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Urine from domestic wastewater greatly increases the nutritional value of wastewater. Urine has a high concentration of nutrients and minerals that can be utilized as plant growth agents, according to a chemical analysis. Due to its high phosphate, nitrogen, and potassium content, human urine can serve as a sustainable substitute for chemical fertilizers. Struvite, also referred to as MAP, can be considered as a sustainable fertilizer and it is a magnesium ammonium phosphate crystal with the chemical formula of MgNH4PO4·6H2O. Struvite may be formed from many types of wastewater, one inexpensive and reliable source for struvite formation being human urine. However, struvite formation requires an external magnesium supply due to the extremely low concentration of magnesium in human urine. In this work, magnesium ammonium phosphate was precipitated from human urine by examining the effects of Mg2+ dose, temperature, and pH on struvite crystals. Several experiments for struvite precipitation were created using the Box–Behnken design. Struvite crystals formed upon the addition of a magnesium source at 20 °C, pH 10, and a mole ratio of 1:1. The results show that the large-scale application and nutrient recovery of struvite crystals from human urine are promising.

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  • Optimum conditions were determined for the recovery of magnesium ammonium phosphate from human urine.

  • The Box-Behnken design was used to create struvite precipitation test sets.

  • When Mg2+ source was added at 20 °C, pH 10, and a mole ratio of 1:1, struvite crystals were formed.

assessment of human urine, nutrient recovery, optimization, struvite formation
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Local water scarcity issues arise as a result of urbanization's acceleration of the water cycle's usage. Food and water in particular are essential for the long-term viability of human society, and efficient wastewater treatment is essential to the long-term viability of water resources in major cities. Water supply needs a lot of energy, and instead of being considered waste, ‘used water’ is becoming recognized as a potential source of recyclable materials and valuable energy (Capodaglio 2020, 2023). Traditional sewerage systems combine human excreta with large amounts of flushing water and other wastewater. However, decentralized toilets process urine and feces independently, providing new opportunities for recovering valuable resources (Koulouri et al. 2024).

Human urine is a good source of many micronutrients, including iron, fluoride, and calcium, and it is structurally rich in important plant macronutrients, including 93–96% water and 7–4% nitrogen, potassium, urea, ammonia, creatinine, and phosphorus (Udert et al. 2006; Jiang et al. 2023). Furthermore, human urine is a valuable waste product, particularly for the recovery of macronutrients, especially when one considers that humans produce roughly 2.5 L of urine per day (Santoro et al. 2020). Human urine also undergoes spontaneous processes when it is stored, separated, or transported. Researchers briefly define these processes as follows: fresh urine generally refers to urine that has been freshly collected or frozen immediately after collection and whose pH has been reduced, while hydrolyzed urine refers to urine that has been completely broken down into urea after storage (Liu et al. 2024). Among the previously mentioned spontaneous processes, urea hydrolysis has a notable impact on urine composition. Urea is hydrolyzed into ammonia and bicarbonate with the help of the urease enzyme, which is produced by the bacteria that hydrolyze urea. The pH of the solution and the ammonia concentration both significantly rise as a result of this reaction. According to Lahr et al. (2016), the precipitation of struvite (MgNH4PO4·6H2O), hydroxyapatite (Ca5(PO4)3(OH)2), and calcite (CaCO3) is affected by the rise in pH and ammonia concentration.

In recent years, struvite precipitation from source-separated urine has become increasingly attractive for fertilizer production. Struvite has been of great interest for decentralized fertilizer production because it can be produced in simple, hand-held reactors and requires only magnesium reagent. Struvite precipitation from source-separated urine has the potential to recover more than 90% of phosphate and some ammonia by adding a suitable magnesium source (Soltani et al. 2023). Global research and implementation are underway on nutrient recovery technologies, with a focus on P recovery from used water (Capodaglio 2020). Struvite recovery from human urine has two important advantages: struvite contains both nitrogen (N) and phosphorus (P) and both elements are removed from the wastewater; therefore, struvite can be used as fertilizer. Struvite recovery can be achieved in the presence of magnesium, phosphate, and ammonium ions in human urine. Therefore, human urine is becoming an important and useful source for struvite precipitation and recovery.

So far, numerous research studies have been carried out on struvite recovery from human urine such as adsorption (Koulouri et al. 2024), membrane capacitive deionization (MCDI; Jiang et al. 2023), membrane filtration (Pradhan et al. 2019), microbial fuel cells (MFCs; You et al. 2016), forward osmosis integrated bioelectro-concentration and recovery system (OsBCRS; Jiang et al. 2022), and hybrid membrane bioreactor-membrane capacitive deionization (MBR-MCDI) processes (Jiang et al. 2023). Among these processes, struvite precipitation is the most straightforward way to extract P and N from urine. Based on a thorough cost analysis, it was possible to make 0.26$/m3 of urine profit by using this technology (Patel et al. 2020).

This first contribution to the literature focuses on optimizing the struvite precipitation from human urine using the surface response methodology. For ion precipitation and struvite production, variables like temperature, pH, and magnesium concentration were chosen in the experiments to create the Box–Behnken design (BBD; Design-Expert software 11.0). The produced struvite was characterized using scanning electron microscopy (SEM), SEM-energy dispersive X-ray (EDX), and X-ray diffraction (XRD) analyses. The concentration of the ions precipitated from the urine was ascertained using ion chromatography.

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Human urine composition

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Fresh human urine was collected and analyzed in this study. pH, COD, conductivity, total nitrogen (TN), total phosphorus (TP), cations, and anions species were measured. Table 1 displays the findings of the analyses. As can be clearly seen from the results, the high COD, TN, and TP contents of human urine draw attention.

Table 1

Analysis results of fresh human urine

ParametersUnitValue
pH – 8.32 
Conductivity mS/cm 11.85 
COD mg/L 7,795 
Total nitrogen mg/L 8,520 
Total phosphorus mg/L 865 
Na+ mg/L 1,625 
 mg/L 358 
K+ mg/L 1,020 
Mg+2 mg/L 49 
Ca+2 mg/L 110 
Cl− mg/L 5,285 
 mg/L 408 
 mg/L 605 
ParametersUnitValue
pH – 8.32 
Conductivity mS/cm 11.85 
COD mg/L 7,795 
Total nitrogen mg/L 8,520 
Total phosphorus mg/L 865 
Na+ mg/L 1,625 
 mg/L 358 
K+ mg/L 1,020 
Mg+2 mg/L 49 
Ca+2 mg/L 110 
Cl− mg/L 5,285 
 mg/L 408 
 mg/L 605 
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Ion precipitation and struvite production using the Box-Behnken method

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Optimization processes in traditional methods are based on independent factors that change while keeping the others fixed. This method is quite time-consuming and ignores the interaction between parameters (Khoshkroodi et al. 2022). Experimental design techniques like response surface methodology (RSM) can be used to solve the limitations of traditional methods by optimizing all parameters at the same time (Hasan et al. 2023). BBD based on RSM has great advantages over traditional applications due to lesser experiments, easy implementation, high accuracy, and statistical analysis (Belibagli et al. 2022).

In this study, Box-Behnken was designed (Design-Expert software 11.0) by choosing parameters such as Mg amount, pH, and temperature which are for ion precipitation and struvite production. Preliminary experiments were used to determine the range of each variable. The ranges of the parameters for ion precipitation and struvite production are shown in Table 2.

Table 2

Parameters for ion precipitation and struvite production

VariableUnitFactorLowHigh
Mg2+ amount mg A 75 195 
pH – B 8 10 
Temperature °C C 20 60 
VariableUnitFactorLowHigh
Mg2+ amount mg A 75 195 
pH – B 8 10 
Temperature °C C 20 60 
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Table 3

ANOVA statistical analysis of the model for Mg2+, ⁠, and precipitation

SourceMg2+ precipitation (%)
 precipitation (%)
 precipitation (%)
SSdfMSF-valueP-valueSSdfMSF-valueP-valueSSdfMSF-valueP-value
Model 12,308.66 9 1,367.63 105.16 <0.0001 599.45 3 199.82 108.77 <0.0001 1,458.66 9 162.07 1,101.81 <0.0001 
A 58.32 1 58.32 4.48 0.0878 223.87 1 223.87 121.86 <0.0001 104.76 1 104.76 712.20 <0.0001 
B 5,682.85 1 5,682.85 436.97 <0.0001 62.83 1 62.83 34.20 0.0001 95.15 1 95.15 646.85 0.0001 
C 1,402.38 1 1,402.38 107.83 0.0001 312.75 1 312.75 170.24 <0.0001 1,203.93 1 1,203.93 8,184.54 <0.0001 
AB 335.44 1 335.44 25.79 0.0038      1.80 1 1.80 12.21 0.0174 
AC 34.63 1 34.63 2.66 0.1636      0.8930 1 0.8930 6.07 0.0570 
BC 2.36 1 2.36 0.1812 0.6881      0.5256 1 0.5256 3.57 0.1173 
A2 861.84 1 861.84 66.27 0.0005      20.99 1 20.99 142.68 <0.0001 
B2 995.86 1 995.86 76.57 0.0003      27.91 1 27.91 189.71 <0.0001 
C2 3,531.83 1 3,531.83 271.57 <0.0001      10.07 1 10.07 68.48 0.0004 
Residual 65.03 5 13.01   20.21 11 1.84   0.7355 5 0.1471   
Lack of fit 49.30 3 16.43 2.09 0.3399 15.30 9 1.70 0.6931 0.7139 0.2856 3 0.0952 0.4233 0.7580 
Pure error 15.73 2 7.86   4.91 2 2.45   0.4499 2 0.2249   
Cor. total 12,373.69 14    619.66 14    1,459.40 14    
SourceMg2+ precipitation (%)
 precipitation (%)
 precipitation (%)
SSdfMSF-valueP-valueSSdfMSF-valueP-valueSSdfMSF-valueP-value
Model 12,308.66 9 1,367.63 105.16 <0.0001 599.45 3 199.82 108.77 <0.0001 1,458.66 9 162.07 1,101.81 <0.0001 
A 58.32 1 58.32 4.48 0.0878 223.87 1 223.87 121.86 <0.0001 104.76 1 104.76 712.20 <0.0001 
B 5,682.85 1 5,682.85 436.97 <0.0001 62.83 1 62.83 34.20 0.0001 95.15 1 95.15 646.85 0.0001 
C 1,402.38 1 1,402.38 107.83 0.0001 312.75 1 312.75 170.24 <0.0001 1,203.93 1 1,203.93 8,184.54 <0.0001 
AB 335.44 1 335.44 25.79 0.0038      1.80 1 1.80 12.21 0.0174 
AC 34.63 1 34.63 2.66 0.1636      0.8930 1 0.8930 6.07 0.0570 
BC 2.36 1 2.36 0.1812 0.6881      0.5256 1 0.5256 3.57 0.1173 
A2 861.84 1 861.84 66.27 0.0005      20.99 1 20.99 142.68 <0.0001 
B2 995.86 1 995.86 76.57 0.0003      27.91 1 27.91 189.71 <0.0001 
C2 3,531.83 1 3,531.83 271.57 <0.0001      10.07 1 10.07 68.48 0.0004 
Residual 65.03 5 13.01   20.21 11 1.84   0.7355 5 0.1471   
Lack of fit 49.30 3 16.43 2.09 0.3399 15.30 9 1.70 0.6931 0.7139 0.2856 3 0.0952 0.4233 0.7580 
Pure error 15.73 2 7.86   4.91 2 2.45   0.4499 2 0.2249   
Cor. total 12,373.69 14    619.66 14    1,459.40 14    
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A quadratic polynomial model is depicted in Equation (1), and the mathematical application of RSM aims to provide the relationship between variables and experimental study results that can fit into it (Sharma et al. 2022):
(1)

The coefficients of the regression equation are represented by the numbers β1, β2, β3, … β10 in this equation.

Ion precipitation and struvite production experiments

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Struvite production tests were carried out in 100 mL flasks. The mixture was shaken in an incubator and left for 24 h to settle down. Then, the soluble and solid phase were separated by centrifugation (Hettich EBA 280S, Germany) at 6,000 rpm for 10 min. After that, the concentration of Mg2+, ⁠, and ions in the solution was analyzed using ion chromatography (DIONEX Ion Chromatography, ICS 3000 DUAL). The precipitated fraction was characterized by SEM and XRD analyses. Figure 1 shows an ion precipitation and struvite production experiments optimization.
Figure 1
Summary of ion precipitation and struvite production experiments optimization.
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Summary of ion precipitation and struvite production experiments optimization.

Figure 1
Summary of ion precipitation and struvite production experiments optimization.
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Summary of ion precipitation and struvite production experiments optimization.

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Characterization of struvite

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X-ray diffraction (PANalytical, Empyrean, USA) was used to define the structure of struvite. Scanning electron microscopy with energy dispersive X-ray (SEM-EDX, FEI, Quanta 650 Field Emission, USA) was used to examine the surface morphology and elemental analyses of struvite.

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Characterization of struvite

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In this study, the surface morphology of struvite crystals was determined using SEM images taken under optimum conditions (Figure 2). Struvite are octahedral in shape. It can be clearly seen from SEM images, struvite crystals are rod-like irregularly shaped crystals, similar to the findings of other researchers (Kemacheevakul et al. 2015).
Figure 2
SEM images of struvite obtained from human urine at different magnifications: (a: 10 μm; b: 4 μm; c: 1 μm; and d: 500 nm).
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SEM images of struvite obtained from human urine at different magnifications: (a: 10 μm; b: 4 μm; c: 1 μm; and d: 500 nm).

Figure 2
SEM images of struvite obtained from human urine at different magnifications: (a: 10 μm; b: 4 μm; c: 1 μm; and d: 500 nm).
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SEM images of struvite obtained from human urine at different magnifications: (a: 10 μm; b: 4 μm; c: 1 μm; and d: 500 nm).

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EDX analysis, in conjunction with SEM images of struvite obtained under ideal conditions, confirmed that the crystals had struvite composition, with EDX spectra indicating the presence of Mg2+, ⁠, and (Figure 3).
Figure 3
EDX spectrum of struvite obtained from human urine.
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EDX spectrum of struvite obtained from human urine.

Figure 3
EDX spectrum of struvite obtained from human urine.
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EDX spectrum of struvite obtained from human urine.

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The crystal structure of struvite was determined qualitatively using XRD (Figure 4). The results were consistent with the literature (Shaddel et al. 2019), confirming the presence of struvite under optimal experimental conditions, with the intensity and positions of the XRD patterns matching the reference powder diffraction file (PDF 00-015-0762) for struvite crystals.
Figure 4
Precipitated struvite XRD patterns.
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Precipitated struvite XRD patterns.

Figure 4
Precipitated struvite XRD patterns.
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Precipitated struvite XRD patterns.

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ANOVA statistical analysis for struvite precipitation

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Table 3 displays the statistical analysis results of the data obtained from the experiments. In ANOVA statistical analysis, a high F-value versus a low P-value is required for each parameter's significance. The model terms are significant when the F-value for magnesium precipitation is 105.16 and the P-value is less than 0.0500. B, C, AB, A2, B2, and C2 are meaningful model terms in magnesium precipitation in this case. Equation (2) depicts the model equation representing the magnesium removal efficiency. pH (A), Mg2+ amount (B), and temperature (C) are all expressed in the equation:
(2)
The ammonium precipitation pattern is significant, with a value of 108.77 F. P-values of less than 0.0500 also indicate that the model terms are significant. A, B, and C are meaningful model terms in this case. Equation (3) depicts the model equation describing the ammonium removal efficiency. The equation is represented by pH (A), Mg2+ amount (B), and temperature (C):
(3)
For phosphate precipitation, F-value of 1,101.81 indicates that the model is significant, and values less than 0.0500 indicate that the model terms are significant. That is, the model terms A, B, C, AB, A2, B2, and C2 are all meaningful. Equation (4) shows the model equation describing the potassium removal efficiency. The pH (A), Mg2+ amount (B), and temperature (C) are all expressed in the equation:
(4)

The R2 values of the model for magnesium, ammonium, and phosphate precipitation are given in Table 4. R2 values near one indicate a strong relationship between adjusted and predicted values. R2 values for magnesium, ammonium, and phosphate precipitation are greater than 0.95, indicating good agreement between variables and outputs. The difference between adjusted R2 and predicted R2 must be less than 0.20 for fit, otherwise it indicates that the experimental and predicted data do not fit the model and there may be errors. The R2 predicted in this study is reasonably close to the adjusted R2, so the difference is less than 0.2. In this study, a value difference of 0.05 for each response indicated that the model fit well. In ANOVA, high adequate precision values are also expected. Sufficient precision values for magnesium, ammonium, and phosphate precipitation are 30.3684, 32.9820, and 102.4176, respectively.

Table 4

R2 of the model for Mg2+, ⁠, and precipitation

R2Adjusted R2Predicted R2Adequate precisionCV %
Mg2+ precipitation (%) 0.9947 0.9853 0.9334 30.3684 7.87 
precipitation (%) 0.9674 0.9585 0.9398 32.9820 10.45 
precipitation (%) 0.9995 0.9986 0.9962 102.4176 1.16 
R2Adjusted R2Predicted R2Adequate precisionCV %
Mg2+ precipitation (%) 0.9947 0.9853 0.9334 30.3684 7.87 
precipitation (%) 0.9674 0.9585 0.9398 32.9820 10.45 
precipitation (%) 0.9995 0.9986 0.9962 102.4176 1.16 
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Figure 5(a)–5(f) shows experimental designs for externally pupillary residues–normal % probability and actual–predicted values for ion precipitation. The efficiency of Mg2+ removal (Figure 5(a) and 5(b)) demonstrates that it is compatible with (Figure 5(c) and 5(d)) and precipitation (Figure 5(e) and 5(f)).
Figure 5
Predicted and actual values and externally studentized residuals and normal % probability, (a and b) for Mg2+ removal efficiency of struvite precipitation, (c and d) for removal efficiency of struvite precipitation, and (e and f) for removal efficiency of struvite precipitation.
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Predicted and actual values and externally studentized residuals and normal % probability, (a and b) for Mg2+ removal efficiency of struvite precipitation, (c and d) for removal efficiency of struvite precipitation, and (e and f) for removal efficiency of struvite precipitation.

Figure 5
Predicted and actual values and externally studentized residuals and normal % probability, (a and b) for Mg2+ removal efficiency of struvite precipitation, (c and d) for removal efficiency of struvite precipitation, and (e and f) for removal efficiency of struvite precipitation.
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Predicted and actual values and externally studentized residuals and normal % probability, (a and b) for Mg2+ removal efficiency of struvite precipitation, (c and d) for removal efficiency of struvite precipitation, and (e and f) for removal efficiency of struvite precipitation.

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Effect of Mg2+ source amount precipitation on struvite production

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Figure 6 depicts 3D and contour plots of the effect of temperature, pH, and Mg2+ content on Mg2+ precipitation for struvite production (a–f). The formation of struvite is controlled by a number of key parameters, including pH, the initial concentration of ionic species in solution, and temperature (Le Corre et al. 2005). The pH level is also important in the precipitation of struvite. The quality of the struvite crystal and thus precipitated struvite is also pH dependent, so pH can be used as an indicator of struvite nucleation (Hao et al. 2008). Most of the magnesium, on the other hand, in the urine is removed from the solution as insoluble minerals during self-precipitation and is therefore unsuitable for the formation of struvite (Tilley et al. 2008). As a result, magnesium was added to the urine solution to precipitate struvite. The struvite precipitation efficiency was greatest when the pH was 10 and the Mg2+: molar ratio was 1:0.5 (Figure 6(a) and 6(b)). The struvite precipitation efficiency increased proportionally as the pH increased and the temperature reached 20 °C (Figure 6(c) and 6(d)). The struvite precipitation efficiency increased proportionally as the Mg2+: molar ratio increased and the pH increased (Figure 6(e) and 6(f)). The highest efficiency of struvite precipitation was obtained at 20 °C, pH 10, and a Mg2+: molar ratio of 1:0.5.
Figure 6
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, (e and f) temperature–Mg2+ source amount on Mg2+ precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, (e and f) temperature–Mg2+ source amount on Mg2+ precipitation.

Figure 6
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, (e and f) temperature–Mg2+ source amount on Mg2+ precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, (e and f) temperature–Mg2+ source amount on Mg2+ precipitation.

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Effect of NH4+ precipitation on struvite production

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Figure 7 depicts 3D and contour plots of the effect of temperature, pH, and Mg2+ content on precipitation for struvite production (a–f). When the operating conditions were pH 10 and the Mg2+: molar ratio of 1:2, the reduction in the amount of NH4 in the urine was maximum (Figure 7(a) and 7(b)). As the pH increased and the temperature was 60 °C, a decrease in the amount of in the urine was observed (Figure 7(c) and 7(d)). When the Mg2+: molar ratio was 1:2 and the temperature increased, the amount of also decreased (Figure 7(e) and 7(f)). In 3D NH4 graphs, it is seen that as the temperature increases, the amount of ammonium in the urine decreases, but this is inversely proportional to the formation of struvite. Ali et al. stated that increasing temperature during struvite formation would cause bobierrite (Mg3(PO4)2·8H2O) formation and ammonia gradually decreased (Ahmad & Gupta 2019).
Figure 7
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.

Figure 7
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.

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Effect of PO43- precipitation on struvite production

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Figure 8 depicts 3D and contour plots of the effect of temperature, pH, and Mg content on NH4 precipitation for struvite production (a–f). When the Mg2+: molar ratio was set to 1:1 and the pH was raised, the efficiency of precipitation increased proportionally (Figure 8(a) and 8(b)). The efficiency of precipitation was again greatest when the temperature was 60 °C and the pH was 10 (Figure 8(c) and 8(d)). When the Mg2+: molar ratio was 1:2 with temperature, the efficiency of precipitation was greatest (Figure 8(e) and 8(f)).
Figure 8
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.

Figure 8
(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.
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(a and b) Effect of Mg2+ source amount–pH, (c and d) effect of temperature–pH, and (e and f) temperature–Mg2+ source amount on precipitation.

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Comparison of research in other studies published in the literature

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Table 5 lists various ion precipitation-based studies that are commonly used to extract struvite from urine.

Table 5

Various studies on struvite derived from urine

SamplesOperational conditions
Struvite precipitationReferences
pHT (°C)Mg ratio
Fresh urine 8–10 20–60 Different Mg2+: ratio >90% Struvite recovery This study 
Real urine 7–11 5–30 Different Mg2+:P ratio >90% Struvite recovery Ronteltap et al. (2010)  
Real urine and cow urine 6.7–6.8 – 1:1 (⁠⁠:Mg2+) molar ratio Struvite recovery Krishnamoorthy et al. (2021)  
Real urine 8.9 – Different Mg:P dosage ratio Struvite recovery Hug & Udert (2013)  
Synthetic urine 9–10 – 1–2 (Mg:P) molar ratio Struvite recovery Seodigeng et al. (2022)  
SamplesOperational conditions
Struvite precipitationReferences
pHT (°C)Mg ratio
Fresh urine 8–10 20–60 Different Mg2+: ratio >90% Struvite recovery This study 
Real urine 7–11 5–30 Different Mg2+:P ratio >90% Struvite recovery Ronteltap et al. (2010)  
Real urine and cow urine 6.7–6.8 – 1:1 (⁠⁠:Mg2+) molar ratio Struvite recovery Krishnamoorthy et al. (2021)  
Real urine 8.9 – Different Mg:P dosage ratio Struvite recovery Hug & Udert (2013)  
Synthetic urine 9–10 – 1–2 (Mg:P) molar ratio Struvite recovery Seodigeng et al. (2022)  
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In this investigation, to precipitate struvite, batch experiments were performed using fresh urine samples with external magnesium supplementation. Box–Behnken (Design-Expert software 11.0) was created by selecting parameters for ion precipitation and struvite production such as Mg2+ amount, pH, and temperature. As a magnesium source, magnesium chloride hexahydrate (MgCl2·6H2O) was used. When a magnesium source was added at 20 °C, pH 10, and a mole ratio of 1:1, struvite crystals were obtained as a result of experimental studies. The study's findings were found to be consistent with the literature. Ronteltap et al. (2010) investigated the effect of process parameters on particle size in one-step struvite precipitation. The Mg/P molar ratio was kept constant while the study was conducted at various temperatures and pH levels. Struvite precipitation was performed at various pH levels in this study, but the obtained struvite crystals had an average size of >90 μm at pH 9 and 20 °C (Ronteltap et al. 2010). Another study compares the precipitation of struvite from cow and human urine. Changes in the physicochemical properties of urine, as well as their effect on the amount and quality of struvite, were investigated in the study. Experiments with precipitation revealed that the yield of struvite in human urine nearly doubled (Krishnamoorthy et al. 2021). Hug et al. synthesized struvite from urine by electrochemically dissolving the magnesium dosage from a magnesium electrode (Hug & Udert 2013). Seodigeng et al. (2022), on the other hand, investigated struvite crystallization using Mg(NO3)2. The effect of four parameters on the yield was investigated in the study, namely settling time, pH, Mg:P ratio, and mixing time, and it was discovered that the mixing speed had the least effect on the yield and had the least effect on the crystal size distribution (Seodigeng et al. 2022).

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The effects of Mg2+ dose, temperature, and pH on struvite crystals were investigated in this study in order to obtain magnesium ammonium phosphate by precipitation from human urine. The magnesium concentration in urine is extremely low, necessitating an external supply of magnesium for struvite formation. Fifteen experimental sets were created for the study's optimization process. Struvite were obtained as a result of experimental studies when a magnesium source was added at 20 °C, pH 10, and a 1:1 molar ratio. The ANOVA statistical analysis revealed that the precipitation experiment results were compatible. This research demonstrates that magnesium ammonium phosphate precipitation results in the formation of struvite. The obtained crystals' SEM images and EDX spectra were found to be consistent with the literature. Finally, other compounds in real human urine do not have the same impact on struvite precipitation as temperature, pH, and ion concentration (Mg2+, ⁠, and NH4). A more economical way to produce struvite from human urine separated at the source could consider using Mg-rich waste sources.

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This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 120Y138. The authors thank TUBITAK for their support.

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All relevant data are included in the paper or its Supplementary Information.

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The authors declare there is no conflict.

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