PRE2019 3 Group5

Group members

Name	Student ID	Department
Ana Maria Risnoveanu	1230444	Electrical Engineering
Stacey Elshove	1279998	Psychology and Technology
Petru Radulescu	1371320	Psychology and Technology
Yiqin Hou	1281135	Electrical Engineering
Tobias Hilpert	1281070	Chemical Engineering

Problem Statement

Today the technology for recycling already exists and is put to use in most first world countries, but at times it can be inefficient because of mistakes made by all of us when throwing the trash, either unwillingly or because of our ignorance. So to mitigate this and help create a better planet for the future generation, we have come up with the idea of a smart trash can, that will use a huge array of sensors and cameras to determine whether the waste should go in the glass, plastic or paper bin. This product will be small enough to fit in our homes and, in the future, it could be upscaled to service a full residence, like a block of flats.

Objectives

• Design an affordable smart trashcan that fits into our homes.

• Use machine learning and AI to sort the trash into their corresponding compartment.

• Find suitable technologies to achieve this.

USE Aspects

User

The user group we wish to target is households, we would like to improve recycling at the source and make it easier for users. To better understand the users' needs, a survey was carried out. Survey

A Data analysis was performed under Data analysis and the requirements needed can be found in Product specifications.

A second survey is currently being created to finalize the design based on the users needs and help determine our products success.

Society

Automatizing recycling will shift the topic from a widely recognized theme to an issue to be solved by experts. Recycling campaigns will likely get far less attention than they do now and did in the recent past. This might slightly counteract the intended effect of increasing recycling. These effects seem unlikely to outweigh the advantages gained in the efficiency of sorting, however. There will also be a shift of who holds the responsibility for recycling. This responsibility is currently largely held by the consumer who is expected to throw garbage into the designated bins. If this process is largely automated the responsibility will either be carried by a few enterprises or, shared among society as a whole if the adoption of smart cans is facilitated through government owned utilities.

Enterprise

Recycling firms are struggling to be profitable in large part due to expensive and inefficient separation of waste feed-streams. An automated bin will judge much more accurately which products are likely to be recyclable than the average consumer. This improved recycling prior to waste collection would drastically lower the cost of recycling and increase profitability of these plants, therefore encouraging new investments into such businesses. The increase of separated waste arriving at recycling plants rather than landfills will also allow for these plants to invoke the economics of scaling up to further increase profitability, making such businesses even more attractive. It thus likely that widely accessible smart bins will grow the recycling industry.

Task distribution

Week1

Name[total hours of work]	Tasks and hours
Ana Maria Risnoveanu [8h]	learn wikitext[1h], Material sensors [5h] , Approach-Milestones-Deliverables [1h], look over internet for other ideas [1h]
Stacey Elshove[10.5h]	learn wikitext[2h], Reasons whyand summary[4h], What already exists and summary[4.5h]
Petru Radulescu[6h]	Leran wikitext[2h]; Objectives[1h]; Problem statement[1h]; Sorting systems[2h]...
Yiqin Hou[13h]	Papers about X-ray imaging/Object recognition searched and studied[7h]; summary of X-ray and coputer vision[2h]; Learn wikitext and make template for the group wiki[3.5h]; User aspect[0.5h]
Tobias Hilpert[14h]	Literature study into waste sorting[4h], Research into Infrared spectroscopy[8h], Learned wikitext[2h], Wrote Enterprise and Society aspect[0.5h]

Week2

Subgroups

User survey form: Ana Maria Risnoveanu, Stacey Elshove, Yiqin Hou.

Recycling center: Petru Radulescu, Tobias Hilpert.

Name[total hours of work]	Tasks and hours
Ana Maria Risnoveanu [h]	Two group meetings[3h]
Stacey Elshove[6h]	Two group meetings[3h], Prepare questions, finalize survey and distribute survey[1.5h], learn wikitext- links within a page and to external pages and add links[0.5h], edit wiki-User, Data analysis [1h]
Petru Radulescu[8h]	Two group meeting[3h], How the Dutch recycle[4], editing wiki[1h]
Yiqin Hou[8.5h]	Two group meetings[3h], Prepare quetions and distribute the user form[1h], edit wiki[0.5h]，think and sketch possible bin designs (the draft will not be uploaded to Wiki)[3h]
Tobias Hilpert[8h]	Two group meetings[3h], Research into Dutch recycling plants[2h], research into recycling mechanisms[2h], research into household waste composition[1h]

Week3

Name[total hours of work]	Tasks and hours
Ana Maria Risnoveanu [7.5h]	Three group meetings[7h], part of feature analysis for the trash from the survey Basket categories,Size[30min];
Stacey Elshove[8.5h]	Two group meetings[3.5h], Data analysis on Stata[4.5h], Survey 2 questions [0.5h]
Petru Radulescu[h]	Three group meeting[7h], Fun to recycle[1h],
Yiqin Hou[9h]	Three group meetings[5h], General plan made, possible problems or hazards listed[1h], Analyze erplies of question: "What do you recycle?"[1h], Adjust product specifications according to data analysis results and merge hazards into the form[1h], New sketch of the bin: Sketching[1h]
Tobias Hilpert[6.5h]	Two group meetings[3.5h], Exploratory data analysis in Python [3h]

Week4

Name[total hours of work]	Tasks and hours
Ana Maria Risnoveanu [h]
Stacey Elshove[h]	Two group meetings[h], make paper prototype[h],
Petru Radulescu[h]
Yiqin Hou[h]
Tobias Hilpert[h]

SotA

Reasons why

Too much rubbish

Nowadays there is an over load in rubbish in cities and in rubbish dumps, he main reason for this is that there are many recyclable materials that just get throw away in the normal rubbish. Having smart bins will help reduce this waste and the resources we use to make produces. Sharma and Singha, found that the pile up of rubbish is leading to many diseases and came up up with a solution about a Smart Bin that will alert the collector when the bin is full and they will come a collect it. This will prevent rubbish over load for example, rubbish falling out of bins or piling up next to them because they are full. However this is just a start to our problem as this still does not solve the recycling issue, this is dealt with in the next section.

Easy to recycle

The best way to get people to do things is to make it easy for them to do it. When it is easy to do something people are more inclined to do it than when they would have to put lots of effort in for example to have to take their separate plastics bag to the supermarket to recycle it when they could just add it to the normal rubbish.

Lack of knowledge

Thomas etal, found that many people are unaware of what happens after they throw something away, or the benefits of recycling. Creating more awareness will cause people to understand what their actions will lead to and hopefully impact the way they behave.

Fun to recycle

When something is fun to do as when it easy to do people are more inclined to do it. This can be seen in the Efteling in the Netherlands, they have paper bins which are shaped like people who have their mouth open. People can through paper into their mouths and then they are thanked for recycling (feeding them). This make people more inclined to recycle as now it is fun to throw the paper into his mouth and they also receive positive feedback.

Another idea is to create an accompanying app that can comper your recycling proficiency with your friends or other people from the same city. This could make the process of recycling competitive pushing owners to do it more thoroughly and maybe give potential customers enough incentive to do the final step. Such systems are used in smartwatch products like Fitbit.

What already exists

Bin e is a smart bin that sorts anything you throw away . It sorts the items into plastic, glass, paper and metal. This bin is only for recyclable materials thus people still have to put some thought into whether they can recycle it or not.

As mentioned above the Smart bin, which informs the collectors when it is full. The bin uses Ultrasonic sensors to determine how full the bin is.

How the Dutch recycle

There is a curbside collection service that collects:

• Biodegradable waste in the green bins (almost all cities)
• Paper in yellow bins (almost all cities)
• Plastics in blue bins (major cities)
• Household chemicals, batteries in red bins (most cities)
• Clothing in green clothing containers (most cities)

For example, the cities of Eindhoven, Valkenswaart and Geldrop-Mierlo employ the company “Cure Afvalbeheer” to collect refuse and waste paper and recycle it into new products. Cure takes care of the collection of waste paper and cardboard, plastics, bulky household waste, glass and chemical waste.

On the collection of recyclable waste:

What paper can be recycled:

• Newspaper and fliers
• Copying, writing, drawing paper
• Envelopes
• Gift-wraps
• Egg boxes
• Paper bags and cardboard boxes
• Cardboard and paper packaging

What plastics can be recycled:

• Plastic bags and bread-, pasta and rice bags
• Wraps from magazines and advertising materials
• Plastic trays
• Yoghurt, cream and ice cream pots
• Plastic lids from jars
• Plastic bottles (from drinks, cooking products and sanitary products)
• Shampoo, shower gel, hand soap and bath foam bottles and similar

What glass can be recycled:

• Glass packaging of beverages
• Glassware of food (jars)
• Glass packaging of cosmetics
• Glass packaging of medicines
• Glass deodorant rollers
• Perfume bottles

What textile can be recycled:

• Clothing
• Bath towels
• Sheets, blankets
• Kitchen textiles
• Curtains
• Shoes (paired together)
• Bags, belts and scarves

What chemical waste can be recycled:

• Batteries, low-energy light bulbs, strip lights, liquid drain cleaner, lamp oil, kerosene, pesticides/ herbicides/insecticides
• Medicines, mercury thermometers, injection needles
• Paint, varnish, stain and wood preservation agents, products used when painting such as turpentine, thinner, paint stripper, paintbrush cleaner, paintbrush softener and benzene, mercury switches

The percentage of different wastes collected in the Netherlands:

Recycling firms in the Netherlands

Existing facilities

The market for waste consists of consumers, collectors, processors and disposers. Collectors in the Netherlands are contractors hired by local municipalities or services directly employed by said municipallities. Depending on the size of their waste streams industrial facilities may either employ the local services or hire a private waste collector. This choice is still regulated on the provincial level by the distribution and requirement of permits. Collected waste is then further distributed to processors, who recycle waste streams into new secondary material, and disposers, who make use of landfilling and incineration to remove the unrecyclable waste. There are close to 600 individual recycling facilities and support firms located in the Netherlands recycling wood, metals, plastics, paper, electronics and glass. This large presence of companies lead to an substantial import in recyclable waste into the Netherlands in recent years

Recycling mechanisms

Recycling mechanisms naturally differ largely by the type of waste inside a given waste stream.

Metals

In the Netherlands most ferrous metals are directly sorted from general waste with the help of electromagnets, whereas pure sources of non-ferrous metals, most notably aluminium, are collected separately. Both kinds are then processed in a similar way, first being compressed for transport, then shredded for sorting and more efficient melting. The melted metal is then purified, usually using electrolysis.

Paper

Paper waste is grouped into several different groups depending on the kind of paper and the print on it. These groups range from wood-free white paper without print to wood-containing coloured or heavily printed paper. The recycling mechanism differs for different categories, but usually includes multiple washing, kneading and dewatering steps. The main difference between the categories is the number and placement of deinking steps

Organic

Organic waste is most commonly recycled by the use of Composting, Biofuel Production or Algae Production, the latter being the least common in the Netherlands. Most household waste are categorized as solid food spills and are converted into biogas in anaerobic bioreactors.

Plastics

There are close to 40 plastic recycling plants in the Netherlands. About 1/3 of these facilities accept a wide range of different plastic types, including PET, PP, PS, HIPS, PVC, HDPE, LDPE, ABS, and PMMA. The most common types present in household waste are PET, PP, and PE (both HDPE and LDPE). Most of the other facilities specialize in one more of the these common plastics.

Waste composition

A comparison of the waste composition of both produced and recycled solid waste is given below:

Percentages of waste recycled in the Netherlands, units in kg/person/year

Kind of waste	Amount collected [/person/year]	Amount recycled (percentage) [/person/year]
Biological	144kg	86kg (60%)
Paper	66 kg	52 kg (78%)
Tetrapaks	3,6 kg	1,8 kg (50%)
Glas	28 kg	20 kg (73%)
Plastic packaging	25 kg	14 kg (59%)
Metal cans	7,1	6,7 kg (95%)
Electrical equipment	18 kg	9 kg (49%)
Textiles	15 kg	4,7 kg (31%)
Small chemical disposables	1,4 kg	1,2 kg (86%)

There are notable deficits in the recycling statistics in biological waste, Tetrapaks, plastic waste, electrical equipment and textiles. The drop in percentage point for electrical equipment and plastic packaging can be explained by the complex recycling processes necessary for these materials . Biological waste and Textiles, however, are recycled with relative ease. These deficits are mainly due to improper collection of the waste streams: bio-waste is difficult to separate from general waste and textile collection is difficult for the consumer.

Detection

X-ray

Inspired by security machines, X-ray imaging could be a usefull tool in detcting the type of rubbish. X-ray imaging makes use of the property of X-ray that it attenuates differently accorss difderent materials. For example metal atoms and ions attenuate more X-ray than normal organic tissue, such as fat and protein. Some new X-ray imaging techniques could even determine the chemical structures that form within the materials. With such techiniques and some morden X-ray imaging detectors , the bin can distinguish materials much more accurate than using normal X-ray imaging. It is also possible to distinguish different types of plastics with X-ray imaging, which further increases the recycling sorting process.

Despite its reliable performance in detecting metal and different kinds of organic materials, an X-ray imaging system is too expensive to implement in a household trash bin. Even the cheapest X-ray tubes cost $100 to $500 each, let alone the detectors, power supply and other systems.

Computer vision and image processing

An alternative could be object recognition, by simply mounting a camera in the container and train an AI system to recognize different types of wastes. With the technique of using discriminative parts, the system could be even more precise. For example, it can distinguish the words on the package of the waste. Computer vison could be implemented together with other techniuqes like infrared spectroscopy, to increase overall effectiveness.

Infrared Spectroscopy

Infrared spectroscopy can be classified into different categories based on the wavelengths used. This detection method is both cheap and fast enough for practical applications in waste sorting. Near Infrared (NIR, wavelength 1-1.7µm) Spectroscopy is already deployed for uses in plastic separation. NIR has the ability to excite overtones of molecular vibrations and therefore can give information on the chemical composition of the sample. NIR spectroscopy can also be used for different kinds of waste, such as construction debris . There are clear limitations when analysing black and coloured samples though, as their reflectivity in the NIR spectrum is too low to allow for signal-to-noise ratios high enough for proper identification. To overcome this difficulty NIR spectroscopy can be enhanced with longer wavelengths. It has been shown that combining NIR with Midwave Infrared (MWIR, wavelength 3-12µm) significantly improves the performance of detectors for black and opaque products. This can be extended to multiple spectra in hyperspectral imaging techniques, which probe a wide range of wavelengths even in the visible spectrum for material identification. These systems have also been shown to work at the speeds which would be needed for practical applications. FTIR spectrometers are simple enough devices that handheld versions with sensitivities good enough to separate materials exist. .

Available sensors

There are several sensors available on the market: electro-mechanical, pneumatic, magnetic, inductive, capacitive, photoelectric, ultrasonic. The electromechanical sensor only shows how resistive an object is, which might be important but not sufficient here since two different objects could output the same value . The same is the case with a pneumatic sensor, which just differs in the way it is built . The magnetic sensors are of no use since there is no magnetic field created by rubbish . On the other hand, although it also uses an induced magnetic field, the inductive sensor (also known as inductive proximity sensors) may prove successful in the detection of metallic materials. Unfortunately, domestic trash mostly includes other types of materials and aluminium cans, for instance, are usually recycled in waste. Then, there is little difference between the applications of inductive and capacitive sensors and spending time in choosing between them will only be useful if the team decides on the categories of rubbish. A photoelectric sensor is another proximity sensor, which can only be used to check the absence of an object which leads to the last option on the list: ultrasonic sensors. Although these sensors cannot detect different types of materials, they do offer more information: the distance to an object or the speed of the object; they are reliable and can be used in many applications . Some models of ultrasonic sensors come with detection of some aspects of the material (softness, colour) and therefore they are the best choice among simple and cheap detection methods. In order to know if they are a suitable choice, we must set the desired sensitivity of our system and check the limitations of different ultrasonic sensors.

Sorting systems

For sorting the trash itself the use of a conveyor type mechanism could be employed, such as a roller cylinder system that moves to create the right path to the corresponding bin for the trash.

Data analysis

Planning

The data analysis will be done on the program Stata. First the data will be checked and cleaned. One of the questions (Why is it difficult/easy to recycle?) will need to be analysed qualitatively, the rest can be analysed quantitatively. A factor analysis could be run on the data to help determine what is best for all the users needs. Correlations of variable can give more information about the effects they have on each other.

Summary

The summary shown on the Google survey shows, that most people do want a smart bin. 39% feel sorting their waste takes too much time or effort, this is shows that their is a need for this Smart bin. Around 70% of people would like all their waste collected and around 65% of people are willing to pay extra to have all their waste collected from this is could be said that people prefer when things are easy or done for them. Currently only 65.9% recycle glass and 22% recycle metal, this can show that there is a need for our Smart bin because around 70% of people do think that recycling is important which means they want to recycle but something is holding them back. From the question ,Why is it difficult/easy to recycle?, it can be seen that many people struggle to recycle because it is difficult to find or take it to the bins. If our smart bin would be implemented in many homes maybe more services would be provided to collect these recyclables because of the money that is saved from having to sort waste, this is something that would still need to be researched and investigated.

When asked about what do you recycle, 100% respondents recycle paper, most people recycle plastic and glass. The detailed result is shown in the figure below.

The minorities can be left out not only because few people recycle them, but also due to other reasons. For example, metals are usually mixed with other materials, which cannot be separated easily. Electronics are hard to detect, since they also contain different materials. Chemical wastes are very difficult to detect, and the price of Flase Negative is too high. Clothing can be easily separated manually so it is not necessary to include this type in the smart bin. However, it does not mean only three categories are needed for the bin. One needs to be aware that not all papers can be recycled and there are also different types of plastic. Moreover, glass can be easily distinguished by people, the smart bin would not make the recycling of glass any more efficient. Combining with the information given in How the Dutch recycle, a more proper solution could be that, the bin contains four categories. One is for paper,one for PBD plastic, one for other plastics and one for wastes that cannot be recycled or detected by the bin. It is notable that, paper like cardboards should be excluded by the bin, because they can be easily sorted by people while takes too much space in a bin. The principle is that, the smart bin should not be made to detect everything, but aim to help users distinguish wastes that seem recyclable while actually are not, such as contaminated plastic, photos and milk boxes.

Analysis in Stata

The survey was closed on 19 February 2020. The data was downloaded and put into Stata, it was cleaned and then analysed. The Data analysis was done on Google drive Data analysis and only the main findings are found below.

At a first glance it is clear that there is a moderate correlation between how often people recycle and how important recycling is to them. This could mean that people want to recycle but something is holding them back. There is quite a strong correlation between how easy and how often people recycle. It can also be seen that people find recycling important but not easy to do as there is a very strong correction, this could be the something that is holding them back, it is not easy to recycle and people just may not have the ability or time to do it in the way it is currently being done. People who find it difficult to recycle seem to want our bin more as there is a strong correlation between how much you want the bin and how easy it is for you to recycle.There is a moderate negative correlation between how important it is to recycle and if they feel it takes a long time or too much effort to sort, thus a lot of people find it important and that it doesn’t take up a lot of time or effort. As this is not a strong correction there is still a need for our bin because some people still feel it does take a long time, according to the survey 39% said it takes too much time or effort.

From this we can see there there is a need for our bin and from looking at the responses about our actual product, it is clear that most people are not willing to pay too much for this product (50-100 euros). They would also not want to pay extra or too much extra to have all their waste collected, up to approximately 100 euros. A bin of 30l to 50l is best for the users needs according to their responses.

Exploratory Analysis in Python

The dataset from the survey was also analysed in Python using pandas, matplotlib and the SciKit Learn package to see if other correlations can be discovered. The full code can be viewed in a Jupyter Notebook on the same google drive (Link to download the notebook). While cleaning the data in case of multiple answers for a single question the geometric average was taken to supply a single numeric value.

One notable finding is visualized in the graph below:

It depicts how important respondents preceived recycling vs how big their desire for the smart bin was. Additionally, red coloured dots correspond to respondents who answered that they do not find recycling too cumbersome, whereas blue coloured dot found recycling too difficult. A more opaque dot corresponds to multiple answers for a single value. The size of the dots is different for visualization purposes only and bears no statistical significance.

In the plot it is visible that mainly respondents who did not find recycling important and also found it too difficult have a strong desire for the smart bin. This defies the intuition one might have that respondents who consider recycling important have a high desire for a smart bin.

To further see if these correlations hold when asked how much respondents are willing to pay for the smart bin several linear regressions were preformed with the Seaborn library. The result is visible below:

As visible by the large confidence intervals and nearly horizontal regressions in all four cases no statistically significant correlation could be found.

A further attempt to categorize the data was to perform create a model using the k-nearest-neighbour clustering algorithm. Multiple models were set with a cluster size of 2, 3 and 4 and then fitted to the data. However in all cases the normalized mean square distance from the cluster centers to the attributed data entries was larger than 10 000, clearly showing that these models were unfit for our data-set and had no predictive value.

Approach

Solving one of the recycling problem aspects has a difficult set of objectives: first, the team must evaluate if a smart trash can is a suitable solution or if there are easier ways; the implementation is next in line. Our goal is to first design the bin with: all the necessary instructions, design of the smaller components, draw the circuits, write the code; everything necessary to build the smart bin if we had the resources.

Milestones

A certain milestone of the problem is finding a suitable way, preferably non-contact, to detect the trash that is put inside the bin; choosing the method depends on dividing the type of trash in the right categories, setting certain features of trash that the bin must detect. Next, the method must ensure it takes little time such that the bin can cope with high demand. Another milestone would be to make the bin robust to deviations from the model (it can happen that 2 types of trash are scanned together or it can happen that the queue of trash to be sorted becomes too long). The biggest milestone of the project is to actually finish the development (on paper) at the end of the 2 months.

Week number	Tasks
Week1	Background study
Week2	User survey and factory study
Week3	Preliminary design, make specifications of the product
Week4	Design and make model, chose sensors, decide which part to focus on, study on sensors
Week5	Second survey for feedbacks
Week6	Test sensors/actuators and mount to the bin (if applicable)
Week7	Validation of the design, analyze limitations and prepare for presentation

Deliverables

The output of the project should be a full description, including the methods (such as pseudocode) for implementing the idea. The description should ensure that anyone who will read it, follow the steps and buy the necessary parts can build the smart trash bin. This might change due to the complexity of the project to simply finding the right method to sort the trash by categories.

Development

Hazards

This part has been merged into Product specifications.

Product specifications

The principle is that, the smart bin should not be made to detect everything, but aim to help users distinguish wastes that seem recyclable while actually are not, such as contaminated plastic, photos and milk boxes. The detailed requirements can be found in this file: Requirements

Size

51.2% of responses in the survey claimed the desired size of the bin is 30 litres, and the same percentage have chosen 50 litres (there were more options and therefore the percentage does not add up to 100%). This is a clear preference since about 19.6% of the responses include other sizes: around 5% would choose a smaller size of 10l which is not feasible for our design. The trend is explainable, the most preferred sizes usually feature in the main trash can of the household, usually placed in the kitchen where most mixed waste is disposed of. The result complies with our vision perfectly since that main bin of the household, exchanged with a smart bin would make the most impact on the recycled quantities.

Number of baskets

Since there is limited space in the trash bin, it was decided that certain categories do not need to be recycled by the device, but it would be more effective if they are recycled directly by the user. For example, cardboard is usually very voluminous and easy to categorise; for this reason, it would be more space-efficient if it was not included in the smart bin categories. The survey has shown that 100% of people who responded already recycle it and this is for good reasons: it is easy to save, it is made of light material and most importantly, it already gets collected every 2 weeks (In the Netherlands). In case of glass, only 65.9% of our sample recycle it and this can be due to the fact that glass is a heavy material which must be transported to the collection points: since it is easy to categories in the three types (white, brown and green glass) and is also voluminous, the better solution than a smart bin can be more collection points in the city, which will facilitate the transportation of glass from the house to the designated bins and make use of the willingness of people to recycle it. Electronic, clothes and chemical waste will be left out since there are hazardous materials and must be dealt with carefully (for instance, simply puncturing a battery can cause a fire). This waste is rare and the risk of fire or contamination is too high in case of electronics and chemical waste. About clothes, not that many people recycle them, the numbers show 31%. This might be due to the lack of collection points which are usually in stores. Sometimes clothes are thrown away in waste because they cannot be donated (they have holes or the material is ripped) and people do keep clothing for longer periods. A solution to this is public awareness: as long as people know the need to recycle these categories and know which stores accept them, then the percentage might change on its own. For now, it is not of maximum utility to use the space of a smart bin for rare waste.

Sketching

A new sketching indicating the rough design of the bin is shown below.