Wastewater Treatment Models Research Articles

Progress and Hotspot Analysis of Bibliometric-Based Research on Agricultural Irrigation Patterns on Non-Point Pollution

With the constant advancement of irrigation technology and the continuous expansion of irrigation areas, non-point source pollution (NPS) caused by agricultural activities has posed a persistent threat to ecosystems and biological safety. Against this backdrop, it is imperative to lay scientific foundations for green, sustainable, and high-quality agricultural development through a thorough review of the relevant research progress. In this study, bibliometric methods are adopted to comprehensively analyze and visualize the current state and key literature on agricultural irrigation and NPS pollution from 2010 to July 2024. The focus of this study is specifically on summarizing the research hotspots and development trends of different irrigation methods and the mechanisms behind their impacts on NPS pollution. The results indicate that publications from the United States and China account for 63.8% of the total, but the fragmentation of research efforts remains, suggesting a necessity to strengthen international and regional collaboration. There are three institutions with the highest publication output, namely Northwest A&F University, Hohai University, and the Chinese Academy of Sciences. The subjects identified as the key areas of research on irrigation-related NPS pollution (IRR-NPS) include precision irrigation, rapid water pollution response, spatiotemporal management, interdisciplinary integration, wastewater treatment, and crop models. Regarding future research, it is necessary to focus attention on real-time precision irrigation, standardized crop models, data accuracy, spatiotemporal pollution coordination, pollution purification technology development, interdisciplinary integrated governance, and the innovative applications of soil improvement technologies. In addition to offering theoretical support and practical guidance for the management of agricultural NPS pollution, this study also provides management and technical support for policymakers, which is beneficial for advancing agricultural irrigation technology and environmental preservation.

Public health implications of antibiotic resistance in sewage water: an epidemiological perspective

The emergence and rapid spread of antibiotic resistance pose a major threat to global health, attributing to misuse and overuse of antibiotics resulting in antibiotics-resistant bacteria through natural mutation or transfer of resistance genes. A cross-sectional study was carried out, in which a total of 36 samples were systematically collected; of these, 26 were derived from the wastewater efflux and 10 from the receiving waters at several critical junctures along the Sutlej River. Herein, this study elucidated elevated levels of antibiotic resistance among bacterial isolates sourced from urban wastewater. Escherichia coli (E. coli) was the highest at 90% among the isolates, followed by Klebsiella pneumoniae (K. pneumoniae) at 58%, Pseudomonas aeruginosa (P. aeruginosa) at 55%, and Salmonella spp. at 53%. Many antibiotics were found to be more resistant including Ciproflaxacin, Co-Trimaxazole, Ampicillin and Tetracycline. Several antibiotic-resistance genes were found in isolated bacterial spp., such as Aminoglycosides (aadA), Sulfonamides (Sul1, Sul3), Tetracyclines (Tet (A/B/D)) and Cephalosporins (Bla_CTM X) at 41%, 35%, 29% and 12% respectively. Furthermore, the development of innovative wastewater treatment models and surveillance programs are crucial to counteract the dissemination of antibiotic resistance. To investigate the genetic determinants of antibiotic resistance, molecular analysis was performed, including DNA isolation, PCR amplification, and sequence analysis. The study helps investigate a diverse range of ARBs and ARGs in wastewater, which highlights the need of better laws for antibiotic usage and wastewater treatment processes. This investigation also stresses on regular monitoring of ARBs and ARGs in sewage wastewater. Through proactive interventions and sustained scientific inquiry, we can strive toward preserving environmental integrity and public health for successive generations.Graphical

A Review On Revolutionizing Waste Water Collection and Recycling Processes with IoT

This paper provides an extensive review of IoT-driven solutions addressing critical challenges in sectors like wastewater treatment, agriculture, waste management, e-waste recycling, and water quality monitoring. Innovative models leverage IoT technology for real-time monitoring, control, and optimization. Examples include an industrial IoT cloud-based model for wastewater treatment, sensor integration in aquaculture systems for improved resource management, and intelligent waste collection systems in smart cities. The review identifies gaps and opportunities for advancements, highlighting the importance of real-time data collection, cloud-based analytics, and machine learning for informed decision- making in environmental and resource management

Machine learning-assisted optimization and evaluation of methylene blue adsorption kinetics on citrus aurantifolia leaves: Insights from isotherm and thermodynamic studies

BackgroundMethylene Blue (MB) is a cationic dye widely used in various industrial and pharmacological applications. However, its improper disposal poses significant environmental and health risks due to its toxicity, carcinogenicity, and resistance to biodegradation. Despite extensive efforts have been done to remove MB using green synthesis approaches, however; there remains a critical need to enhance the efficacy of these methods through proper optimization and the use of novel green sources. Addressing these challenges is essential to mitigate the environmental impact of MB. MethodsCitrus aurantifolia leaves powder (CALP), a simple, environmentally friendly, adsorbent has been utilized to remove MB dye. The adsorption kinetic efficacy of CALP was studied under different experimental conditions, including pH (3–9), contact time (2–8 min), and MB dye concentrations (10–70 mg/L). Additionally, machine learning (ML) was employed to optimize experimental conditions, predict, and validate the adsorption kinetic efficacy. The machine learning (ML) optimized experimental dataset which included parameters of pH (9), contact time (2 min), CALP adsorbent (0.3 gm), and MB dye concentration (70 mg/L) has shown maximum adsorption efficacy. To enhance and assess the extraction efficiency of MB dye, a response surface methodology (RSM) was employed utilizing a central composite design (CCD). Significant findingsThe adsorption efficacy of CALP was verified by the Freundlich isotherm model, which demonstrated an adsorption capacity of 415.05 mg/g with a coefficient of determination R2 of 0.999. The nonlinear pseudo-second-order kinetic model with an R2 value of 0.998 further confirms MB dye adsorption. In addition, spontaneous nature of the process was determined by calculating thermodynamic parameters such as ΔG°, ΔH°, and ΔS°. The outcomes demonstrate that CALP is a highly effective, reliable, and easily available adsorbent for the removal of MB dyes from wastewater. To the best of the author's knowledge, there are substantial unmet research gaps in the fields of wastewater treatment and adsorption modeling optimization. In particular, research that combine cutting-edge machine learning methods with conventional adsorption models to enhance process efficiency and prediction accuracy are scarce. This study is the first to employ CALP, a cost-effective and environmentally sustainable natural adsorbent, for the adsorption of MB dye utilizing a machine learning-optimized approach.

Chitosan decorated magnetic nanobiocatalyst of Bacillus derived α-amylase as a role model for starchy wastewater treatment, detergent additive and textile desizer

In this study, Bacillus tequilensis TB5 α-amylase from rice-milled by-products (rice bran and de-oiled rice bran) was successfully immobilized onto biologically synthesized magnetic nanoparticles fabricated with chitosan (MNP-Ch) and characterized via different biophysical techniques. Furthermore, the study emphasized incorporating this nanostructure framework (MNP@2mgchitosan_DORB-amy and MNP@3mgchitosan_RB-amy) to offer diverse applications, including enzymatic desizing, cleaning starchy stains, and treating synthetic starchy wastewater. An enzyme loading of > 90 % for both enzymes indicated increased binding sites due to the functional moieties of chitosan on the MNP. The Km was 0.28 and 0.31 mg/mL for the immobilized and free forms of DORB-amy, respectively, and 0.18 and 0.27 mg/mL for the immobilized and free forms of RB-amy, respectively. A low Km indicated an increased affinity of MNP-Ch-immobilized forms of enzymes toward the substrate. The performance of both immobilized enzymes improved at a wide range of pH and temperature, which may be attributed to the covalent binding of the enzyme on to the MNP-Ch. The nanobiocatalysts in the detergent act synergistically to fade the starchy stains. Furthermore, an 8–9 TEGEWA scale rating with > 11 % of starch removal was obtained through the biodesizing of starch-sized cotton fabric. The nanobiocatalyst efficiently decomposed starch and liberated 650–670 mg/L of reducing sugar from the synthetic wastewater, therefore offering promising opportunities for its exploration in a wastewater treatment plant. Thus, the study recommends the potential exploration of sturdy matrices like MNP to offer remarkable applications with maximum operational stability, easier recovery, and higher efficiency.

Greywater treatment using lab-scale systems combining trickling filters and constructed wetlands with recycled foam glass and water spinach

A low energy-consuming system for greywater treatment was developed with a trickling filter (TF) followed by constructed wetlands (CWs). Roles of substrates, plants, and microorganisms were intentionally explored. TFs and CWs were filled with recycled foamed glass. Water spinach and parsley planted in the CWs provided additional value. Microorganisms in the system were characterized through carbon-utilization tests and 16S rRNA gene next-generation sequencing. In 5.5 months, TOC, TN, and TP removals from greywater were, respectively, 83.5, 85.5, and 92.1 %. TOC was removed in the TF at 22.6 g-C/m2/d. The CWs exhibited TN and TP removals at 0.44 g-N /m2/d and 0.19 g-P/m2/d. Water spinach was harvested at 792 g-wet/m2/week, accounting for 7.1 % and 12.6 % for TN and TP removals. Denitrification, microbial diversity, and carbon source utilization potentials were enhanced by water spinach. This is a sustainable wastewater treatment model by using recycled materials and edible plants.

Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks

ABSTRACT Computational intelligence models for wastewater treatment utilise mathematical algorithms to simulate and optimise the processes involved in the removal of pollutants from wastewater, aiding in the design and operation of the wastewater treatment plant. The aim of this work was to compare the efficiency of computational models in modelling the dynamic adsorption of dyes. Metal-organic frameworks (MOFs) are considered as adsorbents because of their excellent properties. Further, the dataset used for this study was sourced from literature pertaining to the adsorption of methylene blue (MB), which is one of the commonly found dyes in wastewater. Adsorption parameters considered for the study are initial concentration, bed height, flow rate, pH and time, for the continuous adsorption process. The efficacy of the selected computational models was compared by employing various statistical metrics. Moreover, the results show the efficiency of artificial neural network and radial basis function neural network models is superior to other models based on R2 and mean square error, which were in the range of 0.95–0.999 and 10–3–10–5, respectively. In summary, the computational intelligence models serve as the best tools for the prediction of the adsorption performance of MOFs for MB.

Advancing Environmental Sustainability through Implementation of Artificial Neural Networks for Wastewater Treatment Model Prediction and Remediation

Advancing Environmental Sustainability through Implementation of Artificial Neural Networks for Wastewater Treatment Model Prediction and Remediation

An extended bio-electrochemical model for wastewater treatment and water desalination: Insights into the performance of microbial desalination cells

This paper presents an extended mathematical model for a microbial desalination cell (MDC), integrating Haldane kinetics for substrate inhibition and proton translocation theory for substrate dissociation under dynamic flow. The model is capable of predicting exoelectrogenic and methanogenic biomass dynamics, acetate removal efficiencies, salt concentration changes, and current production. Notably, it can be used to explore the influence of operational parameters such as dilution rates, temperatures, and external resistances. Our simulations reveal the optimal conditions for maximizing exoelectrogen concentration (500 mg/L) with minimal methanogen presence (50 mg/L) in the anode chamber at an anolyte dilution rate of 0.5 d−1, a temperature of 20 °C, and an influent acetate concentration of 1500 mg/L. The model accurately predicts the salt reduction in the middle chamber (from 0.2 to 0.09 mol/L) with a salt dilution rate of 0.5 d−1 and an external resistance of 20 Ohm. The model predicts a peak power output of 2.5 mW at external resistances between 20 and 50 Ohm. The model was validated against a suite of experimental literature data, including our previously published lab-scale studies, verifying its accuracy in predicting organic and salt removal rates, current production, and desalination efficiency. This extended model is a valuable tool for understanding the complex interactions between factors that influence MDC performance and can guide the optimization of MDC design for wastewater treatment and desalination.

Design of an improved process optimization model for enhancing the efficiency of the wastewater treatment process

ABSTRACT Efficient wastewater treatment is crucial for environmental protection and ensuring the availability of potable water. However, existing optimization models often fall short in effectively analyzing historical data, optimizing dosages, or predicting optimal process parameters. This paper aims to address these challenges by proposing an advanced process optimization model that enhances the efficacy of wastewater treatment processes. Our objectives include the development of a novel method for optimizing various aspects of the treatment procedure using Auto Encoders (AE), Genetic Algorithm (GA), and Vector Autoregressive Moving Average (VARMA) models. Extensive experimentation with multiple datasets and samples, including the Melbourne Wastewater Treatment Dataset, Urban Wastewater Treatment, and Global Wastewater Treatment, was conducted to validate our model. The results demonstrate significant improvements in our proposed model, with an average improvement of 8.5% in treatment quality, a 4.9% reduction in delay, and a 9.5% increase in water purity compared to recently proposed models. We conclude that our model provides a valuable framework for optimizing wastewater treatment in a variety of settings due to its adaptability and effectiveness. Future research could explore the integration of additional advanced techniques and real-time monitoring systems to further enhance the model's precision, efficiency, and robustness.

Novel collector design and optimized photo-fenton model for sustainable industry textile wastewater treatment

Textile industry wastewater containing toxic dyes and high COD poses environmental hazards and requires treatment before discharge. This study addresses the challenge of treating complex textile wastewater using a novel integrated system. The system combines sedimentation, screening, adsorption, and an optimized solar photo-Fenton process to provide a sustainable treatment solution. A novel parabolic collector with a larger absorber tube diameter enhances solar radiation utilization at lower catalyst concentrations. This design is versatile, treating all types of wastewaters, especially those that contain colors, smells, solid and suspended materials, in addition to its importance for the treatment of difficult substances that may be present in industrial and sewage wastewaters that are difficult to dispose of by traditional treatment methods. Multivariate experiments optimized key photo-Fenton parameters (pH, catalyst dose, etc.) achieving significant pollutant removal (85% COD, 82% TOC, complete color) under specific conditions (pH 3, 0.2 g/L Fe(II), 1 mL/L H2O2, 40 °C and 100 L/h flow rate after 60 min irradiation). Kinetic modeling revealed second-order reaction kinetics, and multivariate regression analysis led to the development of models predicting treatment efficiency based on process factors. The key scientific contributions are the integrated system design combining conventional and advanced oxidation technologies, novel collector configuration for efficient utilization of solar radiation, comprehensive process optimization through multivariate experiments, kinetic modeling and predictive modeling relating process factors to pollutant degradation. This provides an economical green solution for textile wastewater treatment and reuse along with useful design guidelines. The treatment methodology and modeling approach make valuable additions for sustainable management of textile industry wastewater.

An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances: An approach for approximating complex biological wastewater treatment models

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

Research Advancements in Swine Wastewater Treatment and Resource-Based Safe Utilization Management Technology Model Construction

Swine wastewater contains large amounts of organic matter, nutrients, toxic metal elements, and antibiotics. If it is directly discharged or not properly treated, it poses a significant threat to the environment and human health. Currently, the management of swine wastewater has become a focus of social attention, and it adopts a dual-track parallel model of standard discharge supplemented by resource utilization. If treated properly, it can achieve the recycling of water resources and promote the effective recovery of resources. Based on the pollution characteristics of swine wastewater, this paper analyzes its impact on the environment, society, and the economy in detail and expounds on the research progress of swine wastewater treatment technology. From the perspective of resource utilization and recycling of anaerobic digestion liquid (biogas slurry) from swine wastewater and the carrying capacity of the soil environment and cumulative ecological environmental risks, this study explores new development trends and application prospects for swine wastewater treatment technology.

The Efficiency of a Biological Reactor in a Domestic Wastewater Treatment Plant Operating Based on ABS (Acrylonitrile Butadiene Styrene) Material and Recycled PUR (Polyurethane) Foam

The primary objective of this research was to assess the efficacy of a novel solution under conditions closely resembling those of real-world scenarios. Biological beds, or filters, hold significant potential for widespread implementation in individual households, particularly in areas with dispersed housing. The system’s aim was to improve the quality of wastewater treated in on-site domestic biological treatment plants. A pivotal aspect of the project involved developing a prototype research installation for conducting comprehensive testing. Our installation system consisted of several components designed to create a laboratory-scale model for domestic wastewater treatment. The model comprised four biological reactors filled with ABS material and secured by a PUR frame. Additionally, the tested model included a controller for wastewater dosing control, a septic tank as a reservoir, and four tanks for collecting purified wastewater. Through regression analysis using the Generalized Linear Model (GLM), a correlation between CODCr and TSS was revealed. This study presents the research findings concerning the development of a prototype installation that incorporates an advanced reactor or filter. The data derived from this research have the potential to contribute to the creation of products that enhance the performance and efficiency of household wastewater treatment systems.

DEVELOPMENT OF CONTROL AND FORECASTING SYSTEM FOR THE EFFECTIVE FUNCTIONING OF BIOLOGICAL WASTEWATER TREATMENT PLANTS IN THE CITY OF VITEBSK BASED ON NEURAL NETWORKS

The relevance of implementing modern approaches to improving the efficiency of sewage treatment facilities has been analyzed. Functional modeling of biological wastewater treatment has been performed using the IDEF0 methodology. It has allowed the identification of the nomenclature of input and control factors, mechanisms, and the results of corre-sponding technological processes. A block diagram of information flows in the context of control of wastewater treatment facilities is constructed. Correlation analysis of the relationships between wastewater quality parameters has been con-ducted, and expert opinions have justified the further use of neural networks for modeling the processes of purification of aqueous solutions. Neural model has been built on the Deductor analytical platform for forecasting the functioning of biological wastewater treatment plants.

Wastewater treatment plant design and modeling for the city of Erzurum

Currently large amounts of wastewater are produced by domestic and industrial activities. Discharge of wastewater to the receiving environment without treatment causes significant health and environmental problems. Modeling and optimization of Wastewater Treatment Plants (WWTP) developed to treat domestic wastewater play key roles in determining unit components, design parameters and operation conditions. Several models were proposed to predict the treatment performance in WWTP. The Activated Sludge Model No. 1 (ASM1) is one of the commonly-used standard models developed to better understand removal of carbonaceous and nitrogenous materials. In this study, a WWTP is proposed for domestic wastewater using grit chamber, circular primary and secondary clarifiers, completely-mixed aeration tank, sludge thickener, sludge dewatering and anaerobic digestion processes together. The WWTP was modeled with ASM1 noting the topographic and meteorological features of the city. The treatment performances with wastewater temperatures of 10°C and 20°C were investigated for this plant, operating at high elevation. Removal efficiencies at 20°C were 95.7%, 92.2%, 97.9% and 99.2% for MLSS, COD, BOD and NH4, while effluent concentrations were 14.83, 48.51, 6.55 and 0.3 mg L-1, respectively. At 10°C, removal efficiencies were 88.9%, 88%, 93.2%, and 26.9%, while effluent concentrations were 38, 75, 21.83 and 26.13 mg L-1, respectively. A clear reduction was observed in nitrogenous material removal at low temperatures. Additionally, keeping dissolved oxygen concentration in the aeration tank at 1.5 mg L-1 with PID control increased nitrification efficiency by 30%. The findings reveal the importance of modeling studies during planning of WWTP.

Activated Carbon from Cashew Nut Husk and Cashew Nut Shell Wastes: Synthesis, Characterization, and Adsorption Studies

AbstractActivated carbon (AC) is a key material in numerous industrial applications, including wastewater treatment, catalysis, personal care products, and pharmaceutical industry. Producing AC from waste biomass is of much interest since the environmental footprint is low and the economics are favorable for most applications. Cashew nut husk (CNH) and cashew nut shell (CNS) are typically considered wastes in the cashew nut processing industry. Synthesis of AC from CNH and CNS allows value addition of these materials and improves the economic prospects of the cashew nut processing industry. Herein, we report the production of AC by carbonization of CNH and CNS using orthophosphoric acid as the activating agent. The results showed that 700 °C is a suitable activation temperature for CNH, whereas 500 °C was enough for activating CNS. The AC samples were extensively characterized by FTIR, PXRD, BET, and FESEM‐EDX analysis. The AC produced from CNH at 700 °C (H‐700) exhibited a very high specific surface area and iodine number of 1511 m2/g and 961 mg/g, respectively. Therefore, H‐700 was used for adsorbing methylene blue from water as a model for wastewater treatment. The H‐700 sample showed an adsorption capacity of 520 mg/g with good recyclability up to five cycles.

Machine learning-based model construction and identification of dominant factor for simultaneous sulfide and nitrate removal process

Accurate water quality prediction models are essential for the successful implementation of the simultaneous sulfide and nitrate removal process (SSNR). Traditional models, such as regression and analysis of variance, do not provide accurate predictions due to the complexity of microbial metabolic pathways. In contrast, Back Propagation Neural Networks (BPNN) has emerged as superior tool for simulating wastewater treatment processes. In this study, a generalized BPNN model was developed to simulate and predict sulfide removal, nitrate removal, element sulfur production, and nitrogen gas production in SSNR. Remarkable results were obtained, indicating the strong predictive performance of the model and its superiority over traditional mathematical models for accurately predicting the effluent quality. Furthermore, this study also identified the crucial influencing factors for the process optimization and control. By incorporating artificial intelligence into wastewater treatment modeling, the study highlights the potential to significantly enhance the efficiency and effectiveness of meeting water quality standards.

A Comparative Analysis of Rural Domestic Wastewater Discharge Standards in the Yangtze River Delta Region, China

With the deepening of comprehensive improvement of rural living environments in China, various regions have issued rural domestic wastewater discharge standards based on their economic foundations, management levels, and characteristics of rural wastewater discharge. The Yangtze River Delta region, in particular, has been at the forefront of exploring these standards nationwide. By analyzing the local standards for rural domestic wastewater discharge in Shanghai, Jiangsu, Zhejiang, and Anhui provinces within the Yangtze River Delta region, along with the "Discharge standard of pollutants for municipal wastewater treatment plant" and the "Standard for irrigation water quality", it was found that the standards in each region primarily consider the classification based on the water environmental grade of the receiving water body. The pollutant concentration control requirements are generally consistent with the pollutant concentration limits for urban wastewater treatment plant discharge, but the total nitrogen and total phosphorus limits are more lenient than those for urban wastewater treatment plants. This paper can guide the transformation of rural domestic wastewater discharge from high standards to resource utilization, and provide references for the selection of wastewater treatment technologies, models, and governance strategies in the Yangtze River Delta region.

Gradient-Boosted Decision Tree with used Slime Mould Algorithm (SMA) for wastewater treatment systems

Abstract One way to improve the infrastructure, operations, monitoring, maintenance, and management of wastewater treatment systems is to use machine learning modelling to make smart forecasting, tracking, and failure prediction systems. This method aims to use industry data to treat the wastewater treatment model. Gradient-Boosted Decision Tree (GBDT) algorithms were used gradually to predict wastewater plant parameters. In addition, we used the Slime Mould Algorithm (SMA) for feature extraction and other acceptable tuning procedures. The input and effluent Chemical Oxygen Demand (COD) prediction for effluent treatment systems applies to the GBDT approaches employed in this study. GBDT-SMA employs artificial intelligence to provide precise method modelling for complex systems. Several training and model testing techniques were used to determine the best topology for the neural network models and decision trees. The GBDT-SMA model performed best across all methods. With 500 data, GBDT-SMA achieved an accuracy of 96.32%, outperforming other models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Deep Convolutional Neural Network (DCNN), and K-neighbours RF, which reached an accuracy of 82.97, 87.45, 85.98, and 91.45%, respectively.