Operation Of Wastewater Treatment Plants Research Articles

Resilience Adaptation Through Risk Analysis for Wastewater Treatment Plant Operators in the Context of the European Union Resilience Directive

To combat new threats to critical infrastructure, the European Union enacted new regulations for their member states. With the directive on measures for a high common level of cybersecurity (NIS2) and the directive on critical infrastructure resilience (EU RCE), EU member states must identify critical infrastructure (CIs) and enable measures to reduce the risk of default in stress situations. The topic of resilience in urban water systems has already been of interest in previous research. However, there are still open questions. As it is a multidisciplinary term, understanding resilience and its adaptation into management systems is not an easy task for practitioners. This study will provide an overview of resilience within the framework of municipal wastewater treatment plants (WWTP) and show the current situation of existing implementation of safety and security regulations, taking Germany as an example. One of the main requirements of the EU RCE is a risk assessment (RA) for CIs. Until now, risk analysis for WWTP in research was mostly carried out for individual WWTP. By applying guidelines from the drinking water sector, this paper shows a possible methodology for a risk analysis. This paper aims to support practitioners by forming a common understanding of resilience and risk as well as providing an example for a risk analysis.

Practical implications of adding powdered activated carbon in advanced wastewater treatment for process and plant design

ABSTRACT The addition of powdered activated carbon (PAC) in advanced wastewater treatment is increasingly recognized for its effectiveness in removing micropollutants from secondary effluents. This study investigates the implications of PAC dosing on treatment performance, particularly in terms of natural and effluent organic matter (OM) removal, turbidity reduction, and sludge characteristics. Results indicate that while PAC incorporation significantly enhances the adsorption of OM, it necessitates higher coagulant dosages to achieve comparable or improved turbidity levels. The study also reveals that lower pH levels facilitate the removal of OM, thereby increasing the availability of adsorption sites on PAC for micropollutants. Moreover, PAC addition results in the formation of larger, denser flocs that settle faster, leading to a reduction in sludge volume by approximately 20%. However, the increased coagulant demand and the subsequent rise in sludge volume highlight the need for the optimized process design to balance treatment efficiency with operational costs. This research provides valuable insights for the design and operation of wastewater treatment plants, emphasizing the importance of tailored coagulant dosing and process adjustments when integrating PAC into existing treatment frameworks.

Assessment of carbon efficiency in wastewater treatment plants through Stochastic non-parametric data envelopment analysis (StoNED): Insights from Spanish facilities

Evaluating the carbon efficiency (CE) of wastewater treatment plants (WWTPs) is crucial for guiding these facilities towards carbon neutrality. Nevertheless, enhancing carbon performance ought not to be pursued to the detriment of pollutant removal efficiency. In this study, the CE of WWTPs is calculated as a composite index that integrates carbon emissions with the volume of pollutants removed from wastewater, including organic matter, suspended solids, nitrogen, and phosphorus. To achieve this, the Stochastic Non-parametric Envelopment of Data (StoNED) method is employed. This approach, distinct from the commonly used Data Envelopment Analysis (DEA), accounts for both empirical data and random variations in the estimation of CE, thereby enhancing the reliability of the CE evaluation. The study assesses the CE of 109 Spanish WWTPs, finding that none of them are fully carbon efficient. This involves that all plants have potential to reduce greenhouse gas emissions without sacrificing pollutant removal efficiency. The average CE of the WWTPs is 0.529, indicating a possible reduction in carbon emissions by approximately 0.076 kg of CO2 equivalent per cubic meter of wastewater treated. The analysis also reveals that neither the volume of wastewater treated nor the type of reactor used for secondary treatment has a significant impact on the CE of the facilities. The CE metric proposed in this study serves as an important decision-support tool for advancing towards the carbon neutrality of wastewater treatment processes. By providing a more comprehensive understanding of the environmental performance of WWTPs, it helps identifying areas for improvement and guiding policy and operational decisions.

Generic and site-specific social life cycle assessment of municipal wastewater treatment systems in Spain: challenges and limitations of the method when applied to resource recovery systems

Abstract Purpose This work aims to provide insights on the application of social life cycle assessment (S-LCA) in evaluating the social impacts associated with municipal wastewater treatment (WWT). The study assesses the social risks and social performance of two municipal WWT systems in Catalonia, Spain: a conventional wastewater treatment plant (WWTP) (Reference System) and a novel system that recovers water and other valuable resources (Novel System). Methods S-LCA was conducted at Generic and Site-Specific levels using 1 m3 of wastewater treatment as the functional unit (FU). The Generic assessment was conducted via the Product Social Impact Life Cycle Assessment (PSILCA) database, while the Site-Specific assessment employed the Subcategory Assessment Method (SAM) with four-level reference scales to assess the social performance of the WWTP operator and its first-tier suppliers. Furthermore, activity variables were calculated based on organizations’ shares in the total costs per FU, and the Novel System’s multifunctionality was solved through economic allocation. Results were aggregated by (i) assigning equal weights to organizations and (ii) factoring in organizations’ weights and the allocation factor, leading to results per FU. Results and discussion The Generic analysis results indicated that the Novel System entailed fewer social risks than the Reference System. Most social risks in both systems occurred in the subcategories “Access to material resources,” “Fair salary,” “Freedom of association and collective bargaining,” “Contribution to economic development,” and “Corruption.” In the Site-Specific assessment, the Novel System presented better social performance than the Reference System per 1 m3 of wastewater treatment. The latter’s performance per FU did not meet the basic requirement in four out of eleven subcategories, mainly due to the performance and weight of a chemical supplier. Allocation greatly benefitted the Novel System’s results per FU compared to the results obtained when equal weights were applied. Conclusions Activity variables were used to connect organizations’ conduct with particular WWT systems, and multifunctionality was solved. This approach allowed for obtaining results per FU at both assessment levels. However, social performance was also evaluated by calculating the average social performance of each system without considering activity variables and the FU, leading to different results. The social performance of the Novel System per FU was satisfactory across all subcategories but required improvement in four subcategories based on the average results. Given the limitations of using activity variables and allocation in S-LCA, further research is necessary to appropriately evaluate and compare the social effects of novel resource recovery systems.

Nanostructured materials as hazardous wastewater micropullutants: Sources, behavior, and impact on functional bacterial community of activated sludge

Unique properties of nanoscale materials make them attractive for industrial, medical, agricultural, and environmental applications. Nevertheless, the release of nanoparticles into the environment is a major concern due to the lack of knowledge about their behavior in the environment and potential widespread environmental impacts. On the one hand, nanomaterials are perceived as pollutants that may affect activated sludge microorganisms and, consequently, the efficiency of wastewater treatment processes. On the other hand, some nanomaterials can be intentionally added to activated sludge systems to improve their performance in terms of, e.g., sludge settling and removing heavy metals or organic pollutants. As a result, nanoparticles are frequently accumulated in wastewater, which is considered to be a major source of nanoparticle release to the surrounding environment. Processes that involve the action of activated sludge are used worldwide in wastewater treatment plants due to their excellent capacity of removing nutrients, degrading toxins, and retaining biomass. High concentrations of nanoparticles entering activated sludge systems can affect their growth and metabolism. The research studies, which are reviewed in the present article, show that nanoparticles significantly reduce the relative abundance of the activated sludge microbial community associated with nitrification, denitrification, and phosphorus removal. The knowledge about the structure of the activated sludge microbial community with an assessment of nanomaterial toxicity can contribute to optimizing the sludge population and improving the performance of wastewater treatment plants.

Deep insights into the assembly mechanisms, co-occurrence patterns, and functional roles of microbial community in wastewater treatment plants

The understanding of activated sludge microbial status and roles is imperative for improving and enhancing the performance of wastewater treatment plants (WWTPs). In this study, we conducted a deep analysis of activated sludge microbial communities across five compartments (inflow, effluent, and aerobic, anoxic, anaerobic tanks) over temporal scales, employing high-throughput sequencing of 16S rRNA amplicons and metagenome data. Clearly discernible seasonal patterns, exhibiting cyclic variations, were observed in microbial diversity, assembly, co-occurrence network, and metabolic functions. Notably, summer samples exhibited higher α-diversity and were distinctly separated from winter samples. Our analysis revealed that microbial community assembly is influenced by both stochastic processes (66%) and deterministic processes (34%), with winter samples demonstrating more random assembly compared to summer. Co-occurrence patterns were predominantly mutualistic, with over 96% positive correlations, and summer networks were more organized than those in winter. These variations were significantly correlated with temperature, total phosphorus and sludge volume index. However, no significant differences were found among microbial community across five compartments in terms of β diversity. A core community of keystone taxa was identified, playing key roles in eight nitrogen and eleven phosphorus cycling pathways. Understanding the assembly mechanisms, co-occurrence patterns, and functional roles of microbial communities is essential for the design and optimization of biotechnological treatment processes in WWTPs.

Some Possible Process Configurations for Modern Wastewater Treatment Plants for Per- and Polyfluoroalkyl Substances (PFASs) Removal

Per- and polyfluoroalkyl substances (PFASs) have been detected in the influent, effluent, and sludge/biosolids of wastewater treatment plants (WWTPs) globally. Due to their potential to bioaccumulate in humans, wildlife, and the environment over time owing to their seriously persistent nature and/or strong C-F bonds, PFASs can cause public health concerns. Conventional processes in full-scale WWTPs are usually inefficient in PFASs removal from wastewater and sludge, and advanced treatment technologies are needed for PFASs removal. This study intends to briefly (i) summarize the technologies for PFASs remediation in wastewater and sludge; (ii) review PFASs removal in full-scale WWTPs; (iii) discuss some possible theoretical configurations for the wastewater processing train of modern WWTPs for PFASs remediation; and finally (iv) provide future directions. Further research regarding the techno-economic assessment and optimization of treatment technologies in removing PFASs (especially short-chain PFASs) from real wastewater as well as the performance of full-scale WWTPs consisting of advanced innovative efficient treatment technologies for PFASs removal and associated costs (i.e., construction, operation, maintenance, chemical, energy, and amortization) is still required.

Performance of Dairy Factory Wastewater Treatment Plant (Case Study of Pasuruan Dairy Factory East Java)

The 'ILP' company produces bottled milk with chocolate, melon, strawberry, and vanilla flavors in colorless HDPE (High-Density Polyethylene) plastic bottles and aluminum foil covers. The 'ILP' dairy industry wastewater treatment process stages include bar screen, grease trap, equalization tank, flocculation-coagulation, primary clarifier, aeration tank/activated sludge, and secondary clarifier. The research aims to determine the performance of the dairy factory's WWTP (Wastewater Treatment Plant) in terms of TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) parameters. This field-scale research was carried out for approximately 5 weeks (35 days) at the wastewater treatment plant of the ILP company. Wastewater sampling is carried out at the inlet point, equalization tank, primary sedimentation, aeration tank, and outlet point of WWTP. Wastewater examination conducted in the company's internal laboratory, including temperature, pH, total suspended solids (TSS), and chemical oxygen demand (COD). The dairy factory WWTP (wastewater treatment plant) can generate TSS (total suspended solids) effluent of 22 mg/L and COD (chemical oxygen demand) of 26.8 mg/L to meet the applicable wastewater quality standards. The treatment efficiency of the dairy industry WWTP for TSS is 94.7% and COD is 98.1%.

Quorum-sensing molecules regulate biochar-assisted anaerobic digestion system for methane production: Single-stage vs. two-stage digestion

Promoting methane production performance from waste activated sludge is of great significance for energy self-sufficiency operation of wastewater treatment plants. This study evaluated the methane production enhancement strategies of biochar and quorum-sensing molecules like N-acylated homoserine lactone (AHL) in single-stage and two-stage anaerobic digestion systems. The methane yield increased to 134.9 mL/g volatile suspended solids in single-stage digestion system, which was 41.7 % higher than that of control group, while it in two-stage digestion system was not only lower than that obtained in single-stage digestion system but also less than that observed in groups adding AHL either at the beginning or on the third day of experiment period. The mechanism studies indicated that the single-stage digestion strategy promoted solubilization, hydrolysis, acetogenesis, and methanogenesis stages and improved the conversion and utilization of organic matters with the enhanced syntrophic metabolisms between microorganisms. In addition, the electron transfer activity between microorganisms was promoted, with an increase ratio of 54.7 %, meanwhile, the syntrophic relationships between methanogens such as Methanosarcina and electroactive bacteria such as norank_f__Bacteroidetes_vadinHA17 were promoted. As a result, both acetotrophic and hydrogenotrophic methanogenesis pathways were improved. This study provided some novel approaches for promoting anaerobic digestion performance while using biochar as the additive.

A nutrient circular economy framework for wastewater treatment plants

Given the criticality of phosphorous and the harm eutrophication causes, wastewater treatment plants (WWTP) are undergoing circular economy (CE) conversions into becoming centres for resource recovery. This framework paper identifies the environmental, societal, political, commercial, economic, consumer, regulatory, legal, infrastructural, technological, international compliance, academic, agricultural, and plant-operator factors affecting the micro, macro and meso success and failure of nutrient CE WWTP across scope classifications. From this, nutrient CE framework is not as simplistic as it looks, given the multitude of impingers that need to be considered. The lack of technological readiness and vast selection methodologies for some nutrient recovery technologies, low private-sector investment, divisive consumer perceptions, unique economic situations, localised regulatory specifications, threats of inefficient technology lock-ins, poor acceptance among farmers and WWTP operators due to infrastructural incompatibilities, lack of efficient CE supply chains, legacy infrastructure, poor digitised solutions for nutrient CE management, low training and awareness, and integration of nutrient CE WWTPs with broader society are barriers. On the contrary, nutrient CE WWTP will mainly be driven by regulations, subsidies and consumer-WWTP-farmer nutrient CE acceptances. This framework aims to tie in and provide solutions to overcoming barriers to nutrient CE WWTPs that is universally applicable. Consequently, the framework provides a holistic structure for policy, legislative, academic, agricultural, plant operators, commercial and consumer stakeholders a roadmap for the success and difficulties of nutrient CE WWTP adoption.

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4–6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.

Risk Assessment of Hospital Waste Water Treatment Plant Operation – A Case Study of a Class B Hospital in Indonesia

Risk Assessment of Hospital Waste Water Treatment Plant Operation – A Case Study of a Class B Hospital in Indonesia

Investigating upflow anaerobic sludge blanket process to treat forward osmosis effluents of concentrated municipal wastewater under psychrophilic temperature

This work investigated the stability of the Upflow Anaerobic Sludge Blanket (UASB) reactor under psychrophilic temperatures with varying feed streams, simulating typical and concentrated sewage. In Phase I, treating municipal wastewater, chemical oxygen demand (COD) removal dropped from 77 ± 6 % to 41 ± 2 % as hydraulic retention time decreased from 24 to 12 h and organic loading rate (OLR) increased from 0.6 to 1.3 gCOD/(L∙d). In Phase II, at a similar OLR (≈1.2 gCOD/(L∙d)), the UASB treated organic-rich effluents (from 1.0 to 2.1 ± 0.1 gCOD/L) resulting from the pre-treatment of the forward osmosis (FO) process. The UASB performance improved significantly, achieving 87 ± 3 % COD removal and 63 ± 4 % methane recovery, with microbial analysis confirming methanogen growth. The COD mass balance showed up to 30 % more electrical energy recovery from sewage compared to conventional wastewater treatment plants (WWTPs), indicating that the FO-UASB combination is a promising approach to achieve energy-neutral operation in WWTPs.

A Review of the Dissemination of Antibiotic Resistance through Wastewater Treatment Plants: Current Situation in Sri Lanka and Future Perspectives.

The emergence of antibiotic resistance (AR) poses a significant threat to both public health and aquatic ecosystems. Wastewater treatment plants (WWTPs) have been identified as potential hotspots for disseminating AR in the environment. However, only a limited number of studies have been conducted on AR dissemination through WWTPs in Sri Lanka. To address this knowledge gap in AR dissemination through WWTP operations in Sri Lanka, we critically examined the global situation of WWTPs as hotspots for transmitting antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) by evaluating more than a hundred peer-reviewed international publications and available national publications. Our findings discuss the current state of operating WWTPs in the country and highlight the research needed in controlling AR dissemination. The results revealed that the impact of different wastewater types, such as clinical, veterinary, domestic, and industrial, on the dissemination of AR has not been extensively studied in Sri Lanka; furthermore, the effectiveness of various wastewater treatment techniques in removing ARGs requires further investigation to improve the technologies. Furthermore, existing studies have not explored deeply enough the potential public health and ecological risks posed by AR dissemination through WWTPs.

Assessing the effects of salinity on microbial communities and pollutant removal in urban wastewater treatment plants

This study assessed the impact of electrical conductivity (EC) on the microeukaryotic community and pollutant removal efficiency in full-scale wastewater treatment plants (WWTPs) in Catalonia, Spain. Monthly samples from seven WWTPs (2010−2021) were collected, with microbial communities identified by microscopy and effluent quality parameters measured. Linear mixed-effects models (LMER), indicator species analysis (IndVal), and structural equation modeling (SEM) were used to evaluate the effects of EC on WWTP operation and microbial communities. EC levels varied widely among WWTPs (800 to 15,000 μS cm−1), with mean removal rates generally low, not exceeding 26 %. EC removal was inversely correlated with organic matter removal (p < 0.05). IndVal analysis revealed distinct microbial communities associated with each WWTPs along the EC gradient (e.g., Beggiatoa spp. and Epistylis sp. were associated with high and low EC, respectively). High EC and peaks negatively affected the abundance of certain microorganisms (e.g., Acineria uncinata) (p < 0.05). Ciliate genera were most affected by EC peaks, with different species showing different salinity tolerance ranges (e.g. Holophyra discolor was affected by EC > 3000 μS cm-1). Seasonal variations did not significantly alter community sensitivity to salinity (p > 0.05). LMER and SEM analyses revealed strong adverse effects of high EC and EC peaks on microbial diversity, richness, and the efficiency of organic matter and TKN removal (p < 0.05). This highlights the importance of monitoring and controlling EC levels in WWTP influent to maintain optimal treatment performance and the need for effective technologies or biological processes to mitigate saline discharges into rivers.

In-vitro multiparametric analysis of imipenem resistance in bacteria caused by acquisition of foreign DNA and natural mutations

Several studies have demonstrated that antimicrobial resistance (AMR) can result from natural mutations or the acquisition of foreign antimicrobial resistance genes. Many environmental parameters are known to affect AMR, including trace antibiotic concentrations in water, pH, hydraulic retention time, and nutrient concentration, among others. However, most analyses that investigate these parameters analyze them independently. This study investigated the development of natural resistance in Salmonella sp., and the horizontal transfer of resistance genes from an imipenem- resistant (IR) E. coli strain under varying environmental conditions. An automated continuous-culture device was employed to investigate the effect of external parameters of relevance to wastewater treatment plants and to conduct a multiparametric analysis to determine the relative importance of the different variables. In terms of mutation-driven resistance, hydraulic retention time, trace antibiotic concentration, and initial cell concentrations are the leading factors contributing to higher IR in Salmonella spp. In the case of acquired resistance, the initial trace antibiotic concentrations, and hydraulic retention times were also important, but pH, initial nutrient concentration, and, very importantly, the initial concentration of IR E. coli, were the leading factors contributing to IR in Salmonella spp. These results point out some important factors that need to be considered during the design and operation of wastewater treatment plants to minimize the appearance and transmission of antibiotic resistance.

Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network

Activated sludge models are increasingly being adopted to guide the operation of wastewater treatment plants. Chemical oxygen demand (COD) is an indispensable input for such models. To ensure that the activated sludge mathematical model can adapt to various water quality conditions and minimize prediction errors, it is essential to predict the parameters of the COD components in real-time based on the actual influent COD concentrations. However, conventional methods of determining the components' contributions are too intricate and time-consuming to be really useful. In this study, the chemical oxygen demand in the actual waste water treatment plant was disassembled and analyzed. The research involved determining the proportions of each COD component, assessing the reliability of the measurement parameters, and examining potential factors affecting measurement accuracy, including weather conditions, pipeline conditions, and residents' habits. Then, a backpropagation neural network was developed which can deliver real-time predictions for five important contributors to COD in real time. In addition, using the receiver operating characteristics curve and prediction accuracy to evaluate the performance of the prediction model. For all five components, which SS, XS, SI, XA, and XH, the prediction accuracy of model was more than 80 %. The maximum deviation values of these parameters fall within the range of the actual detected values, suggesting that the model's predictions align well with real-world observations, and demonstrated prediction performance adequate for practical application in wastewater treatment. This article can provide research basis for the engineering application of activated sludge model and help for the intelligent upgrading of waste water treatment plants.

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

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

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.