PRE2022 3 Group9

Implementation and Simulation of Debris Recognition for Autonomous Drum Oil Skimmers

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Group members

Name	Student Number	Email
Eryk Gruszecki	1731483	e.s.gruszecki@student.tue.nl
Mathilda Fogato	1656376	m.fogato@student.tue.nl
Oyku Sanlibayrak	1654519	o.a.sanlibayrak@student.tue.nl
Maud van Bokhoven	1664387	m.m.v.bokhoven@student.tue.nl
Siiri Jokiranta	1614207	s.h.jokiranta@student.tue.nl
Nika Tersteeg	1750828	n.n.q.y.tersteeg@student.tue.nl

Introduction

(Oyku) Oil spills in oceans and other large bodies of water has devastating effects on environment and human health. The toxic chemicals found in crude oil can cause both short-term and long-term damage to marine ecosystems, with effects ranging from physical harm to changes in behavior and reproduction. These impacts can be particularly severe for fish, marine mammals, and birds, which may come into direct contact with the spilled oil or ingest contaminated prey. In addition to the impact on wildlife, oil spills can also have negative consequences for human health. Exposure to the toxic chemicals found in oil can cause respiratory problems, skin irritation, and other health issues, particularly for those involved in oil spill cleanup operations. Coastal communities may also experience economic losses as tourism and fishing industries are affected. Therefore, it is critical that effective measures are taken to prevent and mitigate the damage caused by oil spills. Especially marine oil spills are the most concerning ones. Since oil is a primary source of energy, it's being used daily and worldwide, which causes these spills to happen very often. Most of the oil that is being spilled stays on the surface and it spreads very quickly. Traditional methods of oil spill cleanup, such as the use of booms and skimmers, have been effective in cleaning the oil that is spilled in the ocean, but they are often time-consuming, costly, and require significant human intervention. Clogging due to floating debris is one of the most significant challenges faced by oil skimmers, hindering their efficiency and effectiveness in oil spill cleanup operations. Hence, this research paper aims to explore deeper into a potential solution for addressing the issue of debris clogging in oil skimmers, by equipping the skimmers with a camera, and using neural networks to classify debris images in ocean.

Approach / methodology

(Nika)

The research will focus on the stimulation of the oil spills in oceans and the performance of oil skimmers in cleaning the oil spills. An attempt to simulate an implementation of the suitable camera system to oil skimmers which can lead to a more effective way of cleaning oil spills in the ocean. This research will be conducted mainly by literature research, simulating the oil skimmer and contacting oil skimming companies and finally creating a simulation.

First of all, literature review will be needed to obtain relevant information on oil spills, the impact on the environment, current methods and existing oil skimmers. This information is needed to provide a clear overview and possibly new insights to design the autonomous oil skimmer.

Second, by contacting oil skimming companies another view will be obtained about the effectiveness of their current methods and machines. This will help to understand the perspective of the people in the industry, which will give a deeper understanding on the problems that oil skimmers face.

Third, we have chosen to use an image recognition system for plastics in oceans. By creating a database of images the robot will learn and optimize to minimize false positives and false negatives by learning by recognition plastics in the ocean and the distinction between plastics covered in oil, big chunks of plastics etc.

Fourth, the performance metrics play a big role in designing the robot to evaluate the performance and the effectiveness of the autonomous oil skimmer and compare the measurements with a "regular" oil skimmer. Performance metrics such as speed of cleaning, price/ production costs, lifespan, sustainable battery and the percentage of oil recovery (not sure about this).

Fifth and last, taking all these four aspects together in creating a simulation of the autonomous oil skimmer to obtain an insight how to robot eventually will work in a realistic environment. The simulation will be conducted using Unity in order to vary different conditions and aspects to test and optimize the robot.

Limitations:

- it is hard to design a large database for machine learning

- it is difficult to take every floating object into account. Think about the distinction between a dead fish and a floating piece of wood.

- what are we going to do when we do find a piece of plastic? avoid the plastic or pick it up?

- how are for example vessels going to recognize the robot in the sea or see the robot on a map? Is everyone be able to track the robot?

- How are we going to prove that this is more effective and safe than a regular skimmer, since it is autonomous?

- only using a simulation to prove the effectiveness and quality is weak. further research is needed for confirmation

Reliability and validity:

- internal validity: no influences by other factors

- external validity: generalizable/ results can be applied to other situations

- internal reliability: consistency of results across items within a test

- external reliability; the extent to which a measure varies from one user to another

Analysis technique:

Analyzing in Unity. How are we going to analyze the simulation? What is important to look at? What is our goal?

Reason for investigation -> problem statement

Problem Statement

To answer the question of why focus on the oil spills: the research that is done by R.Manivel and R. Sivakumar in the paper “Boat type oil recovery skimmer” can be a relevant source to understand. In the paper it's stated that oil spilling is a growing problem with increasing ship accidents and oil transporting, which puts the human health in danger. That’s why it’s crucial to take action and do what is best for the environment as soon as possible. While researching the problems that the oil skimmers face while collecting oil, one of the biggest problems that stood out is the debris being clogged in the machine, which causes time and money loss. From the research that is done by the Faculty of Maritime Studies and Transport, it’s observed that the drum oil skimmers face this problem very often. This causes a big disadvantage managerial wise, including issues with time management and efficient allocation of financial resources since losing time being stuck on debris, costs money for the user.

An autonomous drum oil skimmer equipped with plastic recognition software is a new technology that has the potential to avoid debris while effectively cleaning up oil spills. However, the effectiveness of this technology compared to a normal drum oil skimmer in real-world scenarios is unknown. The objective of this project is to evaluate the effectiveness of an autonomous drum oil skimmer with plastic recognition software to avoid debris in the ocean compared to a normal drum oil skimmer in terms of oil recovery, efficiency, and environmental impact.

(Nika)

Oil spills in the ocean can have significant environmental impacts, including damage to marine life and habitats. Current oil spill cleanup technologies, such as normal drum oil skimmers, are effective at collecting oil from the surface of the water. The floating and rotating drum in the water separates the oil from the water. The oil adheres to the drums and consequently wiper blades remove the oil from the drums. Drum oil skimmers is regarded as an efficient oil skimmer since they are lightweight, reliable and efficient . However, the oil skimmer has its limitations. First of all, since the drum oil skimmer is non-autonomous it needs to be under supervision of a human in order to work. Secondly, the oil skimmer may get stuck when pieces of plastics are floating in the water and enter the machine. In that case, the oil skimmer needs maintenance and thus requires a lot of time. When an oil spill occurs, it is important that the oil needs to be picked up quickly, since the oil disperse quickly in water. In addition, when time passes it will get harder and harder to clean the spillage. Therefore, it is important to act and clean as quickly as possible to minimize the damage of the oil spill.

In order to minimize the time, it is needed to minimize the maintenance and the speed of extracting the oil. One possible solution to this problem is to design an autonomous oil skimmer that can operate without human assistance, which results in longer working hours. Furthermore, with the implementation of an searching algorithm that finds the fastest way to pick up the oil. And with the ability to recognize plastics in the ocean using a camera, it will save time in maintenance. In short,

Weekly Tasks

Functionalities

What is an oil spill?

Oil spills can be defined as the leak or spillage of petroleum, which is a mixture of hydrocarbons in a gaseous, liquid or solid form, into the environment. They are common in the sea and have been occurring for a long time, causing life-threatening scenarios. Some oil spills occur naturally due to the activity of earth movements that cause liquid or gas to escape on the surface. However, the most oil spills occurs due to human involvement such as accidents in which tankers release oil, but even intentional spills were made in history to.

Nowadays, shipping and environmental regulations are made to prevent oil spills, but still many oil spills happens. According to ITOPF, which is an company that maintains a database of oil spills from tank vessels, the average number of oil spills from 1970's till 2022 is reduces by almost 90%

Figure 1 shows that the number of medium and large tanker spills is reduced from an average of 78.8 to 5.7 oil spills per year. Furthermore, ITOPF stated that the main causes of oil spills were caused by collisions and groundings.

Oil distributes easily across the water since it is The size and location of an oil spill are important factors in the overall impact. Larger spills require more oil to clean up and the process is expensive. However, it happened that small oil spills incurred greater overall costs than larger spills, due to the lack of equipment and the difficulty of detecting such a small leak. Moreover, the cost of the spill depends on the distance from the coast, as the impact of the spill is greater when the oil hits the coast . Moreover, the type of oil and the concentration of chemicals play a role in the severity of the leak. In short, the impact of an oil spill depends on a lot of factors. The main factors are the size, location and the type of oil, but also the cause the of accidents, the type of oil skimmer, the currents / winds and the effectiveness of the clean-up are important for the total costs.  

Another important aspect is the planning and the decisions that are needed to be made concerning making predictions in the distribution of oil when an oil spill occurs. Computer and mathematical models could provide insights to minimize the risks and impacts and in effectively clean up the oil with for instance a searching algorithm. The size and the location of an oil spill are important factors for the impact. In addition, the type of oil and the concentration of the amount of chemicals plays a role in the seriousness of the leak.

How long does it take to clean oil with a drum skimmer? (Nika)

As it is shown that the amount of medium, 7-700 tons, and large spills, >700 ton has been decreased, but over 80% of spills since 1970 are small oil spills, <700 ton . Since small oil spills occurs often, it is important to look further into these and questioning the impact of such a small oil spill compared to medium or large spills.

(1) The article "Calculation of oil droplet size distribution in ocean spills" by I. Nissanka and P. Yapa reviews the impact of an single droplet of oil in the ocean based on model predictions

As it is said before, using models and simulations could make predictions in the possible risks and according to the I. Nissanka and Y. Yapa, the calculation of an oil Droplet Size Distriubtion (DSD) are essential in model predictions of ocean oil spills. The calculation of a droplet size can be split up into equilibrium models and droplet population dynamic models with both promising promising, but the equilibrium model is preferred since it can be used for field situations. As believed by I.Nissanka and Y.Yapa, modeling this DSD is complex and contains several processes but it is impossible to include all these processes in the model.

Some important equations:

Description	Equation
The maximum stable droplet size surface to breaking waves	VMD /d0 = δ(1 + 10 Oh) ^p We^q
The collision frequency of droplet size class i with an eddy (current) λj	θiλj = NiNλjSi,λj sqrt(u'i^2 + u'λj^2)
The frequency of droplet collision due to turbulent fluctuations	θij = NiNjSij sqrt(u'i^2 +u'j^2)
The coalescence efficiency	Pij = exp(-Tij/ Tau/ij)
Shear rate S by a droplet	Rmax =
Volume of oil into the water when waves break into oil area	V(t) = FsAs(t)h(t)

(2) J. Montewka attempts to design a probabilistic and systematic model to estimate the costs of clean-up operations for the Gulf of Finland in "A probabilistic model estimating oil spill clean-up costs - A case study for the gulf of Finland" .

The author presents a model based on Bayesian Belief Networks that analyses the cleanup costs for the Gulf of Finland in risk framework. A cost model includes utility-, decision-, independent- and conditional variables. In addition, the oil type, spill size, season, wave height, evaporation, effect of booms, time for vessel to arrive, time to collect the oil, oil-combating efficiency, number of vessels, manual and machine costs.

In the first scenario, an oil spill of 5000 tons of medium oil the total clean-up costs: 12.1 M

Second scenario: oil spill of 30000 tons of heave crude oil: 95 M.

Doesn't include socioeconomic, environmental and waste management costs.

(3) "Performance of rotating drum skimmer in oil spill" recovery by A. Hammoud and M. Khali investigates the effectiveness of the oil skimmer and finally predict the oil recovery rate of the device.

"According to the authors, the rate of the maximum oil recovery is dependent on drum rpm and oil viscosity. Oil increases with oil thickness for both low and high viscosity oils. Furthermore, oil recovery rate decreases with wave height for both low and high viscosity oil and increases with current velocity."

An experiment was conducted in which a rotating drum skimmer was tested in a water reservoir spilled with oil. For each test, the oil thickness, which varied between 10 to 40 mm, and the distance between the drum and the surface were measured.

In another similar experiment, the oil recovery rate and the oil recovery efficiency were measured.

The results show the effects between the rotation speed and the recovery rate in crude oil. The main factors to indicate the performance of the drum skimmer are the speed of the rotating drums and the drum diameter. This can be explained as the speed of the rotation increases, the oil recovery rate increases since the drum contact area with the oil increases. In addition, the oil recovery rate also increases when the distance between the oil and the surface decreases.

The same results were seen for diesel oil and SAE 10 w oil

Empirical equation:

f1(Q; d; L; o; h; t; r; m; s) = 0 where Q = the oil recovery rate (m3 /s), d = drum outer diameter (m), L= drum length (m), o = rotational speed (rad/s), h = height of the drum centre above the oil/water interface surface (m), t = oil film thickness (m), r =oil density (kg/m3 ), m = dynamic viscosity of oil (Pas), and s = oil surface tension (N/m).

The oil recovery rate Q= 0:026521 x (w ^1,23 x d1,75 x h0,17 x t 0,1 x L x m0,65) / (r0,21 x s0,44 )m3 /s.

Types of oil skimmers

Oil skimmers come in a variety of types, each with special benefits and restrictions:

Belt skimmers: Belt skimmers are devices that gather oil from the water's surface and move it to a collection container using a revolving belt. They work best when cleaning significant amounts of oil from open water, like harbors or lakes.

Drum skimmers: To collect oil from the water's surface, drum skimmers rotate a drum. They work well in both calm and rough waters, effectively removing both light and heavy oils.

Disc skimmers: Disc skimmers extract oil from a surface using a rotating disc, they are bet used in shallow water.

Weir skimmers: This kind of skimmers collect oil from the surface of water, which is best used in calm waters like ponds.

In this study the drum skimmers are going to be the focus. The reason for that can be described in two main points: One of them is that the drum oil skimmers can operate in open sea and ocean. Since this research focuses on the debris in the open sea, which is a much more complicated environment for the oil skimmer, focusing on a drum-type oil skimmer is a good option. Also, the drum oil skimmer is not stationary. It can move around by itself to collect oil. This is a crucial characteristic that is need to be taken into account while choosing the oil skimmer that this research is going to focus on because our exploration towards the oil skimmer needs to be able to move, in order to detect debris in the ocean by the image recognition in the camera system that is going to be implemented and ignore the debris that might clog the skimmer.

USE Assessment

User

Society

Enterprise

Hardware

The ocean environment presents unique challenges for industrial image recognition systems, as cameras must be able to withstand the harsh conditions of saltwater, strong currents, and changing light conditions.

The hardware used for image processing systems, namely cameras, are distinguished by two groups. The first group being industrial/machine vision (MV) cameras and the second group being network/IP (Internet Protocol) cameras.

(https://www.edge-ai-vision.com/2019/03/camera-selection-how-can-i-find-the-right-camera-for-my-image-processing-system/#:~:text=Cameras%20for%20image%20processing%20systems,in%20combination%20with%20industrial%20cameras.)

Industrial image recognition requires high-resolution cameras capable of capturing clear and detailed images even in low light conditions. High-resolution cameras can capture images up to 4K or 8K, allowing for detailed analysis of underwater images. Remote sensing cameras can capture images from a distance, allowing for long-range monitoring of underwater environments. They are useful for monitoring oceanic currents and weather patterns

In this research, the focus is on a camera system that can work with a drum oil skimmer, which captures images of debris and processes them in real-time to detect and ignore the plastic that could clog the skimmer. In this case, it can be considered to use a machine vision camera with image recognition capabilities.

Weekly Work

Week 4

Eryk: Comparibility, what is the final goal?

A normal drum oil skimmer and an autonomous drum oil skimmer equipped with plastic recognition software are both designed to clean up oil spills in the ocean, but they differ in their effectiveness and capabilities.

A drum oil skimmer is operated by humans and requires a vessel or platform to be mounted on. It uses a large rotating drum that is partially submerged in the water covered in a material that attracts and collects oil to scoop up the oil from the surface of the water. While normal drum oil skimmers are effective at collecting oil, they do not have the ability to distinguish between oil and other materials, such as plastic waste. This means that they can inadvertently collect debris along with the oil, which can reduce their effectiveness and potentially harm marine life.

Drum oil skimmers can be operated using various power sources, including electricity, hydraulic power, or air power. They are typically mounted on a vessel, such as a boat or barge, which is used to transport the collected oil to shore for disposal.

An autonomous drum oil skimmer equipped with plastic recognition software, on the other hand, is designed to operate without human intervention and is equipped with sensors and software that allow it to identify and avoid debris in the water. This type of skimmer uses machine learning algorithms to analyze images captured by its cameras and distinguish between oil and other materials, such as plastic waste. This means that it can effectively clean up oil spills without inadvertently collecting debris, reducing its environmental impact and protecting marine life.

In terms of effectiveness, an autonomous drum oil skimmer equipped with plastic recognition software is generally more effective than a normal drum oil skimmer. This is because it can operate continuously, even in rough seas or adverse weather conditions, and is able to distinguish between oil and other materials in the water, allowing it to focus solely on the oil spill.

Overall, while normal drum oil skimmers are effective at cleaning up oil spills, an autonomous drum oil skimmer equipped with plastic recognition software represents a major advancement in oil spill cleanup technology and has the potential to significantly improve the effectiveness of oil spill response efforts.

Notes from tutor session:

what is the final goal?

How do you plan to acheieve it?

How do you compare our simulation to a real life?

could compare different algorithms

look at the dependencies of the input parameters

Try to make them realistic based on literature

Then compare the times (Very hard to accept this as a proof)

Ask oil skimming companies if they have

Make explicit what the point is of the simulation

Add the storage capacity of the oil skimmer to make it as real as possible

How do you plan to acheieve it?

How do you compare our simulation to a real life?

could compare different algorithms

look at the dependencies of the input parameters

Try to make them realistic based on literature

Then compare the times (Very hard to accept this as a proof)

Ask oil skimming companies if they have

Make explicit what the point is of the simulation

Add the storage capacity of the oil skimmer to make it as real as possible

Week 3

Tasks

Mathilda:

-Requirements for problem

-Unity Simulation

Eryk

-Report Structure on the Wiki

-Netlogo help

   -How we want the skimmer to behave in the environment (how we want it to move)

-Fluid Dynamic behavior

I tried looking at existing Netlogo models to maybe see if there are any that resemble the type of behavior we want. I found two that may provide a lot of help in developing the environment. The first model called "Membrane Formation" models the interaction of lipid groups surrounded by water molecules. Since lipids are essentially oil, the behavior is exactly what is needed. These are the following advantages and disadvantages to this model:

+Interaction with water and oil

+Links large groups of lipid molecules

-Has a shaking behavior

-No possibility to add a disturbance to the system such as an ocean current

The second model found is called "Slime Mold Network". It demonstrates the growth of a certain fungus and how it is able to expand throughout the environment. Here are the advantages and disadvantages to this model:

+Links with other patches

-Starts its own path

-Ever more expanding

+Very small particles

-Makes paths instead of groups

I believe that the combination of these two models could provide us with a dynamic Netlogo environment that could help us in training our simulation to avoid floating debris in the water.

-Unity Physics behavior

Oyku

-Contact with oil skimming companies

-Report Work

Maud

-Netlogo Simulation

Sirii

-Model the oil skimmer in CAD (NX model)

Nika

-Report Work

Week 2:

New problem statement

Oil skimmers, used to collect oil from the surface of the water during oil spills, get easily clogged by floating plastic and debris. By equipping the skimmers with a camera, and by using neural networks to classify images, the skimmers could become fully automated, moving away from debris without the need for human intervention.

Users: Government organizations that focus on the environment.

What do they require:

Approach, milestones and deliverables: start with literature research to see what already exists, identify points that research is still needed and propose a way to combine multiple (existing) technologies to create a working robot.

Who's doing what?:

Matilda: image recognition model with TensorFlow

- Sort training data by size/looks of plastic

- Goal: 100 pictures

- https://www.tensorflow.org/tutorials/images/classification

• DeepPlastic: project in which deep learning and computer vision has been used to identify plastic underwater. Researchers found that, compared to YOLOv4 and YOLOv5, the deep learning model was sufficiently accurate and fast enough to be used in applications like underwater autonomous plastic collectors.
• Another study applied deep learning to estimate the volume of macro-plastics in oceans, proposing a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.
• Vito remote sensing: company using satellites and AI vision for multiple applications, including detection of marine plastic litter.

Nika:

• Research to problem statement
  • Oil skimmers, used to collect oil from the surface of the water during oil spills, get easily clogged by floating plastic and debris. By equipping the skimmers with a camera, and by using neural networks to classify images, the skimmers could become fully automated, moving away from debris without the need for human intervention.

• Definition of oil skimmers: devices that separate oil from water in order for it to be collected for the purposes of recovery of remediation. Skimmers can be installed into two different ways, either floating or fixed/ mounted.

• Different types of oil skimmers.
  • Oleophilic: using an element to which the oil adheres. The oil is wiped from the oleophilic surface and collected in a tank
    • Drum skimmers: wiper blades remove the oil from the rotating drums, depositing it into the collection trough where it is pumped to a storage location
    • Brush skimmers: stiffness and density of the bristles impacts the amount and type of oil they can recover.
    • Disc skimmers: Capable of recovering high volumes of oil with very little water
    • Belt skimmers: a belt that attracts the oil which is then scraped clean and collected in a tank. Effectively remove all kinds of floating oil
  • Non-Oleophilic:
    • Weir skimmers: oil flows into the central hopper where it's pumped to storage. Used for rivers, lakes, etc.

• Limitations to oil skimmers
  • Trash may block the skimmer to become stuck: when a piece of plastic become stuck in the machine, which could block the machine
  • Effectiveness of different oils: some oil skimmers are more effective for rivers or oceans, but the effectiveness also depends on the type of oil for different oil skimmers.
  • Skimming direction: because most oil skimmers don't use a camera but only sensors it could be hard to find the oil and therefore to do its job

• Existing robots
  • 1. Unmanned floating waste collecting robot
    • Collecting floating trash in oceans using a robot hand and collecting it using a belt, which is connected to the bin.
    • Assist humans in removing trash, so it is not autonomous.
    • We could work further on this design to improve the robot by making it autonomous. We could use machine learning that detects plastics in the ocean and therefor collects the plastics or avoid the plastics to solve the problem that the oil skimmer become stuck. In addition, by making the robot autonomous it is possible that the robot works more hours and thus be more effective to collect oil since it disperse quickly in the water.

• Questions for next time:
  • We have to decide which type of oil skimmer we focus on.
  • Research how the oil skimmer can become stuck.
  • Provide research to distinguish our prototype with existing robots, how and what could we improve to make it more effective and solve problems
  • How can we make sure that the robot detects the plastics by implementing machine learning?
  • What are the effects for the user by designing an autonomous oil skimmer?
  • What are the effects for the environment / fish?

Eryk:

Since the previous meeting with the tutors, it was made clear that our group should focus on solving a very small issue within a large topic. We want to add to the current research about a topic, which got us thinking about the issue of oil spills in the ocean. Within this topic, we chose to focus on implementing an image recognition algorithm that helps to avoid solid debris on the ocean surface for autonomous oil skimmers.

Initial Research:

https://www.oilskim.com/blog/the-better-belt-skimmer-solving-the-issues-customers-experience-with-traditional-belt-skimmers#:~:text=%E2%80%9CThe%20oil%20skimmer%20belt%20is%20not%20picking%20up%20enough%20oil.&text=Since%20the%20belt%20only%20operates,leading%20to%20poor%20oil%20removal.

Performing a simple google search about the problems concerning oil skimmers, it is quite evident that oil skimmers require a lot of maintenance due to debris becoming stuck in the mechanism. From OilSkim.com, "since the belt only operates on a small section of the tank or pit, debris can build up and form a dam of sorts, preventing oil from reaching the belt. This adversely affects the skimmer’s efficiency, leading to poor oil removal." The skimmer belt often jumps off track due to this debris, which leads to the high maintenance efforts.

In addition to this, the only way to perform maintenance on the oil skimmer is by removing it from the water "...or the tank may need to be drained, which incurs additional downtime and expenses". Stated by Manufacturing.com, " An operator is responsible for ... preventing the lip from being blocked by floating debris... Because overflow weirs require constant supervision, they are not an efficient separation method." Therefore this already creates an incentive for multiple societal entities such as oil companies, oil skimming companies, governments and wildlife conservationists to name a few.

https://www.manufacturing.net/home/whitepaper/13222228/the-challenges-of-removing-surface-freefloating-oil

The efficiency of oil skimmers is calculated using the recovery efficiency definition. The Recover Efficiency percentage (RE%) is defined as the ratio between the volume of oil recovered and the volume of total fluid recovered by the skimmer. Apparently, the RE in ideal conditions ranges between 50% and 85-90%, however we can say that in real conditions it will hardly exceed 50%.

https://sedosr.com/the-problem-of-mechanical-recovery-efficiency-i/

Then the question arises, why would this active avoidance system be better than an additional system that passively sorts the larger debris before collecting oil. Something as simple as a mesh guard has been seen on oil skimmer before however do create problems. Mesh is usually used in these oil collection systems as a drainage system. The way that it works is that as water flows through the mesh, the oil adheres to the mesh as the water just drains away. The oil that collects on the mesh then needs to be scraped manually. Therefore having a mesh to collect debris would actually inhibit the collection of oil from the water surface. Any other way could result in turbulent surface currents which fragment the oil and spread it out more.

https://www.ukpandi.com/media/files/imports/13108/articles/8435-tip-5-use-of-skimmers-in-oil-pollution-response.pdf

The need for our system is present and is reasonable for the duration of our project.

References

Oyku:

• Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution: https://www.mdpi.com/2077-1312/10/7/925 (page 13)

- We can divide the oil skimmers into 2 categories: oleophilic skimmers(such as disc skimmers) and weir skimmer.

- Authors recommended that disc skimmer should be used for marine pollution (with oils such as gasoline)

- But, disc skimmers are not sensitive to small and solid contaminants such as plastic, so it can just collect them too --> We can focus on implementing our camera system to these kind of skimmers but is it good for the disc skimmer to avoid small solid plastics instead of collecting them unpurposely?

- " The most significant disadvantage of disc skimmers is the weak efficiency in persistent oils such as crude oil and the sensitivity to ropes and river grasses due to constriction and discs"

- I came to the conclusion that most of the oil that is spilled in oceans are marine oil spills.

• Use of Skimmers in Oil Pollution Response:https://www.ukpandi.com/media/files/imports/13108/articles/8435-tip-5-use-of-skimmers-in-oil-pollution-response.pdf

- Limitations of oil recovery: adverse weather conditions, oil viscositiy and effects of currents and waves (nothing is written on this research about plastics)

• Question/concern: I am not sure if it makes sense to develope a camera system which ignores the plastics in the ocean in order to skim the oil in the ocean faster and in a more smooth process. Does it make sense to ignore the plastics on the way?

• Some research about what is oil pollution, where does it come from and what happens to it (we can use these info. in introduction when we are explaining our problem) : https://www.bigblueoceancleanup.org/oil-pollution

- What is it? There are many different types of oils. Because of their wide range of different characteristics, they can have different effects on the ocean, since they have compositional differences.

- Where does it come from? Oils are primary sources of energy and they are used worldwide everyday. That's why daily oil spills also increased a lot. Especially Marine oil spills are the ones that are most concerning. These spills usually occur because of human errors such as equipment failure or illegal dumping.

- What happens to the oil that is spilled? Most of the oil that is spilled stays on the surface of the ocean, and it spreads rapidly by bforming a thinner and thinner layer at the surface of the sea.

- How does it affect humans ? Health: As the oil chemicals are being found in the ocean and inside the fish, as humans being fish eaters it is possible that contaminated fish consumption may have significant consequences in public health.

Economy: " With a high proportion of the world’s population living by and having dependence on the ocean for income, resources and food, the impacts of oil spills are of significant concern socioeconomically. Damage to the environment from oil impacts tourism, industrial and localised fisheries. "

Siiri:

Two options for the prototype:

1.    Full prototype that can move around on the surface of water and collect oil

2.    Simple prototype that only has a camera for testing image recognition

Parts needed for the (full) prototype:

Electronics:

·       Arduino Uno (Arduino Uno R3 - A000066 (tinytronics.nl))

·       Wires, breadboard, resistors etc

·       Battery + battery holder

·       Camera (OV7670 CMOS Camera Module - OV7670 (tinytronics.nl))

·       Pump (for skimming oil) + hose

·       Servo motor S3003 Servo - S3003 (tinytronics.nl)

Other parts:

·       Robot body (3D print)

·       Rotor (Bitcraze Propeller Pack for Crazyflie 2.X - 2x4 pieces - SEEED-110990162 (tinytronics.nl))

·       Pulleys + belt for attaching the rotor (GT2 Pulley - 16 teeth - 5mm axle - GT2PULLEY16T5MM (tinytronics.nl) , GT2 Timing Belt - 6mm - 110mm - Closed - GT2-BELT-110MM-CLOSED (tinytronics.nl))

Things needed for testing the prototype:

·       Bucket of water

·       Plastic

How to use a camera with Arduino (circuit and Arduino code):

How to Use OV7670 Camera Module with Arduino​ Uno (circuitdigest.com)

Week 1

Topic: We want to do something more prototype based

Everyone must come up with sources of research for the Deep Sea Passive collection robot

Problem statement and objectives: We want to design an autonomous robot for deep sea garbage collection.

Maud: I found the following sources, which, based on their abstract, are about under water waste collection or surface waste collection. When I have read them I will add a summary.

• An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning. DOI: 10.1109/TITS.2022.3190278
  • This article discusses a method for surface garbage cleaning robots to find a good route to collect all the garbage in an environment that contains obstacles. It is assumed that it is known where all the garbage and obstacles are located.
  • For the path planning, first the order is determined in which the robot will visit all the pieces of garbage and then the route is determined.
    • To determine the order in which the robot will visit the garbage, the problem is modeled as a Traveling Salesman Problem. However, instead of the Euclidian distance, that does not take the obstacles into account, the authors used a heuristically guided FM* based distance (FM* stands for fast marching, I don't know exactly what FM* distance is, but it makes sure that the path goes around the obstacles, creates a smooth path and is fast to compute. FM* is similar to Dijkstra, so it computes the shortest distance form one point to the other, but it does not uses a partial differential equation to estimate the distance).
    • For the route of the robot, it is not always possible to chose the shortest path, because the robot needs to keep enough distance to the obstacles to not accidentally bump into them if they move and the robot might not be able to follow all paths, for example because it is not able to make a curve smaller than a certain diameter (the angle is to small). To make sure that the robot does not need to make to steep curves, a Gaussian filter was applied. The bigger the sigma in this filter, the bigger the curves and the further the robot stayed away from the obstacles. (There are formula's in the article about exactly how they applied the Gaussian filter, but I could not follow exactly what they were doing).
  • During the execution of the plan, the robot can get new information about the location and movement vector of the garbage it is currently heading for. If the garbage has shifted due to the current, the robot uses a trained neural network to determine its new route. For as far as I understand, the location of the obstacles and possible movement of the obstacles is not taken into account. The system does not re-compute the order in which it collects the garbage, even if the new situation as a different optimal collection order.
  • There are a lot of formulas describing what they do, but they often do not make it clearer. If we need them we can look at them later.
• A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot. https://doi.org/10.1631/FITEE.2100473
  • This article discusses an algorithm for detecting and classifying garbage underwater and determining its location compared to the robot.
  • This detection needs to happen fast and in real time, because the robots environment will be constantly changing.
  • The researchers use the YOLOv4 network, which is a one stage neural network that takes in the image and returns both the class and location of the objects in the image. The researchers pruned to reduce the number of calculations needed.
  • The network was trained on a data set, where it had to distinguish nets, plastic bags and stones. The data consisted of images from multiple sides and in multiple conditions made in a swimming pool. For the robot to perform in the real world, it needs to be able to detect more different kinds of garbage and also things like fish and under water plants. More realistic images could also pose a problem for this data set.
  • Pruning is used to significantly increase the detection speed. Pruning is achieved by not calculating the channels with the smallest contributions. This method works now, because there are very few categories, however, for a more realistic model, this would not work anymore.
• Open-Frame Underwater Robot Based on Vector Propeller Control. DOI: 10.1109/NetCIT54147.2021.00044
• DAMONA: A Multi-robot System for Collection of Waste in Ocean and Sea. https://doi.org/10.1007/978-981-16-8721-1_15/
• Design of water surface collection robot based on deep sea cage culture. DOI: 10.1088/1742-6596/2229/1/012005

Research technology:

• https://www.itopf.org/knowledge-resources/documents-guides/tip-05-use-of-skimmers-in-oil-pollution-response/
  • There are different kinds of skimmers. Some are only useful for oleophilic oils, oils that stick to certain surfaces, where the oil sticks to a part of the skimmer and is scraped off and collected. Other skimmers use different methods to suck up the oil, similar to a vacuum cleaner or using gravity. Oleophilic skimmers are sometimes quite resistant to debris, dependent on the model, but non oleophilic skimmers all can be clogged by medium and large debris, and only some are not clogged by small debris.
  • Problems encountered by skimmers:
    • Rough water
    • The oil is spread over a large surface, low encounter rate. Booms can help concentrate the oil
    • Debris
      • Debris screens get blocked by oil or debris
    • The skimmers are not selective enough, causing the storage to be filled up quick with a lot of water as well
    • The oil has a to high viscosity

Nika:

• Review of Underwater Ship Hull Cleaning Technologies | SpringerLink (tue.nl)
  • This paper presents a comprehensive review and analysis of ship hull cleaning technologies. Various cleaning methods and devices applied to dry-dock cleaning and underwater cleaning are introduced in detail,
  • Using the analysis of these technologies, we could take the positives and negatives into account in our research.
• Analysis of a novel autonomous underwater robot for biofouling prevention and inspection in fish farms | IEEE Conference Publication | IEEE Xplore (tue.nl)
  • Biofouling is a challenge for finfish farming as it can impact cage stability and fish health. Amongst others, current strategies against biofouling rely heavily on removal of biofouling using in-situ pressure cleaning of nets. The cleaning waste is released into the water where it can impact the health of the cultured fish
  • We can take the health of fish into account when designing the robot

Siiri:

• Plastic Waste is Exponentially Filling our Oceans, but where are the Robots? | IEEE Conference Publication | IEEE Xplore

• Robotic Detection of Marine Litter Using Deep Visual Detection Models | IEEE Conference Publication | IEEE Xplore

• Deep learning-based waste detection in natural and urban environments - ScienceDirect

References

Manivel, R., & Sivakumar, R. (2020). Boat type oil recovery skimmer. Materials Today: Proceedings, 21, 470–473. https://doi.org/10.1016/j.matpr.2019.06.632

International Conference on Advanced Logistics and Transport. (2013). https://doi.org/10.1109/icalt31205.2013