Operation Of Treatment Plants Research Articles

Simulating the performance of Kerbala wastewater treatment plant using GPS-X

Simulating the performance of Kerbala wastewater treatment plant using GPS-X

Biofilm Reactors: A Potential Alternative to Current Treatment Technology for Wastewater in Kathmandu Valley

Kathmandu Valley faces challenges managing its growing wastewater volume, compounded by the complex composition of unregulated industrial discharges. Releasing untreated wastewater poses a severe risk to public health and the environment. Existing wastewater treatment infrastructure, primarily reliant on conventional activated sludge processes (ASP) struggles to meet growing demands. These systems require substantial land area, are sensitive to influent variations, produce a high volume of sludge, and incur high operational and maintenance costs. Biofilms, naturally occurring assemblages of microorganisms adherent to surfaces and embedded within an extracellular polymeric matrix (EPS), present a compelling alternative for wastewater treatment due to their diverse pollutant removal capabilities. When implemented as biofilm reactors, they offer distinct advantages, including tolerance to fluctuations in wastewater composition, minimal land requirements, and reduced energy consumption. Notably, microbes residing within a biofilm are capable of biodegradation of persistent materials such as pharmaceuticals, metals, and plastics. Globally, biofilmmediated wastewater treatment has been implemented successfully, while a knowledge gap remains for the treatment of Kathmandu's wastewater. This review critically assesses biological wastewater treatment methods, providing insight into: a) suspended growth process with their configuration, application, and limitations, b) wastewater treatment infrastructures of Kathmandu Valley, and c) biofilm process with their configuration, factors influencing biofilm development and performance, application of specific microbial strains for enhanced treatment efficiency, and factors to be considered during implementation. Furthermore, the paper recommends: a) an extensive study of laboratory-scale biofilm reactors evaluating and optimizing their performance for local integration and b) investigating the role of diverse microbial communities to further enhance the treatment plant's operation. By prioritizing research and development towards biofilm technology, Kathmandu Valley can achieve efficient and environmentally friendly wastewater management.

The short-term effect of nitrogen on freshwater cyanobacteria and cyanotoxins

Toxic cyanobacterial blooms are a worldwide concern. Nutrients are among the numerous factors that trigger cyanobacterial blooms and the production of cyanotoxins. This study aimed to assess the effect of nitrogen on cyanobacteria and cyanotoxins through a short-term mesocosm experiment. To achieve this, mesocosms were installed in situ in two lakes (the Missisquoi Bay of Lake Champlain and Petit-Lac-St-François) and received 700 μg/L of ammonium or 500 μg/L of nitrate. A shift to Microcystis in the mesocosms with nitrogen addition was observed 2 days after the addition in Missisquoi Bay, where nitrogen was more limited than Petit-Lac-St-François based on nutrient ratios. There was a significant increase of extracellular ΣMCs and MC-LR in mesocosms with the addition of ammonium or nitrate after 48 h compared to control mesocosms. Intracellular and extracellular microcystin concentrations were associated with Microcystis. Microcystis presence was associated with ammonium 48 h after N was added at both sites. Therefore, toxin production following sudden nitrogen addition can occur on short time scales relevant to drinking water treatment plant operations. This information will help treatment plant operators better anticipate the arrival of potentially higher concentrations of cyanotoxins.

Environmental and energy efficiency of a selected municipal wastewater treatment plant – a case study

Based on the analysis of environmental and energy efficiency in the years 2019–2021 in the selected sewage treatment plant (WWT) located in the southern Poland with mechanical-biological wastewater treatment, it was found that the highest level of pollution reduction of 98% enabled the highest BOD5 concentration to be reduced more than 800 times. The operation of the sewage treatment plant was characterized by variable efficiency of the treatment process, which was demonstrated by significant differences in chemical indicators (BOD5, COD, P, TSS and N). Low average concentration of total nitrogen below 4 mg l−1 occurred with a reduction of pollutants at a level not exceeding 90%. Noticeable energy consumption of 1.58 kWh m−3 related to the increase in the amount of treated sewage, confirmed by a moderate correlation, indicated the need to optimize the treatment process.

Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Climate change induced precipitation changes can impact wastewater influent volumes and, in turn, the design and operation of wastewater treatment plants (WWTPs). As influent volumes approach or exceeds the design capacity of the WWTP, the likelihood of poor treatment or untreated discharges that result in environmental damage, increases. To date, there has been a lack of research analysing the impact changes in precipitation may have on influent volumes of WWTPs with combined sewerage systems. This study leverages data driven models of observed precipitation and influent volumes for 14 Irish WWTPs with combined sewerage systems, to project monthly wastewater influent volumes in 2041 – 2060 using Ireland’s most up-to-date high resolution multi-model RCM projections under RCPs 4.5 and 8.5. With changing monthly average daily precipitation, influent volumes for all the WWTPs demonstrated maximum decreases during summer months for both RCP 4.5 and RCP 8.5. Winter months showed increasing trends in influent volumes, particularly under RCP 8.5. The results indicate that with projected increase in high and very high precipitation days in Ireland (under CMIP5), the return periods for influent volumes that exceed the hydraulic capacity of 5 of the WWTPs will reduce. This work also presents a framework by which newer high-resolution downscaled climate models can be used to developed updated impact analysis of changing rainfall patterns on wastewater. It likely that wastewater treatment infrastructure will need to adapt to more intense precipitation to minimise the occurrence of combined sewer overflows.

An intelligent predictive and optimized wastewater treatment plant.

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.

Alternatives to 137Cs for 210Pb dating validation in south America and in the Carribean region: Point and diffuse sources

As today the 137Cs fallout peak, in sediment cores, corresponds only to 25% of its initial concentration, alternatives to the use of 137Cs as a210Pb sediment dating validation tool are proposed. In highly industrialized bays, such as Guanabara Bay in the Rio de Janeiro metropolitan region, several chemical/compounds from the surrounding industry releases may be applied as validation tools. Chromium and copper profiles in a sediment core adequately fit the expected pattern due to the implementation of a chemical plant in 1958, reaching their maximum discharge in 1982 and subsequently decreasing due to the operation of a new wastewater treatment plant. A diffuse source such, as the PCB-based mixture Askarel, was also applied as an alternative validation tool, and the observed concentration profile reproduced the expected behavior, with increasing concentration after the Second World War and a decrease after the ban of this product in 1981. The observed Aroclor 1254 and 1260 mixture chlorination rates fit the most widely distributed PCB-based products in the country.

Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach.

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Green business process management in a Polish municipal waste treatment plant-regional case study

Despite COVID-19, the world economy still contributes to the growth of production and consumption worldwide. Waste disposal, recycling management and energy generation are challenges for many companies in developing economies, including Poland. This article aims to assess the operation of a municipal waste treatment plant (MWTP) from the perspective of green business process management (BMP) solutions. The processes implemented in the MWTP were discussed, with specific consideration of the mechanical waste processing (sorting) process, including the reuse and recycling of materials, composting, energy production (anaerobic process), landfill storage and efficiency parameters of the sorting line. A sustainable waste management system was identified; the cost as well as social and environmental perspectives were analyzed. Also, strategic goals and key performance indicators were considered. The performed analysis included costs, environmental criteria and key environmental indicators. This paper has shown the successful implementation of green BPM, with potential cost and material savings results. The findings of this case study are expected to inspire other waste management companies to adopt green BPM. The presented case study might help raise awareness and promote the implementation of green BPM in municipal plants in Eastern and Southern Europe.

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works. In the first stage, Fenton and SPF were carried out on a laboratory scale using a Taguchi L9-type experimental design. From the statistical analysis of this design, the operating parameters: pH, time, hydrogen peroxide concentration [H2O2], and iron ferrous concentration [Fe2+] that maximize the response variables: Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and color were chosen. From these, a cascade forward neural network was implemented to establish a correlation between data from the variables to the physicochemical parameters to be measure being that a great fit of the data was obtained having a correlation coefficient of 0.99 which permits to optimize the pollutant degradation and predict the removal efficiencies at pilot scale but with a projection to a future industrial scale. A relevant result, it was found that the optimal values for maximizing the removal of physicochemical parameters were pH = 3, time = 60 min, H2O2/COD = 1.5 mg L−1, and H2O2/Fe2+ = 2.5 mg L−1. With these conditions degradation percentages of 91.44%, 47.14%, and 97.89% for COD, TOC, and color were obtained from the Fenton process, while for SPF the degradation percentage increased moderately. From the ANN analysis, the possibility to establish an intelligent system that permits to predict multiple results from operational conditions has been achieved.

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.

Innovative sanitation solutions: evaluating the performance of faecal sludge treatment plant in humanitarian settings: A case study from Rohingya camps in Bangladesh

The Rohingya crisis has precipitated one of the most significant humanitarian emergencies, resulting in over 900,000 refugees residing in Cox's Bazar, Bangladesh. The rapid influx of refugees has posed substantial challenges in providing essential services, particularly sanitation, in a densely populated and resource-constrained environment. This study examines the performance of faecal sludge treatment plants (FSTPs) implemented in the Rohingya camps, focusing on key performance indicators such as treatment efficiency, operational challenges, health outcomes, and resource recovery potential. Analysis reveals that FSTPs have played a crucial role in reducing pathogen levels in treated sludge, thereby mitigating the risk of waterborne diseases and environmental contamination. Despite these successes, operational challenges such as frequent desludging, infrastructure maintenance, and the logistical complexities of waste collection and transport persist. The study highlights the importance of addressing these challenges through regular maintenance, infrastructure upgrades, and innovative logistical solutions. Health outcomes have shown improvement with a reduction in sanitation-related diseases, underscoring the health benefits of effective FSTP implementation. Additionally, resource recovery from treated sludge presents a sustainable approach, with potential applications in agriculture and energy production. Community engagement has emerged as a critical factor in the success of these interventions. Effective community involvement in the planning, implementation, and maintenance of sanitation facilities has been instrumental in ensuring their acceptance and proper use. The study emphasizes the need for continued community education and participation to sustain these benefits. In conclusion, while FSTPs have significantly improved sanitation conditions in the Rohingya camps, ongoing efforts are required to enhance operational efficiency, address logistical challenges, and foster community involvement. The findings provide valuable insights for the design and implementation of innovative sanitation solutions in similar humanitarian settings worldwide.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD – chemical oxygen demand, NH4-N – ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

A comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge for nutrient removal in full-scale wastewater treatment plants

Understanding the microbial community structure of sludge is crucial for improving the design, operation and optimisation of full-scale wastewater treatment plants (WWTPs). This study aimed to have a comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge from two full-scale sequential batch reactors-based WWTPs with nutrient removal for the first time. To better understand key functional bacteria such as polyphosphate accumulating bacteria (PAOs), competitive bacteria such as glycogen accumulating bacteria (GAOs) and nitrifying bacteria for both nitrogen and phosphorus removal, another two full-scale WWTPs with only carbon (C) removal and C and nitrogen (N) removal were compared too. It was found that the richness and diversity of the microbial population in sludge increased with pollutant removal from only C, C and N, to C,N, P removal. For C, N P removal, granule structure led to a more diverse and rich microbial community structure than flocculent structure. Although more abundant nitrifying bacteria were enriched in granular sludge than flocculent sludge, the abundance of total putative PAOs was equivalent. However, the most typical putative PAOs such as Tetrasphaera and Candidatus Accumulibacter seemed to be more correlated with biological phosphorus removal performance, which might be more proper to be used as an indication for P removal potential. The higher abundance of GAOs in flocculent sludge with better phosphorus removal performance might suggest that further investigation is needed to understand the functions of GAOs. In addition, the equivalent abundances of PAOs in the WWTPs with only C removal and with C, N, and P removal, respectively, indicate that many newly reported putative PAOs might not contribute to P removal. This study provides insight into the microbial communities and functional bacteria in aerobic granular sludge and flocculent sludge in full-scale SBRs, which can provide microbes-informed optimisation of reactor operation for better nutrient removal.

Prediction of wastewater treatment plant performance through machine learning techniques

This study investigates the use of developed machine learning techniques for modeling the performance of AlHayer, Saudi Arabia, wastewater treatment plant (ALWTP). Three physio-chemical characteristics were measured and predicted, including chemical oxygen demand (COD), biological oxygen demand (BOD), and suspended solids (SS), at ALWTP. The pre-evaluation of collected data revealed the effective capabilities of ALWTP for the removal of suspended solids, organic, and nutrient pollutants. To estimate the physio-chemical characteristics of ALWATP, four developed machine learning techniques were evaluated and compared. Logistic regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR) were designed. The evaluation of the proposed models showed RF outperformed other proposed models for estimating COD and SS with accuracy 91 % and 95 % in terms coefficient of determination (R2); however, GB was found the best, with accuracy 92 %, for detecting the BOD performance of ALWATP. This indicates ensemble learning models, RF and GB, can be considered a superiority soft solution for estimating physio-chemical characteristics of wastewater treatment plant.

Modelling reverse osmosis process and multiple effect evaporator process of common effluent treatment plant using artificial neural network

This paper presents the study on modeling the performance of Textile and Tannery Common Effluent Treatment Plant (CETP) using Artificial Neural Networks (ANN). The process of Reverse Osmosis (RO) Process and Multiple Effect Evaporator (MEE) process of CETP is modelled using ANNs. Two ANN models were developed to predict the performance of CETP dealing with highly saline effluents. Model-I (RO Model) predicts TDS Rejection (%) by taking pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Chemical Oxygen Demand (COD) of RO feed as input parameters thus providing estimate of salt concentration. Model-II (MEE Model) predicts Steam Output needed by taking pH, TDS, Feed flow as input parameters. Levenberg-Marquardt backpropagation (trainlm) algorithm was used as training function for creation of both of ANN models and Hyperbolic Tangent Sigmoid (tansig) was used as transfer function. Separate model validation has been performed to test the predictability of the model and at last prediction accuracy were analysed to show its efficiency in prediction.

The Effect of Energy Saving in Wastewater Treatment Plant on the Environmental Sustainability of the Plant

Wastewater treatment plants (WWTPs) have significant function for the urban water management. However, they can consume large amount of energy for reducing the pollutant concentration in aquatic environments. In this work, the effect of energy saving in the selected wastewater treatment plant (before and after the energy saving revisions) on the environmental sustainability of the plant was investigated by life cycle assessment (LCA). Two situations were assessed comparatively: WWTP-1 (before energy saving) and WWTP-2 (after energy saving). Life cycle impacts were evaluated in terms of both mid-point and end-point impact categories by ReCiPe 2016 methodology. The results showed that contribution of electricity consumption in WWTP-2 significantly decreased in almost all mid-point impact categories compared with WWTP-1. Considering damage assessment, overall environmental burden of WWTP-2 was determined to be 36% lower than WWTP-1. It was also noted that in addition to electricity saving, the method chosen for sludge disposal was decisive in the environmental performance of the wastewater treatment plant.

Optimization of Gambier Industry Wastewater Treatment Plant (WWTP) Performance: a Systematic Approach for Environmental Sustainability

Optimization of Gambier Industry Wastewater Treatment Plant (WWTP) Performance: a Systematic Approach for Environmental Sustainability

Performance of wastewater treatment plants in emission of greenhouse gases

The present work has estimated greenhouse gas emissions in aerobic and anaerobic Wastewater Treatment Plants in Southern Italy. Greenhouse gases emissions from each treatment unit were calculated based on emission factors related to Chemical Oxygen Demand removal for biogenic CO2 and CH4 assessment and on Nitrogen removal for N2O. N2O, biogenic CO2, and CH4 emissions vary for aerobic and anaerobic-based WWTPs respectively from 73 kgCO2eq/PE*y for anaerobic plants to 91 kgCO2eq/PE*y for aerobic plants. In aerobic and anaerobic digestion systems WWTPs the contributions to CO2eq total emissions from N2O, CH4, biogenic CO2, and fossil CO2 are 30 %–33 %, 20 %–29 %, 22 %–25 %, and 26 %–16 %, respectively. N2O emissions from biological processes were found the most contributing sources of greenhouse gases while in the physical processes higher contribution is indirect carbon dioxide related to energy consumption. Compensatory measures are reported to reduce greenhouse gases emissions.