A novel design and application of anti-biofouling system of buoy sensor based on sampling measuring method

The stability and reliability of sensors significantly influence the quality of the ocean buoy data. The long-time submersion of sensors increases the risk of biofouling, leading to a decline in data quality and malfunctions. To resolve this problem, this paper presents an automatic seawater sampling and drainage system based on the formation principles and characteristics of biofilms. The sensor is exposed to seawater only during the measurement cycle, minimizing the contact time with seawater. This disrupts the conditions for the formation and growth of marine microorganisms. A comparative experiment was conducted at a shore station by using different methods, and the measurement data were analyzed. The study demonstrated the applicability and efficiency of the proposed method compared to traditional methods in measuring temperature, salinity, and chlorophyll. In conclusion, we point out the disadvantages of the method and emphasize future research.


Introduction
The ocean data buoy is an important observation equipment for obtaining in-situ environmental parameters of the ocean over the years.However, biofouling poses a significant challenge for submerged sensors, leading to a reduction in accuracy, diminished reliability, and a shortened sensor lifespan [1~8].Figure 1 illustrates the biofouling on the sensor occurring during the vigorous growth stage of marine organisms [9~11].
To address this challenge, researchers have developed three types of methods for anti-biofouling investigations: physical methods, chemical methods, and biological methods [4,12,13].Physical anti-biofouling methods involve mechanisms such as wipe brushes and ultrasonic and ultraviolet technologies.The wipe brush cleans the sensor surface at regular intervals.However, this method is inadequate for several reasons.Firstly, it has higher requirements for the installation of the brush mechanism, making it unsuitable for sensors with complex shapes.Secondly, the wiping mechanism can lose effectiveness due to marine corrosives.As for the application of ultrasonic and ultraviolet technologies, high-power consumption is often an ill-posed problem [2,4,12,14,15].Chemical and biological anti-biofouling methods are frequently employed in marine environments to weaken marine organisms through the release of toxic substances or the application of non-stick coating materials, typically based on silicone materials or fluorinated polymers.However, due to the complexity of the biofouling process, the method's applicability requires further validation.Additionally, it is essential to acknowledge that this approach may lead to pollution in the marine environment and seawater samples [10,[16][17][18][19][20][21].Consequently, there is a crucial need for research into effective methods to prevent biofouling on sensors mounted on buoys.This paper offers a fresh perspective on the issue of buoy sensor biofouling in underwater environments.

Design principle
Research indicates that the process of biofilm formation can be divided into four stages: the formation of a conditioning film, biofilm, soft macrofouling, and hard macrofouling.The initial two stages are reversible, generally lasting from several minutes to hours.It is considered the key stage for preventing the growth and formation of biofouling [21][22][23][24][25].
Based on this point, this paper presents a novel method, where the sensor is periodically immersed in seawater samples rather than being continuously submerged underwater.The sensor is deployed in a system designed to sample, drain, and measure automatically; it is immersed in the seawater only during the brief measurement time.By disrupting the continuous seawater immersion conditions for biofouling, this method enables the maintenance of sensor effectiveness.
As illustrated in Figure 2, the system includes several key components, such as a measurement chamber, a pure water tanker, and a filtration system.Two pumps are controlled by a controller.Additionally, connecting lines for sampling and draining seawater are included in the system.The workflow of this system is as follows: Sampling: The controller turns on submersible pump P1 and opens valve V1 when the system is running.P1 pumps seawater samples from the ocean into the measurement chamber where the sensor is deployed.Once the sample reaches the predetermined level, the controller turns off P1 and closes valve V1.
Measuring: The sensor begins measuring for 1 to 2 minutes.After completing the measurements, the controller opens valve V5 to drain the seawater sample from the measurement chamber.
Cleaning by filter seawater: After the measurement chamber was empty, the controller turned on the submersible pump P2 and opened valve V2 in the filter system.The purified seawater was injected into the measurement chamber, flushing the measuring chamber and sensor.When the flushing process is complete, a measurement process is completed, and the system proceeds to the next cycle.
Cleaning by pure seawater: The controller opens valve V3 in the pipeline connected to the pure water tank every 24 hours, which allows pure water to flow, facilitating the cleaning of the sensor and the measurement chamber.The periodic cleaning process helps keep the system and sensor clean over time.
Chemical protection: The pump P2 is turned on, and valve V4 is opened.The cleaning process is completed.At the same time, CuSO4 solution is injected into the chamber.This sterilizes the sensor.Figure 3. Experiment system on the shore.

Comparing experiments
Figure 3 shows the comparative experiment conducted at coastal stations in Qingdao Zhongyuan, spanning from May 9th to June 9th, 2020, totaling 31 days.In this experiment, three sensors of the CLW2-CAR model, manufactured by JFE Advantech Co., Ltd., were simultaneously tested in three different ways.The technical specifications of the sensor are detailed in Table 1.Table 1.Specification of the ACLW2-CAR.

Parameters
Measuring principles Range Precision Temperature Thermal resistor -5-45℃ ±0.02℃ Chlorophyll Fluorescence scattering 0-400 μg/l (±1%FS) (0~200 μg/l) Turbidity Infrared backscattering 0~1000 FTU ±0.3 FTU (2%) The control group, abbreviated as 'CS', deploys the CS sensor, which is immersed in seawater for the entire experiment.It is cleaned twice daily at 8:00 AM and 6:00 PM, with its measurements considered as the true values.Experimental Group 1, abbreviated as 'ES1', deploys the ES1 sensor in the measuring chamber without undergoing any wiping by the sensor's brush throughout the entire experimental process.The measurement cycle is set at 12 minutes.Experimental Group 2, abbreviated as 'ES2', deploys the ES2 sensor submerged in seawater and subjected to brushing by the sensor's wipe throughout the entire experimental process.The measurement cycle is 12 minutes.

Comparison of fouling
Figure 4 displays the biofouling comparison among the three sensor groups over the course of the experiment.Figures 4a and 4b show the fouling degree of the optical window's surface for ES1 and ES2 over the 31 days, respectively.Figure 4c provides a direct comparison between ES1 and ES2 after 31 days.It can be seen that, despite frequent brush wipes throughout the 30-day experiment, ES2 experienced severe biofouling in the Yellow Sea in May.In contrast, ES1's optical window surface remained unaffected even without regular brush wiping.However, it can be observed from Figures 6c and 6d that, compared with ES1, the chlorophyll deviation between ES2 and CS increases as time progresses.In Figures 7b, 7c, and 7d, it is evident that the deviation of turbidity between ES1 and ES2 tends to decrease from the 1st day to the 31st day, with ES1 outperforming ES2.Data trends suggest that the error deviation from the true value of ES1 and ES2 grows larger from the 1st day to the 31st day for turbidity.However, even better results are achieved when using our method.

Data analysis.
To provide a more detailed illustration of the experiment, Tables 2, 3, and 4 present the data of different sensor groups, where σ is the average relative error between ES1 or ES2 and CS, and r is the correlation coefficient.2, the σ for ES1 temperature exhibits minimal variation, ranging from 0.264% to 0.333%.For ES2, the σ values fluctuate between 0.211% and 0.534%.Both ES1 and ES2 temperature r are consistently greater than 0.99.However, it is notable that ES2's σ increases and r decreases noticeably after 16 days.
As indicated in Table 3, Figure 8, and Figure 10, the σ for chlorophyll in ES1 ranges from 0.443% to 0.710%, with r consistently exceeding 0.930.However, for ES2, the σ increases, and r decreases after day 8, suggesting a less favorable performance in comparison.
As observed in Table 4, Figures 8 and 11, regarding turbidity, both the σ for ES1 and ES2 increased over time, with the r o f ES2 e xpe rie nc ing a si gni fi c an t de c re ase af te r 11 d ay s.E S1 performed better than ES2 in terms of turbidity measurements.
The picture above shows that ES1 is still very clean.Marine biofouling did not pollute it during the 31-day experiment.The measurement error for temperature and chlorophyll is minimal, indicating excellent reliability in ES1.We compare the proposed method with traditional brush wipe methods.
In summary, the features above indicate that ES1 remained clean and did not exhibit significant biofouling throughout the 31-day experiment.The measurement errors for temperature and chlorophyll were minimal, emphasizing the reliability of ES1.
The results demonstrate that the anti-biofouling system and method are both feasible and effective, making them applicable in real marine environments.However, it's important to note that the method has limitations in turbidity measurement.This is due to the accumulation of sediment particles in sampled water at the bottom of the measurement chamber, leading to cumulative errors.In response to this issue, the installation of a filter in the inlet pipe of the sampling line is recommended to eliminate potential errors.
It should be specifically pointed out that the design of the anti-biofouling system in this paper should be based on the measuring principles of the sensor.If the system is designed for optical sensors, various considerations, such as the structure and material of the measuring chamber, need to be taken into account due to factors like reflection and scattering.

Conclusions
In conclusion, we propose a novel anti-biofouling method and the design of an anti-biofouling system for buoy sensors.Through a shore comparison experiment and data analysis, the results show that the periodic sampling and cleaning method is more effective compared to traditional methods in the m e a s u re m e n t o f t e m pe r a t u r e a n d c h l o r o ph y l l p a r a m e te r s .I t i s i m po r t a n t t o n o te t h a t t h e re a r e limitations in its application, such as the need for optimization according to different sensor types.Despite the limitations of specific sensors, these findings are valuable.Future research should consider system optimization more carefully, including water filter and chamber design.
IOP Publishing doi:10.1088/1742-6596/2770/1/0120083This innovative approach changes from the traditional continuous measurement mode in seawater to a periodic and shorter duration.

Figure 2 .
Figure 2. System architecture diagram.Figure 3. Experiment system on the shore.

3. 2
Measurement data comparison 3.2.1 Comparison of data.The measurement curves drawn from the experimental data of temperature, chlorophyll, and turbidity on days 1, 11, 21, and 31 are illustrated in Figures 5, 6, and 7, respectively.

Figure 7 .
Figure 7. Graph of turbidity.As shown in Figures5, 6, and 7, it is evident that the measuring curves of ES1 and ES2 are highly consistent with CS, especially at the beginning of the experiment.The temperature curves of ES1 and ES2 match CS for temperature.However, it can be observed from Figures6c and 6dthat, compared with ES1, the chlorophyll deviation between ES2 and CS increases as time progresses.In Figures7b, 7c, and 7d, it is evident that the deviation of turbidity between ES1 and ES2 tends to decrease from the 1st day to the 31st day, with ES1 outperforming ES2.Data trends suggest that the error deviation from the true value of ES1 and ES2 grows larger from the 1st day to the 31st day for turbidity.However, even better results are achieved when using our method.

Figure 10 .
Figure 10.Variation of correlation coefficient (r) of chlorophyll within 31 days.

Figure 11 .
Figure 11.Variation of correlation coefficient (r) of turbidity within 31 days.

Table 2 .
Statistics of the temperature.

Table 3 .
Statistics of the chlorophyll.

Table 4 .
Statistics of the turbidity.