Mixotrophic denitrification of waste activated sludge fermentation liquid as an alternative carbon source for nitrogen removal: Reducing N2O emissions and costs.

Heterotrophic-sulfur autotrophic denitrification (HAD) has been proposed to be a prospective nitrogen removal process. In this work, the potential of fermentation liquid (FL) from waste-activated sludge (WAS) as the electron donor for denitrification in the HAD system was explored and compared with other conventional carbon sources. Results showed that when FL was used as a carbon source, over 99% of NO3--N was removed and its removal rate exceeded 14.00 mg N/g MLSS/h, which was significantly higher than that of methanol and propionic acid. The produced sulfate was below the limit value and the emission of N2O was low (1.38% of the NO3--N). Microbial community analysis showed that autotrophic denitrifiers were predominated in the HAD system, in which Thiobacillus (16.4%) was the dominant genus. The economic analysis showed the cost of the FL was 0.062 €/m3, which was 30% lower than that in the group dosed with methanol. Our results demonstrated the FL was a promising carbon source for the HAD system, which could reduce carbon emission and cost, and offer a creative approach for waste-activated sludge resource reuse.

Valorization of sewage sludge for facile and green wood bio-adhesives production.

A method is presented herein for the design of wood bio-adhesives using sewage sludge extracts (SSE). SSE was extracted from SS using deep eutectic solvents and processed with glycerol triglycidyl ether (GTE) to disrupt the secondary structure of proteins. An additive was also used to improve mechanical performance. The resulting bio-adhesive (SSE/GTE@TA) had a wet shear strength of 0.93 MPa, meeting the Chinese national standard GB/T 9846-2015 (≥0.7 MPa). However, the high polysaccharide content in SSE would weaken the mechanical properties of wood bio-adhesives. The key to improve bio-adhesive quality was the formation of a strong chemical bond via Maillard reaction as well as higher temperatures (140 °C) to reduce polysaccharide content via dehydration. This approach has lower environmental impact and higher economic efficiency compared to incineration and anaerobic digestion of sewage sludge. This work provides a new perspective on the high-value utilization of SS and offers a novel approach to developing bio-adhesives for the wood industry.

Recovery of phosphorus from wastewater containing humic substances through vivianite crystallization: Interaction and mechanism analysis.

Vivianite crystallization has been regarded as a suitable option for recovering phosphorus (P) from P-containing wastewater. However, the presence of humic substances (HS) would inevitably affect the formation of vivianite crystals. Therefore, the influences of HS on vivianite crystallization and the changes in the harvested vivianite crystals were investigated in this study. The results suggested the inhibition effect of 70 mg/L HS on vivianite crystallization reached 12.24%, while it could be attenuated by increasing the pH and Fe/P ratio of the solution. Meanwhile, the addition of HS altered the size, purity, and morphology of recovered vivianite crystals due to the blockage of the growth sites on the crystal surface. Additionally, the formation of phosphate ester group, hydrogen bonding, and COOH-Fe2+ complexes are the potential mechanisms of HS interaction with vivianite crystals. The results obtained herein will help to elucidate the underlying mechanism of HS on vivianite crystallization from P-containing wastewater.

Revealing the mechanism of quartz sand seeding in accelerating phosphorus recovery from anaerobic fermentation supernatant through vivianite crystallization.

The recovery of phosphorus (P) through vivianite crystallization offers a promising approach for resource utilization in wastewater treatment plants. However, this process encounters challenges in terms of small product size and low purity. The study aimed to assess the feasibility of using quartz sand as a seed material to enhance P recovery and vivianite crystal characteristics from anaerobic fermentation supernatant. Various factors, including seed dosage, seed size, Fe/P ratio, and pH, were systematically tested in batch experiments to assess their influence. Results demonstrated that the effect of seed enhancement on vivianite crystallization was more pronounced under higher seed dosages, smaller seed sizes, and lower pH or Fe/P ratio. The addition of seeds increased P recovery by 4.43% in the actual anaerobic fermentation supernatant and also augmented the average particle size of the recovered product from 19.57 to 39.28 µm. Moreover, introducing quartz sand as a seed material effectively reduced co-precipitation, leading to a notable 12.5% increase in the purity of the recovered vivianite compared to the non-seeded process. The formation of an ion adsorption layer on the surface of quartz sand facilitated crystal attachment and growth, significantly accelerating the vivianite crystallization rate and enhancing P recovery. The economic analysis focused on chemical costs further affirmed the economic viability of using quartz sand as a seed material for P recovery through vivianite crystallization, which provides valuable insights for future research and engineering applications.

Sludge reduction and microbial community evolution of activated sludge induced by metabolic uncoupler o-chlorophenol in long-term anaerobic-oxic process.

Excess sludge management is a restrictive factor for the development of municipal wastewater treatment plants. The addition of metabolic uncouplers has been proven to be effective in sludge reduction. However, the long-term effect of metabolic uncoupler o-chlorophenol (oCP) on the biological wastewater treatment system operated in anaerobic-oxic mode is still unclear. To this end, two parallel reactors operated in anaerobic-oxic mode with and without 10 mg/L of oCP addition were investigated for 91 days. The results showed that 56.1 ± 2.3% of sludge reduction was achieved in the oCP-added system, and the nitrogen and phosphorus removal ability were negatively affected. Dosing oCP stimulated the formation of microbial products and increased the DNA concentration, but resulted in a decrease in the electronic transport activity of activated sludge. Microbial community analysis further demonstrated that a significant reduction of bacterial richness and diversity occurred after oCP dosing. However, after stopping oCP addition, the pollutant removal ability of activated sludge was gradually increased, but the sludge yield, as well as species richness and diversity, did not recover to the previous level. This study will provide insightful guidance on the long-term application of metabolic uncouplers in the activated sludge system.

Phosphorus recovery from synthetic anaerobic fermentation supernatant via vivianite crystallization: Coupling effects of various physicochemical process parameters.

Recovery of phosphorus (P) via vivianite crystallization is an effective strategy to recycle resources from the anaerobic fermentation supernatant. However, the presence of different components in the anaerobic fermentation supernatant (e.g., polysaccharides and proteins) might alter conditions for optimal growth of vivianite crystals, resulting in distinct vivianite characteristics. In the present study, the effect of different components on vivianite crystallization was explored. Then, the reaction parameters (pH, Fe/P, and stirring speed) for P recovery from synthetic anaerobic fermentation supernatant as vivianite were optimized using response surface methodology, and the relationship between crystal properties and supersaturation was elucidated using a thermodynamic equilibrium model. The optimized values for pH, Fe/P, and stirring speed were found to be 7.8, 1.74, and 500 rpm respectively, resulting in 90.54 % P recovery efficiency. Moreover, the variation of reaction parameters did not change the crystalline structure of the recovered vivianite but influenced its morphology, size, and purity. Thermodynamic analysis suggested the saturation index (SI) of vivianite increased with increasing pH and Fe/P ratio, leading to a facilitative effect on vivianite crystallization. However, when the SI was >11, homogenous nucleation occurred so that the nucleation rate was much higher than the crystal growth rate, causing a smaller crystal size. The findings presented herein will be highly valued for the future large-scale application of the vivianite crystallization process for wastewater treatment.

Rapid quantification of intracellular polyhydroxyalkanoates via fluorescence techniques: A critical review.

Polyhydroxyalkanoates (PHA) are promising bioplastics with excellent physicochemical properties and biodegradability, whereas PHA products suffer from high manufacturing costs. To reduce costs of PHA production, experiments with mixed microbial cultures and low-cost substrates have been conducted widely, where rapid and robust PHA quantification methods are necessary. Compared with traditional gas chromatography methods, PHA fluorescence quantification (PHA-FQ) methods may be quicker, safer and more suitable for modern experiments with high throughput requirements. However, practical applications of PHA-FQ methods are still limited. Therefore, this review provides a comprehensive understanding of PHA-FQ methods. Performance of PHA-staining fluorochromes, relevant spectral properties, and important staining procedures are summarized. Current developments of PHA-FQ protocols are critically reviewed. Main considerations needed to make PHA-FQ protocol reliable are comprehensively discussed. Finally, potential improvements in various aspects of PHA-FQ methods are highlighted. This review could help researchers develop more effective PHA-FQ methods and facilitate future experiments related to PHA.

Integrating mechanistic and deep learning models for accurately predicting the enrichment of polyhydroxyalkanoates accumulating bacteria in mixed microbial cultures.

The enrichment of polyhydroxyalkanoates (PHA) accumulating bacteria (PAB) in mixed microbial cultures (MMC) is extremely difficult to be predicted and optimized. Here we demonstrate that mechanistic and deep learning models can be integrated innovatively to accurately predict the dynamic enrichment of PAB. Well-calibrated activated sludge models (ASM) of the PAB enrichment process provide time-dependent data under different operating conditions. Recurrent neural network (RNN) models are trained and tested based on the time-dependent dataset generated by ASM. The accurate prediction performance is achieved (R2 > 0.991) for three different PAB enrichment datasets by the optimized RNN model. The optimized RNN model can also predict the equilibrium concentration of PAB (R2 = 0.944) and corresponding time, which represents the end of the PAB enrichment process. This study demonstrates the strength of integrating mechanistic and deep learning models to predict long-term variations of specific microbes, helping to optimize their selection process for PHA production.

Automated machine learning-based prediction of microplastics induced impacts on methane production in anaerobic digestion.

Microplastics as emerging pollutants have been heavily accumulated in the waste activated sludge (WAS) during biological wastewater treatment, which showed significantly diverse impacts on the subsequent anaerobic sludge digestion for methane production. However, a robust modeling approach for predicting and unveiling the complex effects of accumulated microplastics within WAS on methane production is still missing. In this study, four automated machine learning (AutoML) approach was applied to model the effects of microplastics on anaerobic digestion processes, and integrated explainable analysis was explored to reveal the relationships between key variables (e.g., concentration, type, and size of microplastics) and methane production. The results showed that the gradient boosting machine had better prediction performance (mean squared error (MSE) = 17.0) than common neural networks models (MSE = 58.0), demonstrating that the AutoML algorithms succeeded in predicting the methane production and could select the best machine learning model without human intervention. Explainable analysis results indicated that the variable of microplastic types was more important than the variable of microplastic diameter and concentration. The existence of polystyrene was associated with higher methane production, whereas increasing microplastic diameter and concentration both inhibited methane production. This work also provided a novel modeling approach for comprehensively understanding the complex effects of microplastics on methane production, which revealed the dependence relationships between methane production and key variables and may be served as a reference for optimizing operational adjustments in anaerobic digestion processes.

Exploring the feasibility of nitrous oxide reduction and polyhydroxyalkanoates production simultaneously by mixed microbial cultures.

Nitrous oxide (N2O), as a powerful greenhouse gas, has drawn increasing attention in recent years and different strategies for N2O reduction were explored. In this study, a novel strategy for valuable polyhydroxyalkanoates (PHA) production coupling with N2O reduction by mixed microbial cultures (MMC) using different substrates was evaluated. Results revealed that N2O was an effective electron acceptor for PHA production. The highest PHA yield (0.35 Cmmol PHA/Cmmol S) and PHA synthesis rate (227.47 mg PHA/L/h) were obtained with acetic acid as substrate. Low temperature (15℃) and pH of 8.0 were beneficial for PHA accumulation. Results of the thermogravimetric analysis showed that PHA produced with N2O as electron acceptor has better thermal stability (melting temperature of 99.4℃ and loss 5% weight temperature of 211.4℃). Our work opens up new avenues for simultaneously N2O reduction and valuable bioplastic production, which is conducive to resource recovery and climate protection.

An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process.

Data-driven models are suitable for simulating biological wastewater treatment processes with complex intrinsic mechanisms. However, raw data collected in the early stage of biological experiments are normally not enough to train data-driven models. In this study, an integrated modeling approach incorporating the random standard deviation sampling (RSDS) method and deep neural networks (DNNs) models, was established to predict volatile fatty acid (VFA) production in the anaerobic fermentation process. The RSDS method based on the mean values (x¯) and standard deviations (α) calculated from multiple experimental determination was initially developed for virtual data augmentation. The DNNs models were then established to learn features from virtual data and predict VFA production. The results showed that when 20000 virtual samples including five input variables of the anaerobic fermentation process were used to train the DNNs model with 16 hidden layers and 100 hidden neurons in each layer, the best correlation coefficient of 0.998 and the minimal mean absolute percentage error of 3.28% were achieved. This integrated approach can learn nonlinear information from virtual data generated by the RSDS method, and consequently enlarge the application range of DNNs models in simulating biological wastewater treatment processes with small datasets.

Characterization of interactions between a metabolic uncoupler O-chlorophenol and extracellular polymeric substances of activated sludge.

Metabolic uncouplers are widely used for the in-situ reduction of excess sludge from activated sludge systems. However, the interaction mechanism between the metabolic uncouplers and extracellular polymeric substances (EPS) of activated sludge is unknown yet. In this study, the interactions between a typical metabolic uncoupler, o-chlorophenol (oCP), and the EPS extracted from activated sludge were explored using a suite of spectral methods. The binding constants calculated for the four peaks of three-dimensional excitation-emission matrix fluorescence were in a range of 1.24-1.76 × 103 L/mol, implying that the tyrosine protein-like substances governed the oCP-EPS interactions. Furthermore, the results of Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy and 1H nuclear magnetic resonance indicated that the carboxyl, carbonyl, amine, and hydroxyl groups of EPS were the main functional groups involved in the formation of the oCP-EPS complex. The results of this study are useful for understanding the interactions between metabolic uncouplers and the EPS of activated sludge as well as their fates in biological wastewater treatment systems.