A new method to measure and model dynamic oxygen microdistributions in moving biofilms

Development of a flow-cell bioreactor for immobilized sulfidogenic sludge characterization using electrochemical H2S microsensors

The sulfate-reduction process plays a crucial role in the biological valorization of SOx gases. However, a complete understanding of the sulfidogenic process in bioreactors is limited by the lack of technologies for characterizing the sulfate-reducing activity of immobilized biomass. In this work, we propose a flow-cell bioreactor (FCB) for characterizing sulfate-reducing biomass using H2S microsensors to monitor H2S production in real-time within a biofilm. To replace natural immobilization through extracellular polymeric substance production, sulfidogenic sludge was artificially immobilized using polymers. Physical and sulfate-reducing activity studies were performed to select a polymer-biomass matrix that maintained sulfate-reducing activity of biomass while providing strong microbial retention and mechanical strength. Several operational conditions of the sulfidogenic reactor allowed to obtain a H2S profiles under different inlet sulfate loads and, additionally, 3D mapping was assessed in order to perform a hydraulic characterization. Besides, the effects of artificial immobilization on biodiversity were investigated through the characterization of microbial communities. This study demonstrated the appropriateness of immobilized-biomass for characterization of sulfidogenic biomass in FCB using H2S electrochemical microsensors, and beneficial microbiological communities shifts as well as enrichment of sulfate-reducing bacteria have been confirmed.

Integrated control strategy for dual sludge ages in the high-concentration powder carrier bio-fluidized bed (HPB) technology: Enhancing municipal wastewater treatment efficiency

The high-concentration powder carrier bio-fluidized bed (HPB) technology is an emerging approach that enables on-site upgrading of wastewater treatment plants (WWTPs). HPB technology promotes the formation of biofilm sludge with micron-scale composite powder carriers as the core and suspended sludge mainly composed of flocs surrounding the biofilm sludge. This study proposed a novel integrated strategy for assessing and controlling the sludge ages in suspended/bio-film activated sludge supported by micron-scale composite powder carrier. Utilizing the cyclone unit and the corresponding theoretical model, the proposed strategy effectively addresses the sludge ages contradiction between denitrifying bacteria and polyphosphate-accumulating organisms (PAOs), thereby enhancing the efficiency of municipal wastewater treatment. The sludge age of the suspended (25 d) and bio-film (99 d) sludge, calculated using the model, contribute to the simultaneous removal of nitrogen and phosphorus. Meanwhile, the model further estimates distinct contributions of suspended and bio-film sludge to chemical oxygen demand (COD) and total nitrogen (TN), which are 55% and 42% for COD, 20% and 57% for TN of suspended sludge and bio-film sludge, respectively. This suggests that the contribution of suspended sludge and bio-film sludge to COD and TN removal efficiency can be determined and controlled by the operational conditions of the cyclone unit. Additionally, the simulation values for COD, ammonia nitrogen (NH4+-N), TN and total phosphorus (TP) closely align with the actual values of WWTPs over 70 days (p < 0.001) with the correlation coefficients (R2) of 0.9809, 0.9932, 0.9825, and 0.837, respectively. These results support the theoretical foundation of HPB technology for simultaneous nitrogen and phosphorus removal in sewage treatment plants. Therefore, this model serves as a valuable tool to guide the operation, design, and carrier addition in HPB technology implementation.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Effects mechanism of bio-carrier filling rate on rotating biofilms and the reactor performance optimization method

Benefited from the massive filling bio-carriers, the packed cage rotating biological contactors (RBCs) have better performance and application potentiality in wastewater treatment. Investigating the effects mechanism of bio-carrier filling rate is crucial for such reactors management. In this study, the pollutants removal performance, biofilms physical characteristics, and microbial communities of the biofilms under a series of bio-carrier filling rates were analyzed. The results shown, the pollutant removal rate and amount were quite different under different filling rates, and biofilms structure and microbial composition were the main factors affecting the pollutants removal performance. With the increasing filling rates, the biofilms were more mass increased (dry weight from 0.066 to 0.148 g/per carrier), thicker (from 340.30 to 850.84 μm) and lower dense (from 0.068 to 0.060 g/cm³). The microbial community composition of those biofilms was also quite different at the genus level. The effects mechanism of bio-carrier filling rate can be summarized: the filling rates affect the physical and biological characteristics of biofilms, which will further affect the microenvironment and microbial distribution in biofilms, and then determines the pollutant metabolic rate and metabolic pathway. This study will contribute to design better bio-carrier filling rate according to different wastewater treatment scenario, and promote the performance optimization of packed cage RBCs.

Microbial biofilms in biorefinery - Towards a sustainable production of low-value bulk chemicals and fuels

Harnessing the potential of biocatalytic conversion of renewable biomass into value-added products is still hampered by unfavorable process economics. This has promoted the use of biofilms as an alternative to overcome the limitations of traditional planktonic systems. In this paper, the benefits and challenges of biofilm fermentations are reviewed with a focus on the production of low-value bulk chemicals and fuels from waste biomass. Our study demonstrates that biofilm fermentations can potentially improve productivities and product yields by increasing biomass retention and allowing for continuous operation at high dilution rates. Furthermore, we show that biofilms can tolerate hazardous environments, which improve the conversion of crude biomass under substrate and product inhibitory conditions. Additionally, we present examples for the improved conversion of pure and crude substrates into bulk chemicals by mixed microbial biofilms, which can benefit from microenvironments in biofilms for synergistic multi-species reactions, and improved resistance to contaminants. Finally, we suggest the use of mathematical models as useful tools to supplement experimental insights related to the effects of physico-chemical and biological phenomena on the process. Major challenges for biofilm fermentations arise from inconsistent fermentation performance, slow reactor start-up, biofilm carrier costs and carrier clogging, insufficient biofilm monitoring and process control, challenges in reactor sterilization and scale-up, and issues in recovering dilute products. The key to a successful commercialization of the technology is likely going to be an interdisciplinary approach. Crucial research areas might include genetic engineering combined with the development of specialized biofilm reactors, biofilm carrier development, in-situ biofilm monitoring, model-based process control, mixed microbial biofilm technology, development of suitable biofilm reactor scale-up criteria, and in-situ product recovery.

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

It is widely believed that effective prediction of wastewater treatment results (WTR) is conducive to precise control of aeration amount in the wastewater treatment process (WTP). Conventional biochemical mechanism-driven approaches are highly dependent on complicated and redundant model parameters, resulting in low efficiency. Besides, sharp increase in business volume of wastewater treatment requires automatic operation technologies for this purpose. Under this background, researchers started to introduce the idea of data mining to model the WTP, in order to automatically predict WTR given inlet conditions and aeration amount. However, existing data-driven approaches for this purpose focus on modelling of the WTP at independent timestamps, neglecting sequential characteristics of timestamps during the long-term treatment process. To tackle the challenge, in this paper, a novel prediction and control framework through combination of convolutional neural network (CNN) and recurrent neural network (RNN) is proposed for prediction of the WTR. Firstly, the CNN model is utilized to automatically extract the local features of each independent timestamp in the WTP and make them encoded. Next, the RNN model is employed to represent global sequential features of the WTP on the basis of local feature encoding. Finally, we conduct a large number of experiments to verify efficiency and stability of the proposed prediction framework.

This work proposes a novel data-driven mechanism for prediction of wastewater treatment results through mixture of two neural network models.

Estimation of oxygen effective diffusion coefficient in a non-steady-state biofilm based on response time

In wastewater treatment, oxygen effective diffusion coefficient (D _eff ) is a key parameter in the study of oxygen diffusion-reaction process and mechanism in biofilms. Almost all the reported methods for estimating the D _eff rely on other biokinetic parameters, such as substrate consumption rate and reaction rate constant. Then, the estimation was complex. In this study, a method independent of other biokinetic parameters was proposed for estimating the dissolved oxygen (DO) D _eff in biofilms. It was based on the dynamic DO microdistribution in a non-steady-state inactive biofilm, which was measured by the oxygen transfer modeling device (OTMD) combining with an oxygen microelectrode system. A pure DO diffusion model was employed, and the expression of the DO D _eff was obtained by applying the analytical solution of the model to a selected critical DO concentration. DO D _eff in the biofilm from the bioreactor was calculated as (1.054 ± 0.041) × 10^-9 m²/s, and it was in the same order of magnitude with the reported results. Therefore, the method proposed in this study was effective and feasible. Without measurement of any other biokinetic parameters, this method was convenient and will benefit the study of oxygen transport-reaction process in biofilms and other biofouling deposits. Graphical abstract ᅟ.