ABSTRACT
One of the most costly stages of activated sludge wastewater treatment plants is the treatment and dewatering of waste sludge. Chemical conditioning of sludge, as one of the most widespread methods to enhance sludge dewaterability, accounts for a significant portion of operational expenses due to the consumption of expensive polymeric compounds. This research aims to assess the cost-effectiveness of ochre soil, modified with hydrochloric acid, as an affordable mineral for conditioning waste sludge in an activated sludge system. The optimal conditions for acid modifications are obtained using response surface methodology. Then, its performance is compared with conventional coagulants (ferric chloride and alum) and in combination with cationic polyacrylamide (CPAM). To assess the conditioning process efficiency, the specific resistance to filtration (SRF) parameter was employed. At an optimal dose of modified ochre soil (MOS) equal to 300 (mg/g dry solids), the SRF value decreased from 31.96 to 2.7 Tm/kg. The combination of 100 (mg/gDS) MOS with 0.5 (mg/gDS) CPAM showed as the most cost-effective among the coagulants tested, with a 31% greater SRF reduction compared to CPAM used alone. This study shows the practical efficacy of an eco-friendly natural mineral as a polymer alternative, with the potential for sludge dewatering.
HIGHLIGHTS
Introducing modified ochre soil as a new, effective coagulant for sludge dewatering.
Repurposing mining byproducts, promoting environmental sustainability.
Enhancing sludge dewatering, and reducing operational costs.
Offering a low-cost alternative to synthetic chemicals in wastewater treatment.
Opening avenues for future studies on natural and modified materials in wastewater treatment.
INTRODUCTION
Due to population growth, urbanization, and industrialization, wastewater treatment needs have steadily increased to protect the environment and water resources (Obaideen et al. 2022; Sathya et al. 2023). The activated sludge process is one of the most important and widely used biological methods in wastewater treatment plants (WWTPs), while one of its main drawbacks is the production of waste sludge, which accounts for about 60% of the expenses in a municipal WWTP (Lee et al. 2015; Kim et al. 2017). Since nearly 99% of sludge is composed of water, efficient dewatering is essential to minimize sludge volume and associated management costs. In the conventional approach to sludge dewatering, chemical agents and mechanical techniques are used to reduce the water content in sludge (Chen et al. 2016).
Chemical conditioning includes the use of inorganic coagulants such as ferric and aluminum salts (Kocbek et al. 2022), organic flocculants such as polyelectrolytes and natural chitosan (Yang et al. 2019), and combinations of these agents (Zemmouri et al. 2014). Each method has specific benefits and drawbacks that affect dewatering efficiency, costs, and environmental impacts (Dai et al. 2018). The chemical conditioning mechanisms include charge neutralization, flocculation, skeletal formation, and the creation of bridging bonds between sludge particles. The integration of coagulants induces the creation of a robust skeletal structure within the sludge matrix. This, in turn, facilitates the efficient passage of water among sludge particles under mechanical pressure, ultimately resulting in a more dewatered sludge cake (Zaki et al. 2023).
Among various synthetic polymeric flocculants applied to sludge conditioning, cationic polyacrylamide (CPAM) is the most commonly used. It is a high-molecular-weight, water-soluble linear polymer with cationic charges (Hyrycz et al. 2022). It is typically introduced during the mechanical dewatering process to significantly improve the separation of solids and liquids by efficiently neutralizing sludge charges and forming connections between its particles. The CPAM flocculants are mainly used to agglomerate particles in wastewater with high acidity, organic content, or a higher concentration of positive charges (Fu et al. 2021).
In recent years, there have been growing concerns about the presence of flocculants in sludge and their potential impact on the sludge digestion process (Wang et al. 2018a; Hamza et al. 2022). Furthermore, the high cost of chemical materials in the treatment process necessitates the exploration of natural and high-efficiency, cost-effective, and environment friendly substitute materials. Research has been conducted on the use of natural coagulants and their combinations to enhance sludge conditioning while reducing treatment costs (Kurniawan et al. 2020).
For instance, Zhu et al. (2018) enhanced sludge dewatering by combining NaCl, CPAM, and rice husk, reducing specific resistance to filtration (SRF) and sludge cake moisture content (MC) by 91 and 29%, respectively. The CPAM and rice husk combination re-flocculated and reconstructed the sludge floc structure, increasing floc size by 38% and creating a porous and rigid sludge cake, thus improving dewaterability.
Liang et al. (2021) studied the combination of pyrite (FeS2), a naturally occurring mineral, and peroxymonosulfate (PMS) to improve waste-activated sludge dewatering and triclosan removal. This combination demonstrated superior cost-effectiveness compared to the iron + PMS combination, as it generated highly active radicals and strong flocculants. Wei et al. (2018) developed synthetic cationized starch-based flocculants with high charge density and combined them with ferric chloride in sludge dewatering. These synthetic materials facilitated flocculation while ferric chloride acted as a charge neutralizer, leading to improved dewatering.
Zemmouri et al. (2014) evaluated the potential use of chitosan as an eco-friendly flocculant in municipal sludge conditioning. A reduction of 95% of the filtrate turbidity was obtained with the optimal dosage of chitosan. The findings underscored its potential as an environmentally friendly, available alternative to conventional coagulants for sludge conditioning. An investigation by Masihi & Badalians Gholikandi (2020) revealed that modification of bentonite with hydrochloric acid improved sludge dewatering by reducing the sludge SRF by up to 96%. Acidic-modified bentonite functions as an effective and economically affordable conditioner for anaerobically digested sludge, making it a promising option for reducing sludge conditioning costs. Another research by Qiang & Yi-jun (2015) aimed to investigate the influence of establishing a skeleton on sludge dewatering. It was found that combining 15% quicklime with 83% moisture sludge and ≥80% slag in the range of 0.075–0.85 mm particle diameter achieves optimal dewatering, suitable for various applications with MC below 60%.
In recent years, there has been a rising interest in utilizing natural, eco-friendly materials such as clays, biochars, and soils across various applications, including enhancement of flocculation and sludge dewatering and reducing chemical material usage (Liu et al. 2020; Badaou & Sahin 2022). Zhong et al. (2017) investigated the efficacy of raw coal fly ash, a cost-effective byproduct derived from coal combustion, modified by sulfuric acid, to enhance sludge dewatering. The enhancement observed was attributed to consequential structural alterations leading to increased adsorption capacity and permeability. In separate research endeavors, biochar, renowned for its cost-effective and environmentally sustainable attributes, underwent acid modification, demonstrating substantial promise in the removal of pharmaceuticals and algae from water and wastewater. These advancements were attributed to the surface properties of biochar, which were enhanced by acid modification, increasing the adsorption capacity of the particle surface (Han et al. 2022; Nie et al. 2023).
The combinations of different chemicals and methods for sludge combination were applied in some recent studies. Feng et al. (2023) studied the effects of rice husk and thermal hydrolysis on sludge characteristics. They found that the combination could effectively destroy sludge particles and release more bound water. They optimized the process using the response surface method. Hua et al. (2023) examined a solution of polyamine (PA) with polyferric sulfate (PFS) and demonstrated that using PFS + PA could achieve the same filtering rates as using PFS + polyacrylamide with less cost. Sen et al. (2024) applied hydrogen peroxide and ultrasonic radiation to improve dewatering performance. Yang et al. (2024) studied the combination of ultrasonic, chitosan, and sludge-based biochar to regulate sludge dewaterability.
Ochre soil is a naturally occurring mineral pigment characterized by its predominant composition of iron oxide along with variable clay and mineral content. The use of ochre soil as a low-cost and eco-friendly adsorbent in wastewater and sludge treatment has gained attention in recent years. It has been used in experiments for phosphorus removal from sludge in WWTPs (Öfverström et al. 2020). Ochre soil has also been extensively studied for its potential to remove heavy metals (Sahoo et al. 2014; Olimah et al. 2015) and phosphorus (Heal et al. 2005; Fenton et al. 2009) from wastewater. Ochre soil can enhance the formation of flocs, improving the dewatering characteristics of the sludge. The acid-modified soil can serve as a primary mechanism for neutralization and skeletal structure formation and production of metal salts in sludge conditioning (Olimah et al. 2015).
In summary, extensive research has been focused on optimizing the combination of natural coagulants as cost-effective and efficient alternatives while also emphasizing the importance of high-performance sludge dewatering processes to minimize sludge volume. However, to the best of our knowledge, the use of acid-modified ochre soil (MOS) for excess sludge conditioning in the activated sludge process has not been explored yet. Thus, this paper aims to examine MOS as a novel low-cost, eco-friendly agent for sludge conditioning. Using response surface methodology (RSM), the study identifies the optimal conditions for modifying ochre soil with acid to maximize its dewatering efficiency. In addition, a cost-effectiveness comparison is made between the MOS and conventional coagulants such as ferric chloride and alum, as well as the combination of MOS with CPAM.
MATERIALS AND METHODS
Materials
Raw sludge
The raw sludge utilized in this study was collected from the waste-activated sludge of the south WWTP of Isfahan, Iran. Following the sample collection, the sludge was immediately conveyed to the laboratory and maintained below 4 °C before being utilized. Before the experiments, the sludge was allowed to equilibrate to room temperature of 20–25 °C.
Ochre soil
Ochre soil, a natural component of the Earth's geological crust, is also a significant byproduct of iron ore processing. One of its key components is iron oxide (III) (Khan & Chhibber 2020). Prior to the experiments, the ochre soil samples were initially subjected to three washes with distilled water. Then, they underwent a drying process in an oven at a temperature of 105 °C for 24 h, ground into powder, and were sieved through a 200-mesh filter.
Other substances
Other substances include hydrochloric acid 37%, 12 molar, and common coagulants such as ferric chloride 98%, and alum 30%, used in this study purchased from a domestic dealer in Iran, namely Mojalal company. CPAM with a purity of 98% was supplied from ZETAG company. The initial solutions of ferric chloride and alum utilized in sludge conditioning were prepared by dissolving them in 10 mL of distilled water to provide 800–3,300 ppm solutions. The CPAM solution was provided at a concentration of 500 ppm suitable for use in the experiments.
Analytical methods
A variety of analyses were conducted on raw sludge to investigate its overall composition and inherent characteristics according to Table 1. Volatile suspended solids (VSS) represent the concentration of biodegradable solids while total suspended solids (TSS) includes all suspended solids. Total solids (TS) account for all solids, while volatile solids (VS) indicate the organic portion of TS.
Specifications of tests and analysis methods
Parameter . | Analysis method . | Detection limit . | Measurement uncertainty (%) . | Device . |
---|---|---|---|---|
TSS | SMa-2540-D | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
VSS and VS | SMa-2540-E | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
TS | SMa-2540-G | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
Turbidity | SM-2130-B | 0.1 NTU | ±2 | Hach-2100N |
pH | SM-4500-H + | 0.1 | ± 2% | 3045-Ion Analyzer |
Parameter . | Analysis method . | Detection limit . | Measurement uncertainty (%) . | Device . |
---|---|---|---|---|
TSS | SMa-2540-D | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
VSS and VS | SMa-2540-E | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
TS | SMa-2540-G | 0.1 mg/L | ±4 | Sartorius Entris 224-1S Balance |
Turbidity | SM-2130-B | 0.1 NTU | ±2 | Hach-2100N |
pH | SM-4500-H + | 0.1 | ± 2% | 3045-Ion Analyzer |
aStandard methods 23rd- edition- 2017.
Specific resistance to filtration (SRF)
Time to filter (TTF)
In addition to SRF, the assessment of sludge dewatering capability commonly involves the widely used test known as TTF. TTF refers to the time (in s) required to collect 100 mL of liquid separated from a 200 mL conditioned sludge sample. This sample is subjected to a pressure of 51 kPa during the vacuum filtration process using a 9 cm diameter Buchner funnel and Whatman No. 40 filter paper, as per APHA et al. (2017).
Sludge volume index (SVI)
The SVI is commonly employed to assess the settleability of sludge in wastewater treatment. It is a measure of the volume of settled sludge after a specified settling period. A lower SVI value indicates enhanced settleability of the sludge. The standard SVI test necessitates a 1-L graduated cylinder for conducting the mixed liquor suspended solids (MLSS) settling test (APHA et al. 2017).
MC of the sludge cake
Error analysis



Soil modification process
The soil obtained from the washing, drying, and sieving processes undergoes modification with 37% hydrochloric acid, wherein the soil and acid were blended on a hot plate at 666 rpm. Afterward, the acid-mixed soil was placed in the environment for 3 days to let the corrosive acid content vaporize preventing damage to the apparatus. Then, the samples were put in the oven at 105° for 2 h to ensure complete dryness (Betatache et al. 2014). Ultimately, the modified soil was utilized for sludge conditioning tests.
A series of lab-scale experiments were conducted with a mixture of 1 g of MOS in 10 mL of distilled water, added to 500 mL of sludge sample. The mixture was then subjected to rapid stirring at 120 rpm for 20 s using a jar test (RNG-Arm-50P), followed by slow stirring at 40 rpm for 2 min to promote sludge flocculation (Betatache et al. 2014). Subsequently, various tests were performed for each conditioned sludge sample, and the results were used for further analyses.
It is worth noting that for onsite production, a closed, acid-resistant reactor with temperature and pressure controls is recommended to safely conduct the process. This setup controls HCl vapor emissions, minimizes odor, and ensures a safe environment without high capital investment. By recycling condensed HCl vapor within the reactor, environmental risks and waste are minimized. Local production also allows for immediate use, reducing costs associated with bulk storage and transportation. For large-scale applications, establishing a dedicated facility for mass production could further enhance environmental and safety controls while benefiting from economies of scale. Such a facility would centralize HCl handling, simplifying emissions management and reducing costs for multiple WWTPs by providing a consistent, ready-to-use supply of modified ochre.
Experimental design using RSM
The experimental design is used to determine the optimum parameters range in the ochre soil modification process to produce the most efficient MOS. The initial phase of the experimental design involves one-factor-at-a-time experiments to determine effective parameter ranges, where each factor is changed individually to optimize the response (Montgomery 2017). Subsequently, RSM is employed to design the experiments and statistical analyses for model presentation and validation using Design Expert 11 software. The Box–Behnken method, a three-level response surface approach capable of fitting models with continuous variable values, was utilized. This method offers advantages, including the estimation of second-order equations, assessment of model adequacy and efficiency, and a reduced number of experiments compared to other methods. Unlike the central composite design, the Box–Behnken approach does not require testing beyond specified ranges and has fewer selection constraints (Montgomery 2017).
The influential variables in the modification of ochre soil with acid, including acid concentration (molarity), mixing temperature, acid volume-to-soil mass mixing ratio, and acid contact time, are examined with the response variable being the SRF. Analysis of variance (ANOVA) determines the significance of the model and the parameters based on the p-value. RSM models the relationship between factors and the response variable (SRF), and optimal variable values for ochre soil modification are obtained. The optimal modified soil is then prepared for subsequent experiments.
Cost-effectiveness analysis
Final experiments
RESULTS AND DISCUSSION
Raw sludge and ochre soil characteristics
Table 2 shows the average values and standard deviation of raw sludge characteristics. The chemical analysis of the ochre soil utilized in this research was conducted using the X-ray fluorescence spectroscopy (XRF) method, as outlined in Table 3. As shown, about 45% of the soil content is referred to as hematite, which can release the iron ions in the solution.
Average ± standard deviation values of the raw sludge characteristics
pH . | MC (%) . | TTF (s) . | SRF (Tm/kg) . | VSS/TSS (%) . | VSS (mg/L) . | TSS (mg/L) . | VS/TS (%) . | VS (mg/L) . | TS (mg/L) . | SVI (mL/g) . |
---|---|---|---|---|---|---|---|---|---|---|
6.73 ± 0.41 | 92.4 ± 5.2 | 526 ± 29 | 31.96 ± 2.5 | 68.8 ± 7.3 | 5,047 ± 234 | 7,340 ± 437 | 71.1 ± 5.2 | 5,843 ± 325 | 8,213 ± 458 | 128 ± 11 |
pH . | MC (%) . | TTF (s) . | SRF (Tm/kg) . | VSS/TSS (%) . | VSS (mg/L) . | TSS (mg/L) . | VS/TS (%) . | VS (mg/L) . | TS (mg/L) . | SVI (mL/g) . |
---|---|---|---|---|---|---|---|---|---|---|
6.73 ± 0.41 | 92.4 ± 5.2 | 526 ± 29 | 31.96 ± 2.5 | 68.8 ± 7.3 | 5,047 ± 234 | 7,340 ± 437 | 71.1 ± 5.2 | 5,843 ± 325 | 8,213 ± 458 | 128 ± 11 |
XRF analysis results of ochre soil (wt%)
Composition type . | Fe2O3 . | Al2O3 . | SiO2 . | TiO2 . | K2O . | Other compounds . |
---|---|---|---|---|---|---|
Percentage in soil | 44.6 | 19.2 | 23.5 | 2.7 | 0.8 | 9.1 |
Composition type . | Fe2O3 . | Al2O3 . | SiO2 . | TiO2 . | K2O . | Other compounds . |
---|---|---|---|---|---|---|
Percentage in soil | 44.6 | 19.2 | 23.5 | 2.7 | 0.8 | 9.1 |
Experimental design and optimization of the soil modification process
After conducting preliminary experiments to determine the effective ranges of soil modification factors, the following ranges in three levels were selected for each factor: 3–9 molar concentration for acid molarity, 30–90 °C for the mixture temperature of acid, and ochre soil, 1–5 mL/g for the acid-to-soil mixing ratio, and 1–5 h for the contact time of acid with soil. Based on the Box–Behnken method using Design Expert software, a total of 29 experiments were designed, with five randomized replications to minimize bias. Moreover, each response has been recorded as the average value of three replications. Table 4 presents the designed experiments and the response values.
Values of designed experiments and measured response
Run number . | Factor A: acid molarity (mol/L) . | Factor B: temp (°C) . | Factor C: acid-to-soil mixing ratio (mL/g) . | Factor D: acid-soil contact time (h) . | Response: SRF (Tm/kg) . |
---|---|---|---|---|---|
1* | 6 | 60 | 3 | 3 | 33.8 |
2 | 3 | 30 | 3 | 3 | 65 |
3 | 6 | 30 | 1 | 3 | 63.1 |
4 | 9 | 60 | 3 | 1 | 21.4 |
5 | 6 | 90 | 1 | 3 | 38.7 |
6 | 6 | 90 | 3 | 1 | 16.9 |
7 | 6 | 60 | 5 | 5 | 21.2 |
8 | 9 | 30 | 3 | 3 | 27.6 |
9 | 6 | 30 | 5 | 1 | 29.3 |
10* | 6 | 60 | 3 | 3 | 28.1 |
11 | 6 | 30 | 2 | 3 | 35.7 |
12* | 6 | 60 | 3 | 3 | 36.3 |
13 | 6 | 30 | 3 | 5 | 26.1 |
14 | 3 | 60 | 5 | 3 | 51.5 |
15 | 6 | 90 | 3 | 5 | 10.1 |
16* | 6 | 60 | 3 | 3 | 31.1 |
17 | 6 | 90 | 5 | 3 | 7.9 |
18 | 3 | 60 | 3 | 1 | 77 |
19 | 3 | 90 | 3 | 3 | 39.1 |
20 | 3 | 60 | 1 | 3 | 96 |
21 | 6 | 60 | 1 | 5 | 45.4 |
22 | 6 | 30 | 3 | 1 | 39.5 |
23 | 9 | 90 | 3 | 3 | 7.5 |
24 | 3 | 60 | 3 | 5 | 73.6 |
25 | 9 | 60 | 1 | 3 | 22.7 |
26 | 9 | 60 | 5 | 3 | 11.5 |
27 | 9 | 60 | 3 | 5 | 10.6 |
28 | 6 | 60 | 1 | 1 | 74 |
29* | 6 | 60 | 3 | 3 | 36.8 |
Run number . | Factor A: acid molarity (mol/L) . | Factor B: temp (°C) . | Factor C: acid-to-soil mixing ratio (mL/g) . | Factor D: acid-soil contact time (h) . | Response: SRF (Tm/kg) . |
---|---|---|---|---|---|
1* | 6 | 60 | 3 | 3 | 33.8 |
2 | 3 | 30 | 3 | 3 | 65 |
3 | 6 | 30 | 1 | 3 | 63.1 |
4 | 9 | 60 | 3 | 1 | 21.4 |
5 | 6 | 90 | 1 | 3 | 38.7 |
6 | 6 | 90 | 3 | 1 | 16.9 |
7 | 6 | 60 | 5 | 5 | 21.2 |
8 | 9 | 30 | 3 | 3 | 27.6 |
9 | 6 | 30 | 5 | 1 | 29.3 |
10* | 6 | 60 | 3 | 3 | 28.1 |
11 | 6 | 30 | 2 | 3 | 35.7 |
12* | 6 | 60 | 3 | 3 | 36.3 |
13 | 6 | 30 | 3 | 5 | 26.1 |
14 | 3 | 60 | 5 | 3 | 51.5 |
15 | 6 | 90 | 3 | 5 | 10.1 |
16* | 6 | 60 | 3 | 3 | 31.1 |
17 | 6 | 90 | 5 | 3 | 7.9 |
18 | 3 | 60 | 3 | 1 | 77 |
19 | 3 | 90 | 3 | 3 | 39.1 |
20 | 3 | 60 | 1 | 3 | 96 |
21 | 6 | 60 | 1 | 5 | 45.4 |
22 | 6 | 30 | 3 | 1 | 39.5 |
23 | 9 | 90 | 3 | 3 | 7.5 |
24 | 3 | 60 | 3 | 5 | 73.6 |
25 | 9 | 60 | 1 | 3 | 22.7 |
26 | 9 | 60 | 5 | 3 | 11.5 |
27 | 9 | 60 | 3 | 5 | 10.6 |
28 | 6 | 60 | 1 | 1 | 74 |
29* | 6 | 60 | 3 | 3 | 36.8 |
*indicates replicate tests.
Results of the ANOVA for the regression model are provided in Table 5. P-values less than 0.05 and F-values higher than 2.5 for each term indicate its significance in the regression model (Montgomery 2017). According to Table 5, the significant terms were, in order, molarity (A), temperature (B), acid-to-soil ratio (C), B2, time (D), BC, and AB.
Results of the proposed model analysis for MOS
Factors . | Sum of squares . | Average of squares . | F-value . | P-value . |
---|---|---|---|---|
Model | 12.7 | 1.81 | 58.37 | > 0.0001 |
A-molarity | 6.26 | 6.26 | 201.31 | > 0.0001 |
B-temperature | 2.61 | 2.61 | 83.85 | > 0.0001 |
C-acid-to-soil mixing ratio | 2.21 | 2.21 | 71.01 | > 0.0001 |
D-time | 0.5120 | 0.512 | 16.47 | 0.0006 |
AB | 0.1573 | 0.1573 | 5.06 | 0.0353 |
BC | 0.2585 | 0.2585 | 8.32 | 0.0089 |
B2 | 0.7024 | 0.7024 | 22.6 | 0.0001 |
Residual | 0.0311 | 0.0311 | – | – |
Lack of fit | 0.0354 | 0.0354 | 2.78 | 0.1659 |
Pure error | 0.0127 | 0.0127 | – | – |
Factors . | Sum of squares . | Average of squares . | F-value . | P-value . |
---|---|---|---|---|
Model | 12.7 | 1.81 | 58.37 | > 0.0001 |
A-molarity | 6.26 | 6.26 | 201.31 | > 0.0001 |
B-temperature | 2.61 | 2.61 | 83.85 | > 0.0001 |
C-acid-to-soil mixing ratio | 2.21 | 2.21 | 71.01 | > 0.0001 |
D-time | 0.5120 | 0.512 | 16.47 | 0.0006 |
AB | 0.1573 | 0.1573 | 5.06 | 0.0353 |
BC | 0.2585 | 0.2585 | 8.32 | 0.0089 |
B2 | 0.7024 | 0.7024 | 22.6 | 0.0001 |
Residual | 0.0311 | 0.0311 | – | – |
Lack of fit | 0.0354 | 0.0354 | 2.78 | 0.1659 |
Pure error | 0.0127 | 0.0127 | – | – |
The coefficient of determination (R²) value is 0.9944, the adjusted R² is 0.9864, and the predicted R² is 0.9073. The value of R2 = 0.994 indicates that only 0.6% of the total variation could not be explained by the model. Furthermore, the R2 value should also be in close agreement with the adjusted R2. The predicted R2 value should be within 0.20 of the adjusted R2 value, indicating that the model has adequate predictive capability. In this case, the difference between the values is 0.08, which falls within the acceptable range.
The optimal values of the factors obtained from the model are provided in Table 6. Based on these values, 1 g of produced MOS was added to 500 mL of raw sludge, and the SRF was measured with three replicates. The average SRF value for the conditioned sludge was determined to be 3.1, with a standard deviation of 0.26. This result was compared to the predicted value of 2.62, showing a 15% difference, which can be considered acceptable.
Results of variable optimization in the modification of ochre soil
Molarity (mol/L) . | Temp (°C) . | Acid-to-soil mixing ratio (mL/g) . | Time contact (h) . | Predicted SRF (Tm/kg) . | Measured SRF (Tm/kg) . | Satisfaction index . |
---|---|---|---|---|---|---|
9 | 90 | 5 | 5 | 2.62 | 3.1 ± 0.26 | 0.987 |
Molarity (mol/L) . | Temp (°C) . | Acid-to-soil mixing ratio (mL/g) . | Time contact (h) . | Predicted SRF (Tm/kg) . | Measured SRF (Tm/kg) . | Satisfaction index . |
---|---|---|---|---|---|---|
9 | 90 | 5 | 5 | 2.62 | 3.1 ± 0.26 | 0.987 |
Performance comparison of MOS with common inorganic coagulants
According to Figure 4, the MC of sludge cake ranges from 92% for raw sludge to 88% for MOS, 89% for alum, and 88% for ferric chloride. Thus, in terms of MC reduction capacity, the MOS and ferric chloride are nearly similar. However, based on the SRF and TTF criteria, the MOS effectiveness falls within the range of ferric chloride and alum. It could be due to the formation of a permeable and rigid skeleton structure of sludge filter cake with a large number of rigid voids and pores.
In Figure 5, the pH variations of the sludge sample after the application of various doses of inorganic coagulants and MOS are illustrated. The results indicate a decrease in pH with increasing the coagulant doses up to 400 mg/g DS. It is observed that the greatest reduction is associated with ferric chloride, equal to 1.6, while the lowest reduction is attributed to the MOS, amounting to 4. Since low pH enhances the corrosiveness of sludge, the advantages of using MOS over other coagulants include lower corrosion while maintaining acceptable efficiency in sludge conditioning.
In addition, the settleability of raw and conditioned sludge and the turbidity of the supernatant of settled sludge after 30 min are considered as two performance criteria for the investigated coagulant materials. Table 7 presents the results of the SVI and supernatant turbidity for the raw and conditioned sludge by the coagulants and MOS at their optimum doses. The lowest SVI and turbidity for the MOS were observed at 117 mL/g and 3.9 nephelometric turbidity unit (NTU), respectively, indicating superior performance compared to other coagulants, particularly ferric chloride. The lower values of SVI and turbidity obtained with MOS can be attributed to mechanisms such as skeleton building, absorption of sludge particles, and neutralization of colloidal particles. These processes result in sludge densification and improved settleability, which will be discussed further in the following sections.
SVI of sludge and supernatant turbidity
Coagulant type . | Coagulant dose (mg/g DS) . | Sludge settling volume after 30 min (mL) . | SVI (mL/g) . | Supernatant turbidity (NTU) . |
---|---|---|---|---|
Raw sludge | 0 | 940 | 128 | 111 |
MOS | 300 | 880 | 117 | 3.94 |
Ferric chloride | 200 | 920 | 125 | 59 |
Alum | 250 | 900 | 123 | 12.5 |
Coagulant type . | Coagulant dose (mg/g DS) . | Sludge settling volume after 30 min (mL) . | SVI (mL/g) . | Supernatant turbidity (NTU) . |
---|---|---|---|---|
Raw sludge | 0 | 940 | 128 | 111 |
MOS | 300 | 880 | 117 | 3.94 |
Ferric chloride | 200 | 920 | 125 | 59 |
Alum | 250 | 900 | 123 | 12.5 |
Combining CPAM and ochre soil
CPAM can help to form larger and more compact flocs mainly through mechanisms of charge neutralization and the bridging effect (Zhu et al. 2018). This section evaluates CPAM, a commonly used and costly polymer in wastewater treatment, both independently and in combination with the MOS. The main objective behind this combination is to minimize CPAM consumption and thereby reduce the associated costs of its utilization in sludge conditioning. Table 8 presents the application of CPAM alone in the conditioning of sludge samples. The results show that the minimum SRF of 2.1 Tm/kg is attained at a dosage of 2 mg/g DS, indicating the optimal value for sludge conditioning, coinciding with the minimum TTF and MC values.
The effect of different doses of CPAM alone on the studied variables of sludge conditioning
CPAM Dose (mg/g DS) . | SRF (Tm/kg) . | TTF (s) . | MC (%) . | pH . |
---|---|---|---|---|
0 | 32 | 526 | 92.4 | 6.7 |
1 | 14.4 | 178 | 92.2 | 6.9 |
1.25 | 7.9 | 94 | 92.1 | 6.7 |
1.5 | 4.3 | 49 | 90.4 | 6.9 |
1.75 | 3.4 | 51 | 90.2 | 6.9 |
2 | 2.1 | 41 | 89.4 | 7 |
2.25 | 10.5 | 113 | 90.8 | 6.9 |
2.5 | 28.8 | 310 | 92.2 | 7.1 |
CPAM Dose (mg/g DS) . | SRF (Tm/kg) . | TTF (s) . | MC (%) . | pH . |
---|---|---|---|---|
0 | 32 | 526 | 92.4 | 6.7 |
1 | 14.4 | 178 | 92.2 | 6.9 |
1.25 | 7.9 | 94 | 92.1 | 6.7 |
1.5 | 4.3 | 49 | 90.4 | 6.9 |
1.75 | 3.4 | 51 | 90.2 | 6.9 |
2 | 2.1 | 41 | 89.4 | 7 |
2.25 | 10.5 | 113 | 90.8 | 6.9 |
2.5 | 28.8 | 310 | 92.2 | 7.1 |
The bold values denote the optimal dosage value for sludge conditioning.
In Table 9, the results of six different combinations, comprising two doses of MOS and three doses of CPAM, are presented. The findings demonstrate that the combination of CPAM and MOS not only reduces polymer consumption but also enhances water absorption capacity. Combination number 4, utilizing a dose of 200 mg/g DS for MOS and 0.75 mg/g DS for CPAM, yielded an SRF value of 1.54 Tm/kg. This result outperformed the case of CPAM alone with an SRF of 2.1 Tm/kg, indicating a 27% SRF reduction and a 2.7-fold reduction in CPAM consumption (from 2 to 0.75 mg/g DS). However, a slight decrease in pH from 6.5 to 7.6 was observed.
Combination of CPAM with MOS
Combination number . | MOS (mg/g DS) . | CPAM dose (mg/g DS) . | SRF (Tm/kg) . | TTF (s) . | MC (%) . | pH . |
---|---|---|---|---|---|---|
1 | 100 | 0.5 | 5.43 | 78 | 90.7 | 6.13 |
2 | 200 | 2.77 | 56 | 90.2 | 5.47 | |
3 | 100 | 0.75 | 2.02 | 43 | 90.4 | 6.2 |
4 | 200 | 1.54 | 39 | 89.9 | 5.6 | |
5 | 100 | 1 | 2.1 | 41 | 89.8 | 6.27 |
6 | 200 | 1.38 | 39 | 90.9 | 5.67 |
Combination number . | MOS (mg/g DS) . | CPAM dose (mg/g DS) . | SRF (Tm/kg) . | TTF (s) . | MC (%) . | pH . |
---|---|---|---|---|---|---|
1 | 100 | 0.5 | 5.43 | 78 | 90.7 | 6.13 |
2 | 200 | 2.77 | 56 | 90.2 | 5.47 | |
3 | 100 | 0.75 | 2.02 | 43 | 90.4 | 6.2 |
4 | 200 | 1.54 | 39 | 89.9 | 5.6 | |
5 | 100 | 1 | 2.1 | 41 | 89.8 | 6.27 |
6 | 200 | 1.38 | 39 | 90.9 | 5.67 |
The bold values denote the optimal dosage value for sludge conditioning.
Optical microscopic images of raw and conditioned sludge at 400× magnification: (a) raw waste-activated sludge (b) conditioned sludge with 200 mg/gDS MOS, and (c) conditioned sludge with (200 + 0.75 mg/gDS) MOS + CPAM.
Optical microscopic images of raw and conditioned sludge at 400× magnification: (a) raw waste-activated sludge (b) conditioned sludge with 200 mg/gDS MOS, and (c) conditioned sludge with (200 + 0.75 mg/gDS) MOS + CPAM.
Cost-effective analysis
Table 10 shows the unit cost of coagulants and the total cost per kg of dried solids at optimal dosage. The capital, operation, and maintenance costs of making the MOS are considered assuming onsite production with solar drying. Given the small scale of the operation in a WWTP and the relatively low volume of vapor produced, the emissions are not expected to cause significant environmental impacts. The costs were obtained based on the inquiries from the suppliers in 2021 in Iranian Tomans (10 Rials).
Unit cost of coagulants and total cost at optimal dosage
Coagulant . | Optimal dosage (g/kgDS) . | Unit cost of chemicals/soil (Tomans/kg) . | HCl price (tomans/kg MOS) . | Equivalent capital, operation, and maintenance costs (tomans/kg MOS) . | Total cost at optimal dosage (Tomans/kgDS) . |
---|---|---|---|---|---|
MOS | 300 | 800 | 200 | 400 | 420 |
Ferric chloride | 200 | 40,000 | N/A | N/A | 8,000 |
Alum | 250 | 15,000 | N/A | N/A | 3,750 |
CPAM | 2 | 160,000 | N/A | N/A | 320 |
Coagulant . | Optimal dosage (g/kgDS) . | Unit cost of chemicals/soil (Tomans/kg) . | HCl price (tomans/kg MOS) . | Equivalent capital, operation, and maintenance costs (tomans/kg MOS) . | Total cost at optimal dosage (Tomans/kgDS) . |
---|---|---|---|---|---|
MOS | 300 | 800 | 200 | 400 | 420 |
Ferric chloride | 200 | 40,000 | N/A | N/A | 8,000 |
Alum | 250 | 15,000 | N/A | N/A | 3,750 |
CPAM | 2 | 160,000 | N/A | N/A | 320 |
Ferric chloride, despite having the highest SRF reduction percent, gives the highest Ce due to its high price and is therefore ranked last. MOS without CPAM has 19 and 9 times lower Ce ratios compared to ferric chloride and alum, respectively.
On the other hand, comparing the cost efficiency value of the combination of ochre soil + CPAM (Combination 1) with CPAM alone shows a 23% reduction in the Ce parameter and better performance. This ochre/CPAM ratio showed the advantage of reducing ochre soil consumption by about 66% and polymer consumption by 75% compared to using each component alone. Combination 3 (100 MOS + 0.75 CPAM mg/gDS) is more suitable because it achieves a better reduction percentage in SRF than Combination 1. Since its Ce is nearly equal to that of Combination 1, it can be considered a superior option. Additionally, this combination is 22% cheaper than using polymer alone (250 versus 320).
Compared with the previous studies
Table 11 compares the methods and results of the previous studies with the present study. The studies used waste-activated sludge at different stages of treatment, including discharge from secondary clarifiers (as in the current study) and thickeners. Many natural-based materials, such as Chitosan Moringa oleifera, rice husk biochar, and bentonite soil, were applied in combinations with different iron ion compounds or modified with acids prior to application. Acceptable results were achieved in most studies. SRF reduction rates were reported in the range of 73–98%, with the exception of Lin et al. (2023) who reported 52%. The reduction rate of 94% achieved in this study is one of the highest rates among the studies listed. The CEA of the proposed combination of reagents with the conventional flocculants was only performed by Wei et al. (2018). Moreover, except for two cases, most of the studies did not employ an experimental design or utilize statistical modeling and validation tools. These elements are considered advantageous in this study. Consequently, by comparing the results, the novelty and effectiveness of the tested materials were confirmed.
Comparison of the methods and results with the previous studies
Study . | Raw sludge type . | Conditioning reagent . | Effective factor . | Design of experiments . | Optimum condition . | Conditioned sludge SRF(Tm/Kg) +(%SRF reduction) . | MC (%) . | Other notable results . |
---|---|---|---|---|---|---|---|---|
Lin et al. (2023) | Waste-activated sludge | Dual redox cycle system with Fe (III) + H2O2 + EPS | H2O2/Fe(III) dose, time for mixing agent and sludge | N/A | Contanct:10 min, Fe(III) = 0.15 and H2O2 = 1.46 mmol/gVSS | 16.2 (52%) | 78.4% | EPS is damaged during the oxidation process, leading to the effective conversion of bound water to free water |
Wang et al. (2022) | Waste-activated sludge | Electro-coagulation combined with added free nitrous acid | Voltage, process time, free nitrous acid dose | N/A | Voltage of 25 V, process time of 60 min, 1.13 mg/L free nitrous acid | 0.6 (89.6%) | 72.1% | Zn and Mn contents of the sludge cake decreased by 92.3% and 69.0% |
Xiao et al. (2020) | Waste-activated sludge | Fe2+/persulfate/tannic acid (TA) | Reagent dose | RSM-Box Behnken | 0.43 mol/gTS + 0.58 Fe2 + mmol/gTS +0.14 mmol/gTS TA | 48.7 (83.49%) | 75.9% | Elimination of EPS, supernatant viscosity reduction, and particle size increase |
Zhu et al. (2018) | Thickened waste sludge | Combined NaCl-CPAM-Rice husk | Reagent dose | N/A | 0.25 mol/L NaCl + 30 mg/L CPAM + 50% DS RH | 0.21 (91.23%) | 65.4% | Sludge cake porosity increased by 138.6% and compressibility reduced by 39.6% |
Wei et al. (2018) | Thickened waste sludge | Cationized starch-based flocculant (St-WH)and combination with ferric chloride | Flocculant dose, charge densities | N/A | 24 mg/g TSS | 0.25 (87.8%) | 81.2% | A combination of St-WH + ferric chloride was presented as the most cost-effective |
Wu et al. (2016) | Thickened waste sludge | Rice husk biochar modified by ferric chloride (MRB–Fe) | Ferric chloride dose,+ porosity of rice husk biochar | N/A | Ferric chloride dose = 3 mol/L; ultrasound time, 1 h. MRB–Fe dose = 60% DS | 1.13 (97.9%) | 94.70 (19.4%reduction) | Effectively enhanced sludge dewaterability as a skeleton builder |
Zemmouri et al. (2015) | Waste-activated sludge | Chitosan + cationic polyelectrolyte Sedipur CF802 (Sed CF802) and (ferric chloride) | Chitosan dose | N/A | 2–3 kg/tonDS | 0.932 (90.7%) | 82.69 | Chitosan showed the same performance as synthetic polymer |
Current study | Waste-activated sludge | Acid-modified ochre (MOS)+ CPAM, ferric chloride, alum | Flocculant doses | RSM-Box Behnken design | MOS (100 mg/g DS) with CPAM (0.75 mg/g DS) | 1.54 (94%) | 90.4 | Combination of MOS + CPAM was presented as the most cost-effective reagent |
Study . | Raw sludge type . | Conditioning reagent . | Effective factor . | Design of experiments . | Optimum condition . | Conditioned sludge SRF(Tm/Kg) +(%SRF reduction) . | MC (%) . | Other notable results . |
---|---|---|---|---|---|---|---|---|
Lin et al. (2023) | Waste-activated sludge | Dual redox cycle system with Fe (III) + H2O2 + EPS | H2O2/Fe(III) dose, time for mixing agent and sludge | N/A | Contanct:10 min, Fe(III) = 0.15 and H2O2 = 1.46 mmol/gVSS | 16.2 (52%) | 78.4% | EPS is damaged during the oxidation process, leading to the effective conversion of bound water to free water |
Wang et al. (2022) | Waste-activated sludge | Electro-coagulation combined with added free nitrous acid | Voltage, process time, free nitrous acid dose | N/A | Voltage of 25 V, process time of 60 min, 1.13 mg/L free nitrous acid | 0.6 (89.6%) | 72.1% | Zn and Mn contents of the sludge cake decreased by 92.3% and 69.0% |
Xiao et al. (2020) | Waste-activated sludge | Fe2+/persulfate/tannic acid (TA) | Reagent dose | RSM-Box Behnken | 0.43 mol/gTS + 0.58 Fe2 + mmol/gTS +0.14 mmol/gTS TA | 48.7 (83.49%) | 75.9% | Elimination of EPS, supernatant viscosity reduction, and particle size increase |
Zhu et al. (2018) | Thickened waste sludge | Combined NaCl-CPAM-Rice husk | Reagent dose | N/A | 0.25 mol/L NaCl + 30 mg/L CPAM + 50% DS RH | 0.21 (91.23%) | 65.4% | Sludge cake porosity increased by 138.6% and compressibility reduced by 39.6% |
Wei et al. (2018) | Thickened waste sludge | Cationized starch-based flocculant (St-WH)and combination with ferric chloride | Flocculant dose, charge densities | N/A | 24 mg/g TSS | 0.25 (87.8%) | 81.2% | A combination of St-WH + ferric chloride was presented as the most cost-effective |
Wu et al. (2016) | Thickened waste sludge | Rice husk biochar modified by ferric chloride (MRB–Fe) | Ferric chloride dose,+ porosity of rice husk biochar | N/A | Ferric chloride dose = 3 mol/L; ultrasound time, 1 h. MRB–Fe dose = 60% DS | 1.13 (97.9%) | 94.70 (19.4%reduction) | Effectively enhanced sludge dewaterability as a skeleton builder |
Zemmouri et al. (2015) | Waste-activated sludge | Chitosan + cationic polyelectrolyte Sedipur CF802 (Sed CF802) and (ferric chloride) | Chitosan dose | N/A | 2–3 kg/tonDS | 0.932 (90.7%) | 82.69 | Chitosan showed the same performance as synthetic polymer |
Current study | Waste-activated sludge | Acid-modified ochre (MOS)+ CPAM, ferric chloride, alum | Flocculant doses | RSM-Box Behnken design | MOS (100 mg/g DS) with CPAM (0.75 mg/g DS) | 1.54 (94%) | 90.4 | Combination of MOS + CPAM was presented as the most cost-effective reagent |
CONCLUSION
The present study investigated the potential of MOS as a cost-effective and environmentally friendly coagulant for sludge conditioning in WWTPs. The primary objective was to reduce sludge volume and associated costs while improving dewatering efficiency. The optimization of the ochre soil modification process used RSM to identify optimal conditions. These conditions include a 9 mol/L acid concentration, a mixture temperature of 90 °C, a 5 mL/g acid-to-soil ratio, and a contact time of 5 h. The resulting MOS demonstrated significant improvements in sludge dewatering, achieving an SRF of 3.1 Tm/kg, a 93% reduction compared to untreated sludge. Performance comparisons with ferric chloride and alum showed that MOS was equally effective, and sometimes superior, in terms of SRF, TTF, and MC. Moreover, the MOS combined with CPAM further enhanced dewatering efficiency, indicating the potential for synergistic effects. CEA demonstrated that MOS presents a competitive and viable alternative to conventional chemical coagulants, providing a sustainable solution that reduces the environmental impact and operational costs of sludge management. The promising results encourage further exploration and potential application of MOS in full-scale wastewater treatment processes, contributing to more sustainable and cost-effective sludge management practices. Future research should compare MOS with advanced treatments, assessing long-term performance, stability, and environmental impact, aiming for improved efficiency and sustainability.
AUTHOR CONTRIBUTIONS
B.A. developed the methodology, rendered support in data preparation and analysis, and prepared the original draft. M.T. supervised and conceptualized the work, wrote, reviewed, and edited the article. R.M.F. investigated the process, wrote, visualized, reviewed, and edited the article.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.