Coagulant Dosage Research Articles

Coupling coagulation-flocculation-sedimentation with adsorption on biosorbent (Corncob) for the removal of textile dyes from aqueous solutions

This study evaluated the efficacy of combining the coagulation-flocculation-sedimentation (CFS) process with adsorption onto corncob biosorbent for the removal of textile dyes from aqueous solutions. The synthetic dyes tested were Bemacron Blue RS 01 (BB-RS01), a disperse dye, and Bemacid Marine N-5R (BM-N5R), an acid dye. Aluminum sulfate (Al₂ (SO₄)₃·18H₂O) was used as the coagulant, followed by superfloc 8396 as the flocculant. During coagulation, optimal parameters included coagulant doses (50-600 mg/L), flocculant doses (30-125 mg/L), and pH (2-11). For the adsorption phase, factors such as pH (2-11), temperature (25-45°C), contact time (0-480 min), and initial dye concentration (15-100 mg/L) were investigated. The corncob was characterized using FTIR, SEM, and pHpzc. Both Langmuir and Freundlich isotherm models were applied, with the Langmuir model demonstrating the best fit (0.92 < R² < 0.96). The CFS process achieved dye removal rates of 95.1% for BB-RS01 at pH 8 and 92.3% for BM-N5R at pH 6.5. Adsorption efficiency varied with solution pH, yielding removal rates of 26.19% for BB-RS01 at pH 6 and 7.69% for BM-N5R at pH 4. Maximum adsorption capacities were 99.5 mg/g for BB-RS01 and 46.08 mg/g for BM-N5R. This study demonstrates the effectiveness of coupling CFS with corncob adsorption for economical dye removal, utilizing agricultural waste as a biosorbent.

Effectiveness of Different Artificial Neural Network Models in Establishing the Suitable Dosages of Coagulant and Chlorine in Water Treatment Works

Generally, in India, determining the chlorine and coagulant dosage in a WTP depends on the proficiency of operators, which may lead to overdosing or underdosing of coagulants and chlorine. Nevertheless, the determination of both coagulant and chlorine dosages frequently changes as inlet water quality varies which demands extensive laboratory analyses, leading to prolonged experimentation periods in water treatment plants. So objective of the study is to develop the precise relationship between coagulant dose and chlorine dose in a water treatment plant by using an artificial neural network (ANN). As a result, ANN models were developed to predict chlorine dose using coagulant dose by comparing the performance of the number of ANN models. It has been found that radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN) modeling provide better prediction. In RBFNN and GRNN modeling, the spread factor is varied from 0.1 to 15 to establish a stable and accurate model with high predictive accuracy. It is observed that the RBFNN model showed good prediction (R2 = 0.999). The application of a soft computing model for defining doses of coagulant and chlorine that are inextricably linked at a Water treatment plant (WTP) will be highly beneficial for WTP Managers.

Sulfur powder utilization and denitrification efficiency in an elemental sulfur-based membrane bioreactor with coagulant addition

The integration of elemental sulfur-based autotrophic denitrification with membrane bioreactor (MBR) technology offers a cost-effective solution for nitrate removal; however, stable operation demands efficient sulfur utilization and phosphorus management. This study explores sulfur consumption dynamics and the impacts of coagulant injection on denitrification efficiency. Sulfur consumption was closely correlated with nitrate removal rates, highlighting the critical role of stoichiometric sulfur availability for sustained denitrification. While coagulant addition enhanced phosphorus removal, excessive dosing impaired elemental sulfur-based microbial activity, reducing nitrate removal efficiency and increasing nitrite accumulation. Notably, microbial community analysis revealed a decline in the abundance of key sulfur-oxidizing bacteria, such as Sulfurimonas, under high coagulant concentrations. These findings emphasize the need for optimized sulfur and coagulant dosing strategies to balance phosphorus and nitrate removal while preserving microbial diversity and reactor stability. This study provides practical insights into operational parameters for efficient and sustainable ESAD-MBR processes.

Turbidity removal from surface water using Cactus opuntia

Coagulation and flocculation are critical processes in water treatment facilities and typically precede the more intricate secondary and tertiary treatment stages. Currently, the most commonly used coagulants in water treatment are aluminum sulfate (alum) and ferric chloride. In developing countries, synthetic coagulants can be neither economical nor health-friendly. Consequently, natural coagulants have garnered significant research attention over the past decade. This study explored the use of cactus as a natural coagulant to decrease turbidity in surface water from the Sekak dam in Tlemcen, Algeria. Various jar tests were performed to optimize parameters such as coagulant and flocculant dosages, mixing speed, rapid mixing duration, and the pH of the water. The initial turbidity level of 5.45 NTU was reduced by up to 84.03%. The turbidity removal capacity observed in this study suggests that Opuntia cactus has considerable potential for application in surface water treatment.

Image recognition enhances efficient monitoring of the coagulation-settling in drinking water treatment plants

Water pollution is a major issue in the context of increasing population and industrialization, yet many drinking water treatment plants (DWTPs) are not fully efficient countering it. In particular, coagulation-settling stage often faces multiple disturbances and time lags, which lower the efficiency because coagulant dosage cannot be accurately calculated in real-time based on the effluent turbidity. To address this issue, we developed a method using deep learning image recognition to monitor the coagulation-settling stage in real-time. For that we used 5761 operational data and images of flocs from the sedimentation tank of a DWTP in East China in 2022, to build an image recognition regression model that predict the turbidity of the sedimentation tank effluent. Results show that our deep learning regression model, performs better with r-square (R2) of 0.97, mean absolute error (MAE) of 0.016 and mean absolute percentage error (MAPE) of 2.74%, compared with the traditional machine learning giving R2 of 0.76, MAE of 0.045 and MAPE of 8.26%. The model also avoids misclassification at different turbidity intervals. The incorporation operational data of the sedimentation tank, prediction accuracy is improved by 79.6%. By adjusting the turbidity data to correct time misalignment, our model effectively handles the time lag caused by the hydraulic retention time of the sedimentation tank, thus enhancing the timeliness and accuracy of its practical application.

Analysis of the Interrelationship between Water Color and Coagulant Dose and the Possibility of Taking it into Account in Mathematical Modeling

The character of water chromaticity change in a water source, which is the main one for organizing water supply of a large urban agglomeration, is analyzed. It is shown that the correlation analysis characterizes the interrelationship between the coagulant dose and water chromaticity as noticeable (on the Cheddock scale), which predetermines the necessity to take into account water chromaticity as a parameter influencing the choice of coagulant dose. Variation series were constructed, empirical and theoretical functions of water chromaticity distribution by months were obtained. The possibility of modeling the reagent dose on the change of chromaticity taking into account specific conditions of practically constant influence of seasonal and random factors in the selected periods has been achieved. It is concluded that the chromaticity index makes a significant contribution to the value of the predicted value of coagulant dose.

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.

Pilot-scale study of turbid particle evolutional/removal characteristics during coagulation-sedimentation-filtration (CSF): Effects of coagulant dosage and secondary dosing after breakage

Considering the limitations of treated water turbidity, a thorough understanding of the changes in turbid particle size distribution during coagulation-sedimentation-filtration (CSF) is crucial for enhancing the removal efficiency of particulate and dissolved contaminants from raw water. In this study, a pilot-scale experimental system was utilized to track the evolution of turbid particles and assess how the characteristics of these turbid particles affected the quality of treated water in the CSF processes under varying cases of polyaluminum chloride (PAC) coagulant dosage and secondary coagulant dosage after breakage. It was found that differently from the role of PAC coagulant dosage (without high-shear breakage), different levels of secondary coagulant dosing after breakage had minimal influence on the aggregate structure produced by mechanical stirring flocculation. During the overall CSF processes, the changing trend in the total particle number for the filtered water appeared to be more consistent with the corresponding changing trend for the settled water, as the PAC coagulant dosage increased. In addition, the combined effects of shear-induced breakage and secondary dosing could not only improve the removal efficiency of organic matter after inclined-tube sedimentation following mechanical stirring flocculation, but also enhance the removal efficiencies of water turbidity and particulate matter through sand filtration. In order to reduce the frequency of backwashing and extend the service life of the filter media, greater emphasis should be placed on detecting and removing the number of smaller-sized particles existing in the sixth unit of the flocculation tank together with the effluent of the sedimentation tank.

Constructing a visual detection method for coagulation effect based on image feature machine learning

The coagulation process is influenced by several factors, including turbidity, temperature, pH, and hydraulic conditions. Consequently, optimizing the dosage of coagulants is often time-consuming and challenging. To quickly evaluate coagulation efficacy and optimize coagulant dosage, this study utilized a convolutional neural network (CNN) model to analyze the accuracy of effluent turbidity predictions from floc images at different time intervals. Python OpenCV was employed to develop a program that extracts macro features (e.g., texture) and micro features (e.g., particle size distribution) from the images, and these feature parameters were analyzed using a BP-ANN model. The results of the CNN analysis indicated that the middle to late stages of flocculation are the optimal periods for predicting effluent turbidity, with the highest prediction accuracy of 99.81 % achieved during the middle stage of flocculation. The BP-ANN analysis demonstrated that particle size distribution effectively represents the microscopic characteristics of flocs, achieving a maximum prediction accuracy of 96.94 % in the middle stage of flocculation. Furthermore, combining macroscopic and microscopic features yielded a prediction accuracy of 99.44 % at the end of flocculation using BP-ANN. These findings suggest that machine learning techniques applied to floc images can effectively predict effluent turbidity, offering valuable insights for future water quality prediction and the development of flocculation kinetics models.

Optimization of Dissolved Air Flotation (DAF) process for the treatment of oily water from biodiesel production

Biodiesel production has become one of the most efficient ways of diversifying the energy matrix. However, the increase in biodiesel production increases the generation of wastewater. Therefore, this study aimed to characterize the wastewater generated in the biodiesel washing process, obtained by the alkaline transesterification of sunflower oil, and to evaluate its treatment by the dissolved air flotation (DAF) system, using a Central Composite Rotational Design (CCRD). The first DCCR was carried out with 4 independent variables: pH, Coagulant Dose (CD), Polymer Dose (PD), and Recirculation Rate (RR), with the response variables being apparent color removal and turbidity removal. Based on the results of the first DCCR, optimization tests were designed with DC and DP as independent variables and apparent color, turbidity, COD, and oil and grease (OG) as response variables. The process showed high apparent color and turbidity removal efficiency. The best results were obtained in tests 10 (DC = 1545 mg.L-1 and DP = 1050 mg.L-1) and 3 (DC = 700 mg.L-1 and DP = 8.8 mg.L-1), with the removal of 99.88% and 99.22%, respectively. For the COD and OG parameters, the best results were observed in tests 8 (DC= 555 mg.L-1 and DP= 6 mg.L-1) and 5 (DC= 1050 mg.L-1 and DP= 6 mg.L-1), with 98% and 99.42% removal, respectively. FAD treatment using the combination of coagulant and cationic polymer proved to be highly efficient in treating wastewater from biodiesel production.

Implementation and performance monitoring of the treatment system to remove arsenic from dam water in a full-scale urban water treatment plant

Nowadays, health and environmental problems caused by elevated arsenic concentration in water sources have become a major concern for researchers and environmental experts. Therefore, it is necessary to study processes that significantly reduce the arsenic content in aqueous solutions, which is of great interest to environmental experts. This work aimed to evaluate the effect of the pre-oxidation/coagulation process with commercially available coagulants (FeCl3, Al2 (SO4)3·18H2O, and PACl) on the efficiency of arsenic removal (with initial concentration of 73 ± 2.5 μg/L) from Talvar Dam entering the Sheikhi Jan Hamadan water treatment plant. This was demonstrated on a laboratory and operational scale at the water treatment plant under different scenarios (pre-oxidant agents, type of coagulant, solution pH, and different coagulant dosages). It should be noted that, as part of an emergency project, the possibility of arsenic removal at the Sheikhi Jan water treatment plant, which receives Talvar Dam water at a flow rate of 1800 L/s, was investigated. The priority of the researchers and city officials in the present study was to use the potential of the water treatment plant and the existing equipment so that they could remove arsenic quickly and without additional costs. This study was conducted in three stages: laboratory scale, implementation of the process in the water treatment plant, and monitoring of the treatment plant's performance, which continues to this day. The results of this project showed that the pre-oxidation/coagulation process can effectively reduce the concentration of arsenic at the inlet of the treatment plant from to less than national and international recommended values.

Microalgae separation in MP-PVC contaminated wastewater using plant-based coagulant over different extraction methods in Bauru, Brazil

This study investigates the effectiveness of coagulation-flocculation and sedimentation (CFS) for separating microalgae, focusing on the use of various Moringa oleifera extracts as natural coagulants. We examined photobioreactor effluent (PBR) both with and without microplastic PVC (MP-PVC) contamination, referred to as PBR R2 and PBR R1, respectively. Utilising response surface methodology, we identified optimal conditions for the removal of microalgae and MP-PVC. Validation tests demonstrated that the aqueous extract of delipidated Moringa oleifera powder (AEDMOP) achieved high removal efficiencies, with coagulant dosages of 630 mg L−1 for PBR R1 and 625 mg L−1 for PBR R2. Both conditions showed microalgae removal efficiencies exceeding 83% for turbidity, colour, OD540 nm, OD680 nm, and OD750 nm, and 63% for OD254 nm. Interestingly, the optimised conditions for PBR R2 required slightly less coagulant, likely due to the additional particulate matter from MP-PVC. While extracellular polymeric substances (EPS) exhibited a marginal effect on flocculation, further investigation into their role in CFS is necessary. Our findings highlight the potential of AEDMOP for efficient microalgae separation, even in the presence of microplastics.

Constructing a visual detection model for floc settling velocity using machine learning

Optimizing the dosage of coagulant is a time-consuming process, and real-time evaluation of floc settling velocity can quickly predict the coagulation effect and optimize the dosage. This study used a convolutional neural network (CNN) model to analyze the accuracy of floc image recognition of settling velocity. Python-OpenCV was employed to develop a program that segments individual flocs and detects their settling velocity to constructing a dataset of floc images and settling velocity. The results showed that the accuracy of determining the settling velocity of flocs solely based on their particle size was 88%, indicating that the floc structure is complex and a single parameter is not sufficient to accurately identify settling velocity. The results of the CNN analysis indicated that using a relatively simple Lenet5 structure can quickly achieve an accuracy of 88%, while using a Resnet18 structure can achieve recognition accuracy of over 90%. These findings suggest that machine learning techniques applied to floc images can effectively evaluate floc settling velocity, providing theoretical guidance for optimizing coagulant dosage and regulating coagulation processes.

Application of Thymus Species as Natural Coagulant for Elimination of Turbidity of Water and Its Comparison with the Conventional Coagulant

Introduction: The most important pollutant in surface water sources is turbidity. The aim of the study is to determine the most effective thyme plant extract as a natural coagulant to remove water turbidity and compare it with alum as a chemical coagulant.. Materials and Methods: In this study, plant extracts obtained from Thymus vulgaris (Tv), Thymus kotschyanus (Tk), and Thymus eriocalyx (Te) were used as natural coagulants for elimination of water turbidity. Then, the best of plant extract was confirmed by ninhydrin and FTIR analyses and the effect of simultaneous use of plant extract with chemical coagulant of aluminum sulfate (alum) was investigated in the hybrid ratio of 1:1, 2:1, and 1:2. Then, the effect of parameters include of pH, coagulant dose, turbidity removal efficiency, and the most suitable solvent for coagulant extraction were studied. Results: The results of this study showed that among the natural coagulants, Tv had a higher efficiency in elimination of water turbidity, and the highest amounts of turbidity elimination for Tv, alum, and Tv/alum hybrid coagulants was 94, 90, and 96, respectively. According to the results of this study, the most effective solvent for preparing plant extracts was 1M KCl solvent. Conclusion: The natural coagulant removes more turbidity from water than the chemical coagulant, and the simultaneous use of natural and chemical coagulants with a 1:1 ratio was more effective.

The Synergistic Power of Rosehip Seed Powder and Aluminum Chloride in Steel Industry Wastewater Treatment

This study investigates the efficiency of a hybrid coagulation–flocculation process for the treatment of industrial wastewater from the steel industry. The novel method combines a natural coagulant, processed Rosehip Seed Powder (RSP), with a chemical coagulant, aluminum chloride (AlCl3), across varying concentrations and pH levels. The study simulated the pH 8 conditions of iron and steel industrial wastewater and examined the removal of heavy metals, total suspended solids (TSS), chemical oxygen demand (COD), and ammonia–nitrogen (NH3-N). At pH 8, the optimal coagulant dosage was determined to be 0.75:0.75 (g/g) of RSP/AlCl3 powder, resulting in high removal efficiencies across several parameters: 88.29% for COD, 91.85% for color, 99% for TSS, 93.11% for NH3-N, 94.3% for Mn, 98.5% for Fe, 96.7% for Zn, and 99.3% for Ni. The pH optimization demonstrated high removal efficiencies without pH adjudication. The removal of heavy metals at pH 8 demonstrated high efficiencies, with Mn, Fe, Zn, and Al achieving 99.00%, 90.6%, 95.73%, and 92.3%, respectively. These results suggest that no pH adjustment is required when using RSP/AlCl3 for the treatment of iron and steel industry wastewater through the coagulation method.

Siamese based few-shot learning lightweight transformer model for coagulant and disinfectant dosage simultaneous regulation

Deep learning (DL) has emerged as a transformative and promising approach to address inefficient resource management due to imprecise chemical dosing in conventional manual-dependent drinking water treatment. However, the scarcity of high-quality data and limited computing resources of embedded devices hinder the development and deployment of state-of-the-art (SOTA) DL models. Herein, a novel lightweight Transformer (LighT) model integrated with the Siamese structure was proposed for simultaneous regulation of coagulant dosage (FeCl3 and PAC) and disinfectant dosage (NaClO) and compared with other attention-based DL models. The results demonstrate that LighT showed robust few-shot learning effectiveness and outperformed other models in multi-output prediction (R2 values of 0.99 and RMSE of 0.20 mg/L, 0.16 mg/L and 0.45 mg/L, respectively). The LighT model also exhibited enhanced cost-effectiveness with a 79.74 % compression in model parameters and a 36.14 % decrease in inference time. The LighT-based chemical dosage prediction program was sustained for one month of operation, which can save chemical dosage by 15.81 % while maintaining satisfactory product water quality, indicating the potential of LighT for economizing chemical dosing strategy in drinking water treatment.

Machine learning regression-based prediction model for the autonomous control of coagulant dosing in smart water purification plants

ABSTRACT The global issue of water scarcity is escalating due to urbanization and increased demand. This paper proposes a machine learning (ML) regression-based model for automatic coagulant dosing control in smart water purification plants (SWPPs).1 The model uses random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) algorithms. Performance metrics include MAE, MSE, RMSE, MAPE, and R2. The RF algorithm showed superior performance, with MAE of 0.005, MSE of 0.002, RMSE of 0.05, and MAPE of 0.000 for anion-poly aluminum chloride dosing, and MAE of 0.007, MSE of 0.00, RMSE of 0.02, and MAPE of 0.000 for Polymax dosing. The RF model's performance is due to its robust handling of large datasets and ensemble learning approach. Limitations include testing only two coagulants and reliance on historical data. These findings suggest integrating advanced water management, energy systems, and facility management to make SWPPs feasible and efficient, establishing a foundation for future applications of ML in chemical processes.

Optimization of the use of Moringa oleifera in wastewater treatment by rotational central composite design

Textile effluents have a complex waste composition due to the various compounds present and their resistance to degradation. Given the harmful effects of these pollutants on aquatic ecosystems, adequate treatment before disposal is essential, usually involving chemical processes such as coagulation and flocculation. This study optimizes textile wastewater treatment using Moringa oleifera as a natural coagulant through Rotational Central Composite Design (RCCD), a type of Response Surface Methodology (RSM), aiming to improve color removal, a key factor for effluent quality. By exploring Moringa oleifera as a sustainable alternative, the research provides new insights into environmentally friendly coagulant use and treatment optimization. The treatment’s effectiveness depends on the coagulant dosage, the energy applied, and the pollutant concentration. Dye removal was evaluated using spectrophotometry, and the evaluated factors in the optimization process were slow and fast mixing times, coagulant dosage, and pollutant concentration. The study identified pollutant concentration and slow mixing time as the most influential factors in treatment effectiveness. The model achieved a coefficient of determination (R²) of 87.9 %, and maximum dye removal reached 80.2 % under optimal conditions: 30 mL of coagulant, 20 min of slow mixing, 2 min of fast mixing, and a pollutant concentration of 300 mg ∙ L−1. The average removal efficiency was 47.86 %, indicating that natural coagulants offer a viable alternative for textile effluent treatment.

Microplastic removal using Okra (Abelmoschus esculentus) seed from aqueous solutions

The ubiquitous presence of MPs in water bodies presents an escalating concern, as these minuscule plastic particles could ultimately reach humans via the drinking water supply. This study explores the efficacy and underlying mechanisms of removing PE and PVC MPs using Abelmoschus esculentus seeds (commonly known as okra), a natural and environmentally benign coagulant. Through experiments conducted under varying conditions—such as pH level, coagulant dosage, MP concentration, and EC—using the standard method and a Jar test apparatus, the sedimentation rate was assessed. ZP analysis revealed that charge neutralization and bridging cause pivotal in enhancing the removal efficiency of MPs. FESEM and FTIR analyses corroborated the formation of new bonds during the interaction between the MPs and the okra seed-based coagulant. The findings indicate that the optimal parameters for PVC removal were a coagulant dosage of 70 mg/L, a pH of 10, and an MP concentration of 20 mg/L, achieving a removal efficiency of 80.11%. Conversely, for PE, the maximum removal efficiency of 64.76% was realized at a coagulant dosage of 70 mg/L, a pH of 3, and an MP concentration of 20 mg/L. Abelmoschus esculentus seeds offer a practical and eco-friendly option, potentially substituting chemical coagulants, to efficiently eliminate MPs from aquatic environments.

APPLICATION OF PLANT SEED BIO-COAGULANT IN THE REMEDIATION OF ENVIRONMENTAL PAINT EFFLUENT

The discharge of industrial effluents into the environment has been a growing concern worldwide due to its adverse impacts on human health and the ecosystem. Paint effluent, in particular, poses a significant threat due to its high chemical oxygen demand (COD), total suspended solids (TSS), and colour. Conventional methods of treating paint effluent discharge require the use of chemical coagulants, which are expensive, non-sustainable, and can generate hazardous waste. The search for alternative and sustainable methods for treating industrial effluent has led to the exploration of natural coagulants derived from plant sources. One such source is watermelon seeds, which contain significant amounts of proteins that possess coagulating properties. This study aimed to investigate the potential of watermelon seed bio-coagulant (WSB) for the remediation of paint effluent discharge. The effects of the watermelon seed bio-coagulant dosage, pH, and agitation rate on the removal efficiency of the treatment process were evaluated. These results revealed that the bio-coagulant was effective. The optimum coagulant dosage, pH, and agitation rate were found to be 300mg/L,4, and a treatment time of 45 min, respectively, with BOD and COD removal efficiencies of 88.47% and 90.41%, respectively. Statistical optimization of the treatment process was performed using the response surface methodology (RSM). The results revealed that the quadratic model developed using RSM was statistically significant with a high coefficient of determination (R2 = 0.9958). The analysis also revealed that bio-coagulant dosage and pH are the major factors affecting the efficacy of the treatment process. The optimized conditions led to removal efficiencies of88.95% and 90.61% for BOD and COD, respectively, indicating that watermelon seed bio-coagulant could be an effective and sustainable alternative for treating paint effluent. The results of this study suggest that the use of watermelon seed bio-coagulant is a promising approach for optimizing the paint effluent treatment process. It is a cost-effective and eco-friendly alternative to conventional coagulants used in wastewater treatment and has demonstrated efficacy in treating industrial wastewater. This study presents a sustainable and eco-friendly approach to the treatment of paint effluent discharge using a natural coagulant derived from a waste product.