Prediction and quantifying parameter importance in simultaneous anaerobic sulfide and nitrate removal process using artificial neural network

Sulfate-reducing ammonium oxidation: A promising novel process for nitrogen and sulfur removal

Sulfate-reducing ammonium oxidation (sulfammox), a novel and promising process that has emerged in recent years, is essential to nitrogen and sulfur cycles and offers significant potential for the elimination of ammonium and sulfate. This review discussed the development of sulfammox process, the mechanism, characteristics of microbes, potential influencing factors, applicable bioreactors, and proposed the research needs and future perspective. The sulfammox process could be affected by many factors, such as the NH4+/SO42- ratio, carbon source, pH, and temperature. However, these potential influencing factors were only obtained based on what has been seen in papers studying related processes such as denitrification, sulfate-reduction, etc., and have to be further tested in bioreactors carrying out the sulfammox process in the future. Currently, sulfammox is predominantly used in granular activated carbon anaerobic fluidized beds, up-flow anaerobic sludge blanket reactors, anaerobic expanded granular bed reactors, rotating biological contact reactors, and moving bed biofilm reactors. In the future, the operating parameters of sulfammox should be further optimized to improve the processing performance, and the system can be further scaled up for actual wastewater treatment. In addition, the isolation, identification, and characterization of key functional microbes and the analysis of microbial interrelationships will also be focused on in future studies to enable an in-depth analysis of the sulfammox mechanism.

Ordered Mesoporous nZVI/Zr-Ce-SBA-15 Catalysts Used for Nitrate Reduction: Synthesis, Optimization and Mechanism

Excessive concentrations of nitrate (NO3-N) in water lead to the deterioration of water quality, reducing biodiversity and destroying ecosystems. Therefore, the present study investigated NO3-N removal from simulated wastewater by nanoscale zero-valent iron-supported ordered mesoporous Zr-Ce-SBA-15 composites (nZVI/Zr-Ce-SBA-15) assisted by response surface methodology (RSM), an artificial neural network combined with a genetic algorithm (ANN-GA) and a radial basis neural network (RBF). The successful support of nZVI on Zr-Ce-SBA-15 was confirmed using XRD, FTIR, TEM, SEM–EDS, N2 adsorption and XPS, which indicated ordered mesoporous materials. The results showed that ANN-GA was better than the RSM for optimizing the conditions of NO3-N removal and the RBF neural network further confirmed the reliability of the ANN-GA model. The removal rate of NO3-N by the composites reached 95.71% under the optimized experimental conditions (initial pH of 4.89, contact time = of 62.27 min, initial NO3-N concentration of 74.84 mg/L and temperature of 24.77 °C). The process of NO3-N adsorption onto Zr-Ce-SBA-15 composites was followed by the Langmuir model (maximum adsorption capacity of 45.24 mg/g), pseudo-second-order kinetics, and was spontaneous, endothermic and entropy driven. The yield of N2 can be improved after nZVI was supported on Zr-Ce-SBA-15, and the composites exhibited a strong renewability in the short term within three cycles. The resolution of Fe2+ experiments confirmed that nZVI/Zr-Ce-SBA-15 was simultaneously undergoing adsorption and catalysis in the process of NO3-N removal. Our study suggests that the ordered mesoporous nZVI/Zr-Ce-SBA-15 composites are a promising material for simultaneously performing NO3-N removal and improving the selectivity of N2, which provides a theoretical reference for NO3-N remediation from wastewater.

Production and characteristics of elemental sulfur during simultaneous nitrate and sulfide removal

The production and characteristics of elemental sulfur were examined during simultaneous sulfide and nitrate removal, with abiotic assays as control. The biotic assay showed good sulfide and nitrate removal, with the respective removal percentage of which were 90.67-96.88% and 100%. Nitrate reduction resulted in the production of nitrogen gas, while sulfate formed due to sulfide oxidation. The concentration of elemental sulfur in the effluent was greater than that in the sludge, which accounted for 73.70-86.28% of total elemental sulfur produced. Furthermore, the elemental sulfur of the effluent and sludge from the biotic assays was orthorhombic crystal S₈. Elemental sulfur was normally distributed in the effluent, but its average diameter increased with the increasing influent sulfide concentration (60-300 mg S/L), where the average diameter increased from 10 (60 mg S/L) to 29 μm (300 mg S/L).

Sulfate dependent ammonium oxidation: A microbial process linked nitrogen with sulfur cycle and potential application

Sulfate dependent ammonium oxidation (Sulfammox) is a potential microbial process coupling ammonium oxidation with sulfate reduction under anaerobic conditions, which provides a novel link between nitrogen and sulfur cycle. Recently, Sulfammox was detected in wastewater treatments and was confirmed to occur in natural environments, especially in marine sediments. However, knowledge gaps in the mechanism of Sulfammox, functional bacteria, and their metabolic pathway, make it challenging to estimate its environmental significance and potential applications. This review provides an overview of recent advances in Sulfammox, including possible mechanisms, functional bacteria, and main influential factors, and discusses future challenges and opportunities. Future perspectives are outlined and discussed, such as exploration of microbial community structure and metabolic pathways, possible interactions with other microbes, environmental significance, and potential applications for nitrogen and sulfate removal, to inspire more researches on the Sulfammox process.

Application of fuzzy neural networks for modeling of biodegradation and biogas production in a full-scale internal circulation anaerobic reactor

This paper presents the development and evaluation of three fuzzy neural network (FNN) models for a full-scale anaerobic digestion system treating paper-mill wastewater. The aim was the investigation of feasibility of the approach-based control system for the prediction of effluent quality and biogas production from an internal circulation (IC) anaerobic reactor system. To improve FNN performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, and a total of 5 rules were extracted in the IF-THEN format. Findings of this study clearly indicated that, compared to NN models, FNN models had smaller RMSE and MAPE as well as bigger R for the testing datasets than NN models. The proposed FNN model produced smaller deviations and exhibited a superior predictive performance on forecasting of both effluent quality and biogas (methane) production rates with satisfactory determination coefficients greater than 0.90. From the results, it was concluded that FNN modeling could be applied in IC anaerobic reactor for predicting the biodegradation and biogas production using paper-mill wastewater.