Economic assessment of real-time biofouling monitoring using SpectroMarine in a 100,000 m3/day SWRO plant in the gulf region

Biofouling is a significant operational challenge in seawater reverse osmosis (SWRO) desalination, particularly in biologically active environments like the Arabian Gulf. This study assesses the operational and economic impacts of implementing SpectroMarine, an autonomous real-time monitoring system, in a 100,000 m³/day SWRO facility. SpectroMarine leverages in-situ fluorescence and UV-visible absorbance measurements to detect early-stage biological activity in feedwater, enabling predictive maintenance and proactive fouling control. An economic model was constructed using literature-based operational baselines, including membrane lifespan, cleaning frequency, specific energy consumption, chemical dosing, and downtime. Implementation of SpectroMarine is projected to reduce energy consumption by 3%, cleaning-in-place (CIP) frequency by 50%, membrane replacement costs by 20%, and pretreatment chemical usage by 25%. Furthermore, unplanned downtime may be reduced by up to 50%. The model estimates annual savings of approximately 2.89 million SAR, with a payback period of less than 2 months under Gulf-specific operating conditions. The presented results are based on a literature-derived economic model incorporating sensitivity analysis, and no site-specific field validation has been conducted at this stage.

1 Introduction

Seawater reverse osmosis (SWRO) has become the leading desalination technology worldwide, particularly in arid coastal regions such as the Arabian Gulf, where rapid urbanization, industrial expansion, and limited freshwater resources have intensified reliance on desalination. Despite significant advancements in membrane technology and pretreatment systems, biofouling remains a persistent and costly operational challenge.

Biofouling, defined as the accumulation of microorganisms and their extracellular polymeric substances (EPS) on membrane surfaces, results in increased feed channel pressure drop, reduced permeate flux, elevated specific energy consumption (SEC), and accelerated membrane degradation. Severe biofouling events can trigger frequent chemical cleaning-in-place (CIP) operations, emergency shutdowns, and unplanned maintenance, thus reducing plant availability and increasing operational costs.

In the Arabian Gulf, biofouling issues are particularly pronounced due to shallow seawater depths, elevated temperatures (often exceeding 35 °C), and high concentrations of organic matter and nutrients (Abushaban et al., 2020; Hoek et al., 2022). Reported TOC concentrations typically range from 0.5 to 3.9 mg/L, with values of 1.52–2.41 mg/L along the Saudi coastline (Al-Jeshi and Mabrouk, 2006). Additionally, microbial counts in Gulf waters have been reported at 12,100–333,000 cells/mL (Abushaban et al., 2020), confirming the elevated biofouling risk profile.

Operational case studies have documented up to 12–15 unplanned shutdowns per year in some large SWRO plants in the Gulf region, each leading to production losses exceeding 8–12 million SAR annually (Kim et al., 2020), (Vrouwenvelder and van Loosdrecht, 2010). Additionally, membrane cleaning frequencies have increased from standard intervals of 3–6 months to monthly or even biweekly cycles (Voutchkov, 2018).

Conventional monitoring parameters, such as the silt density index (SDI) and turbidity, offer limited predictive capabilities against early-stage microbial fouling (Chong, 2008). These parameters often respond only after significant biofouling has developed, limiting opportunities for preventive intervention.

Real-time water quality monitoring platforms using optical sensing technologies—particularly fluorescence and UV-visible absorbance spectroscopy—have emerged as promising tools for early detection of biofouling potential (Hoek et al., 2022; Mahmoud et al., 2025). Among these, SpectroMarine offers an autonomous, lab-grade solution that continuously monitors biological risk indicators in feedwater, enabling predictive interventions before performance declines occur.

Given these challenges, this study aims to economically evaluate the deployment of the SpectroMarine system in a 100,000 m³/day SWRO facility operating under Arabian Gulf conditions, quantifying its potential impact on operational reliability, membrane longevity, and cost savings.

2 Materials and methods

2.1 Plant configuration and operating conditions

The study examines a 100,000 m³/day SWRO desalination plant located on the Arabian Gulf coast, operating a single-pass RO system with conventional pretreatment (coagulation, dual media filtration, cartridge filtration).

Operating parameters:

• Seawater temperature: 28 °C–35 °C

• SDI: 3.0–5.5

• Cleaning frequency: Monthly

• Membrane lifetime: 5 years

2.2 Biofouling baseline and operational metrics

Baseline operational data were compiled from published studies shown in Table 1, vendor data, and case reports:

Table 1

Table 1. Baseline operational data.

2.3 SpectroMarine monitoring system

SpectroMarine is an autonomous submersible unit providing real-time biological risk assessment through measurement of:

• Fluorescent dissolved organic matter (fDOM)

• Protein-like and humic-like substances

• UV-visible absorbance spectra

Based on (Mahmoud et al., 2025), SpectroMarine integrates excitation–emission matrix (EEM) fluorescence and UV–Vis absorbance (200–750 nm, detection limit <0.1 mg/L DOC equivalent). The system consists of a submersible optical sensor, onboard data logger, and IoT-enabled telemetry. It is typically installed downstream of cartridge filtration with a 1 L/min bypass flow. Maintenance involves monthly optical window cleaning and biannual calibration. Unlike SDI or manual ATP assays, SpectroMarine enables continuous, high-frequency microbial risk assessment and predictive alerts.

2.4 Economic model

2.4.1 Economic model equations

1. Annual energy cost

$\mathrm{E}=\mathrm{Q}\times 365\times \mathrm{SEC}\times {\mathrm{C}}_{\mathrm{k}}\text{Wh}$

where E (SAR/yr), Q (m³/day), SEC (kWh/m³), C_kWh (SAR/kWh).

2. Annual CIP cost

$\text{CIP}\_\text{total}=\mathrm{N}\_\text{CIP}\times \text{Cost}\_\text{CIP}$

3. Annual chemical dosing cost

$\mathrm{C}\_\text{chem}=\mathrm{Q}\times 365\times \mathrm{C}\_{\mathrm{m}}^3$

4. Annual membrane replacement cost

$\mathrm{M}=\left(\mathrm{N}\_\text{mem}\times \text{Cost}\_\text{mem}\right)/\mathrm{L}\_\text{mem}$

5. Downtime loss in production revenue

$\mathrm{D}=\mathrm{H}\times \mathrm{Q}\_\text{hr}\times \mathrm{P}\_{\mathrm{m}}^3,\text{with\hspace{0.17em}}\mathrm{Q}\_\text{hr}=\mathrm{Q}/24$

6. Total annual cost savings

$\text{Savings}\_\text{total}=\text{Savings}\_\mathrm{E}+\text{Savings}\_\text{CIP}+\text{Savings}\_\text{chem}+\text{Savings}\_\mathrm{M}+\text{Savings}\_\mathrm{D}$

7. Payback period (months)

$\text{Payback}=\left(\text{Capex\hspace{0.17em}}/\text{\hspace{0.17em}Savings}\_\text{net}\right)\times 12$

8. Net Present Value (NPV)

$\text{NPV}=\mathrm{\Sigma }\left[\text{Savings}\_\text{net\hspace{0.17em}}/\left(1+\mathrm{r}\right){}^{\mathrm{t}}\right]–\text{\hspace{0.17em}Capex},\text{for\hspace{0.17em}}t=1\dots n$

Assumptions: 3% SEC reduction; 50% CIP reduction; 25% chemical reduction; 20% membrane lifetime extension (supported by (Vrouwenvelder and van Loosdrecht, 2010)); 50% downtime reduction. Electricity tariff used in baseline energy cost is 0.20 SAR/kWh (disclosed for transparency).

The model compares:

• Baseline operation without real-time monitoring

• Intervention with SpectroMarine deployment

Assumed operational improvements:

• 3% reduction in energy consumption

• 50% reduction in CIP frequency

• 25% reduction in chemical dosing

• 20% extension of membrane lifetime

• 50% reduction in unplanned downtime

The assumption of a 50% reduction in CIP events is derived from field observations in Gulf-based SWRO plants where real-time biofouling monitoring reduced cleaning frequency by 40%–55% ((Veolia Water Technologies, 2017)). Similarly, the 20% membrane life extension assumption is supported by (Vrouwenvelder and van Loosdrecht, 2010), who demonstrated reduced pressure drop and slower biofilm accumulation under optimized monitoring and intervention regimes. We acknowledge that these values represent best-case scenarios and emphasize them within a sensitivity analysis (±10%).

2.5 Model assumptions and limitations

Sensitivity analysis was performed assuming ±10% variation in operational savings. Results should be interpreted as indicative estimates.

2.6 Validation considerations

To enhance the robustness of SpectroMarine’s real-time measurements, it is recommended that future field validation campaigns be conducted. Comparative studies measuring Adenosine Triphosphate (ATP) concentrations and Heterotrophic Plate Counts (HPC) alongside SpectroMarine optical readings can provide direct microbial activity confirmation.

Future validation is proposed via a pilot study covering at least two seasonal periods, including ≥20 feedwater samples. Each sample will be analyzed for ATP concentration, HPC counts, and potentially flow cytometry, in parallel with SpectroMarine optical readings. Quantitative correlation (R², RMSE) will be assessed, and operational alarm thresholds will be adjusted based on these results. This validation plan ensures direct microbial confirmation of SpectroMarine signals and minimizes false positives (Abushaban et al., 2020). Additionally, to further refine operational alerts, it is recommended that trend-based analysis be combined with machine learning algorithms to dynamically adjust thresholds based on seasonal water quality variations, minimizing false positive rates.

2.7 SpectroMarine operational costs

Annual operation and maintenance (O&M) costs are estimated at 100,000 SAR.

3 Results

The economic assessment revealed substantial operational savings across all analyzed categories upon the integration of the SpectroMarine real-time monitoring system into the 100,000 m³/day SWRO facility. The key outcomes are summarized below in Table 2.

Table 2

Table 2. Summary of projected operational savings.

Energy costs, representing a major operational expense, are projected to decrease by approximately 3% due to improved system optimization and early intervention before critical biofouling development. This translates to annual savings of approximately 985,500 SAR, based on a baseline energy cost of 32.85 million SAR.

Chemical cleaning-in-place (CIP) operations, traditionally performed on a monthly basis, are expected to reduce by 50% in frequency with SpectroMarine predictive alerts. The corresponding annual cost reduction is estimated at 600,000 SAR, considering a baseline of 12 CIP events per year at 100,000 SAR each.

Membrane replacement costs, another major contributor to lifecycle expenses, are projected to decrease by 20% due to the extended operational life of membranes under lower fouling conditions. This equates to an additional saving of 600,000 SAR per year, based on a baseline annualized membrane cost of three million SAR.

Pretreatment chemical consumption is anticipated to decline by 25% through dynamic dosing strategies informed by SpectroMarine’s continuous microbial risk assessment. This optimization results in annual savings of approximately 456,250 SAR compared to the baseline chemical cost of 1.825 million SAR.

Unplanned downtime, which not only affects production but also leads to revenue losses, could be reduced by up to 50%, generating savings of around 250,020 SAR annually based on historical downtime data from Gulf-based SWRO plants.

The cumulative annual savings from all categories amount to approximately 2.89 million SAR, demonstrating the significant financial and operational benefits associated with real-time biofouling monitoring.

3.1 Sensitivity analysis

To evaluate the robustness of the projected economic benefits, a sensitivity analysis was conducted by varying the assumed operational improvements by ±10% summarized in table 3. This analysis accounts for possible deviations due to site-specific conditions, seasonal variability, or differences in system response.

Table 3

Table 3. Summary of sensitivity analysis results.

Under the pessimistic scenario, assuming a 10% reduction in the expected savings, the total annual savings would decrease to approximately 2.60 million SAR. Conversely, under the optimistic scenario, with a 10% increase in performance, the savings could reach 3.18 million SAR.

Even in the conservative case, where operational benefits are 10% lower than expected, the payback period would still remain well below 2 months, reinforcing the financial viability of integrating real-time biofouling monitoring systems like SpectroMarine.

This analysis highlights that the economic case for SpectroMarine is resilient to reasonable uncertainties in system performance.

3.2 Payback period analysis

Based on the estimated total annual savings of 2.89 million SAR and an assumed initial investment cost of 400,000 SAR, the calculated payback period is approximately 1.66 months as shown in table 4.

Table 4

Table 4. Payback period.

While the initial payback calculation indicates a rapid recovery period, it should be recognized that ongoing operational expenses for calibration, maintenance, and data management may slightly extend the total payback period over multiple years.

When annual operation and maintenance (O&M) costs of 100,000 SAR are included, the net savings are reduced to 2,791,770 SAR/year, yielding a payback period of ≈1.7 months (slightly longer than the initial 1.66 months). A 5-year Net Present Value (NPV) analysis at a 5% discount rate results in an NPV of ≈11.69 million SAR after subtracting the 400,000 SAR initial cost. Table 5 summarizes sensitivity of NPV to discount rates (3%–7%).

Table 5

Table 5. Five-year Net Present Value (NPV) sensitivity analysis for different discount rates.

When annual O&M costs (100,000 SAR) are considered, the net annual savings amount to 2.79 million SAR, corresponding to a payback period of ≈1.7 months. A longer-term Net Present Value (NPV) analysis over 5 years shows that the investment remains economically attractive, with NPV ranging between 10.96 and 12.45 million SAR depending on the applied discount rate (3%–7%). Table 5 summarizes the sensitivity results.

4 Discussion and conclusion

Deploying real-time monitoring systems like SpectroMarine can significantly mitigate biofouling impacts in SWRO plants operating in high-risk environments like the Arabian Gulf. SpectroMarine’s continuous monitoring enables early detection of biofouling indicators, supports predictive maintenance, and reduces operational disruptions.

It is important to note that seasonal fluctuations in seawater quality, such as temperature shifts and organic loading spikes, may affect SpectroMarine’s detection accuracy and thus influence the operational savings achieved.

While the initial payback calculation indicates a rapid recovery period, it should be recognized that ongoing operational expenses for calibration, maintenance, and data management may slightly extend the total payback period over multiple years.

The savings associated with downtime reduction assume immediate recovery of lost production volume upon plant restart; actual savings may vary depending on operational ramp-up protocols.

It should also be noted that membrane characteristics (e.g., surface hydrophilicity, roughness) may affect the early fouling signals detected by SpectroMarine. Future studies should assess system performance across different membrane types such as polyamide thin-film composites from various manufacturers.

Compared to conventional monitoring techniques such as manual SDI measurements or periodic ATP assays, SpectroMarine offers the advantage of continuous, real-time assessment with immediate operator alerts, thus enabling faster preventive interventions.

These findings reinforce the critical importance of integrating real-time water quality protection systems to safeguard membrane integrity, optimize performance, and minimize lifecycle costs under challenging operational conditions.

While this study focuses on Gulf conditions (warm, nutrient-rich seawater), the methodology is transferable to other regions. For example, Mediterranean plants experience lower biofouling loads (TOC ∼0.7–1.2 mg/L, (Al-Jeshi and Mabrouk, 2006), implying smaller but still tangible cost savings. Similarly, multi-pass RO plants may realize greater chemical savings due to their higher pretreatment dosing. These contextual notes broaden the applicability of our findings beyond the Gulf while clarifying that local calibration is required.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

AM: Visualization, Investigation, Resources, Data curation, Project administration, Writing – review and editing, Supervision, Writing – original draft, Methodology, Conceptualization. AA: Project administration, Conceptualization, Supervision, Investigation, Writing – review and editing. SA: Investigation, Validation, Writing – review and editing, Visualization, Methodology.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

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Abbreviations

E, Annual energy cost (SAR/year); Q, Plant daily production capacity (m³/day); SEC, Specific Energy Consumption (kWh/m³); C_kWh, Cost of electricity (SAR/kWh); CIP_total, Total annual cost of chemical cleaning-in-place (SAR/year); N_CIP, Number of CIP events per year; Cost_CIP, Cost per CIP event (SAR); C_chem, Total annual chemical dosing cost (SAR/year); C_m³, Chemical cost per cubic meter of produced water (SAR/m³); M, Annualized membrane replacement cost (SAR/year); N_mem, Number of membrane elements in operation; Cost_mem, Cost per membrane element (SAR); L_mem, Membrane lifetime (years); D, Annual downtime loss in production revenue (SAR/year); H, Total downtime hours per year (hours/year); Q_hr, Hourly plant production rate (m³/hour), calculated as Q/24.

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