Modeling the performance of Single-stage Nitrogen removal using Anammox and Partial nitritation (SNAP) process with backpropagation neural network and response surface methodology

Optimization of catalytic wet air oxidation process in microchannel reactor for TBBS wastewater treatment

Catalytic wet air oxidation (CWAO) process is employed for the treatment of N-tert-butyl-2-benzothiazolesulfenamide (TBBS) wastewater in a microchannel reactor that enables continuous operation of the reaction and allows for thorough mixing of oxygen and pollutants. To achieve the optimal process performance, four key parameters of pressure, temperature, time, and the mass ratio of input oxygen to wastewater COD are optimized using both response surface methodology (RSM) and backpropagation artificial neural network (BP-ANN). According to the correlation coefficients of model results and experimental data, BP-ANN performs better than RSM in simulation and prediction. The analysis of variance in RSM shows that all parameters are significant for the obtained quadratic model, but their interactions with each other are not significant. Connection weights algorithm is used to determine the relative importance of these parameters for the process efficiency, and it is demonstrated that temperature is the most influential parameter with a relative importance of 35.61%, followed by pressure (29.74%), time (19.53%) and ROC (15.12%).

Comparative assessment on ammonia nitrogen adsorption onto a saline soil-groundwater environment: distribution, multi-factor interaction, and optimization using response surface methodology and artificial neural network

The adsorption of soil can reduce the leaching of NH₄⁺-N from the external environment into groundwater. The adsorption of NH₄⁺-N is affected by many factors. It is critical to use statistical model to quantitatively describe the effects of interaction between two or more factors on the system response. In this study, HJ-Biplot was used to analyze the correlation characteristics of soil water, salt, and nitrogen, and the response surface methodology and artificial neural network were used to statistically visualize the interaction between factors, including concentration, total dissolved solids (TDS), temperature, and pH. The results showed that the study soil was a typical saline soil, with maximum soil NH₄⁺-N content of 85.45 mg/kg. For the adsorption experiments of NH₄⁺-N on saline soils, the effects of factors on the adsorption capacity were assessed using the RSM model. The RSM model was coupled with an ANN to predict the adsorption of NH₄⁺-N by saline soils. The NH₄⁺-N concentration and water pH were both significant at a linear level (p < 0.0001). The interaction between NH₄⁺-N concentration and pH was also more significant (p < 0.01). Under optimal conditions (concentration: 800 mg/L; temperature: 24 °C; TDS: 637 mg/L; pH: 7.83), the NH₄⁺-N adsorption capacity was 1650.2 ug/g, which was in general agreement with the calculated values from the Box-Behnken and RSM model. In addition, a statistical error criterion for the model showed that the RSM-ANN model had greater predictive ability than RSM model.

Enhancing Real-Time Prediction of Effluent Water Quality of Wastewater Treatment Plant Based on Improved Feedforward Neural Network Coupled with Optimization Algorithm

To provide real-time prediction of wastewater treatment plant (WWTP) effluent water quality, a machine learning (ML) model was developed by combining an improved feedforward neural network (IFFNN) with an optimization algorithm. Data used as input variables of the IFFNN included hourly influent water quality parameters, influent flow rate and WWTP process monitoring and operational parameters. Additionally, input variables included historical effluent water quality parameters for future prediction. The model was demonstrated in a WWTP in Jiangsu Province, China, where prediction of effluent chemical oxygen demand (COD) and total nitrogen (TN) with large variations were tested. Relative to the traditional feedforward neural network (FFNN) model without considering historical effluent water quality parameter input, the IFFNN enhanced prediction performance by 52.3% (COD) and 72.6% (TN) based on the mean absolute percentage errors of test datasets, after its model structure was optimized with a genetic algorithm (GA). The problem of over-fitting could also be overcome through the use of the IFFNN, with the determination of coefficient increased from 0.20 to 0.76 for test datasets of effluent COD. The GA-IFFNN model, which was efficient in capturing complex non-linear relationships and extrapolation, could be a useful tool for real-time direction of regulatory changes in WWTP operations.

Development of novel experimental and modelled low density polyethylene (LDPE)-water partition coefficients for a range of hydrophobic organic compounds

Knowledge about partitioning constants of hydrophobic organic compounds (HOCs) between the polymer and aqueous phases is critical for assessing chemical environmental fate and transport. The conventional experimental method is characterized by large discrepancies in the measured values due to the limited water solubility of HOCs and other associated issues. In the current work, a novel three-phase partitioning system was evaluated to determine accurate low-density polyethylene (LDPE)-water partition coefficients (K_PE-w). By adding sufficient surfactant (Brij 30) to form the micellar pseudo-phase within the polymer/water system, the K_PE-w values were obtained from a combination of two experimentally measured values, that is, the micelle-water partition coefficient (K_mic-w) and the LDPE-micelle partition coefficient (K_PE-mic). The method presented here is capable of shortening the equilibration time to half a month, and avoiding defects of the traditional method with respect to directly measured aqueous phase concentrations. Herein, the K_PE-w values were determined for HOCs with little errors. Meanwhile, based on the 120 experimental K_PE-w data, several in silico models were also developed as valid extrapolation tools to estimate missing or uncertain values. Analysis of the underlying solubility interactions in the nonionic surfactant micelles were investigated, providing additional support for the reliability of the proposed method.

Startup of pilot-scale single-stage nitrogen removal using anammox and partial nitritation (SNAP) reactor for waste brine treatment using marine anammox bacteria

This study is the first to demonstrate the startup of a pilot-scale single-stage nitrogen removal using anammox and partial nitritation (SNAP) reactor utilizing marine anammox bacteria. A complete mixing type reactor, continuously fed with waste brine obtained from a natural gas plant (salinity 3%, NH₄⁺-N 130-180 mg/L) and having an effective volume of 2 m³, achieved stable operation at temperatures of 20-30°C with a maximum nitrogen removal rate of 1.43 kg-N/m³/day. During the startup process, along with a small amount of seed sludge, granular sludge was additionally inoculated as a biomass carrier for the enrichment of ammonia oxidizing bacteria (AOB), followed by the enrichment of anammox bacteria. A mesh screen equipped at the outlet of the reactor facilitated the successful sludge retention in the reactor. Analysis of bacterial community composition indicated that Candidatus Scalindua was successfully enriched in the pilot SNAP reactor. These methods for stable sludge retention in the reactor greatly contributed to the startup of the first pilot-scale SNAP reactor using marine anammox bacteria.

Microbial community and function in nitrogen transformation of ectopic fermentation bed system for pig manure composting

In this work, agricultural wastes were treated by composting in an ectopic fermentation bed system (EFBS) with a continuous nitrogen addition technique. With decreasing of NH₄⁺-N concentration and increasing of NO₃^--N concentration were observed, and activities of protease, urease and nitrate reductase changed significantly during the fermentation process. To elucidate the key microbes and their function in nitrogen-transforming, microbial diversity and clusters of orthologous groups (COGs) in composting materials were evaluated using metagenomic technology. Comparing with ammonification, the COGs involved in nitrification and denitrification were predominant in the composts. The correlation heatmap revealed that Streptomyces predominant in ammonification was significantly affected by contents of N, NH₄⁺-N and NO₃^--N. Meanwhile, ammonia-oxidizing archaea (AOA) had a positive relationship with moisture. The most abundant genera in denitrification had positive relationships with N and NO₃^--N. The results indicated that EFBS had functionally diverse microbes and COGs for NH₃ removal.

Accelerated start-up, long-term performance and microbial community shifts within a novel upflow porous-plated anaerobic reactor treating nitrogen-rich wastewater via ANAMMOX process

Schematic diagram of the upflow porous-plate anaerobic reactor and nitrogen removal pathways occurring within the reactor.