Techno-Economic Assessment of Microalgae-Based Biofertilizer Production from Municipal Wastewater Using Scenedesmus sp.

Abstract

This research determines the techno-economic feasibility of valorizing as biofertilizer the nitrogen (N) and the phosphorus (P) from a municipal wastewater effluent using the microalgae Scenedesmus sp., contributing to phosphorus recycling, resource optimization, and diminishing eutrophication by capturing 74% of N, 97% of P, and 41% of chemical oxygen demand in effluents. The inoculum was conditioned in 20 L photobioreactors by weekly harvesting and refilling at room temperature (25 °C day, 12 °C night) with a 12:12 photoperiod and 4 L/min atmospheric air bubbling. The improved operational conditions were obtained using a Box–Behnken experimental design, establishing that 70% wastewater concentration (vol./vol.), 4.5% nutrient addition, and 3 days’ harvesting time were the best conditions. The estimated biomass production was 176 tons/year, and this represents a maximum net present value of 1.5 MUSD for a 6.8 Ha plant, capturing 10% of municipal wastewater effluent, which serves 64000 inhabitants. The representative operational costs (OPEX) were 32% for utilities, 30% labor costs, and 25% for raw materials, and the required capital expenditures (CAPEX) were 11 MUSD and are related to photobioreactors (64%) and land (21%). The findings demonstrate the potential of microalgae-based systems as a feasible and profitable approach to wastewater valorization, while also highlighting the need for scale-up validation and integration with existing treatment infrastructures, where land requirements and photobioreactor installation will be relevant for financial feasibility.

1. Introduction

There are important challenges in wastewater treatment systems due to population growth, industrial expansion, and increasingly stringent environmental regulations. Global capital investments in wastewater treatment are increasing as governments and industries seek to modernize outdated infrastructure and adopt sustainable practices to reduce increasing environmental pollution [1]. Total investment in environmental protection in countries from the European Union was EUR 357 billion in 2023, corresponding to an increase of 27% compared to 2018, when EUR 67 billion was invested in wastewater treatment plants, waste disposal, natural reserves, and equipment for cleaner production [2].

Outdated wastewater treatment systems focus on organic matter reduction, currently evaluated using biochemical and chemical oxygen demand (BOD and COD), which is the main purpose of conventional systems such as anaerobic/aerobic digestion or septic tanks, with removal efficiencies around 90%. However, these systems’ removal rates for nitrogen and phosphorus are around 30% in optimal operational conditions [3], and the residues are released, leading to eutrophication in aquatic ecosystems and increasing the risks associated with poor-quality water, as stated in previous research [4].

According to the United Nations, only 58% of wastewater worldwide undergoes treatment classified as “safely managed” [5], which generally refers to primary and secondary processes and does not systematically include tertiary stages for nitrogen and phosphorous removal. In Colombia, it is estimated that there are approximately 368 wastewater treatment plants (WWTPs). However, only a very small share of these facilities applies tertiary treatment, since current discharge regulations are usually met with primary and secondary processes [6].

To address these challenges, many countries are turning to innovative technologies to reduce nutrient pollution, such as the use of microalgae, which offers an effective and eco-friendly solution. Microalgae-based wastewater treatment systems have several advantages over conventional methods. By leveraging the natural metabolic processes of microalgae, such systems can significantly reduce the concentrations of nitrogen and phosphorus in wastewater (from 73 to 99% for both), preventing harmful nutrient overload in rivers and lakes. Additionally, microalgae can capture and store carbon dioxide (CO₂), making them a tool for both water treatment and greenhouse gas mitigation [7]. Likewise, the growth of microalgal biomass makes it possible to generate high-value products, such as biofuels, biochar, biofertilizers, and other outputs related to green economies. This could create a positive economic impact. Moreover, this alternative, unlike others, does not focus solely on treatment capacity and efficiency, but instead provides added value by transforming waste into usable resources [8].

Microalgae not only can help to remove nutrients, but also can produce valuable byproducts like biofuels, biofertilizers, and high-protein biomass, making them a dual-purpose investment that could boost both environmental and economic returns [9], with the significant advantage that they can be adapted to conventional effluents, which, in consortia with Ammonia Oxidizing Bacteria (AOB), will use ammonia (N-NH₃⁺), nitrate (N-NO₃⁻), nitrite (N-NO₂⁻), and phosphorus (P-PO₄³⁻) as a nutrient source for metabolic processes and reproduction [10]. However, these technologies are still undergoing research and innovation to improve the biomass production and reduce the required operational costs and capital expenditures [11].

The ability of microalgae to generate biomass from wastewater is an important improvement of wastewater treatment systems for nutrient removal, contributing to reducing eutrophication risks in ecosystems, with a potential economic gain by the sub-production of valuable materials such as biofuels, animal feed stock [12], and organic fertilizers from algae biomass. The global implementation of these technologies can create new revenue streams for wastewater treatment plants, making the microalgae–bacteria consortium systems not only environmentally sustainable but also enhancing sedimentation of the biomass and the production of value-added products from the microalgae [13]. However, it is important to note the risks associated with microalgae’s capability to interact with pollutants such as heavy metals that must be carefully addressed to avoid bioaccumulation in algae biomass [14], which will be a drawback to biomass reutilization as biofertilizer, but an opportunity to remove heavy metals in wastewater effluents that must be explored.

Wastewater treatment infrastructure faces significant legislative and operational challenges in Colombia, as was stated in previous research. The accomplishment of the 2050 sustainable development goals (SDGs) must be addressed by updating environmental normative and reinforcing wastewater treatment systems [4]. The potential for microalgae to enhance wastewater treatment systems is vast, and strategic investments in this technology could bring both environmental and economic dividends. Integrating these technologies could significantly improve nutrient removal from wastewater. Furthermore, the biomass produced through these systems can be transformed into valuable products, supporting the country’s agricultural and energy sectors. The collaboration of public and private sectors will be key to mobilizing the necessary capital investments to scale up these innovations, making Colombia a leader in sustainable wastewater management while also addressing pressing issues like climate change and water pollution.

Considering the previous statements [15], this study aims to optimize nutrient recovery from a municipal wastewater effluent, with Scenedesmus sp., and to evaluate the economic feasibility of using the so-obtained biomass for biofertilizer applications, under real-world operative scenarios using 20 L photobioreactors (PBRs). Different scenarios will be evaluated by developing a statistical surface response design that identifies optimal operative conditions, such as wastewater dilution, microalgae inoculum rate, and harvesting time, that maximize the overall net present value (NPV) for a 20-year biofertilizer production plant. The economic model considers the annual required raw materials and energy consumption for biomass production and transformation, the investments required for land, equipment acquisition, maintenance, and the administrative services required for plant construction, where the novelty consists in the integration of experimental nutrient removal data with financial modeling for a Colombian case study.

2. Materials and Methods

Previous experiments with microalgae, conducted under control conditions in 20 L photobioreactors, provided essential data on biomass and nutrient removal, where Scenedesmus sp. showed the greatest operational profits in the shortest time at the lowest operational costs (OPEX) when cultivated in municipal wastewater. In this research, the performance of this microalga will be evaluated in municipal wastewater under conditions closer to those of a wastewater treatment plant. The evaluated scenario corresponds to a projection of a tertiary treatment plant that will operate for the next 20 years. Results obtained previously will be validated and operational parameters will be adjusted to maximize economic profits in real operational scenarios, where the cost analysis represents an nth plant design, which assumes that major technical obstacles have been overcome, and required equipment is commercially available. The major assumptions for economic analysis are summarized in

Table 1. Major economic considerations for the economic model.

Element	Assumptions
Plant lifetime (years)	20
Flow (L/s)	10
Operating days per year	333
Inflation	5.81%
Discount rate (TES)	11.62%
Depreciation period (years)
Pumps and PBR	5
Storage tanks	15
Construction period (years)	1
Fraction of investment in year 1	100%
Start-up time (years)	1

.

The economic assessment of the proposed microalgae-based system was carried out using a set of standardized assumptions that reflect typical conditions for industrial-scale projects (Table 1). The construction period was assumed to last one year, during which 100% of the investment is committed, followed by an additional one-year startup phase before full-scale operation. These assumptions provide the framework for estimating capital and operating expenditures, as well as for calculating cash flows and financial indicators such as net present value (NPV) and internal rate of return (IRR).

2.1. Determination of Optimum Operational Conditions

Table 2. Details of equipment and streams for process simulation.

Code	Function	Type	Material	Related Stream	Lifetime
B-1	KOH Solution	Centrifugal pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS304	In: KOH Out: S19	5
B-2	Nutritive Solution	Centrifugal pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS	In: NUT Out: C4	5
B-3	H2SO4 Solution pump	ANSI Plastic pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	ANSI PLASTIC	In: H2SO4 Out: C11	5
B-4	Raw Wastewater pump	Sanitary pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS316	In: WW Out: C1	5
B-5	Reactor filling	Sanitary pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS316	In: S1 Out: IN	5
B-6	Reactor emptying	Sanitary pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS316	In: OUT, Out: S4	5
B-7	Biomass transport	Rotatory lobe pump (Ningbo Bonve Pumps Co., Ltd., Ningbo, China)	SS	In: S3, Out: S5	5
C-1	Air	Vaneaxialfan (Blauberg Motoren, JiangSu, China)	CS	In: AIR Out: C16	15
T-1	KOH Solution	Storage tank ( Zhangjiagang King Machine Tech Co., Ltd., Jiangsu, China)	SS	Out: KOH	15
T-2	Nutritive Solution	Storage tank ( Zhangjiagang King Machine Tech Co., Ltd., Jiangsu, China)	CS	Out: NUT	15
T-3	H2SO4 Solution	Storage tank ( Zhangjiagang King Machine Tech Co., Ltd., Jiangsu, China)	SS	Out: H2SO4	15
D-1	Inoculum divider	Split Flow 1	CS	In: S4, Out: S12, INOCULUM	15
D-2	KOH Solution divider	Split Flow 2	CS	In: S19 Out: S2, S8	15
CE-1	Biomass concentration	Atmospheric basket centrifuge (Dolphin centrifuge, Warren, Michigan)	CS	In: S12 Out: S7, EFFLUENT	15
M-1	Precondition mixer	Mixer 1 (Wenzhou Leno Machinery Co., Ltd., Zhejiang, China)	SS	In: S2, C1, C4 Out: S1	15
M-2	Biomass processing	Reversing anchor agitator (Ruian Xuanli Machinery Co., Ltd., Zhejiang, China)	SS	In: S7, C11, S8 Out: S3	15
PBR	Reactor	40L Vertical PBR (EIA Equipos Ingenieria Y Analisis S.A.S, Medellín, Colombia).	Acrylic	In: IN, INOCULUM C16 Out: AIR + W, OUT	5

describes the equipment required for the process shown in Figure 1, its functionality, materials, and the lifetime expected for repurchase. The wastewater that comes from secondary effluent (WW) is pumped to mixer M-1, where pH is stabilized to 7–8 with potassium hydroxide solution (KOH), and nutritive solution (NUT) was added. This blend is then pumped into vertical photobioreactors (PBRs) with microalgae inoculum, allowed to grow for the desired retention time. PBRs were bubbled with atmospheric compressed air (C-1) to ensure microalgal suspension. PBR effluent is then pumped to a splitter (D-1) that recirculates a fraction of the final algal suspension for inoculum, while the other fraction is conducted to a centrifuge (CE-1) that splits water and wet microalgae, which then is pumped to mixer 2 (M-2), where the hydrolysis process is performed with acid solution and then neutralized with KOH solution. Once hydrolysis has ended, wet biomass is pumped to a solar drier (DRY-1) and packed for their commercialization. Since other studies have compared different drying methods with solar drying and found mineral content differences of less than 10%, industrial solar drying is considered a sustainable technology with high potential to preserve high-quality microalgal biomass. Moreover, this method offers the opportunity to supply various markets at a predictably lower cost compared to more conventional alternatives [14].

To determine the operational conditions that optimize the net profits, results obtained from the experimental design described in

Table 3. Surface response experimental design for company net profit optimization.

Experiment	NUT A (%Vol/Vol.)	WW B (%Vol/Vol.)	Ht C (Days)	Desired Response (USD)
1	2	70	4.5	NPV
2	7	50	6
3	7	30	4.5
4	2	30	4.5
5	4.5	30	3
6	4.5	50	4.5
7	7	70	4.5
8	2	50	3
9	4.5	70	3
10	2	50	6
11	4.5	50	4.5
12	4.5	70	6
13	7	50	3
14	4.5	50	4.5
15	4.5	30	6

Notes: ^A: Nutritive solution factor was evaluated at a low level: 2% Vol.Vol, Central = 4.5% Vol.Vol, and high: 7% Vol.Vol. ^B: Wastewater addition factor was evaluated at a low level: 30% Vol.Vol, Central = 50% Vol.Vol, and high: 70% Vol.Vol. ^C: The Harvesting time factor was evaluated at a low level: 3 Days, Central = 4.5 days, and high: 6 days.

were employed to perform mass balances and determine raw materials, electricity consumption, and the equipment dimension required for the process. This information was employed to determine potential selling prices according to biomass nutrient concentration obtained, and the operational costs (OPEX) and capital expenditures (CAPEX) required to ensure biofertilizer production. It is important to mention that in an all-year operation, the inoculum corresponds to the recirculation required to complete the reactor volume.

2.1.1. Experimental Design

The experimental design selected was Box–Behnken [16]. The Scenedesmus inoculum employed for these experiments was obtained from previous experiments [15], and it was conditioned for this experiment by harvesting and refilling 20 L photobioreactors with 50% (Vol./Vol.) municipal wastewater effluent every 8 days for 4 weeks. The experiments were performed with an average room temperature (12 °C night/25 °C day), solar radiation (4850 Wh/m²/day) [17] and bubbling with atmospheric compressed air to ensure agitation (Q_air = 4 L/min; Efficiency E = 85%) in the Center of Innovation of ARGOS (CAPI) in the Universidad EAFIT in Medellín, Colombia.

The variable of interest to optimize was the net present value (NPV). For each scenario, the physicochemical parameters such as chemical oxygen demand (COD), total nitrogen (TN), and phosphorus in the obtained effluent were measured.

Table 4. Preparation instructions for the nutrient solution added to the experiments.

Molecular Formula	Reactive	Concentration
Phosphorus stock solution (PSS): Transfer 100 mL to 1 L of medium		mg/L
KH2PO4	Monopotassium phosphate (Vizda industrial Co., limited, Shanxi, China)	308
K2HPO4	Dipotassium phosphate (Jiangsu Kolod Food Ingredients Co., Ltd., Jiangsu, China)	165
Total phosphorus (mg P/L)		100
Nitrogen stock solution (NSS): Transfer 200 mL to 1 L of medium		mg/L
KNO3	Potassium nitrate (Huaqiang Chemical Group Stock Co., Ltd., Hubei, China)	1443.64
SO4(NH4)2	Ammonium sulphate (Aegle green fertilizer Co., Ltd., Shandong Province, China)	943.405
Total nitrogen (mg N/L)		500
Micronutrients stock solution 1 (MSS1): Transfer 1 mL to 1 L of medium		g/L
H3BO3	Boric acid (Zhengzhou Langkou Chemical Products Co., Ltd., Henan, China)	2.86
MnCl2.4H2O	Chloride manganese (Shandong WorldSun Biological Technology Co., Ltd., Shandong, China)	1.81
ZnSO4.7H2O	Zinc sulfate heptahydrate (Jiangsu Kolod Food Ingredients Co., Ltd., Jiangsu, China)	0.222
(NH4)6Mo7O24.4H2O	Ammonium molybdate (Anhui Sincerely Titanium Industry Co., Ltd., Anhui, China)	0.285
CuSO4.5H2O	Copper sulfate (Jiangsu Kolod Food Ingredients Co., Ltd., Jiangsu, China)	0.079
CoCl2.6H2O	Cobalt chloride (Powder Pack Chem, Maharashtra, India)	0.0404
Micronutrients stock solution 2 (MSS2): Transfer 1 mL to 1 L of medium		g/L
2H2O.EDTA. Na2	EDTA Disodium (Mingray Technology Group Co., Ltd., Hunan, China)	10
KOH	Potassium hydroxide (Guangxi Kunya Biological Technology Co., Ltd., Guangxi, China)	6.2
Micronutrients stock solution 3 (MSS3): Transfer 1 mL to 1 L of medium		g/L
FeSO4.7H2O	Iron sulphate (Hebei Jinchangsheng Chemical Technology Co., Ltd., Hebei, China)	4.98
H2SO4	Sulfuric acid (98%) (Shijiazhuang Xinlongwei Chemical Co., Ltd., Hebei, China)	0.1 mL/L

shows the raw materials for the solutions which correspond to a modification of Bold’s basal medium [18].

Table 5. Wastewater characterization and methodologies implemented.

Parameter	Method	Municipal
Tracked parameters *
Total Kjeldahl Nitrogen (mg/L)	SM 4500 Norg B, SM 4500 NH3 B, C	59.2
Nitrite (mgN-NO2−/L)	SM. (4500 B)	<0.005
Nitrate (mg N-NO3−/L)	Test 1–65 NANOCOLOR 918-65, MACHEREY NAGEL	<1
Ammoniacal Nitrogen (mg/L)	S.M. (4500 NH3 B, C)	50.3
Phosphates (mg P-PO4/L)	S.M. (4500 P E)	5.6
Phosphorus (mg P/L)	S.M. (4500 P B, E)	7.6
COD (mg O2/L)	S.M. (5220 D)	172
pH (U pH)	S.M. (4500-H+ B)	7.4
Suspended solids (mg SST/L)	S.M. (2540 D)	74.3
Other chemical parameters
BOD5 (mg O2/L)	S.M. (5210 B) ASTM D888-18	39.7
Total Hardness (mg CaCO3/L)	S.M. (2340 C)	48.4
Calcium Hardness (mg CaCO3/L)	S.M. (3500 Ca B)	40.6
Conductivity (µS/cm)	S.M. (2510 B)	410
Total Acidity (mg CaCO3/L)	S.M. (2310 B)	21.6
Total Alkalinity (mg CaCO3/L)	S.M. (2320 B)	136.3
Phenols (mg/L)	S.M. (5530 B, D)	<0.05
Detergents (mg SAAM/L)	S.M. (5540 C)	0.5
Fluorides (mg F-/L)	Test 1–42 NANOCOLOR 918142, MACHEREY NAGEL	1.1
Total Solids (mg TS/L)	S.M. (2540 B)	233
Metals
Total Beryllium (mg Be/L)	EPA 200.7	<0.010
Boron (mg B/L)		<0.100
Cadmium (mg Cd/L)		<0.003
Total Calcium (mg Ca/L)		16.3
Total Cobalt (mg Co/L)		<0.05
Copper (mg Cu/L)		<0.05
Tin (mg Sn/L)		<0.050
Strontium (mg Sr/L)		<0.050
Lithium (mg Li/L)		<0.01
Magnesium (mg Mg/L)		2.1
Manganese (mg/L)		0.1
Molybdenum (mg/L)		<0.05
Nickel (mg/L)		<0.02
Silver (mg Ag/L)		<0.05
Lead (mg Pb/L)		<0.01
Potassium (mg K/L)		9.5
Selenium (mg Se/L)		<0.01
Total Silicon (mg Si/L)		7.6
Sodium (mg Na/L)		16.4
Total Titanium (mg Ti/L)		<0.05
Vanadium (mg V/L)		<0.01
Zinc (mg Zn/L)		0.08
Chromium (mg Cr/L)	EPA 3015A	<0.05
Antimony (mg Sb/L)	EPA 3015A-SM 3120B	<0.010
Barium (mg Ba/L)	EPA 3015A-SM 3120B	0.058
Total Mercury (mg/L)	SM 3112 B	<0.001
Arsenic (mg As/L)	SM 3030 K	<0.010
Aluminum (mg Al/L)	SM 3030 B, SM. 3500 Al B	4.7
Total Iron (mg Fe/L)	SM 3030 G, SM 3500 Fe B	1.9

Note: * Correspond to the parameters that were evaluated during the experiment.

shows the characteristics of the municipal wastewater and the methodologies employed for the characterization. The wastewater comes from an anaerobic treatment system with 60 thousand inhabitants.

2.1.2. Selling Price Estimation for Microalgae Production

To determine a price for dry microalgae biomass (MP_i), a theoretical value was determined by comparison with similar biofertilizers found in the market, based on the sum of nitrogen (C_Ni) and phosphorus (C_Pi) concentrations (% wt./wt.) of the biomass, according to Equation (1). Once the nutrient value in the market was established, a potential value for microalgae biomass is determined according to Equation (2).

$$P_{N+P}={\displaystyle \frac{\sum _{i=1}^n\left(\frac{M{P}_i}{C_{N_i}+C_{Pi}}\right)}{n}}$$

(1)

$$B_p=P_{N+P}\times \left(C_{N_i}+C_{P_i}\right)$$

(2)

where:

• $P_{N+P}$ = Average nutrients price in commercial biofertilizers (USD/Kg);
• $C_{N_i}$ = Nitrogen concentration (%wt./wt.);
• $C_{P_i}$ = Phosphorus concentration (%wt./wt.);
• $M{P}_i$ = Market Price (USD/Kg);
• $n$ = Number of biofertilizers employed;
• $B_p$ = Theoretical biomass price (USD/Kg).

Data obtained for commercial biofertilizers are shown in

Table 6. Nitrogen and phosphorus prices for commercial biofertilizers.

Commercial Fertilizer	N (CN) (%wt./wt.)	P (CP) (%wt./wt.)	Market Price (MP) (USD/Kg)	Nutrient Price (PN+P) (USD/Kg)
Biofertilizer A	3.2%	0.0%	7.65	239.08
Biofertilizer B	2.0%	1.1%	9.26	299.65
Biofertilizer C	0.8%	0.8%	3.64	233.62
Biofertilizer D	6.0%	1.4%	17.14	232.42
Average	3%	1%	9.42	251.19

, where the added price of nitrogen and phosphorus is around USD 251.19 per kilogram. This value was employed to estimate a theoretical price for dried biomass obtained in each run. It is important to note that even if nitrogen and phosphorus specific prices were not obtained, there is expected to be a marked tendency in nutrient and phosphorus composition ratios like commercial biofertilizers due to algae metabolisms and compositions around 5% for nitrogen and 1% for phosphorus [19].

Figure 2 shows the projection of selling prices for biofertilizers, calculated as an average of the projections of oil barrel price [20], urea price [21], and the compound annual growth rate (CAGR) reported to the biofertilizer market of 13.30% until 2032 [22]. The slope (m) and intercept (b) required for urea and crude barrel projections from 2025 at constant price were performed based on Equations (3) and (4), where the slope and intercept in the initial period (i.e., 2015) was determined by simple linear regression between 2015 (Period t₀ = 0) and 2025 (Period t_n = 13). Once base projections were estimated, the changes in biofertilizer price for each projection were estimated according to Equation (5).

$${(m/b)i}_{2024}={\displaystyle \frac{{{C_P}_i}_{2024}\ast {(m/b)}_{t_0}}{{{C_P}_i}_{t_0}}}$$

(3)

$$C_{P_{ni}}={m_i}_{2024}{t}_n+{b_i}_{2024}$$

(4)

$${B_p}_{ni}={B_p}_0{\displaystyle \frac{C_{P_{ni}}}{C_{P_{n0il}}}}$$

(5)

• $m/b$ = Regression coefficients (m = slope, b = intercept) according to historical data.
• $C_{P_{ni}}$ = Market price for urea and oil at period n.
• ${B_p}_{ni}$ = Biofertilizer price estimation at period n according to commodity i.

2.1.3. Operational Cost Estimation (OPEX)

The main considerations to estimate the operational costs of the process were the raw material acquisition and transport from China to Colombia, with an initial price of USD 1000/container with a capacity of 32.5 tons and projected according to oil price as described in Equation (5). The chemicals used were potassium hydroxide (KOH), employed in pH conditioning for wastewater and neutralize hydrolyzed biomass, sulfuric acid (H₂SO₄) employed to reduce pH to 2.5 in the biomass hydrolysis process, and nutritive solution added before photobioreactor feedings to ensure minimum micronutrient presence. The energy costs consider the power consumption to pump wastewater, nutrients, hydroxide, and acid solution, the use of aerators for bubbling compressed air to photobioreactors, the centrifuge for algae harvesting, and blenders for wastewater and microalgae homogenization. The microalgae were dried using conventional solar drying systems, which do not require power consumption [23].

Pumps and centrifuge efficiencies (E) were assumed as 85% and power consumption was estimated using Aspen Plus. The water content in wet biomass was assumed to be 30% (vol./vol.), while biomass concentration in effluent was assumed to be 0.001%. The required power for the air compressor was calculated based on the water column height pressure for each photobioreactor (Equation (6)), considering an atmospheric airflow of 4 L/min (Q_air) operating 24 h per day, along with the number of 40 L PBRs required (see Equations (7) and (8)). The required reactor volume was determined based on a wastewater flow rate of 10 L/s (Q_ww), the volume fraction used in each experiment (%WW), a 10% headspace (H_S), and the harvesting time (Ht), as described in Equation (9).

$$P_W={\rho }_{w}gH$$

(6)

$$E_{fan}={\displaystyle \frac{{(P}_W\ast Q_{air}\ast \#PBR)}{E}}$$

(7)

$$\#PBR={\displaystyle \frac{R_v}{40L}}$$

(8)

$$R_v=\left({\displaystyle \frac{Q_{ww}\ast H_t}{W{W}_{\%}\ast (1-HS)}}\right)$$

(9)

• $R_v$ = Reactor volume dimension (m³);
• $E_{fan}$ = Power required for aeration (KwH);
• $P_W$ = Required pressure for aeration (bar);
• ${\rho }_w$ = Density of water (Kg/m³);
• $H$ = Height of column water (m);
• $Q_{air}$ = Air flow (L/min);
• $Q_{ww}$ = Wastewater flow (m³/s);
• $H_t$ = Harvesting time (s);
• $\#PBR$ = Amount of PBR required.

Based on the previous assumptions and projections, an estimation of annual operational costs was calculated for each experiment according to Equation (10), where raw materials (RM) were calculated as the sum of nutritive, acid and hydroxide solutions flows (MC) (transport is included in the cost) according to Equation (11); personal and labor costs (L) were adapted as USD 0.3/Kg dried algae [24], while energy costs (E_g) associated to the pumps (E_p), compressors (E_fan) and centrifuges (E_c) were calculated according to Equation (12); contingency savings were calculated as 5% from total sales (Sv) and laboratory for quality control as a 15% of labor costs (Ql).

$$OPE{X}_n=R{M}_n+E_{g_n}+L_n+S{v}_n+Q{l}_n$$

(10)

$$R{M}_n={\sum }_{k=1}^n\left({\displaystyle \frac{Q_{k_n}\ast M{C}_{k_0}\ast C_{{P_{oil}}_n}}{C_{{P_{oil}}_0}}}\right)$$

(11)

$$E_{t_n}={\sum }_{i=1}^n{\displaystyle \frac{(E_{p_i}+E_{fa{n}_i}+E_{C_i}){\ast E}_{g_0}{\ast C}_{{P_{oil}}_n}}{C_{{P_{oil}}_0}}}$$

(12)

• $R{M}_n$ = Raw materials cost (USD/year);
• $L_n$ = Labor costs at year n (USD/year);
• $M{C}_{k_0}$ = Cost associated with raw material k at year 0 (USD/Kg);
• $Q_{k_n}$ = Flow of raw material k at year n (Kg/year);
• $C_{{P_{oil}}_n}$ = Market price of oil for period n (USD);
• $E_{g_0}$ = Energy costs at period 0 (USD/Kwh);
• $E_T$ = Energy costs per year at period “n” (USD/year).

It is important to mention that this research focusses on the production process for biofertilizer considering harvesting, acid-hydrolysis and drying of microalgae, however, this model doesn’t consider the presentation of the product and the costs associated with each presentation (i.e., reduction of particle size for microalgae powder, addition of chelating agents for suspensions, compressing processes for pellets, among others.) and their respective packaging and business models [25,26]. The value as biofertilizer is planted in terms of nitrogen and phosphorus concentrations for dried biomass; however, it does not contemplate the value associated with the dose by hectare according to the respective presentation of the biofertilizer. Those characteristics should be evaluated in subsequent experiments that evaluate the microalgae as biofertilizer in real crops.

2.1.4. Total Plant Costs and Financial Indexes

The capital expenditures were calculated according to the equipment required for process operation; their acquisition and installation prices ($E{Q}_p$) were obtained using ASPEN PLUS and corrected using Chemical Engineering Plant Cost Index (CEPCI) according to Equation (14) [27]. The land price was fixed at USD 35.71/m², and the required space for plant operation was calculated according to Equation (13), which considers reactor volume (R_v) and PBR surface/volume ratio for 40 L photobioreactors $(PB{R}_{{\displaystyle \frac{S}{V}}}=15.02 m^{-1})$ plus a security factor of 10%.

$$Land=\left(R_v\ast PB{R}_{{\displaystyle \frac{S}{V}}}\ast \left(1+10\%\right)\right)\ast 35.71$$

(13)

$$E{Q}_{pn}=\left({\displaystyle \frac{CEPC{I}_n}{CEPC{I}_{n-1}}}\ast E{Q}_{pn-1}\right)$$

(14)

$$CAPEX=\sum _{i=1}^{k}E{Q}_{pk}+Land$$

(15)

• $CAPEX$ = Capital expenditures required for plant production;
• $PB{R}_{{\displaystyle \frac{S}{V}}}$ = Surface/volume ratio for 40 L photobioreactors with 70.8 cm height;
• $E{Q}_{pn}$ = Equipment cost at year “n”;
• $Land$ = Required area for biomass production and services.

The lifetime considered for equipment was 5 years for sanitary and centrifugal pumps and PBR, and 15 years for storage tanks, air compressors, and flow splitters. It is important to mention that equipment was dimensioned for all cases, and there were no significant differences between acquisition and installation prices, with the exception of the compressor and the photobioreactors that were calculated for each run due to their significant impact on power consumption and equipment cost, respectively.

Once the CAPEX was estimated according to Equation (15), heuristics were used to estimate the costs of all services required for plan function [28], and the overhead, contingencies, and profit(s) (AIU) required for construction contracts (See

Table 7. Typical industrial services for chemical plant facilities according to Peters and Timmerhaus (2003) [28].

Service Facilities	Typical CAPEX Value%
Electric distribution	0.1
Communications	0.2
Raw material storage	0.5
Finished-product storage	1.5
Fire protection system	0.5
Safety installations	0.4
AIU *	25%

Note: * Typical values are 40–50% for chemical plants; however, 25% was assumed due to the low-complexity construction required.

). Equation (16) was employed to estimate the total plant costs for biofertilizer production. Internal return rate (IRR) and net present value (NPV) were estimated for each experiment, having a constant discount rate (TES) equal to 2 times Colombia’s inflation of 5.81% in 2024 [29].

With the results obtained, a response surface model was estimated to identify the best result obtained, which will be selected to perform sensitivity analysis for the most relevant factors identified in the OPEX and CAPEX calculations.

$$P_C=CAPEX+O_s$$

(16)

• $O_s$ = Other typical services contemplated in Table 7;
• $P_C$ = Total plant cost.

2.2. Sensitivity Analysis for Critical Variables

The sensitivity analysis was performed on the best scenario found in previous steps; this analysis was performed by identifying the conditions required to achieve a net present value (NPV) equal to zero for the base case found. Initially, the minimum selling price was supported, followed by the most important factors that influence the operational costs (OPEX), such as raw material costs, labor and energy costs. For capital expenditures, the maximum land price was identified for photobioreactor equipment, the maximum price that could be accepted, and strategies such as maintenance increases to increase photobioreactor lifetime. Concluding, an analysis of the variation of IRR was considered when different flows of wastewater were modeled. Equation (17) describes the methodology employed to estimate the exponential plant cost scaling to identify the required investment per kilogram of biofertilizer.

$$P_{Cn}=P_{c0}\ast {\left({\displaystyle \frac{B{M}_n}{B{M}_0}}\right)}^{0.6}$$

(17)

• $B{M}_0$ = Base case biofertilizer biomass production.

• $B{M}_n$ = New biomass production associated with wastewater flow.

• $P_{c0}$ = Plant cost at base case.
• $P_{cn}$ = Estimated plant cost according to new wastewater flow.

3. Results and Analysis

3.1. Optimum Operational Condition Determination

3.1.1. Plant Effluent Characterization and Selling Price Determination

The results obtained from microalgae cultivation are summarized in

Table 8. Final concentration of nitrogen and phosphorus (mg/L) at harvesting time (days).

Experiment	Ht	N-NO3−	N-NO2−	N-NH3+	NOrg	N	P-PO43−	COD	pH
1	4.5	0.0	6 × 10−3	1.2	6.1	7.3	7 × 10−3	79	8.0
2	6	0.1	9 × 10−3	4.5	5.2	9.8	3.0 × 10−3	168	9.7
3	4.5	0.0	8 × 10−3	1.0	3.1	4.1	1.5 × 10−2	89	8.7
4	4.5	0.0	4 × 10−3	0.4	3.6	4.1	1.9 × 10−2	196	8.8
5	3	0.2	3 × 10−4	0.0	3.9	4.2	0	79	8.4
6	4.5	0.0	4 × 10−3	0.3	3.8	4.1	0	61	8.8
7	4.5	0.0	9 × 10−3	0.6	2.8	3.4	1.5 × 10−2	92	8.7
8	3	0.1	4 × 10−3	0.0	1.5	1.5	1.6 × 10−2	104	8.5
9	3	0.5	6 × 10−4	0.0	2.2	2.7	0	147	8.1
10	6	0.2	5 × 10−3	0.0	3.8	4.0	1.0x−2	95	9.6
11	4.5	0.3	4 × 10−3	0.3	3.7	4.3	0	60	8.8
12	6	0.1	8 × 10−3	3.3	3.3	6.8	1.8x−2	171	9.7
13	3	0.0	4 × 10−3	0.2	3.3	3.5	0	153	8.3
14	4.5	0.3	4 × 10−3	0.3	3.7	4.3	0	64	8.8
15	6	0.1	9 × 10−3	4.2	4.9	9.3	1.3x−2	150	9.6

, employing the same methodological methods as described in Table 5. From this information it is confirmed that, as in previous research [15], Scenedesmus sp. has the potential to quickly remove ammonia, with removal yield from 46 to 100%; while organic nitrogen was hardly consumed, from 7.1% to a maximum of 83.5%, nitrite was highly consumed, with a minimum of 79.9%, with a general nitrogen removal rate from 56.8% to 92.2%, which is coherent with literature with removal rates for ammonia above 73.7% using this strain [30]. Reactive phosphorus was highly consumed, from 85.7% to 100%. Dissolved chemical oxygen demand varied, with removals rates from 12.5% to a maximum of 71.2%, which variations could be related to harvesting time and secretion of organic compounds instead of taking them up, according to Zenebe Yirgu et al. (2020) [31], while pH shows a trend of increasing with harvesting time and is related to CO₂ capture from algae and needs to be controlled according to Rebeca Nordio et al. (2023) to enhance biomass productivity [32].

Biomass production was variable but increased at higher wastewater concentrations, growing from a minimum of 8.7% in experiment 13 to a maximum of 151.9% for experiment 12. Biomass obtained once the inoculum is recirculated is then separated by centrifugation, obtaining the algae cake and the effluent of the plant. Biomass obtained is then hydrolyzed and neutralized to enter the drying process, where there are no significant losses of biomass, which has a final 7% humidity.

Table 9. Potential selling price (USD/Kg) estimated, dried biomass production, and nutrient content.

Experiment	NUT (%Vol/Vol.)	WW (%Vol/Vol.)	Ht (Days)	Initial SST	Final SST	Biomass (Ton/Year)	N (%wt./wt.)	P (%wt./wt.)	Selling Price (Bp)
1	2	70	4.5	405	746	239	7.2	0.5	19.19
2	7	50	6	567	1066	378	5.2	0.6	14.56
3	7	30	4.5	738	1058	406	5.1	0.5	14.00
4	2	30	4.5	744	1089	361	4.4	0.2	11.69
5	4.5	30	3	790	881	315	5.6	0.6	15.53
6	4.5	50	4.5	595	990	336	5.2	0.5	14.07
7	7	70	4.5	366	736	252	7.1	0.6	19.37
8	2	50	3	635	746	241	5.0	0.7	14.25
9	4.5	70	3	374	533	176	6.8	1.1	19.92
10	2	50	6	635	1003	324	5.0	0.5	13.79
11	4.5	50	4.5	595	990	336	5.1	0.5	13.95
12	4.5	70	6	374	942	312	5.5	0.5	15.00
13	7	50	3	577	627	222	4.1	0.6	11.86
14	4.5	50	4.5	595	990	336	5.3	0.5	14.33
15	4.5	30	6	781	1132	405	3.9	0.5	10.84

summarizes the theoretical dried biomass production per year and the potential fertilizer selling price according to the nitrogen and phosphorus concentrations obtained.

3.1.2. Operational Cost Results

The operational costs were calculated and are shown in

Table 10. Summary of annual operational costs and raw material requirements.

Experiment	OPEX (Million USD/Year)	OPEX (USD/Kg)	Nutritive Solution (m3/Year)	KOH Solution (m3/Year)	H2SO4 (m3/Year)
1	0.25	1.05	8220	1.4	107
2	0.46	1.21	40,280	2.2	169
3	0.56	1.38	67,133	2.4	182
4	0.45	1.25	19,181	2.1	162
5	0.40	1.26	43,157	1.9	141
6	0.37	1.10	25,894	2.0	150
7	0.29	1.15	28,771	1.5	113
8	0.25	1.04	11,508	1.4	108
9	0.20	1.14	18,496	1.1	79
10	0.38	1.17	11,508	1.9	145
11	0.37	1.10	25,894	2.0	150
12	0.34	1.08	18,496	1.8	140
13	0.29	1.29	40,280	1.3	100
14	0.37	1.10	25,894	2.0	150
15	0.58	1.44	43,157	2.4	181

. It can be observed that the main contributor to operational costs corresponds to the utilities required for plant production, the compressor being the most relevant power consumption process, with a total energy consumption ranging from 79.22 kWh for experiment 9 to 325.87 kWh for experiment 15. Labor costs were estimated according to the biomass production heuristic of USD 0.3/Kg, which implies an average of 17% of operational costs and represents two technicians by hectare with a salary of USD 7143/year in Colombia. Raw materials were mainly related to the nutritive solution of 66% and sulfuric acid solution of 29%.

As can be observed in Table 10, the results indicate that ensuring an adequate bubbling for microalgal suspension is a critical factor that must be addressed and explored to reduce operational costs, while acid and nutritive solutions must be carefully considered for the process, and alternatives with supplementary materials such as calcium carbonate to stabilize pH should be considered.

3.1.3. Equipment Acquisition, Land Requirements, and Cash Flow Results

The equipment employed for the simulation of processes was sized using ASPEN PLUS, where the total cost of the equipment corrected with CEPCI is summarized in

Table 11. Equipment acquisition and installation costs according to ASPEN PLUS, corrected with CEPCI to 2024.

Equipment	Acquisition (Million USD)	Installation (Million USD)	Total (Million USD)
Pumps	0.09	0.35	0.45
Storage tanks	0.10	0.63	0.73
Centrifuge	0.11	0.13	0.24
Mixers	0.09	0.20	0.28
PBR	3.63	3.63	7.27
Total equipment cost			8.97

(the example corresponds to scenario 9). The most expensive equipment corresponds to the PBR, which corresponds to 87%, and must be replaced every 5 years.

The reactor volume is calculated according to Equation (9). The land required for the plant was estimated according to Equation (13) for each run, and the capital expenditure proportion is reported in Figure 3. The required utilities and economic indices, such as NPV and IRR, were calculated and are shown in

Table 12. Summary capital expenditures and economic indexes.

Experiment	Reactor Volume (m3)	Sales (Million USD)	Land Requirement (Ha)	CAPEX (Million USD)	Raw Utilities (MUSD)	VNA (Million USD)	IRR (%)	Plant Cost (Million USD)
1	6171	4.6	10.2	16.2	1.4	−0.8	11	20.97
2	11,520	5.5	19.0	28.8	0.6	−20.5	0	37.24
3	14,400	5.7	23.8	35.6	−0.1	−33.3	−5	46.00
4	14,400	4.2	23.8	35.6	−1.4	−44.2	-	46.00
5	9600	4.9	15.9	24.3	0.7	−15.5	2	31.40
6	8640	4.7	14.3	22.1	0.8	−12.1	4	28.48
7	6171	4.9	10.2	16.2	1.6	1.0	12	20.97
8	5760	3.4	9.5	15.3	0.7	−7.0	5	19.72
9	4114	3.5	6.8	11.4	1.2	1.5	13	14.71
10	11,520	4.5	19.0	28.8	−0.1	−27.3	−6	37.24
11	8640	4.7	14.3	22.1	0.8	−12.4	3	28.48
12	8229	4.7	13.6	21.1	0.9	−10.3	5	27.23
13	5760	2.6	9.5	15.3	0.1	−12.9	−3	19.72
14	8640	4.8	14.3	22.1	0.9	−11.5	4	28.48
15	19,200	4.4	31.7	46.9	−3.1	−69.4	-	60.61

, concluding that only the operations around 70% wastewater and Ht between 3 and 4.5 days in the reactor could represent economic profits.

Once all NPVs were obtained, a surface response was performed, as shown in Figure 4, to identify the optimum operational conditions. The model obtained is described in Equation (18), and the statistical parameters are described in

Table 13. Regression model coefficients for Scenedesmus sp. growth in municipal wastewater.

Coefficients:	Estimate	Std. Error	t Value	Pr(>|t|)
(Intercept)	−29.358	34.688	−0.846	0.419
WW	1.104	0.772	1.431	0.186
Ht	−7.649	11.503	−0.665	0.523
WWx Ht	0.331	0.0852	3.879	3.74 × 10−3 **
WW2	−0.017	0.007	−2.516	0.033 *
Ht2	−1.831	1.180	−1.552	0.155

Note: Regression coefficients of the fitted model. Significance levels are indicated as follows: * p < 0.05; ** p < 0.01.

, identifying that the volumetric concentration of wastewater directly affects the VNA obtained, while increases in Ht have a negative effect (model p-value ≤ 0.01), and nutritive solution has no significant effects in the model. The most beneficial conditions are those close to 70% (vol./vol.), 4.5% nutritive solution addition (vol./vol.), 28% inoculum recirculation, and Ht between 3 and 4.5 days.

$$\begin{gathered} VNA=-29.358+1.104\times WW-7.649·Ht+0.331\ast \left(WW\ast Ht\right)-0.0167 \\ ·W{W}^2-1.831\ast H{t}^2 \end{gathered}$$

(18)

The regression model evaluation described in

Table 14. Model adjustment parameters for Scenedesmus sp. growth in municipal wastewater.

Statistical Test	Result
Residual standard error:	5.115 on 9 degrees of freedom
Model fitness:	Multiple R-squared: 0.9489, Adjusted R-squared: 0.9205
F-statistic:	33.43 on 5 and 9 degrees of freedom, p-value: 1.496 × 10−5
Shapiro–Wilk normality test:	W = 0.96921, p-value = 0.8462
Studentized Breusch–Pagan Test:	BP (Test Statistic) = 4.2727, p-value = 0.5109

shows a good fit of the data, with an R² of 0.949 and an adjusted R² of 0.921, indicating that the predictors explain approximately 92% of the variability of the response variable. The overall model is significant (F = 33.43, p = 1.496 × 10⁻⁵), suggesting that at least one predictor contributes to explaining the dependent variable. However, at the individual coefficient level, the interaction term (WWxHt, p = 0.00374) and the quadratic term of (WW², p = 0.03296) are the only statistically significant predictors, implying a non-linear relationship and an interaction effect between wastewater and harvesting time. The main effects (WW and Ht) are not significant, suggesting that their individual contributions are less clear when interaction and quadratic terms are included. The normality assumption is met (Shapiro–Wilk p = 0.8462) and homoscedasticity is confirmed (Breusch-Pagan p = 0.5109), indicating that the residuals are well behaved.

3.2. Sensibility Analysis for Optimal Operational Conditions

The best scenario for microalgae growth observed was experiment 9, with a wastewater concentration of 70% (vol./vol.), an inoculum recirculation of 28% (vol./vol.), and a nutrient addition of 4.5% (vol./vol.) with an Ht of 3 days. The most relevant factors identified in OPEX and CAPEX were sensitized to establish possible risks and scenarios that must be carefully addressed for a pilot-scale plant that requires detailed engineering design.

The actual behavior of NPV is shown in Figure 5. It can be observed that the investment is returned around year 14 of operation. The minimum selling price determined was USD 18.71/Kg of dried biomass, against the theoretical value obtained of USD 19.91/Kg, which are values commonly found in the market of biofertilizers but represent a significant barrier for the stability of the process. For labor costs, the initial heuristic was USD 0.3/Kg dried biomass; however, the plant could support USD 1.50/Kg to mitigate the necessity of personnel for intensive cleaning and maintenance procedures associated with the photobioreactor employed.

The land price is also a relevant factor due to its importance in the investment period, and its maximum price against the base case goes from USD 35.71 to USD 51.5/m², while the photobioreactor price and installation should not be greater than USD 79, while the base case has USD 72. Due to the necessity of replacing photobioreactors, the amplification of reposition time was evaluated by increasing from 5% to 20% the maintenance estimation to increase the lifetime to 8 years, which resulted in an improvement in NPV.

The energy price is also a relevant factor for operational costs, and the investment could support an increase from USD 0.1014/kWh to USD 0.49/kWh. The evaluation of integration of photovoltaic energy into the process, or the purification and use of methane produced in anaerobic digestion steps in wastewater treatment facilities, could reduce the energy that must be acquired [33].

Due to the obtained results and the low margin generated, parameters such as wastewater flow were also evaluated to identify production volumes that could improve the IRR and the required investment for kilograms of microalgae production. According to Figure 6, the expected IRR increases with the flow of wastewater treated, reaching a theoretical limit when treating close to 70L/s with the model developed.

4. Discussion

This study demonstrates the effectiveness of Scenedesmus sp. in bioremediation processes for sustainable water treatment technologies. The strain exhibited a strong capacity to remove nitrogen and phosphorus species under different operational scenarios, suggesting its suitability for pilot-scale wastewater treatment systems. Such implementation could reduce environmental, operational, and public health risks associated with eutrophication [34]. Additionally, the application of microalgae-based processes may contribute to lowering retributive taxes on water contamination [35] and create economic benefits through biomass valorization. For instance, Álvarez et al. (2025) reported a 13% reduction in total production costs by valorizing wastewater nutrients via microalgal biofertilizers, thereby improving the life cycle performance of lettuce production [36]

Wastewater effluents provided nutrient concentrations suitable for Scenedesmus sp. growth, with the strain demonstrating resilience even at low nutrient levels. In most scenarios, ammonium, nitrite, nitrate, and phosphorus were rapidly assimilated, while organic nitrogen degraded more slowly due to the higher energy demand for the breakdown of molecules such as urea or glycine [37]. These results suggest that integrating microalgae into Colombian wastewater treatment plants—many of which currently achieve efficiencies below 50% or rely on ammonia conversion into nitrogen gas—could enhance nutrient recovery and valorization [4]. On average, reductions reached 74% for total nitrogen, 97% for phosphorus, and 41% for chemical oxygen demand (COD).

Future improvements could involve exploring other strains such as Chlorella sorokiniana, Spirulina platensis, or consortia with specific bacteria and fungi, as well as genetic enhancements [38]. Additionally, using greenhouse gases generated in anaerobic wastewater systems (CO₂, CH₄, H₂S) as carbon sources may further improve sustainability by reducing emissions and mitigating odors associated with hydrogen sulfide [39,40].

Biomass production is highly dependent on wastewater concentration and inoculum recirculation, which define reactor volume, equipment requirements, and land area. High wastewater fractions reduce capital expenditure and favor long-term profitability. Optimal conditions identified in this study involved ~70% municipal wastewater, 28% inoculum recirculation, 4.5% nutrient supplementation, and a three-day harvesting cycle. Nevertheless, further experiments beyond 70% wastewater fractions and with tighter control of operational parameters (e.g., pH, dissolved oxygen, mixing) are required to confirm the most profitable configuration. In this regard, previous research with Scenedesmus sp. cultivated in synthetic urban wastewater reported that a 75% dilution scenario maximized protein content (49.97%) in 5 days, enhancing the nutritional quality of the biomass and increasing its value as a biofertilizer [41]. This finding aligns with the present results, where operating near 70% wastewater enabled both efficient nutrient removal and biomass of high agronomic potential.

The nutrient composition of dried biomass is a key determinant of its market value, strongly influenced by strain type, nutrient availability, and harvesting regime. However, assigning a reliable market price requires testing under real agricultural conditions. Future studies should therefore assess biofertilizer performance beyond nutrient content, including impacts on soil moisture retention, environmental footprint, nutrient release dynamics, and microbiological activity [42]. Identifying target crops is also important; hydroponic systems such as tomato and lettuce cultivation represent promising applications [43].

Nevertheless, potential risks must be considered. Microalgae can accumulate heavy metals and other pollutants [44], which may compromise the marketability of biofertilizers. On the other hand, this capacity could provide valorization opportunities for specific industries such as mining, paint, or foundries [45]. Risks can be mitigated by periodically refreshing inoculum from controlled environments and by enforcing stricter regulation of wastewater inputs. Compliance with Decree 2667 of 2012 [46] is also essential, which requires a minimum protein content of 5% for commercialization. This condition was successfully met in the present study.

From an operational perspective, energy consumption was identified as a critical driver of OPEX. In photobioreactors (PBRs), air bubbling to maintain suspension accounted for ~89% of total energy use, while labor for maintenance and cleaning also contributed substantially. These costs could be reduced by evaluating alternative reactor configurations, such as raceways, which require less cleaning but may yield lower biomass productivity [46]. The need for nutrient supplementation could be partly addressed by integrating activated sludge from wastewater facilities, reducing disposal burdens [47].

In terms of CAPEX, reactor volume and surface area were the most significant contributors, accounting for 64% of total costs (including PBR installation), followed by land acquisition at 21%. These expenses could be optimized through reactor geometry, improved layout, and the use of more durable photobioreactor materials, thereby extending service life beyond the current depreciation period of five years (See Figure 5). Raceway systems, although less productive, may also provide a cost-effective alternative, given their expected lifetime of up to 30 years.

5. Conclusions

Nutrient valorization through Scenedesmus sp. cultivation represents a technically feasible and financially promising alternative for upgrading wastewater treatment systems. The strain demonstrated high removal efficiencies for nitrogen and phosphorus, enabling compliance with discharge standards while generating biomass suitable for conversion into biofertilizers and other value-added products. These outcomes highlight the potential of microalgae to simultaneously address environmental goals and create economic opportunities.

The main drivers of process performance are wastewater fraction, harvesting strategy, and reactor configuration, which together define both biomass quality and system economics. Although large-scale deployment requires substantial capital investment, financial viability could be enhanced by integrating the technology into existing treatment infrastructures, developing supportive financial schemes, and recognizing its environmental benefits within policy frameworks.

Future research should focus on long-term operational stability, integration of greenhouse gases and activated sludge to reduce costs and emissions, and field validation of biofertilizer applications in specific crops. In this way, microalgae-based treatment could contribute to national sustainability targets, reduce eutrophication risks, and provide a viable pathway for circular resource use in wastewater management.