convolution auto-encoder (SCAE) algorithm. Fluorescence hyperspectral images of oilseed rape leaves under different Pb stress contents were obtained in the silicon-free and silicon environments. The entire region of oilseed rape leaves was chosen as the region of interest (ROI) to obtain fluorescence spectra. First of all, standard normalized variable (SNV) algorithm was implemented as the preferred preprocessing method, and the fluorescence spectral data processed by SNV was utilized for further analysis. Further, SCAE was used to reduce the dimensionality of the best pre-processed spectral data, and compared with the traditional dimensionality reduction algorithm. Finally, the optimal SCAE deep learning network was transferred to obtain the T-SCAE model to verify the transferability between the deep learning models in silicon-free and silicon environments. The results show that the SVR model based on the depth features extracted by SCAE has the best performance in predicting different Pb concentrations in silicon-free or silicon environments, and the coefficient of determination (Rp2), root mean square error (RMSEP) and residual predictive deviation (RPD) of prediction set in silicon-free or silicon environments were 0.9374, 0.02071 mg/kg and 3.268, and 0.9416, 0.01898 mg/kg and 3.316, respectively. Moreover, the SVR model based on the depth feature extracted by T-SCAE has the best performance in predicting different Pb concentrations in silicon-free and silicon environments, and the Rp2, RMSEP and RPD of the optimal prediction set were 0.9385, 0.02017 mg/kg and 3.291, respectively. The combination of hyperspectral fluorescence imaging and deep transfer learning algorithm can effectively detect different Pb concentrations in oilseed rape leaves in both non-silicon environment and silicon environment.

Nondestructive detection of saponin content in Panax notoginseng powder based on hyperspectral imaging.

This study investigated the feasibility of using hyperspectral imaging (HSI) technique to detect the saponin content in Panax notoginseng (PN) powder. The reflectance hyperspectral images of PN powder samples were collected in the spectral range of 400.6-999.9 nm. Savitzky-golay (SG) smoothing combined with detrending correction was utilized to preprocess the original spectral data. Two model population analysis (MPA) based methods, namely bootstrapping soft shrinkage (BOSS) and iteratively retains informative variables (IRIV) were employed to extract feature wavelengths from the full spectra. A generalized normal distribution optimization based extreme learning machine (GNDO-ELM) model was proposed to establish calibration model between spectra and saponin content, and compared with existing methods (GA-ELM, PSO-ELM and SSA-ELM). The result showed that the IRIV-GNDO-ELM model gave the best performance, with coefficient of determination for prediction (R2P) of 0.953 and root mean square error for prediction (RMSEP) of 0.115%. Therefore, it is possible to determine the saponin content of PN powder by using HSI technique.

Multi-task convolutional neural network for simultaneous monitoring of lipid and protein oxidative damage in frozen-thawed pork using hyperspectral imaging.

Lipid and protein oxidation are the main causes of meat deterioration during freezing. Traditional methods using hyperspectral imaging (HSI) need to train multiple independent models to predict multiple attributes, which is complex and time-consuming. In this study, a multi-task convolutional neural network (CNN) model was developed for visible near-infrared HSI data (400-1002 nm) of 240 pork samples treated with different freeze-thaw cycles (0-9 cycles) to evaluate the feasibility of simultaneously monitoring lipid oxidation (thiobarbituric acid reactive substance content) and protein oxidation (carbonyl content) in pork. The performance of the commonly used partial least squares regression (PLSR) model based on the spectra after pre-processing (Standard normal variate, Savitzky-Golay derivative, and Savitzky-Golay smoothing) and feature selection (Regression coefficients) and single-output CNN model was compared. The results showed that the multi-task CNN model achieved the optimal prediction accuracies for lipid oxidation (R2p = 0.9724, RMSEP = 0.0227, and RPD = 5.2579) and protein oxidation (R2p = 0.9602, RMSEP = 0.0702, and RPD = 4.6668). In final, the changes of lipid and protein oxidation of pork in different freeze-thaw cycles were successfully visualized. In conclusion, the combination of HSI and multi-task CNN method shows the potential of end-to-end prediction of pork oxidative damage. This study provides a new, convenient and automated technique for meat quality detection in the food industry.

Nondestructive testing and visualization of compound heavy metals in lettuce leaves using fluorescence hyperspectral imaging.

This study evaluated the feasibility of nondestructive testing and visualization of compound heavy metals (cadmium and lead) in lettuce leaves using fluorescence hyperspectral imaging. In addition, a method involving wavelet transform and stepwise regression (WT-SR) was proposed to perform dimensionality reduction of fluorescence spectral data. Fluorescent hyperspectral image acquisition and mathematical analysis were carried out on lettuce leaf samples processed with different compound heavy metal concentrations. The entire lettuce leaf sample was selected as a region of interest (ROI). Savitzky-Golay (SG) algorithm, multivariate scatter correction (MSC), standard normalized variable (SNV), first derivative (1st Der) and second derivative (2nd Der) were used to preprocess the ROI fluorescence spectra. Further, the successive projections algorithm (SPA), the competitive adaptive reweighted sampling (CARS), the iteratively retaining informative variables (IRIV) and variable iterative space shrinkage approach (VISSA), and the wavelet transform combined with stepwise regression (WT-SR) were used to reduce the dimension of spectral data. Finally, the multiple linear regression (MLR) algorithm was used to build the compound heavy metal content detection models. The results showed that the MLR models based on the feature data obtained by 1st Der-WT-SR achieved reasonable performance with Rp2 of 0.7905, RMSEP of 0.0269 mg/kg and RPD of 2.477 for Cd content under wavelet fifth layer decomposition, and with Rp2 of 0.8965, RMSEP of 0.0096 mg/kg and RPD of 3.211 for Pb content under wavelet first layer decomposition. The distribution maps of cadmium and lead contents in lettuce leaves were established using the optimal prediction models. The results further confirmed the great potential of fluorescence hyperspectral technology combined with optimization algorithm for the detection of compound heavy metals.

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves.

BACKGROUND: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. RESULTS: Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg-1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. CONCLUSION: The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.

Nondestructive detection and visualization of protein oxidation degree of frozen-thawed pork using fluorescence hyperspectral imaging.

This study evaluated the feasibility of non-destructive detection of carbonyl and total sulfhydryl contents by fluorescence hyperspectral imaging (F-HSI) to visualize the protein oxidation degree of pork during freezing-thawing process. Fluorescence hyperspectral image acquisition and chemical analysis were carried out on pork samples treated with different freeze-thaw cycles. Variational Mode Decomposition (VMD) was used to preprocess the raw spectrum, and Mutual Information-Variance Inflation Factor (MI-VIF) was applied to select the feature wavelengths. The Partial least squares regression (PLSR) models based on selected 19 wavelengths obtained good performance in predicting carbonyl content with R2p of 0.9275 and RMSEP of 0.0812 nmol/mg, and sulfhydryl content with R2p of 0.9512 and RMSEP of 1.2979 nmol/mg. The distribution maps of carbonyl and total sulfhydryl content were established based on the optimal prediction models. The results confirmed that the contents of carbonyl and total sulfhydryl in pork could be successfully predicted by F-HSI, so as to monitor the protein oxidation degree of pork during freezing-thawing.

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage.

S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401-1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.

Development of multi-disturbance bagging Extreme Learning Machine method for cadmium content prediction of rape leaf using hyperspectral imaging technology.

Exploring the cadmium (Cd) pollution in rape is of great significance to food safety and consumer health. In this study, a rapid, nondestructive and accurate method for the determination of Cd content in rape leaves based on hyperspectral imaging (HSI) technology was proposed. The spectral data of rape leaves under different Cd stress from 431 nm to 962 nm were collected by visible-near infrared HSI spectrometer. In order to improve the robustness and accuracy of the regression model, a machine learning algorithm was proposed, named multi-disturbance bagging Extreme Learning Machine (MdbaggingELM). The prediction models of Cd content in rape leaves based on MdbaggingELM and ELM-based method (ELM and baggingELM) were established and compared. The results showed that the model of the proposed MdbaggingELM method performed significantly in the prediction of Cd content, with Rp2 of 0.9830 and RMSEP of 2.8963 mg/kg. The results confirmed that MdbaggingELM is an efficient regression algorithm, which played a positive role in enhancing the stability and the prediction effect of the model. The combination of MdbaggingELM and HSI technology has great potential in the detection of Cd content in rape leaves.

A variable selection method based on mutual information and variance inflation factor.

Feature selection plays a vital role in the quantitative analysis of high-dimensional data to reduce dimensionality. Recently, the variable selection method based on mutual information (MI) has attracted more and more attention in the field of feature selection, where the relevance between the candidate variable and the response is maximized and the redundancy of the selected variables is minimized. However, multicollinearity often is a serious problem in linear models. Collinearity can cause unstable parameter estimation, unreliable models, and weak predictive ability. In order to address this problem, the variance inflation factor (VIF) was introduced for feature selection. Therefore, a variable selection method based on MI combined with VIF was proposed in this paper, called Mutual Information-Variance Inflation Factor (MI-VIF). By calculating the MI between the independent variable and the response variable, the variable with greater MI was selected to maximize the correlation between the independent variable and the response variable. By calculating the VIF between the independent variables, the multicollinearity test was performed. The variables that cause the multicollinearity of the model were eliminated to minimize the collinearity between the independent variables. The proposed method was tested based on two high-dimensional spectral datasets. The regression models (PLSR, MLR) were established based on feature selection through MI-VIF and MI-based methods (MIFS, MMIFS) to compare the prediction accuracy of the models. The results showed that under two datasets, the MI-VIF showed a good prediction performance. Based on the tea dataset, the established MI-VIF-MLR model achieved accuracy with Rp2 of 0.8612 and RMSEP of 0.4096, the MI-VIF-PLSR model achieved accuracy with Rp2 of 0.8614 and RMSEP of 0.4092. Based on the diesel fuels dataset, the established MI-VIF-MLR model achieved accuracy with Rp2 of 0.9707 and RMSEP of 0.6568, the MI-VIF-PLSR model achieved accuracy with Rp2 of 0.9431 and RMSEP of 0.9675. In addition, the MI-VIF was compared with the Successive projections algorithm (SPA), which is a method to reduce the collinearity between variables in the wavelength selection of the near-infrared spectrum. It was found that MI-VIF also had a good predictive effect compared to SPA. It proves that the MI-VIF is an effective variable selection method.

The Tetramethylpyrazine Analogue T-006 Alleviates Cognitive Deficits by Inhibition of Tau Expression and Phosphorylation in Transgenic Mice Modeling Alzheimer's Disease.

T-006, a small-molecule compound derived from tetramethylpyrazine (TMP), has potential for the treatment of neurological diseases. In order to investigate the effect of T-006 prophylactic treatment on an Alzheimer's disease (AD) model and identify the target of T-006, we intragastrically administered T-006 (3 mg/kg) to Alzheimer's disease (AD) transgenic mice (APP/PS1-2xTg and APP/PS1/Tau-3xTg) for 6 and 8 months, respectively. T-006 improved cognitive ability after long-term administration in two AD mouse models and targeted mitochondrial-related protein alpha-F1-ATP synthase (ATP5A). T-006 significantly reduced the expression of phosphorylated-tau, total tau, and APP while increasing the expression of synapse-associated proteins in 3xTg mice. In addition, T-006 modulated the JNK and mTOR-ULK1 pathways to reduce both p-tau and total tau levels. Our data suggested that T-006 mitigated cognitive decline primarily by reducing the p-tau and total tau levels in 3xTg mice, supporting further investigation into its development as a candidate drug for AD treatment.

Removal and recovery of chloride ions in concentrated leachate by Bi(III) containing oxides quantum dots/two-dimensional flakes.

The high concentration of chloride (Cl-) ions in leachate often has negative effects in their harmless treatments, and the common treatments containing the ion exchange method consume excessive antichlors due to their large particle sizes and unfavorable morphologies. Herein, the antichlors of the Bi(III) containing oxides with quantum dots (QDs) or two-dimensional (2D) structures are first explored for the removal and recovery of Cl- ions in concentrated leachate. By using the QDs/2D flakes constructed antichlors of Bi2O3 and the magnetite Bi-Ti composite, the maximum Cl- removal rates of 61.8% and 66.1% are respectively achieved under the optimum conditions. The higher removal efficiency of the magnetite Bi-Ti composite is contributed by its less stable crystal phases of Bi25FeO40/Bi12TiO20, which can proceed more deeply in the removal of Cl- ions compared with that of Bi2O3. The recovered terminal magnetite Bi-Ti precipitate with Bi2O3/BiOCl heterostructure exhibits excellent photocatalytic activity in the degradation of the dechlorinated leachate, where a total organic carbon removal rate of 87.2% is achieved under UV-vis-near-infrared irradiation. Therefore, the selection of Bi(III) containing oxides opens a promising and high-value method for the removal and recovery of Cl- ions in leachate and other waste waters.

Hippocampal Proteomic Alteration in Triple Transgenic Mouse Model of Alzheimer's Disease and Implication of PINK 1 Regulation in Donepezil Treatment.

Donepezil is a clinically approved acetylcholinesterase inhibitor (AChEI) for cognitive improvement in Alzheimer's disease (AD). Donepezil has been used as a first-line agent for the symptomatic treatment of AD, but its ability to modify disease pathology and underlying mechanisms is not clear. We investigated the protective effects and underlying mechanisms of donepezil in AD-related triple transgenic (APPSwe/PSEN1M146V/MAPTP301L) mouse model (3×Tg-AD). Mice (8-month old) were treated with donepezil (1.3 mg/kg) for 4 months and evaluated by behavioral tests for assessment of cognitive functions, and the hippocampal tissues were examined by protein analysis and quantitative proteomics. Behavioral tests showed that donepezil significantly improved the cognitive capabilities of 3×Tg-AD mice. The levels of soluble and insoluble amyloid beta proteins (Aß1-40 and Aß1-42) and senile plaques were reduced in the hippocampus. Golgi staining of the hippocampus showed that donepezil prevented dendritic spine loss in hippocampal neurons of 3×Tg-AD mice. Proteomic studies of the hippocampal tissues identified 3131 proteins with altered expression related to AD pathology, of which 262 could be significantly reversed with donepezil treatment. Bioinformatics with functional analysis and protein-protein interaction (PPI) network mapping showed that donepezil significantly elevated the protein levels of PINK 1, NFASC, MYLK2, and NRAS in the hippocampus, and modulated the biological pathways of axon guidance, mitophagy, mTOR, and MAPK signaling. The substantial upregulation of PINK 1 with donepezil was further verified by Western blotting. Donepezil exhibited neuroprotective effects via multiple mechanisms. In particular, PINK 1 is related to mitophagy and cellular protection from mitochondrial dysfunction, which might play important roles in AD pathogenesis and represent a potential therapeutic target.

Extraction of cobalt(ii) by methyltrioctylammonium chloride in nickel(ii)-containing chloride solution from spent lithium ion batteries.

Spent lithium batteries contain valuable metals such as cobalt, copper, nickel, lithium, etc. After pretreatment and recovery of copper, only cobalt, nickel and lithium were left in the acid solution. Since the chemical properties of cobalt and nickel are similar, separation of cobalt from a solution containing nickel is technically challenging. In this study, Co(ii) was separated from Ni(ii) by chelating Co(ii) with chlorine ions, Co(ii) was then extracted from the aforementioned chelating complexes by methyltrioctylammonium chloride (MTOAC). The effects of concentrations of chlorine ions in the aqueous phase ([Cl-]aq), MTOAC concentrations in organic phase ([MTOAC]org), ratios of organic phase to aqueous phase (O/A), and the initial aqueous pH on cobalt separation were studied. The results showed that [Cl-]aq had a significant impact on cobalt extraction efficiency with cobalt extraction efficiency increasing rapidly with the increase in [Cl-]aq. The effect of initial pH on cobalt extraction efficiency was not significant when it varied from 1 to 6. Under the condition of [Cl-]aq = 5.5 M, [MTOAC]org = 1.3 M, O/A = 1.5, and pH = 1.0, cobalt extraction efficiency reached the maximum of 98.23%, and nickel loss rate was only 0.86%. The stripping rate of cobalt from Co(ii)-MTOAC complexes using diluted hydrochloric acid was 99.95%. By XRD and XRF analysis, the recovered cobalt was in the form of cobalt chloride with the purity of cobalt produced reaching 97.7%. The mode of cobalt extraction was verified to be limited by chemical reaction and the kinetic equation for cobalt extraction was determined to be: R (Co) = 4.7 × 10-3[MTOAC](org) 1.85[Co](aq) 1.25.

Multifunction of Chrysin in Parkinson's Model: Anti-Neuronal Apoptosis, Neuroprotection via Activation of MEF2D, and Inhibition of Monoamine Oxidase-B.

Chrysin, a flavonoid compound existing in several plants, is applied as a dietary supplement because of its beneficial effects on general human health and alleviation of neurological disorders. However, mechanisms underlying neuroprotection of chrysin has not been fully elucidated, and the effects of chrysin on the Parkinson's disease (PD) model in vivo have not been investigated. It is here shown that chrysin protects primary granular neurons against 1-methyl-4-phenylpyridinium ion insult via antiapoptosis by reversing the dysregulated expression of Bcl-2, Bax, and caspase 3. The mechanisms also involved activating transcriptional factor myocyte enhancer factor 2D (MEF2D) via regulation of AKT-GSK3ß signaling. In this in vivo model of PD, chrysin rescued the dopaminergic neurons loss and alleviated the decrease in dopamine level induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine in mice. Moreover, chrysin markedly inhibited monoamine oxidase-B activity in vitro and in vivo. In conclusion, chrysin exerts beneficial effects to PD, possibly through multitarget mechanisms including antineuronal apoptosis, activation of the AKT-GSK3ß/MEF2D pathway, and inhibition of the MAO-B activity.

Effects of stabilization and sludge properties in a combined process of anaerobic digestion and thermophilic aerobic digestion.

A novel process of combining mesophilic (<35°C) anaerobic digestion with the thermophilic (55°C) aerobic digestion process (AN-TAD) was designed to stabilize sludge and economize aeration energy. Effects of stabilization and sludge properties for AN-TAD process were evaluated by batch experiments during a 25 d digestion period. The sludges digested by AN-TAD process achieved the requirements for Class-A sludge standard. The sludge at total solid (TS) 5.4% had the highest value of decay coefficient K(d(55)) at 0.1851 d(-1) among the three TS contents according to the first-order kinetics equation. Oxidation reduction potential at below 0 mV remained for sludges at TSs of 6.5%, 5.4%, and 4.6% for at least 15 d because of initial hydrolytic-acidification. Concentrations of nitrogen and phosphorus in sludges at TSs of 6.5%, 5.4%, and 4.6% gradually increased up to the highest values in the supernatant during the initial 13 d, causing low utilized value in land application as a fertilizer. Prolonging the retention time for more than 15 d was considered because soluble phosphorus precipitated in the solid phase. High content of soluble organic matters of the soluble chemical oxygen demand, protein, and polysaccharide in the supernatant caused deterioration in sludge dewaterability rates.

Influence of temperature on the single-stage ATAD process predicted by a thermal equilibrium model.

Autothermal thermophilic aerobic digestion (ATAD) is a promising biological process that will produce an effluent satisfying the Class A requirements on pathogen control and land application. The thermophilic temperature in an ATAD reactor is one of the critical factors that can affect the satisfactory operation of the ATAD process. This paper established a thermal equilibrium model to predict the effect of variables on the auto-rising temperature in an ATAD system. The reactors with volumes smaller than 10 m(3) could not achieve temperatures higher than 45 °C under ambient temperature of -5 °C. The results showed that for small reactors, the reactor volume played a key role in promoting auto-rising temperature in the winter. Thermophilic temperature achieved in small ATAD reactors did not entirely depend on the heat release from biological activities during degrading organic matters in sludges, but was related to the ambient temperature. The ratios of surface area-to-effective volume less than 2.0 had less impact on the auto-rising temperature of an ATAD reactor. The influence of ambient temperature on the auto-rising reactor temperature decreased with increasing reactor volumes. High oxygen transfer efficiency had a significant influence on the internal temperature rise in an ATAD system, indicating that improving the oxygen transfer efficiency of aeration devices was a key factor to achieve a higher removal rate of volatile solids (VS) during the ATAD process operation. Compared with aeration using cold air, hot air demonstrated a significant effect on maintaining the internal temperature (usually 4-5 °C higher).

Optimization of three operating parameters for a two-step fed sequencing batch reactor (SBR) system to remove nutrients from swine wastewater.

In this study, the effect of three operating parameters, i.e., the first/second volumetric feeding ratio (milliliters/milliliters), the first anaerobic/aerobic (an/oxic) time ratio (minute/minute), and the second an/oxic time ratio (minute/minute), on the performance of a two-step fed sequencing batch reactor (SBR) system to treat swine wastewater for nutrients removal was examined. Central Composite Design, coupled with Response Surface Methodology, was employed to test these parameters at five levels in order to optimize the SBR to achieve the best removal efficiencies for six response variables including total nitrogen (TN), ammonium nitrogen (NH4-N), total phosphorus (TP), dissolved phosphorus (DP), chemical oxygen demand (COD), and biochemical oxygen demand (BOD). The results showed that the three parameters investigated had significant impact on all the response variables (TN, NH4-N, TP, DP, COD, and BOD), although the highest removal efficiency for each individual responses was associated with different combination of the three parameters. The maximum TN, NH4-N, TP, DP, COD, and BOD removal efficiencies of 96.38%, 95.38%, 93.62%, 94.3%, 95.26%, and 92.84% were obtained at the optimal first/second volumetric feeding ratio, first an/oxic time ratio, and second an/oxic time ratio of 3.23, 0.4, and 0.8 for TN; 2.64, 0.72, and 0.76 for NH4-N; 3.08, 1.16, and 1.07 for TP; 1.32, 0.81, and 1.0 for DP; 2.57, 0.96, and 1.12 for COD; and 1.62, 0.64, and 1.61 for BOD, respectively. Good linear relationships between the predicted and observed results for all the response variables were observed.

Characteristics of oxidation-reduction potential, VFAs, SCOD, N, and P in an ATAD system under different thermophilic temperatures.

One-stage autoheated thermophilic aerobic digestion (ATAD) can stabilize sludge to meet class A standard. In this study, batch experiments were conducted to investigate the characteristics of oxidation-reduction potential (ORP), volatile fatty acids (VFAs), soluble chemical oxygen demand (SCOD), and nutrients at different temperatures (45, 55, and 65 °C) in the one-stage ATAD. Results showed that the ORP values remained between approximately -350 and -120 mV in the primary 5-day digestion despite of excessive aeration in the digester, indicating that the aeration level could be decreased in an ATAD system to save energy. The pH exhibited a poor correlation (R (2) < 0.11) with ORP, indicating that some variables synthetically effected on pH. Digestion at 65 °C caused more intercellular compounds released because of the highest concentrations of SCOD, VFA, nitrogen (N) and phosphorus (P) in the solution among three digestion temperatures. The volatile suspended solid (VSS) removal rate for sludge at 55 °C was the highest among three digestion temperatures, reaching 41.4 % on day 13 and meeting Class A standard. VSS removal rate of 30.1 % under 65 °C did not satisfy the effluent standard because of the high soluble content of ammonium nitrogen. The majority of nitrogen and phosphorus left in the sludge supernatant under 65 °C could hinder its further use for land applications. Therefore, the optimal temperature of 55 °C is suitable for the ATAD process.

The influence of factors on dewaterability of one-stage autothermal thermophilic aerobically digested sludges.

One-stage auto thermophilic aerobic digestion (ATAD) could achieve the same sludge stabilization efficiency as two- or multi-stage ATAD process does. However, the dewaterability of sludge might be affected through thermophilic digestion because of the release of intracellular substances under the thermophilic temperatures. This paper investigated which operation factors affect intracellular substances released in the liquid phase of sludge that lead to different extent of dewaterability. The results showed that optimal digestion time needs to be prolonged up to 480 h to avoid a deteriorated dewaterability phase. The deterioration in dewaterability of the sludge could be minimized at a retention time of 360 h (i.e., 15 days) under a digestion temperature of 65 °C. Specific capillary suction time (SCST) had quadratic correlations with SCOD, protein (PN) and polysaccharides (PS) in the liquid phase. The coefficient between SCST and the ratio of PN/PS had a value of 0.9764, indicating that the sludge dewaterability was significantly deteriorated by biopolymer of protein and polysaccharides in supernatant.

Combined mesophilic anaerobic and thermophilic aerobic digestion process: effect on sludge degradation and variation of sludge property.

One-stage autothermal thermophilic aerobic digestion (ATAD) is effective for the reduction of volatile solids (VSs) and pathogen in sewage sludges. A novel process of combining mesophilic (<35 °C) anaerobic digestion with a thermophilic (55 °C) aerobic digestion process (AN/TAD) occurred in a one-stage digester, which was designed for aeration energy savings. The efficiency of sludge degradation and variation of sludge properties by batch experiments were evaluated for the AN/TAD digester with an effective volume of 23 L for 30 days compared with conventional thermophilic aerobic digestion (TAD). The AN/TAD system can efficiently achieve sludge stabilization on the 16th day with a VS removal rate of 38.1 %. The AN/TAD system was operated at lower ORP values in a digestion period with higher contents of total organic compounds, volatile fatty acids, protein, and polysaccharide in the soluble phase than those of the TAD system, which can rapidly decreased and had low values in the late period of digestion for the AN/TAD system. In the AN/TAD system, intracellular substances had lysis because of initial hydrolytic acidification.