Model-based strategy for nitrogen removal enhancement in full-scale wastewater treatment plants by GPS-X integrated with response surface methodology

Assessment and optimization of wastewater treatment plant in terms of effluent quality, energy footprint, and greenhouse gas emissions: An integrated modeling approach

Wastewater treatment plants (WWTPs) can benefit from utilizing digital technologies to reduce greenhouse gas (GHG) emissions and to comply with effluent quality standards. In this study, the GHG emissions and electricity consumption of a WWTP were evaluated via computer simulation by varying the dissolved oxygen (DO), mixed liquor recirculation (MLR), and return activated sludge (RAS) parameters. Three different measures, namely, effluent water quality, GHG emissions, and energy consumption, were combined as water-energy-carbon coupling index (WECCI) to compare the effects of the parameters on WWTPs, and the optimal operating condition was determined. The initial conditions of the A2O process were set to 4.0 mg/L of DO, 100 % MLR, and 90.7 % RAS. Eighty scenarios with various DO, MLR, and RAS were simulated under steady-state condition to optimize the biological treatment process. The optimal operating conditions were found to be 1.5 mg/L of DO, 190 % MLR, and 90.9 % RAS, which had the highest WECCI of 2.40 when compared to the WECCI of the initial condition (1.07). This optimal condition simultaneously reduced GHG emissions by 1348 kg CO2-eq/d and energy consumption by 11.64 MWh/d. This implies that controlling DO, MLR, and RAS through sensors, valves, and pumps offers a promising approach to operating WWTPs with reduced electricity consumption and GHG emissions while attaining effluent quality standards. Additionally, the nitrous oxide stripping rate exhibited linear relationships with the effluent total ammonia and nitrite concentrations in the aerobic reactor, suggesting that monitoring dissolved nitrogen compounds in the effluent and reactor could be a viable strategy to control MLR and DO in the biological reactor. The digital-based assessment and optimization tools developed in this study are expected to hold promise for application in broader environmental management efforts.

Hemodialysis Wastewater Treatment via Electrocoagulation and Electro-Oxidation: Modular Pilot-Level Modeling and Simulation

Hemodialysis treatment in specialized clinics within the same hospital significantly impacts environmental water health due to contaminated wastewater. The issues observed included changes in electrical conductivity, the presence of dangerous bacterial loads, toxicity from heavy metals, total cyanide content, and helminth parasite eggs. The level of damage is dependent on the patient's health under treatment. This research will use a modular system that employs electrocoagulation and electro-oxidation processes at the laboratory and pilot levels to treat hemodialysis wastewater using synthetically prepared and real samples extracted from local clinics. The results showed that these hybrid systems improved various physicochemical parameters. Specifically, decreases in electrical conductivity of 49 %, total suspended solids of 27-100 %, chemical oxygen demand of 49 %, biochemical oxygen demand of 49 %, and cation and anion loading were observed at 96-100 % and pH 8.13 UpH in accordance with the established standards. With these results and the experimental conditions used, the proposed treatment system was modeled using the GPS-X program, and it was concluded that the modular system used and the electrocoagulation/electro-oxidation/activated carbon configuration is suitable for treating wastewater from hemodialysis and that scaling up this process to facilities that have dialysate machines more advanced than those considered in this work is possible.

Development and modeling of an integrated fixed-film activated sludge (IFAS) system for simultaneous nitrogen and carbon removal from an industrial estate wastewater

The potential of an integrated fixed film activated sludge (IFAS) bioreactor for developing simultaneous aerobic and anoxic micro-zones under continuous aeration regime to promote carbon and nitrogen removal from Faraman industrial estate wastewater was evaluated in the present research. The effects of three independent variables on carbon and nitrogen removal were assessed. Overall, the optimum condition with 94 %, 77 %, and 2 NTU of COD (chemical oxygen demand) removal, Total nitrogen (TN) removal, and effluent turbidity has been specified with hydraulic retention time (HRT) of 11 h, air flow rate (AFR) of 3.5 L/min, and filling ratio (FR) of 50 %. To assess the stability of treating processes in the system, the IFAS system was operated in this optimal condition. Moreover, the simulation of the bioreactor was accomplished via calibration and verification of GPS-X model. GPSX simulation results and experimental data were compared using an independent sample T-test, which the T-test result confirmed that there was no significant difference between them.

Coupling process-based modeling with machine learning for long-term simulation of wastewater treatment plant operations

Effective treatment of sewage by wastewater treatment plants (WWTPs) are essential to protecting water environment as well as people's health worldwide. However, operation of WWTPs is usually intricate due to precarious influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTPs can provide valuable decision-making support to facilitate their daily operations and management. In this study, we have built a novel hybrid model by combining a process-based WWTP model (GPS-X) with a data-driven machine learning model (Random Forest) to improve the simulation of long-term hourly effluent ammonium-nitrogen concentration of a WWTP. Our study results have shown that the hybrid GPS-X-RF model performs the best with a coefficient of determination (R²) of 0.95 and root mean squared error (RMSE) of 0.23 mg/L, followed by the GPS-X model with a R² of 0.93 and RMSE of 0.33 mg/L and last the Random Forest model with a R² of 0.84 and RMSE of 0.41 mg/L. Capable of incorporating wastewater treatment mechanisms and utilizing superior data mining capabilities of machine learning, the hybrid model adapts better to the large fluctuations in influent and operating conditions of the WWTP. The proposed hybrid modeling framework may be easily extended to WWTPs of various size and types to simulate their operations under increasingly variable environmental and operating conditions.

Assessment and modeling of effluent quality, economic benefits, and greenhouse gas reduction for receiving brewery wastewater on A2O by GPS-X

Recently, breweries have been allowed to discharge brewery wastewater (BWW) to the sewage pipe network to alleviate the shortage of carbon sources of municipal wastewater treatment plants (MWTPs) under the premise of signing a contract with MWTPs in some countries. This study aims to provide a model-based method for MWTPs to evaluate the threshold, the effluent risk, the economic benefits, and the potential greenhouse gas (GHG) emissions reduction of receiving BWW. In this research, a simulation model of an anaerobic-anoxic-oxic process (A²O) receiving BWW was established based on the data of a real MWTP and brewery using GPS-X. The sensitivity factors of 189 parameters were analyzed, and several sensitive parameters were calibrated stably and dynamically. By analyzing the errors and standardized residuals, the calibrated model was proved to be high-quality and reliable. In the next phase, the impact of receiving BWW on the A²O was evaluated in terms of effluent quality, economic benefits, and GHG emissions reduction. The results showed that receiving a certain amount of BWW can effectively reduce the carbon source cost and GHG emissions for the MWTP compared with adding methanol. Though the chemical oxygen demand (COD), biochemical oxygen demand in five days (BOD₅), and total nitrogen (TN) in the effluent increased in various degrees, the effluent quality still met the discharge standard implemented by the MWTP. The study can also facilitate the modeling work for many researchers and promote more kinds of food production wastewater to be treated equally.

Realizable wastewater treatment process for carbon neutrality and energy sustainability: A review

Despite a quick shift of global goals toward carbon-neutral infrastructure, activated sludge based conventional systems inhibit the Green New Deal. Here, a municipal wastewater treatment plant (MWWTP) for carbon neutrality and energy sustainability is suggested and discussed based on realizable technical aspects. Organics have been recovered using variously enhanced primary treatment techniques, thereby reducing oxygen demand for the oxidation of organics and maximizing biogas production in biological processes. Meanwhile, ammonium in organic-separated wastewater is bio-electrochemically oxidized to N₂ and reduced to H₂ under completely anaerobic conditions, resulting in the minimization of energy requirements and waste sludge production, which are the main problems in activated sludge based conventional processes. The anaerobic digestion process converts concentrated primary sludge to biomethane, and H₂ gas recovered from nitrogen upgrades the biomethane quality by reducing carbon dioxide in biogas. Based on these results, MWWTPs can be simplified and improved with high process and energy efficiencies.

Optimization of the Anaerobic-Anoxic-Oxic Process by Integrating ASM2d with Pareto Analysis of Variance and Response Surface Methodology

Wastewater treatment plants (WWTPs) are high-energy-consuming units. Reasonable operation strategies can enable WWTPs to meet discharge standards while reducing the operating cost. In this study, the activated sludge model 2d (ASM2d), Pareto analysis of variance (ANOVA), and response surface methodology (RSM) were jointly used to simulate and optimize the operation of a lab-scale anaerobic-anoxic-oxic (AAO) reactor. The optimization objective was to determine the optimal design and operational parameters (DOPs) that could enhance both pollutant removal and energy saving. The DOPs that had significant influence on the optimization objective, such as sludge retention time (SRT), dissolved oxygen (DO), and the ratio of biodegradable chemical oxygen demand to total nitrogen (BCOD/TN), were identified by Pareto ANOVA. The optimal DOPs with SRT of 15 days, DO concentration of 0.5 mg/L, and BCOD/TN of 5.21 were determined by RSM. Under the optimal conditions, the removal efficiencies of NH4+-N, total nitrogen (TN), and total phosphorus (TP) were 96.2%, 76.8%, and 92.8%, respectively, and the annual operating cost was $26.4. Furthermore, this combination of DOPs was validated using a pilot-scale AAO system. The TN and TP removal efficiencies were improved by 11.0% and 5.0%, respectively, and the annual operating cost could be reduced by 15.0%. Overall, this study confirmed that the method integrating ASM2d with Pareto ANOVA and RSM was effective in optimizing wastewater treatment processes.

Modeling of novel processes for eliminating sidestreams impacts on full-scale sewage treatment plant using GPS-X7

The novel process consisted of two steps was established by combining all sidestreams lines (supernatant gravity thickener, underflow mechanical thickener, and centrate), treating them together away from the mainstream treatment plant, and returning treated sidestreams effluents to the plant outfall instead of plant head. The two steps novelty treatment combined degradation, nitrification, and dilution processes. To treat combined sidestreams, a novel pilot extended nutrient moving bed biofilm reactor was developed. The effects of sidestream elimination on a full-scale anaerobic/anoxic/oxic system were simulated using GPS-X7. The statistical results of R values greater than 0.8 and NMSE values near zero proved the calibrated model's validation. The novel system successfully removed 98, 93, 100, 85, 98, 100, and 98% of BOD, COD, NH₄, NO₃, TSS, H₂S, and PO₄-P from sidestreams, respectively. Furthermore, the simulation results showed that eliminating sidestreams has reduced volumes of full-scale A²/O facilities, controlled hydraulic and pollutants shocks, and minimized cost and energy. The novel process proved successful in treating combined sidestreams and eliminating their impacts on the A/O² system.

Study on the optimal position of the roof low roadway based on the response surface methodology

For determine the optimum position of the roof low roadway, the optimal solution is derived according to the response surface methodology. The UDEC numerical simulation of the overburden gives the porosity distribution of the strike fractured zone, the upper limit heights of the caving zone and the fractured zone are obtained as 18 m and 65 m, respectively. Based on the porosity distribution, the FLUENT numerical models of the goaf zone, air inlet roadway, air return roadway, working face and roof low roadway were established to simulate the gas concentration in the upper corner and gas drainage volume in roof low roadway during mining. Using the vertical and horizontal distance of the roof low roadway as the influencing factors, the experimental scheme of the position of the roof low roadway was designed according to the response surface method, and the response values were obtained from the FLUENT simulation experiments, predicting that the vertical and horizontal distances of the roof low roadway were 7.7 m and 5.9 m respectively when the interaction between the gas concentration in the upper corner and gas drainage volume in roof low roadway was optimal. Field tests showed that the average gas concentration in the upper corner and the average gas drainage volume in roof low roadway were 0.432% and 40.861 m3/min respectively, both of which were less than 10% of the error from the simulations. The design of the roof low roadway has effectively managed the gas accumulation problem in the upper corner.