Influent Quality Research Articles

Influence of influent C/N on sludge granulation process and nutrient removal pattern

Abstract In this paper, firstly, the relevant parameters of influent C/N quantification were selected, and then, on the basis of the relevant parameters, the apparent shear force formula was deduced, and the apparent shear force was used to reflect the hydraulic condition of the reactor. Next, the experimental device, operation method, influent water quality and analysis method were determined, and the influences of influent C/N on the sludge granulation process were derived from the influences of group I C/N ratio on anaerobic granular sludge and group II C/N ratio on anaerobic granular sludge. Then, the return sludge from the Guangzhou sewage treatment plant in Guangdong Province was selected as the experimental seed sludge, and the methanol concentration of 0.13g/L (COD=200mg/L) was used as the experimental water quality, and the influences of influent C/N on nutrient N2O denitrification were illustrated by the experimental analyses. The results showed that the NO2 − − N accumulation rate was maintained above 81% in all five water intake modes, and when the ratio of water intake (1:1) and (2:1), a higher concentration of NO2 − − N was accumulated in the first nitrification stage of 26.5 mg/L and 24.9 mg/L, respectively, which could give full play to the advantages of nitrifying bacteria to achieve nitrification rapidly and also reduce the production of nutrient salt N2O. This study combines macro-experiment and micro-analysis to provide a theoretical basis for granular sludge technology.

An Extensive Analysis of the Engineering Design of Underground Sewage Plants in China

In recent years, underground sewage treatment plants that can remarkably reduce land occupation with less environmental pollution are gradually entering the popular consciousness and are now being used widely. However, problems associated with the traditional treatment plants, such as high construction and operation cost, severe health and safety risks, and monotonous landscape design have limited their value and restricted their application and promotion. Through the literature and field investigations, the value of underground sewage treatment plants (STPs) was analyzed, their engineering and landscape design were studied, and their development direction was explored in order to supply a theoretical basis for further application and development of underground STPs. The analysis showed that as a new model of environmentally friendly sewage treatment and resource conservation, underground STPs have the apparent advantages of lower cost of land use for construction and pipeline as well as an outstanding value for the urban landscape and ecological environment. These factors can offset its relatively high construction and operating costs to a certain extent, especially when compared with above-ground STPs. The engineering design study results showed that significant differences existed between underground STPs and traditional above-ground STPs, and that the main contents of the engineering design of underground STPs consist of treatment scale and degree, influent and effluent qualities, site selection, design model, underground arrangement and structure, main treatment process, monomer structure, ventilation and deodorization, daylighting and artificial lightings, fire safety, operation and maintenance, and the linkage design between the above-ground landscape and the underground STP.

Prediction of influent wastewater quality based on wavelet transform and residual LSTM

Accurate prediction on influent wastewater quality is of great importance to energy saving and chemical dosage reduction of wastewater treatment plants (WWTPs). However, the existing methods ignore the data noise caused by water sensors working in harsh conditions and the intrinsic variable dynamics inherent in the time series of wastewater quality. To tackle this problem, we propose a novel approach called wt-ResLSTM (wavelet transform and Residual Long Short-Term Memory) to predict the influent wastewater quality. Specifically, we adopt wavelet transform and semi-soft thresholding to remove the noise from influent wastewater quality data adaptively. Then, we use autoencoder to learn the latent representation of the recent fluctuation of wastewater quality to capture its transient uncertainty. Further, the residual LSTM is adopted to learn both the long-term and short-term sequential dependencies of influent wastewater quality from the historical wastewater quality and the latent representation of its recent fluctuation. Experiments on the dataset from a large-scale urban WWTP in Beijing demonstrate that the proposed wt-ResLSTM approach outperforms state-of-the-art techniques in predicting influent wastewater quality in terms of level accuracy and directional accuracy.

Insight into the mechanism of nutrients removal and response regulation of denitrifying phosphorus removal system under calcium ion stress

The influent quality is an important factor affecting the nutrients removal and operational stability of denitrifying phosphorus removal (DPR) system. This study investigated the effects of calcium ion (Ca2+) on the nutrients removal, nitrogen oxide (N2O) release, microbial community, and quorum sensing in DPR system. Results showed that high accumulation of Ca2+ had a significant impact on the carbon footprint of DPR system. Specifically, N2O release reached 2.11 mg/L under Ca2+ of 150 mg/L, which represented 214.93% increase compared to 0 mg/L of Ca2+. The DPR system demonstrated its adaptability to elevated Ca2+ concentrations by modifying key enzyme activities involved in nitrogen and phosphorus removal, altering the microbial community structure, and adjusting the type and content of signal molecules. These findings hold significant implications for understanding the stress mechanism of Ca2+ on DPR system, ultimately aiding in the maintenance and enhancement of stable operational performance in biological wastewater treatment process.

Elevated purification of urban rainwater runoff using a calamus constructed wetland with multi-layer carrier fillers

Urban water body pollution caused by storm-water runoff is increasingly serious due to rising global urbanization. We evaluated the effectiveness of a calamus vertical flow constructed wetland purifying urban runoff. This wetland was constructed using three filler layers of rare earth porcelain sand, ceramsite, and clinoptilolite respectively. The results revealed that under the optimal hydraulic load of 80 L/(m2·h), pollutants removal efficiency with the study wetland reached 89.9 %, 95.0 %, 84.7 %, and 76.2 % for the moderate rain runoff, in terms of COD (chemical oxygen demand), SS (suspended solids), TN (total nitrogen) and TP (total phosphorus) respectively. The removal rates of COD, SS, TN and TP by plants and the soil layer together were up to 64.9 %, 50.0 %, 58.1 %, and 28.6 %, respectively, while the rare earth porcelain sand layer had the highest contribution to the removal rate of TP (42.8 %), and the filler removal effect for phosphorus was more significant than that of plants and the soil layer. Although the influent water quality fluctuates with runoff from moderate to heavy torrential rain intensities, most of the effluent water meets the discharging standards. While the effluent quality of light rain runoff with high pollution load slightly exceeded the standards, it still removal rate as high as 88 %, 96.3 %, 73.3 %, 76.2 % for COD, SS, TN, TP.

Metagenomics and metatranscriptomics suggest pathways of 3-chloroaniline degradation in wastewater reactors

Biological wastewater treatment systems are often affected by shifts in influent quality, including the input of toxic chemicals. Yet the mechanisms underlying the adaptation of activated sludge process performance are rarely studied in a controlled and replicated experimental setting, particularly when challenged with a sustained toxin input. Three replicate bench-scale bioreactors were subjected to a chemical disturbance in the form of 3-chloroaniline (3-CA) over 132 days, after an acclimation period of 58 days, while three control reactors received no 3-CA input. Ammonia oxidation was initially affected by 3-CA. Within three weeks of the experiment, microbial communities in all three treatment reactors adapted to biologically degrade 3-CA resulting in partial ammonia oxidation recovery. Combining process and microbial community data from amplicon sequencing with potential functions gleaned from assembled metagenomics and metatranscriptomics data, two putative degradation pathways for 3-CA were identified. The first pathway, determined from metagenomics data, involves a benzoate dioxygenase and subsequent meta-cleavage of the aromatic ring. The second, determined from intensive short-term sampling for gene expression data in tandem with 3-CA degradation, involves a phenol monooxygenase followed by ortho-cleavage of the aromatic ring. The relative abundances of amplicon sequence variants associated with the genera Gemmatimonas, OLB8, and Taibaiella correlated significantly with 3-CA degradation. Metagenome-assembled genome data also showed the genus OLB8 to be differentially enriched in treatment reactors, making it a strong candidate as 3-CA degrader. Using replicated reactors, this study has demonstrated the impact of a sustained stress on the activated sludge process. The unique and novel features of this study include the identification of putative pathways and potential degraders of 3-CA using long-term and short-term sampling in tandem with multiple methods in a controlled and replicated experiment.

Automated machine learning-based models for predicting and evaluating antibiotic removal in constructed wetlands

Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94–13.68, coefficient of determination = 0.780–0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.

Optimization pollutants removals from wastewater treatment plant using artificial neural networks

Water quality, treatment plant management, and environmental concerns all affect how well a sewage treatment plant performs. Due to the high degree of nonlinearity in the plant as well as the nonuniformity and unpredictability of the influent amount, quality parameters, and operational conditions, modelling the sludge capacity index of the activated sludge method in municipal wastewater treatment plants is a challenging mission. To assess the effectiveness of the al-diwaniyah wastewater treatment plant (WWTP) operation and to estimate quality parameters, the study’s first goal is to improve the WWTP by using artificial neural networks (ANNs). Second, increasing the efficiency of the ANNs model to determine the best WWTP procedure. ANNS were created to predict the sludge volume index (SVI) using the al-diwaniyah WWTP operational and influent quality characteristics. The neural network’s best model for predicting SVI consists of an input node with six input variables, a hidden layer with five nodes, and an output layer with one variable, with an R2 value of 0.965. The outcomes show how effective the right neural network models are at predicting SVI. This is a highly helpful tool that WWTP operators may use in their daily management to improve the effectiveness of the treatment process and the dependability of the WWTP.

Performance and microbial characteristics of completely autotrophic nitrogen removal over nitrite process in different types of reactors

There are various types of reactors for completely autotrophic nitrogen removal over nitrite (CANON) process, but the advantages and disadvantages of different types of reactors and their scope of application still need to be studied. In this paper, high-throughput sequencing technology was used to analyze the microbial characteristics of four CANON reactors with similar and stable influent water quality conditions, and the applicable scenarios of different types of CANON reactors was clarified. At the gene level, the proportions of aerobic ammonia oxidizing bacteria (AerAOB), anaerobic ammonia oxidation bacteria (AnAOB) and denitrifying bacteria in a flocculent sludge sequencing batch reactor (FSBR), a membrane bioreactor (MBR), a biological aerated filter (BAF) and a granular sludge sequencing batch reactor (GSBR) were 16.88 %, 3.49 %, 19.36 % and 29.67 %; 9.51 %, 4.72 %, 5 % and 27.75 %; 7.12 %, 11.5 %, 6.81 % and 13.6 %. The results showed that FSBR was low nitrogen removal load and low stability in practical application. MBR can treat different concentrations of ammonia nitrogen wastewater by controlling the influent load. BAF can treat ammonia nitrogen wastewater with moderate influent load. GSBR has high removal load and can treat high ammonia nitrogen wastewater.

Modeling future land cover and water quality change in Minneapolis, MN, USA to support drinking water source protection decisions

AbstractContinued alteration of the nitrogen cycle exposes receiving waters to elevated nitrogen concentrations and forces drinking water treatment services to plan for such increases in the future. We developed four 2011–2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long‐term planning for the Minneapolis Water Treatment Distribution Service (MWTDS) using Soil Water and Assessment Tool. Projected land cover changes based on relatively unconstrained economic growth led to substantial increases in total nitrogen (TN) loads and modest increases in total phosphorus (TP) loads in spring. Changes in sediment, TN, and TP under two “constrained” growth scenarios were near zero or declined modestly. Longitudinal analysis suggested that the extant vegetation along the Mississippi River corridor upstream of the MWTDS may be a sediment (and phosphorus) trap. Autoregressive analysis of current (2008–2017) chemical treatment application rates (mass per water volume processed) and extant (2001–2011) land cover change revealed that statistically significant increases in chemical treatment rates were temporally congruent with urbanization and conversion of pasture to cropland. Using the current trend in chemical treatment application rates and their inferred relationship to extant land cover change as a bellwether, the unconstrained growth scenarios suggest that future land cover may present challenges to the production of potable water for MWTDS.

Multi-scale Investigation of Ammonia-Oxidizing Microorganisms in Biofilters Used for Drinking Water Treatment.

Ammonia-oxidizing microorganisms (AOMs) include ammonia-oxidizing bacteria (AOB), archaea (AOA), and Nitrospira spp. sublineage II capable of complete ammonia oxidation (comammox). These organisms can affect water quality not only by oxidizing ammonia to nitrite (or nitrate) but also by cometabolically degrading trace organic contaminants. In this study, the abundance and composition of AOM communities were investigated in full-scale biofilters at 14 facilities across North America and in pilot-scale biofilters operated for 18 months at a full-scale water treatment plant. In general, the relative abundance of AOM in most full-scale biofilters and in the pilot-scale biofilters was as follows: AOB > comammox Nitrospira > AOA. The abundance of AOB in the pilot-scale biofilters increased with increasing influent ammonia concentration and decreasing temperature, whereas AOA and comammox Nitrospira exhibited no correlations with these parameters. The biofilters affected AOM abundance in the water passing through the filters via collecting and shedding but exhibited a minor influence on the composition of AOB and Nitrospira sublineage II communities in the filtrate. Overall, this study highlights the relative importance of AOB and comammox Nitrospira compared to AOA in biofilters and the influence of filter influent water quality on AOM in biofilters and their release into the filtrate.

Predicting Influent and Effluent Quality Parameters for a UASB-Based Wastewater Treatment Plant in Asia Covering Data Variations during COVID-19: A Machine Learning Approach

A region’s population growth inevitably results in higher water consumption. This persistent rise in water use increases the region’s wastewater production. Consequently, due to this increase in wastewater (influent), Wastewater Treatment Plants (WWTPs) are required to run effectively in order to handle the huge demand for treated/processed water (effluent). Knowing in advance the influent and effluent parameters increases the operational efficiency and enables cost-effective utilization of diverse resources at wastewater treatment plants. This paper is based on a prediction/forecasting of an influent quality parameter, namely total MLD, as well as effluent quality parameters, namely MPN, BOD, DO, COD and pH for the real-time data collected pre-, during and post-COVID-19 at the Bharwara WWTP in Lucknow, India. It is the largest UASB-based wastewater treatment facility in Uttar Pradesh and the second largest in Asia. In this paper, we propose a novel model namely, wPred comprising extensions of SARIMA with seasonal order and ANN-based ML models to estimate the influent and effluent quality parameters, respectively, and compare it with the existing machine learning models. The lowest sMAPE error for the influent parameters using wPred is 2.59%. The findings of the paper show a strong correlation (R-value), up to 0.99, between the effluent parameters actually measured and predicted. As a result, the model designed in this paper has an acceptable level of accuracy and generalizability which efficiently predicts/forecasts the performance of Bharwara WWTP.

Lake management under severe drought: Lake Mead, Nevada/Arizona

AbstractDrought can affect both the quantity and quality of water in lakes and reservoirs, yet larger, highly managed waterbodies, such as Lake Mead, may be somewhat buffered from drought effects. From 2000 to present, Lake Mead has experienced a 71% decline in volume; however, influent water quality has remained high and consistent outflow volumes through Hoover Dam have been maintained. Furthermore, management activities, such as increased removal of phosphorus by wastewater dischargers and legacy contamination cleanup efforts, have been initiated since the drought began. These efforts have led to small improvements in values of water quality parameters, such as phosphorus, nitrogen, and perchlorate, despite loss of volume for dilution of constituents, and consequently, decreased residence time. As the drought continues, Lake Mead is projected to continue declining in volume, inflows are projected to become warmer, and the population of Las Vegas is projected to grow, potentially adding additional stress to the hydrologic system. Maintenance of outflow may mitigate some potentially negative consequences, and understanding the drivers behind continued high water quality despite prolonged drought is important to continue to maintain the health and vitality of the entire Lower Colorado River Basin.

Long-term variation characteristics of nutrients in the water and sediments of a surface flow constructed wetland with micro-polluted water sources

This study aimed to evaluate the long-term effects of a large-scale constructed wetland purifying micro-polluted water and explore accumulation characteristics and influencing factors of nutrients in sediments. For this purpose, the water quality and nutrients in sediments of the long-running large-scale constructed wetland of Yanlong Lake were monitored for eight years since the wetland began operation. The results demonstrated that the average removal rates of permanganate index (CODMn), ammonia nitrogen (NH4+-N), total nitrogen (TN) and total phosphorus (TP) were 4.39 ± 12.64%, 41.57 ± 16.36%, 32.61 ± 23.36% and 47.05 ± 20.39% in the constructed wetland from 2013 to 2020, respectively. The CODMn removal effects were the lowest. Sediments of the constructed wetland were moderately polluted and the cumulative level of nutrients was mainly affected by the organic matter content, influent water quality, water depth and plant function. Firstly, TN and TP in sediments are significantly positively correlated with total organic carbon. Besides, the nutrient concentrations in sediments increased along the water flow direction. Furthermore, the nutrient concentrations in sediments increased with water depth, and the carbon was greatly affected. In addition, the overgrowth and lodging of wetland plants could promote the nutrients accumulation in sediments. This research results provide a scientific basis for improving the purification capacity of long-running surface flow constructed wetlands.

Long-Term Monitoring of an Urban Stormwater Infiltration Trench in South Korea with Assessment Using the Analytic Hierarchy Process

Evaluating the functionality of small and decentralized low-impact development (LID) technologies often requires extensive labor, time, and costs for water quality analysis. In order to reduce these in an infiltration trench in South Korea, monitoring data gathered over a period of 8 years were used to determine its long-term performance, establish a stormwater quality estimation model, and develop a comprehensive evaluation tool. Our findings show that the infiltration trench can treat up to 90% of the stormwater runoff from a paved road but would require annual maintenance to minimize the reduction in infiltration capacity. The facility was able to remove an average of 83% of total suspended solids (TSS), 75% of biochemical oxygen demand (BOD), 80% of chemical oxygen demand (COD), 76% of total nitrogen (TN), and 79% of total phosphorus (TP), with the highest removal efficiencies observed after maintenance was conducted. Rainfall depth and air quality parameters (i.e., PM2.5 and PM10) were found to be positively correlated with TSS, COD, TN, and TP. These parameters were then used to develop a model for the estimation of influent stormwater quality, which can help in estimating the effluent water quality based on the average removal efficiencies. Furthermore, a comprehensive evaluation tool considering indicators such as treatment efficiency, cultural benefits, and facility and operating conditions was established through the analytic hierarchy process (AHP). Aside from determining the facility’s overall efficiency, this can also serve as a diagnostic tool to identify whether maintenance is needed or not. While atmospheric and hydrological characteristics differ in different regions, and the results may vary if applied in other facilities, this study can serve as a guide to the effective and efficient evaluation of similar stormwater management facilities in South Korea.

Nitrogen removal performance and microbiological characteristics for the landfill leachate treatment in a three-stage vertical flow constructed wetlands system

Landfill leachate (LL) has the characteristics of high NH 4 + -N and low C/N ratio, increasing the cost and difficulty of efficient treatment. Constructed wetlands system (CWs) has proven to be feasible for the treatment of LL. A constructed three-stage series of vertical flow CWs (termed CW1, CW2, and CW3) based on effluent recirculation (T-VF) was used to treat LL, and nitrogen removal effect and microbiological characteristics was investigated. The results show that the nitrogen removal efficiency showed a increased trend from CW1 to CW3. The average removal efficiencies of TN, NH 3 + -N, and NO 3 − -N in T-VF were 91.43%, 94.19%, and 98.11%, respectively, which showed good nitrogen removal performance. Furthermore, application of the combined CW1, CW2 and CW3 increased diversity of substrate bacterial communities and decreased the relative abundances of the dominant nitrifying(e.g. Nitrosomonas , Nitrobacter ) and heterotrophic denitrifying bacteria (e.g. Thauera ). The relative abundances of autotrophic denitrification (e.g. Thiobacillus ) increased. Pathway of nitrogen metabolism and functional genes analysis showed that the abundance of enzymes associated with the denitrification and dissimilatory nitrate reduction increased and that the abundance of enzymes associated with the nitrification decreased. Application of the combined CW1, CW2 and CW3 could be respectively formed corresponding nitrogen removal microbial community, influent quality and pathways of nitrogen to enhance the nitrogen removal effect of T-VF to treat LL. • The removal rate of nitrogen was over 90% in vertical flow constructed wetlands. • Nitrosomonas and Nitrobacter were the dominant nitrification bacteria. • Denitrifying bacteria Thauera , Thiobacillus , and Lautropia predominated.

Comparison of anthracite and GAC biofilter performance for surface‐water manganese removal

AbstractAlameda County Water District has observed increased biofilter effluent manganese concentrations during winter operations. To investigate manganese removal across surface water biofilters during cold‐water conditions, trends were analyzed between water temperature and manganese removal across multiple testing scales, multiple biofilter influent water qualities, and both anthracite and GAC media. During acclimation of new biofilters, 100% removal of manganese was observed sooner across both anthracite and GAC biofilters brought online at 20°C compared to 12°C. Acclimation at 12°C required 18 extra days for the GAC biofilters and 48 additional days for the anthracite biofilters. For fully acclimated biofilters, a decrease in manganese removal across both GAC and anthracite biofilters at temperatures below 15°C was observed. However, greater and more consistent manganese removal was observed across GAC compared to anthracite biofilters. Performance differences between locations also suggest that operational and water quality conditions also likely affect manganese removal.

Plant Carbon Sources for Denitrification Enhancement and Its Mechanism in Constructed Wetlands: A Review

Nitrogen pollution in water bodies is a serious environmental problem worldwide. Plant carbonsource (PCS) enhanced denitrification in constructed wetlands (CWs) for wastewater with low chemical oxygen demand to total nitrogen (COD/N) has been one of the most exciting research topics. This paper summarized the related studies with VOSviewer software and found that the major interests were denitrification performance and mechanism in CWs. This article mainly focused on the PCSs’ characteristics, denitrification rate, the influences of key environmental and operational parameters, surface morphology variation, microbial community structure, and denitrification genes. Engineering prospects and existing problems were also introduced. PCSs’ degradation consumes DO and creates favorable conditions for denitrification. The COD/N of wastewater should be maintained at 4–5 by adding PCSs, thus improving denitrification performance and reducing nitrous oxide emission. Aerobic degradation, anaerobic fermentation, dissimilatory nitrate reduction to ammonium, and sulfate reduction processes may consume the carbon released by PCSs depending on the influent quality and environmental conditions. More attention should be paid to the reduction of greenhouse gases and emerging pollutants in CWs with PCSs.

Impact of nitrite and oxygen on nitrous oxide emissions from a granular sludge sequencing batch reactor

Maximizing nutrient removal and minimizing greenhouse gas (GHG) emissions is imperative for the future of wastewater treatment. As municipalities focus on minimizing their carbon footprints, future permits could regulate GHG emissions from wastewater treatment plants. This study investigates how nitrous oxide (N2O) emissions are affected by dissolved oxygen and nitrite concentrations, providing potential strategies to meet possible gaseous emission permits. A lab-scale sequencing batch reactor (SBR) was enriched with aerobic granular sludge (AGS) capable of phosphate removal and simultaneous nitrification-denitrification (SND). N2O emissions were tracked at varying dissolved oxygen (DO) and nitrite (NO2−) concentrations, with >99% SND efficiency and 93%–100% phosphate removal efficiency. Higher DO and NO2− concentrations were associated with higher N2O emissions. Emissions were minimized at a DO concentration of 1 mg L−1, with an average emission factor of 0.18% of oxidized NH3–N emitted as N2O–N, which is lower than factors from many full-scale treatment plants (Vasilaki et al., 2019) and similar to a Nereda® full-scale AGS SBR (van Dijk et al., 2021). This challenges assertions that AGS emits more N2O than conventional activated sludge, although more research at full-scale with influent quality variations is required to confirm this trend. Molecular analyses revealed that the efficient SND was likely achieved with shortcut nitrogen removal facilitated by a low presence of nitrite oxidizing bacteria and a large population of denitrifying phosphate accumulating organisms, which far outnumbered denitrifying glycogen accumulating organisms.

Improved neural network with least square support vector machine for wastewater treatment process

This research offers a unique interval by using the predicting approach for discharge indicators of water quality data such as biochemical oxygen demand (BOD) and ammonia nitrogen (NH3–N). This is considered one of the significant quality metrics in wastewater treatment plants for water quality management as well as surveillance. To begin, the effluent information for BOD/NH3–N and their supplementary parameters are gathered. Hence BOD and NH3 are considered major feature sources for estimating water pollutants. BOD is high then oxygen level is very low in the water due to pollutants or algae. Ammonia nitrogen is an organic waste component in water from sewage. The significant characteristics with good correlation levels of BOD and NH3–N are examined and identified using a grey correlation analysis method after certain basic data pre-processing procedures. The BOD/NH3–N effluent information of a water treatment plant is predicted using an upgraded feed-forward neural network with the least square support vector machine (FFNN-LSSVM) method. An optimization approach for an enhanced feed-forward neural network (IFFNN) is built by Machine Learning Algorithms. The IFFNN used regular influent water quality, influent rate of flow, and Wastewater performance monitoring and operational conditions as input parameters. For future prediction, input variables were previous different wastewater quality measurements. Lastly, the analysis shows that, when compared to other current algorithms, the proposed methodology can forecast wastewater quality of water with high accuracy in predicting BOD and NH3 levels, limited computation duration, mean error less than 10% and R2 is 90% proves better than existing techniques.