Wastewater Flow Rate Research Articles

Time series modelling for wastewater-based epidemiology of COVID-19: A nationwide study in 40 wastewater treatment plants of Belgium, February 2021 to June 2022

BackgroundWastewater-based epidemiology (WBE) has been implemented to monitor surges of COVID-19. Yet, multiple factors impede the usefulness of WBE and quantitative adjustment may be required. AimWe aimed to model the relationship between WBE data and incident COVID-19 cases, while adjusting for confounders and autocorrelation. MethodsThis nationwide WBE study includes data from 40 wastewater treatment plants (WWTPs) in Belgium (02/2021–06/2022). We applied ARIMA-based modelling to assess the effect of daily flow rate, pepper mild mottle virus (PMMoV) concentration, a measure of human faeces in wastewater, and variants (alpha, delta, and omicron strains) on SARS-CoV-2 RNA levels in wastewater. Secondly, adjusted WBE metrics at different lag times were used to predict incident COVID-19 cases. Model selection was based on AICc minimization. ResultsIn 33/40 WWTPs, RNA levels were best explained by incident cases, flow rate, and PMMoV. Flow rate and PMMoV were associated with −13.0 % (95 % prediction interval: −26.1 to +0.2 %) and +13.0 % (95 % prediction interval: +5.1 to +21.0 %) change in RNA levels per SD increase, respectively. In 38/40 WWTPs, variants did not explain variability in RNA levels independent of cases. Furthermore, our study shows that RNA levels can lead incident cases by at least one week in 15/40 WWTPs. The median population size of leading WWTPs was 85.1 % larger than that of non‑leading WWTPs. In 17/40 WWTPs, however, RNA levels did not lead or explain incident cases in addition to autocorrelation. ConclusionThis study provides quantitative insights into key determinants of WBE, including the effects of wastewater flow rate, PMMoV, and variants. Substantial inter-WWTP variability was observed in terms of explaining incident cases. These findings are of practical importance to WBE practitioners and show that the early-warning potential of WBE is WWTP-specific and needs validation.

Desain Perencanaan Instalasi Pengolahan Air Limbah Domestik

This research aims to understand the existing condition of domestic wastewater treatment at Pondok Pesantren Attabiyatussakilah Kendari. It aims to identify the characteristics of wastewater generated at Pondok Pesantren Attabiyatussakilah Kendari. It also aims to design a domestic wastewater treatment installation for Pondok Pesantren Attabiyatussakilah Kendari. The methods used in this research are quantitative, qualitative, and experimental. The research findings indicate that the existing domestic wastewater treatment installation at Pondok Pesantren Attabiyatussakilah Kendari is currently absent. Generally, the wastewater is discharged into the drainage system without undergoing any treatment process. The calculated wastewater flow rate is 19.75 m3/day. The planned dimensions of the wastewater treatment plant (IPAL) consist of a sedimentation tank (settler) with dimensions of 3.35 m × 3.30 m × 3.54 m and an Anaerobic Filter (AF) tank with dimensions of 1 m × 3.32 m × 2.40 m.

Pilot-scale study on an advanced Fe-Cu process for refractory wastewater pretreatment

The extreme pH, high color, and poor biodegradability of refractory wastewater have severe impacts on its biological treatment. To address this issue, an advanced Fe-Cu process with redox reaction and spontaneous coagulation was investigated and applied for pilot-scale (wastewater flow rate of 2000 m3·day−1) pretreatment of separately discharged acidic chemicals and alkaline dyeing wastewater. The advanced Fe-Cu process had five functions: (1) increasing the pH of chemical wastewater to 5.0 and above, with an influent pH of approximately 2.0; (2) transforming refractory organics of chemical wastewater with 10.0% chemical oxygen demand (COD) and 30.8% color removal, thereby enhancing the ratio of biological oxygen demand after five days (BOD5) to COD (B/C) from 0.21 to 0.38; (3) neutralizing the pH of the pretreated chemical wastewater for coagulation application with alkaline dyeing wastewater to avoid adding alkaline chemical; (4) achieving average nascent Fe(II) concentrations of 925.6 mg∙L−1 using Fe-Cu internal electrolysis for mixed wastewater coagulation, resulting in an average of 70.3% color removal and 49.5% COD removal; (5) providing more efficient COD removal and B/C enhancement than FeSO4∙7 H2O coagulation while avoiding secondary pollution. The green process offers an effective, easy-implemented solution for the pretreatment of separately discharged acidic and alkaline refractory wastewater.

A demonstration project for desulfurization wastewater evaporation technology: Field test and performance analysis

Desulfurization wastewater evaporation technology is an effective approach to achieve zero liquid discharge. However, limited information is available on the practical engineering performance and related risks of the technology. In this study, we conducted a field test of desulfurization wastewater evaporation technology in a demonstration project. The results indicated the technology was highly feasible and applicable to three operating conditions with varying desulfurization wastewater flow rates. The migration characteristics of Cl were investigated, revealing that less than 3 % of Cl was released into gaseous HCl. The concentrations of other typical pollutants, such as NOx and SO2, remained unchanged after treatment, indicating that the wastewater evaporation technology did not significantly affect the distribution of gaseous pollutants or the comprehensive fly ash utilization. Moreover, the study found that the evaporation process promoted the agglomeration of particulate matter, as confirmed by larger particle sizes and XRD analysis. The total operating cost of wastewater evaporation ranged from 25 to 30 yuan per ton of wastewater, primarily consisting of coal consumption for extracting flue gas. The data obtained in this work complements and verifies existing research on wastewater evaporation technology.

Oxygen requirement analysis in the wastewater treatment plant using machine learning

The research presented in this article refers to the optimization of oxygen requirement in biological wastewater treatment using machine learning (ML) techniques with the highest efficiency of the process. The treatment technology of wastewater treatment plant consists of sequential biological reactor with nutrient removal. The analysis is based on the daily data over a period of 2 years, influent, respectively effluent wastewater characteristics, wastewater flow rate and concentration of oxygen in biological reactor. Octave software was used to build the model and it was obtained an accuracy of 85%.

A multistate assessment of population normalization factors for wastewater-based epidemiology of COVID-19.

Wastewater-based epidemiology (WBE) has become a valuable tool for monitoring SARS-CoV-2 infection trends throughout the COVID-19 pandemic. Population biomarkers that measure the relative human fecal contribution to normalize SARS-CoV-2 wastewater concentrations are needed for improved analysis and interpretation of community infection trends. The Centers for Disease Control and Prevention National Wastewater Surveillance System (CDC NWSS) recommends using the wastewater flow rate or human fecal indicators as population normalization factors. However, there is no consensus on which normalization factor performs best. In this study, we provided the first multistate assessment of the effects of flow rate and human fecal indicators (crAssphage, F+ Coliphage, and PMMoV) on the correlation of SARS-CoV-2 wastewater concentrations and COVID-19 cases using the CDC NWSS dataset of 182 communities across six U.S. states. Flow normalized SARS-CoV-2 wastewater concentrations produced the strongest correlation with COVID-19 cases. The correlation from the three human fecal indicators were significantly lower than flow rate. Additionally, using reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) significantly improved correlation values over samples that were analyzed with real-time reverse transcription quantitative polymerase chain reaction (rRT-qPCR). Our assessment shows that utilizing flow normalization with RT-ddPCR generate the strongest correlation between SARS-CoV-2 wastewater concentrations and COVID-19 cases.

Simultaneous abatement of Ni2+ and Cu2+ effectually from industrial wastewater by a low cost natural clay-chitosan nanocomposite filter: Synthesis, characterization and fixed bed column adsorption study

Untreated industrial wastewaters are so hazardous object for environment. It is straightforwardly contaminates ground/surface water fonts and extremely damage the ecology, and human health safety. This article elicits some neoteric upliftment and latent significance of fixed bed activated chitosan-clay bio nano-filter (ACCBNF) composite for remarkable adsorption of Ni2+ and Cu2+ from wastewater. However, chitosan was synthesized following demineralization, deproteinized and finally by 50 % NaOH treatment. On contrary, ACCBNF were contrived by solution casting method. The characterization of ACCBNF composites was done by FTIR, XRD, SEM and TGA techniques. The influence of different parameters such as initial concentration of wastewater (40–60 ppm), flow rate of wastewater (3–5 mL/min) and bed height of column (1.5–3.5 cm) were explored appropriately for this study, where the amount of adsorbents were inter between to 2.57–5.98 g. This study revealed that the fictitious ACCBNF composites were highly crystalline, thermally stable, and have worthy surface activity and exhibit a very astonishing adsorption capacity against Ni2+ and Cu2+. It might be happened due to the association of nucleophilic groups of the chitosan edifice and the porosity of activated clay (modified). The uppermost R% was found around 81.13 % for Ni2+ and 88.88 % for Cu2+ ions respectively additionally the maximum adsorption capacity were found particularly for Ni2+ around 101.24 mg/g and for Cu2+ around 94.14 mg/g. Finally, two eminent column adsorption models, namely Thomas and Yoon nelson model were applied to fit the experimental data. The obtained data are in blameless agreement with both of the models in which the value of R2 lies between 0.971 and 0.998 for Thomas model in addition between 0.982 and 0.998 for Yoon nelson model. Besides this, they are suitable for the explanation of breakthrough curve at all the experimental conditions and it supports the Langmuir isotherm, also obeys second-order reversible reaction kinetics.

Visible-light-driven photodegradation of xanthate in a continuous fixed-bed photoreactor: Experimental study and modeling

Photocatalysis has been widely employed as a promising method for wastewater treatment. However, existing studies are primarily conducted in batch reactors. To the best of our knowledge, a continuous reactor has not been used to degrade xanthate residual in mineral beneficiation wastewater. In this work, a continuous fixed-bed photoreactor (CFPR) was first designed, and Bi2WO6 photocatalysts coated on silica sands were used to degrade sodium isobutyl xanthate (SIBX) under visible light irradiation. The effects of various parameters, including catalyst loading, pH level, initial SIBX concentration, wastewater flow rate, calcium ion (Ca2+) concentration, magnesium ion (Mg2+) concentration, and zinc ions (Zn2+), on the degradation efficiency, were systematically investigated. The results show that the photodegradation percentage of SIBX increases with increasing catalyst loading, decreasing initial concentration, and decreasing flow rate within the scope under investigation. Additionally, low concentrations (30 mg·L−1) of Ca2+ and Mg2+ ions favor the photodegradation of SIBX, whereas excessive Ca2+ and Mg2+ ions (240 mg·L−1) inhibit photocatalytic activity. The presence of Zn2+ ions inhibits photodegradation percentage. Furthermore, the photodegradation percentage is slightly higher at alkaline than at acidic pH. The maximum degradation percentage reached 95.40 % after 70 min of visible light irradiation under optimal conditions. In addition, a fixed bed reactor model considering surface reaction kinetics and mass transfer constraints was established by combining the Langmuir-Hinshelwood (L-H) kinetic equations and the series resistance theory. This model can accurately estimate the intrinsic kinetic parameters and can well predict degradation efficiency with a root-mean-square-error (RMSE) of 4.24 %. Finally, the bottleneck for improving the performance of the reactor can be identified under different conditions.

Aqueous Bromide Discharges from U.S. Coal-Fired Power Plants: Points of Origin, Concentration Ranges, and Effluent Treatment Costs

Bromide discharges from coal-fired power plants have received increased attention from regulatory bodies due to their contribution to the formation of disinfection byproducts (DBPs) in downstream drinking water treatment plants. This paper characterizes the relative contributions of bromide from coal feedstocks and bromine-based mercury control processes, estimates the distribution of bromide concentrations at 85 active coal-fired power plants across the United States (U.S.) with wet flue gas desulfurization units, and estimates the cost of bromide removal from wastewater discharge using year 2020 data. Bromide discharges are estimated at the plant level using a combination of the reported coal rank and composition combusted, estimates of bromide addition in mercury control techniques under multiple halogen addition scenarios, and the estimated flue gas desulfurization (FGD) wastewater flow rate. The median, simulated plant-level estimation of total FGD wastewater flow is 18.3 gallons/min at a bromide concentration of 319 mg/L, equivalent to ∼11.6 tonnes/year of bromide discharges to the environment. Next, we evaluated the expected cost of employing the best available technology (BAT) to control bromide discharges in FGD wastewater to prevent contributions to DBP formation. Treatment would need to remove more than 99.8% of bromide to reach the 0.2 mg/L voluntary incentive program (VIP) limit. The total cost of treatment depends on whether disposal is on- or off-site; the average costs for all plants combined come to an average of $110 million ($95.2/kgal) in 2021 U.S. dollars for on-site disposal, or $134 million ($115/kgal) for off-site disposal.

Investigating the fate and transport of microplastics in a lagoon wastewater treatment system using a multimedia model approach

Effluents of municipal wastewater treatment plants (WWTPs) are a major source of microplastics (MP) in the terrestrial and aquatic environment; there is growing concern over the environmental and health impacts of MP pollution. In this study, the MP removal (MP cut-off size= 25 µm) in a lagoon-based wastewater treatment system was predicted by developing a model based on the multimedia modelling approach and utilising MP-specific properties for improving the understanding of the fate and transport of MP in such treatment processes. The high MP removal efficiency of the lagoon treatment system as predicted by the model (99.3%) and determined with the site wastewater samples (97%) could be attributed to its high HRT (>200 days, including that for the storage lagoons) that would allow effective MP removal with the system. Evaluation of the model predictions of MP concentration demonstrated reasonable alignment with measured concentrations in the facultative, maturation and winter storage lagoons of the system. Further evaluation of model predictions for various MP size classes (25–100, 100–200, 200–500 and >500 µm) obtained reasonable predictions for MP within the size range of 25–500 µm, indicating that the model is better used for predicting MP within that size range. The sensitivity analysis revealed the model predictions to be sensitive towards the operating/water quality parameters in the order of influent wastewater flowrate, MP concentration in influent wastewater, and MP settling rate in the water column of the lagoon. The study showed the potential of the developed model as a quantitative assessment tool for better management of MP in lagoon-based WWTPs.

Performance Analysis of Integral Process of Bio-Oil Production, Bio-Oil Upgrading, and Hydrogen Production from Sewage Sludge

This research investigated the production of bio-oil through the hydrothermal liquefaction (HTL) process using sewage sludge from wastewater, along with the hydrotreating of the bio-oil. The simulation process began with a wastewater flow rate of 460 tonnes/day, where the feedstock was divided into two streams. The first stream underwent the HTL process, while the other was directed towards hydrogen production. The resulting products included gaseous products, crude bio-oil, and heavy liquid. The crude bio-oil was further upgraded by introducing hydrogen, which was obtained through gasification and purified by gas separation using a palladium membrane. The primary product mainly comprised alkane, with a carbon content of 85.89% and hydrogen content of 14.11%. For the purification of gasoline, kerosene, diesel, and fuel oil, a fractionation distillation tower arrangement was designed. In addition, Additionally, the gaseous products underwent fractionation distillation to obtain 98% nitrogen and 99.9% liquid carbon dioxide. Considering the carbon footprint, it was observed that the bio-oil production process resulted in the highest greenhouse gas (GHG) emissions.

Bi-objective optimization and thermal economy analysis of a novel coke quenching process with wastewater gasification

• An exergy-and-cost bi-objective model for a new coke quenching process is proposed. • The parameters of furnace structure and cooling medium flowrate are considered. • The best processes under different objective weight schemes are obtained. • Higher weights on annual cost than exergy income could bring greater profits. • Annual profit of the new process reaches 1.41 times of that via coke dry quenching. Coke wastewater gasification quenching (CWGQ) process with waste heat recovery and wastewater treatment has a good application prospect. The process optimization considering the exergy and cost contributes to its economic evaluation and future application. In this work, a bi-objective optimization model of the CWGQ process is established to find the best structural and operating parameters under the comprehensive optimization of net exergy income and total annual cost. We focus on the influence of the weights of objective functions on the optimization result. With the same weight for the two objective functions, the optimal structural parameters of height-to-diameter ratio and volume ratio are 0.267 and 0.723, respectively, while the optimal operating parameters of injected wastewater flowrate and intermediate steam extraction rate are 0.239 t-water/t-coke and 0.364, respectively. The optimal structural parameters are independent of the weights of objective functions. In contrast, the reduction rate of operating parameters at high-weight net exergy income is about 2 times of the increase rate at high-weight total annual cost. When the total annual cost is more significant, the net annual profit gets greater via the CWGQ process, which is 1.41 times of that via widely used coke drying quenching process.

Estimation of wastewater flowrate in a gravitational sewer line based on a low-cost distance sensor

Abstract Wastewater flowrate exhibits valuable information on the conditions in a sewer line. However, existing hardware flowrate sensors are rather expensive, vulnerable to fouling and breakages, and require frequent and laborious maintenance. Therefore, they are typically only mounted in a few key locations of a sewer system, leading to a lack of important information in a major part of the network. Utilizing more cost-effective sensors and a soft sensor approach is advisable for estimating the flowrate at locations where hardware sensors are lacking. Here, the development and testing of a data-driven soft sensor for estimating the wastewater flowrate based on the water level information obtained from a low-cost ultrasonic distance sensor are presented. The research included a long-term functionality testing period of the sensor in a cold region. The soft sensor-based flowrate was applied to estimate inflow and infiltration, indicating the conditions of the sewer line. The harsh conditions inside the sewer manhole caused challenges for the reliability of the distance measurement based on an ultrasonic principle. With the developed model-based soft sensor, it seems possible to accurately estimate the wastewater flowrate. Together with additional information, it might also enable accurate monitoring of inflows and infiltrations.

A comparative LCA of three WWTPs in a tourist area: Effects of seasonal loading rate variations

Although the wastewater treatment is a fundamental utility for the protection of human health and the environment, non-evident drawbacks are associated with it. Wastewater treatment plants (WWTPs) located in tourist areas generally suffer from the seasonal increase in wastewater flow-rate and associated pollution loads. In this study, a Life Cycle Assessment (LCA) of three medium-size urban WWTPs, located in a tourist area in central Italy, was carried out. The study compared the environmental impacts generated by 1 m3 of treated wastewater in low season (LS) and high season (HS). All the material and energy flows involved in the operational phase of wastewater treatment were considered within the system boundaries, including the disposal or recovery treatment of the waste streams generated by the WWTPs, namely screenings, waste from grit removal and wastewater sludge. The analysis was conducted using almost only real data from full-scale plants. In each WWTP, the environmental impacts produced in HS were higher than those generated in LS; therefore, the environmental impacts increased as the loading rate increased. Furthermore, a correlation between WWTP size and environmental performance was observed. Indeed, in 8 out of 11 environmental indicators, the percentage increase in pollutant emissions due to wastewater treatment in HS decreased as the WWTP size increased. Results revealed that larger WWTPs suffered less from seasonal loading rate variations, showing greater flexibility. The existence of a scale factor suggests that the centralization of WWTPs in tourist areas can be an option to enable better treatment performance in terms of environmental impacts. A sensitivity analysis was performed, increasing N2O emission factors from wastewater treatment in LS: considering a 75 % increase, the outcomes found in default LCA were not confirmed. Future research should investigate the operational factors and biological mechanisms that most affect wastewater treatment when significant seasonal variations are present.

Municipal Wastewater Treatment uses Vertical Flow Followed by Horizontal Flow in a Two-Stage Hybrid-Constructed Wetland Planted with Calibanus hookeri and Canna indica (Cannaceae).

The online version contains supplementary material available at 10.1007/s11270-022-05984-0.

Performance Evaluation of Waste Stabilization Pond for Treatment of Wastewater from a Tertiary Institution Campus Located in Jos North Local Government Area, Plateau State, Nigeria

Appropriate treatment of wastewater before disposal into the environment or reuse is very important in the quest to protect the environment and safeguard public health. This paper investigated the performance evaluation of a waste stabilization pond (WSP) for treatment of wastewater from a tertiary institution campus located in Jos North Local Government Area of Plateau State in North Central Nigeria. The waste stabilization pond of the tertiary institution campus was designed for a wastewater flow rate of 12,945 m3/day. In order to evaluate the performance of the WSP, a model was developed following the Froude’s number dimensional analysis technique. The model was evaulated based on a wastewater flow of 0.4 m3/day. The final effluent from the maturation pond had 80 mg/L BOD, 195 mg/L COD, 75 CFU/100ml Total Coliforms, 610 mg/L Total solids, 19 mg/L Total Nitrogen, 210 mg/L Chloride, 28 mg/L Phosphate, 1.3 mg/L Ammonia and 7.0 pH. The WSP was able to achieve an overall efficiency of 77.78% reducing all quality parameters to recommended limited with exception of total nitrogen and phosphate. The implementation of this design will go along way to aid the treatment of wastewater from the campus of the university.

Prediction model of sparse autoencoder-based bidirectional LSTM for wastewater flow rate.

Sanitary sewer overflows caused by excessive rainfall derived infiltration and inflow is the major challenge currently faced by municipal administrations, and therefore, the ability to correctly predict the wastewater state of the sanitary sewage system in advance is especially significant. In this paper, we present the design of the Sparse Autoencoder-based Bidirectional long short-term memory (SAE-BLSTM) network model, a model built on Sparse Autoencoder (SAE) and Bidirectional long short-term memory (BLSTM) networks to predict the wastewater flow rate in a sanitary sewer system. This network model consists of a data preprocessing segment, the SAE network segment, and the BLSTM network segment. The SAE is capable of performing data dimensionality reduction on high-dimensional original input feature data from which it can extract sparse potential features from the aforementioned high-dimensional original input feature data. The potential features extracted by the SAE hidden layer are concatenated with the smooth historical wastewater flow rate features to create an augmented previous feature vector that more accurately predicts the wastewater flow rate. These augmented previous features are applied to the BLSTM network to predict the future wastewater flow rate. Thus, this network model combines two kinds of abilities, SAE's low-dimensional nonlinear representation for original input feature data and BLSTM's time series prediction for wastewater flow rate. Then, we conducted extensive experiments on the SAE-BLSTM network model utilizing the real-world hydrological time series datasets and employing advanced SVM, FCN, GRU, LSTM, and BLSTM models as comparison algorithms. The experimental results show that our proposed SAE-BLSTM model consistently outperforms the advanced comparison models. Specifically, we selected a 3 months period training dataset in our dataset to train and test the SAE-BLSTM network model. The SAE-BLSTM network model yielded the lowest RMSE, MAE, and highest R2, which are 242.55, 179.05, and 0.99626, respectively.

Economic optimization of hollow fiber membrane contactors for ammonia nitrogen recovery from anaerobic digestion effluents

This study presents the optimization and economic analysis of the membrane contactors to recover ammonia nitrogen from anaerobic effluents. The effects of ammonia nitrogen concentrations and alkalinity of the anaerobic effluents on the operating cost and total recovery cost were investigated, and the optimization of wastewater flowrate and pH was carried out.The sensitivity analysis revealed that the wastewater pH was the dominant parameter of the operating cost, followed by the percentage Am-n recovery and wastewater flowrate. The increase of anaerobic effluent’s alkalinity reduced the optimized wastewater pH and flow rate. The total operating recovery cost, including the production cost of solid ammonium sulfate, was found to be in the range of 0.16–0.31 USD/ kg·ammonium sulfate for the Am-n and alkalinity concentrations of 100–700 mg/L and 400–2800 mg/L as CaCO3, respectively, which are much lower than those of the conventional vacuum thermal stripping/acid absorption technique. The total costs, considering the capital investment of the membrane contactor’s unit, were also determined, and found to be 0.242–0.741 USD/kg·ammonium sulfate in the investigated range of anaerobic effluents

Model-Based Theoretical Evaluation of the Feasibility of Using Wastewater-Based Epidemiology to Monitor Monkeypox

Monkeypox is an orthopoxvirus that is spreading rapidly globally. Gaps in current clinical testing availability and the lag from infection to symptoms suggest the potential utility of wastewater-based epidemiology (WBE) to monitor community level spread and prevalence. Here, we estimate the distributions of Monkeypox DNA wastewater concentrations based upon prior shedding reports and use Monte Carlo modeling to estimate the probability of detection in wastewater. Stool shedding drives total Monkeypox DNA wastewater concentrations, with a median daily shedding of 7.78 log10 genome copies (GC) per infected individual. Using a process limit of detection of 1.0 log10 GC/L, the average United States wastewater treatment plant (WWTP) could feasibly detect 7 infections out of 100 000 people. The UK, Spain, Nigeria, and the Democratic Republic of Congo (DRC) have respective minimum infection rates of 54.7%, 44.5%, 3.1%, and 8.0% that of the US due to decreased median per capita wastewater flow rates. We also develop a strategy to select the number of replicate PCR assays based upon expected case rates to optimize sewage Monkeypox detection. This model demonstrates a framework to evaluate WBE applications without the associated laboratory expense and highlights the key need for improved shedding data to develop robust WBE programs and interpretation of results.

Optimization of water management strategies for shale gas extraction considering uncertainty in water availability and flowback water

This paper addresses the multi-objective optimization of water management used in the extraction of shale gas through the hydraulic fracturing process. The optimization aims to minimize the total annual cost and simultaneously minimize the total water requirements. Two uncertain factors are considered about the available fresh water and flowrate of wastewater (flowback water). Due to the complexity of the problem, a hybrid optimization method is proposed by combining deterministic and metaheuristic optimization approaches. The deterministic model is programmed in the General Algebraic Modeling System (GAMS) commercial software. The metaheuristic algorithm is implemented in Microsoft Excel®. The stochastic tool for uncertainty consideration is a code declared in the Visual Basic for Applications environment. The communication of the necessary programs in the methodology is achieved by the action of code sets also programmed in Visual Basic for Applications. To illustrate the merits of the proposed approach, a case study is solved to address water management for a shale gas field where a variable number of wells from 20 to 60 with different scheduling are considered. The results offer the optimal operation scheduling of the wells accounting for the minimum total annual cost and total freshwater consumption. Additionally, the maximum number of wells is obtained up to where it is possible to find feasible solutions. The best results were found with a maximum number of 51 wells prioritizing the economic objective and with 59 wells if the environmental objective is prioritized.