Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge

The responses and tolerance of photosynthetic system in Chlorella vulgaris to the pharmaceutical pollutant carbamazepine

Screening for sensitive toxicological indicators and understanding algal tolerance to pharmaceutical contaminants (PhCs) are essential for assessing PhCs risk and their removal by microalgae. Carbamazepine (CBZ) showed adverse effects on microalgae, but the specific toxicity mechanisms on the most sensitive algal photosynthetic system (PS) remain limited. This study delved into the impact of CBZ exposure on the growth, cell viability, pigment content, and PS of Chlorella vulgaris. The findings revealed a notable inhibition of C. vulgaris growth by CBZ, with an IC50 value of 27.2 mg/L at 96 h. CBZ exposure induced algal membrane damage and cell viability. Intriguingly, CBZ drastically diminished intracellular pigment levels, notably showing "low promotion and high inhibition" of chlorophyll b (Chl b) by 72 h. Moreover, the study identified a decreased number of active reaction centers (RCs) within algal PSII alongside inhibited electron transport from QA to QB on the PSII receptor side, leading to PSII disruption. As an adaptive response to CBZ stress, C. vulgaris stimulated its Chl b synthesis, increased non-photochemical quenching (NPQ), and adapted its tolerance to bright light. Additionally, the alga attempted to compensate for the CBZ-induced reduction in electron transfer efficiency at the PSII receptor side and light energy utilization by increasing its electron transfer from downstream. Principal component analysis (PCA) further verified that the parameters on non-photochemical dissipation, electron transport, and integrative performance were the most sensitive algal toxicological indicators for CBZ exposure, and algal PS has energy protection capability through negative feedback regulation. However, prolonged exposure to high doses of CBZ will eventually result in permanent damage to the algal PS. Hence, attention should be paid to the concentration of CBZ in the effluent and the exposure time, while methods to mitigate algal photodamage should be appropriately sought for algal treatment of dense effluents.

Analysis of the effect of intrinsic sludge properties on sludge drying characteristics from both sludge composition and type scales

Convective drying is an effective method for reducing the moisture content of the sludge. Fewer studies have discussed the effect of sludge physicochemical properties on drying compared to air parameters. Eleven types of sludge were collected, and ultimate analysis, proximate analysis, and heat value analysis were performed. Meanwhile, the maximum drying rate (umax) of sludge convection drying at 70 °C was determined. The results showed that the cumulative variance contribution of the two extracted principal components (PCs) was 92.5 %. Then, a regression model of umax was developed based on the extracted PCs. The coefficient of determination of this model was 0.788, and the difference was statistically significant, with a negative correlation between umax and PC2. Further, the principal component score plot enabled the traceability of the integrated sludge, and based on this classification results, the drying characteristics of various types of sludge were discussed, and a high correlation (R2 = 0.9590) between the initial moisture content of sludge and umax was found. Mathematical models between sludge physicochemical properties and drying characteristics can be effectively developed from both sludge composition and type scales. This exploration deepened the knowledge of sludge drying and facilitates the prediction of drying rate.

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model

Electrocoagulation (EC) is a promising alternative for decentralized drinking water treatment in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality in influent remain a significant challenge for rural decentralized water supply, which was a potential threat to water safety. The frequent operation was required to ensure the effluent water quality by the experienced technicians, who were in shortage in rural areas. If the operational parameters prediction model based on water quality could be established, it might reduce the dependence on technicians. Therefore, an artificial neural network (ANN) model combined with genetic algorithm (GA) was used to establish a prediction model for unattended intelligent operation. Data on water quality and operational parameters were collected from a practical EC system in a decentralized water treatment plant. Seven water quality parameters (e.g., turbidity, temperature, pH and conductivity) were selected as input variables and the operational current was employed as the output. A non-linear relationship between water quality parameters and the operational current was verified by correlation analysis and principal component analysis (PCA). The mean squared error (MSE) and coefficient of determination (R²) were used as evaluation indexes to optimize the structure of the GA-ANN model. Influent turbidity was identified to be crucial in the GA-ANN model by model interpretation using sensitivity analysis and scenario analysis. The Garson weight of turbidity in the seven input variables achieved 45.4%. The predictive accuracy of the GA-ANN model sharply declined from 90% to 67.1% when influent turbidity data were absent. In addition, it was estimated that energy consumption savings of the GA-ANN method declined by 14.2% in comparison with the gradient control method. This study verifies the feasibility and stability of machine learning strategy for unattended operation in the rural decentralized water treatment plant.

Double-edged sword effects of dissimilatory nitrate reduction to ammonium (DNRA) bacteria on anammox bacteria performance in an MBR reactor

Dissimilatory nitrate reduction to ammonium (DNRA) bacteria imposing double-edged sword effects on anammox bacteria were investigated in an anammox-membrane bioreactor (MBR) experiencing an induced crash-recovery event. During the experiment, the anammox-MBR was loaded with NH₄⁺-N:NO₂^--N ratios (RatioNH₄⁺-N: NO₂^--N) of 1.20-1.60. Initially, the anammox-MBR removed over 95% of 100 mg/L NH₄⁺-N and 132 mg/L NO₂^--N (RatioNH₄⁺-N: NO₂^--N = 0.76, the well accepted stoichiometric RatioNH₄⁺-N: NO₂^--N for anammox) in the influent (Stage 0). Then, we induced a system crash-recovery event via nitrite shock loadings to better understand responses from different guilds of bacteria in anammox-MBR, loaded with 1.60 RatioNH₄⁺-N: NO₂^--N with 100 mg/L NO₂^--N in the influent (Stage 1). Interestingly, the nitrogen removal by anammox bacteria was maintained for about 20 days before starting to decrease significantly. In Stage 2, we further increased influent nitrite concentration to 120 mg/L (1.33 RatioNH₄⁺-N: NO₂^--N) to simulate a high nitrite toxicity scenario for a short period of time. As expected, nitrogen removal efficiency dropped to only 16.8%. After the induced system crash, anammox-MBR performance recovered steadily to 93.2% nitrogen removal with a 1.25 RatioNH₄⁺-N:NO₂^--N and a low nitrite influent concentration of 80 mg/L NO₂^--N. Metagenomics analysis revealed that a probable causality of the decreasing nitrogen removal efficiency in Stage 1 was the overgrowth of DNRA-capable bacteria. The results showed that the members within the Ignavibacteriales order (21.7%) out competed anammox bacteria (17.0%) in the anammox-MBR with elevated nitrite concentrations in the effluent. High NO₂^--N loading (120 mg N/L) further caused the predominant Candidatus Kuenenia spp. were replaced by Candidatus Brocadia spp. Therefore, it was evident that DNRA bacteria posed negative effects on anammox with 1.60 RatioNH₄⁺-N: NO₂^--N. Also, when 120 mg/L NO₂^--N fed to anammox-MBR (RatioNH₄⁺-N: NO₂^--N = 1.33), canonical denitrification became the primary nitrogen sink with both DNRA and anammox activities decreased. They probably fed on lysed microbial cells of anammox and DNRA. In Stage 3, a low RatioNH₄⁺-N: NO₂^--N (1.25) with 80 mg/L NO₂^--N was used to rescue the system, which effectively promoted DNRA-capable bacteria growth. Although anammox bacteria's abundance was only 7.7% during this stage, they could be responsible for about 90% of the total nitrogen removal during this stage.

Aerobic granular sludge development using diatomite for low-strength wastewater treatment

This paper presents an assessment of the start-up performance of aerobic granular sludge (AGS) for the treatment of low-strength (chemical oxygen demand, COD < 200 mg/L) domestic wastewater by the application of a diatomite carrier. The feasibility was evaluated in terms of the start-up period and stability of the aerobic granules as well as COD and phosphate removal efficiencies. A single pilot-scale sequencing batch reactor (SBR) was used and operated separately for the control granulation and granulation with diatomite. Complete granulation (granulation rate ≥ 90%) was achieved within 20 days for the case of diatomite with an average influent COD concentration of 184 mg/L. In comparison, control granulation required 85 days to accomplish the same feat with a higher average influent COD concentration (253 mg/L). The presence of diatomite solidifies the core of the granules and enhances physical stability. AGS with diatomite recorded the strength and sludge volume index of 18 IC and 53 mL/g suspended solids (SS) which is superior to control AGS without diatomite (19.3 IC, 81 mL/g SS). Quick start-up and achievement of stable granules lead to an efficient COD (89%) and phosphate removal (74%) in 50 days of bioreactor operation. Interestingly, this study revealed that diatomite has some special mechanism in enhancing the removal of both COD and phosphate. Also, diatomite has a significant influence on microbial diversity. The result of this research implies that the advanced development of granular sludge by using diatomite can provide promising low-strength wastewater treatment.

Odor emission assessment of different WWTPs with Extended Aeration Activated Sludge and Rotating Biological Contactor technologies in the province of Cordoba (Spain)

In this study, five urban WWTPs (Wastewater Treatment Plant) with different biological treatment (Extended Aeration Activated Sludge - EAAS; Rotating Biological Contactor - RBC), wastewater type (Urban; Industrial) and size, were jointly evaluated. The aim was twofold: (1) to analyze and compare their odor emissions, and (2) to identify the main causes of its generation from the relationships between physico-chemical, respirometric and olfactometric variables. The results showed that facilities with EAAS technology were more efficient than RBC, with elimination yields of organic matter higher than 90%. In olfactometric terms, sludge managements facilities (SMFs) were found to be the critical odor source in all WWTPs compared to the Inlet point (I) or Post primary treatment (PP), and for seasonal periods with ambient temperature higher than 25 °C. Moreover, the global odor emissions quantified in all SMFs revealed that facilities with EAAS (C-WWTP, V-WWTP and Z-WWTP) had a lower odor contribution (19,345, 14,800 and 11,029 ou_E/s·m², respectively) than for those with RBC technology (P-WWTP and NC-WWTP) which accounted for 19,747 ou_E/s·m² and 80,061 ou_E/s·m², respectively. In addition, chemometric analysis helped to find groupings and differences between the WWTPs considering the wastewater (71.27% of total variance explained) and sludge management (64.52% of total variance explained) lines independently. Finally, odor emissions were adequately predicted from the physico-chemical and respirometric variables in the wastewater (r² = 0.8738) and sludge (r² = 0.9373) lines, being pH, volatile acidity and temperature (wastewater line), and pH, moisture, temperature, SOUR (Specific Oxygen Uptake Rate) and OD₂₀ (Cumulative Oxygen Demand at 20 h) (sludge line) the most influential variables.

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

N-Acyl-Homoserine Lactone (AHL)-Mediated Microalgal-Bacterial Communication Driving Chlorella-Activated Sludge Bacterial Biofloc Formation

N-acyl-homoserine lactones (AHLs) as autoinducers of Gram-negative bacteria for quorum sensing regulation have shown positive effects on the production of aromatic proteins in extracellular polymeric substances (EPSs) during bioflocculation. To investigate the role of AHLs in aromatic protein production, a Chlorella-bacteria system with great bioflocculation was established via fed-batch cultivation. Tryptophan and aromatic proteins as the main compounds in the EPS of bioflocs showed an increasing trend during fed-batch cultivation. The Chlorella cells only secreted tryptophan rather than aromatic proteins during axenic cultivation. N-dodecanoyl-l-homoserine lactone (C12-HSL) was correlated with the flocculation activity and extracellular protein content of bioflocs during fed-batch cultivation. The addition of exogenous C12-HSL enhanced the flocculation activity of the Chlorella-bacteria system and aromatic protein production in the EPS. Chlorella cells sensed exogenous C12-HSL and significantly upregulated the aromatic protein synthesis pathway during axenic cultivation. In addition, vanillin as a quorum-sensing inhibitor suppressed the positive effect of C12-HSL on flocculation activity and aromatic protein production and synthesis. This result indicated that vanillin intercepts the response of Chlorella cells to C12-HSL. Overall, C12-HSL is supposed to be an important signal molecule to achieve communication between Chlorella and Gram-negative bacteria and subsequently induce Chlorella cells to produce aromatic proteins for biofloc formation.

Application of data smoothing and principal component analysis to develop a parameter ranking system for the anaerobic digestion process

A parameter ranking system to enhance data interpretation of the anaerobic digestion process was developed using principal component analysis (PCA) and data smoothing. The experimental data collected from the start-up operation of a pilot-scale, two-stage anaerobic digester for food wastewater treatment was used to demonstrate the enhanced PCA procedures. The correlation of multiple parameters in the anaerobic digestion process could be identified by the ranked parameters based on statistically scored outcomes. According to the parameters ranked for their impact on biogas production and methane yield, the biochemical oxygen demand (BOD) in the food wastewater was shown to have the highest correlated ranks with a score of 0.246, and the pH and ammonium in the food wastewater were shown to have lower ranked scores of 0.834 and 1.019, respectively. Therefore, ammonia toxicity and pH shock might not have had significant influence, and the operating priority should be focused on the organic loads. This application of PCA to anaerobic digestion can be helpful for addressing stabilization issues by identifying the core parameters that primarily contribute to early detection of operating problems.

Understanding the combination of fractional factorial design and chemometrics analysis for screening super-saturable quercetin-self nano emulsifying components

Quercetin is formulated in a super saturable - self-nano emulsifying (SS-SNE) to increase its stability and bioavailability. This study focuses on the screening design for SS-SNE components with a fractional factorial design (FrFD) approach and chemometric analysis. The FrFD method was chosen because it provides comprehensive benefits. The oil components used are canola and grape seed oil. Croduret 50-SS was selected as a surfactant and PEG 400 as a co-surfactant. The interaction of SNE components was evaluated using FTIR-ATR instrumentation. SNE droplet morphology was observed using a transmission electron microscope (TEM). The selected formulas were grape seed oil as oil phase at 19.6%, croduret at 60%, and PEG 400 as co-surfactant with a concentration of 16.6%. The selected formula has a droplet size of 133.27 nm, PDI of 0.181, the zeta potential of 17.00 mV, electrophoretic mobility of 1.332 µmcm/Vs, emulsification time of 10.05 seconds, a viscosity of 370.147 mPa.s, and a drug load of 31.70 mg/mL. The components of grape seed oil, croduret, and PEG 400 resulted in a quercetin carrier SNE formula that met the criteria. FrFD design and chemometric analysis in the screening process can help determine the selected formula very effectively and efficiently.

Co3O4@TiO2@Y2O3 nanocomposites for a highly sensitive CO gas sensor and quantitative analysis

In order to predict the early failure of organic insulator, Co₃O₄@TiO₂@Y₂O₃ nanocomposites was prepared and characterized (XRD, SEM, EDS, FTIR, UV-vis-NIR, XPS) to detect decomposition CO gas. A simple experimental platform was built to verify the excellent adsorption, stability, selectivity and repeatability of the composite. Then, the mechanism of adsorption enhancement was analyzed by heterojunction. Aiming at 170 sets of gas sensing data sets, Successive Projections Algorithm (SPA) was used to extract data features, and grey wolf optimization vector machine regression (GWO-SVR) model was established to predict carbon monoxide concentration. The correlation coefficient (R_P), root mean square error (RMSEP) and calculation time of prediction set were 99.3025%, 0.0418 and 1.47 s, respectively. Therefore, the combination of the superior properties of a composite sensitive material and the small sample quantitative prediction model is a promising method for gas sensors in the future.

Dual-mode spectrum of transmission and fluorescence using single ultraviolet LED light source and their application in analyzing total bilirubin in serum

In order to improve the accuracy of spectral analysis of complex solutions, based on the "M + N" theory, this paper proposes to use single (365 nm ± 5 nm) ultraviolet LED as emission light to detect transmission spectrum and excited fluorescence spectrum. Taking the total bilirubin in serum as the measurement object, the dual-mode spectrum of transmission and fluorescence about serum is collected, which increases the amount of information. In order to verify the effectiveness of the method proposed in this paper, the transmission and fluorescence spectra of the samples were also collected. Then three models of total bilirubin concentration are established by transmission spectrum, fluorescence spectrum and dual-mode spectrum of transmission and fluorescence respectively. Through the comparison of the parameters of the three models, the model established by dual-mode spectrum of transmission and fluorescence is good. The Rc of the model is 0.91 and the RMSEC is 3.00 (μmol/L). The Rp is 0.92, the RMSEP is 3.53 (μmol/L). Compared with transmission spectrum modeling and fluorescence spectrum modeling, the RMSEP of dual-mode spectrum modeling was reduced by 34.8% and 22.6% respectively. The experimental results show that the measurement method of dual-mode spectrum of transmission and fluorescence by using a single ultraviolet light source proposed in this paper based on the "M + N" theory increases the information of solution composition, which provides a new method for the analysis of the same characteristic components.