WO2024104616A1

WO2024104616A1 - Non-consumable item recycling

Info

Publication number: WO2024104616A1
Application number: PCT/EP2023/050785
Authority: WO
Inventors: Divya Bansal; Padhraig RYAN; Suchandra MANDAL
Original assignee: Eaton Intelligent Power Ltd
Current assignee: Eaton Intelligent Power Ltd
Priority date: 2022-11-17
Filing date: 2023-01-13
Publication date: 2024-05-23
Anticipated expiration: 2025-05-17

Abstract

A method of recycling non-consumable items is described. A lifetime measurement method for at least one non-consumable item is established. Using this lifetime measurement method, an end of life for the non-consumable item according to the lifetime measurement method is determined. When it is determined that the end of life for the non-consumable item is near, a recycling process is initiated. On confirmation that the non-consumable item is to be recycled, recycling options are established for selection. This method is particularly effective for use for the recycling of electrical appliances. A suitable system for implementing the method is also described.

Description

NON-CONSUMABLE ITEM RECYCLING

TECHNICAL FIELD

The present disclosure relates generally to recycling of non-consumable items, such as appliances, and provides methods and apparatus adapted to manage and support the recycling of such non-consumable items, particularly from the home.

BACKGROUND

Waste disposal is a major challenge for all economies. In 2018 alone, 24% of all municipal wastes in EU were landfilled. By 2050 the amount of household waste are predicted to grow by 70% reaching 3.4 billion tons a year, meaning that the increase in waste generation will double the pace of human population growth.

A major element in addressing this issue is more widespread and effective use for recycling. However, current recycling methodologies, particularly for household waste, rely heavily on householder actions. The recycling of large appliances such as dryers, dishwashers, and fridges is a significant part of the overall problem. This is particularly challenging is it imposes both cognitive demands and logistical burden on individuals. It is unusual for householders to have any clear picture of when such large appliances need to be replaced, and recycling sites for large appliances are limited in number and the logistics of the recycling action may be daunting.

Limited information is currently available for householders looking to recycle items. Directory-type applications (such as iRecycle and Recycle Nation) exist, and these allow users to construct their own recycling strategies, but this does little to lower the user burden or to help the user to an optimised recycling approach.

It would be desirable to address these issues in such a way to make the recycling of appliances, particularly large appliances, less burdensome and more effective so that unrecycled waste is reduced and the recycling process is more effective. It is against this background that the disclosure has been devised.

SUMMARY OF THE DISCLOSURE

According to an aspect of the present disclosure there is provided a method of recycling non-consumable items, comprising: establishing a lifetime measurement method for at least one non-consumable item; determining an end of life for the non-consumable item according to the lifetime measurement method; initiating a recycling process determined by proximity to the determined end of life for the non-consumable item; and on confirmation that the non-consumable item is to be recycled, establishing recycling options for selection.

While a particular purpose of aspects of the disclosure is to enable and encourage recycling of non-consumable items, it will be appreciated that in some cases recycling of non-consumable items is not possible, but that on end of life they may create specific issues for the householder - for example, they may have specific end-of-life processing requirements, or they may simply be so bulky that disposal itself is challenging. Here, “recycling” is considered to encompass all end-of-life conditions that require specific handling of a non-consumable item at end of life.

Using this approach, the householder (or other user) is relieved of the burden of determining when recycling is needed, and is appropriately supported through the recycling process. Recycling activity is therefore encouraged.

In embodiments, one or more of the non-consumable items is an electrical appliance, and the lifetime measurement method for the electrical appliance comprises measurement of operational cycles performed by the electrical appliance. The remaining useful lifetime for the electrical appliance may be determined as a number of operational cycles assessed to be remaining in the lifetime of the electrical appliance, and may be determined using a purchase date, a rate of usage and a predetermined operational lifetime for the electrical appliance. Operational cycles may be determined by measuring voltage and/or current in an electrical power supply to the electrical appliance. Voltage and/or current may be measured in an electrical power supply to a building, wherein the measurements of voltage and current are disaggregated to determine whether an operational cycle has been performed by the electrical appliance. The voltage and/or current may be measured in one or more circuit breakers of an electrical power system of the building.

Such an approach is particularly appropriate for the recycling of electrical appliances, in particular for large “white goods” such as refrigerators, freezers, washing machines, dryers and dishwashers. Such electrical appliances are typically not replaced until they break down, and householders typically have little idea when they have reached their working lifetimes. At best, they may know when the appliance was bought - and so may be able to estimate a service life - but they will typically have little idea whether their use of the appliance has been heavy or light, which could lead to significant miscalculation of service lifetime. This approach enables the useful life of an appliance to be assessed accurately and for a recycling (and replacement) decision to be taken at an appropriate time.

In embodiments, the method may comprise identifying a plurality of non-consumable items and by establishing a lifetime measurement and determining an end of life for each of the plurality of non-consumable items. For some of the plurality of non-consumable items, identifying the item may comprise photographing or scanning elements of the physical item and performing a recognition process to identify the non-consumable item. The recognition process may comprise determining a make and/or model of a non-consumable item and interrogating data sources relating to the make and/or model. In identifying the plurality of non-consumable items, a user may be prompted to enter manually some or all data necessary to identify the non-consumable item and/or to establish its end of life. User manual entry may be provided wholly or partly through a structured user interface.

To be particularly effective, this approach can be used for some or all of the recyclable appliances and other non-consumable items in the house - that is, items that have a nontrivial lifetime and which need recycling or other end-of-life processing. This may be limited to items for which recycling itself is a non-trivial process - for example, it may be limited to items that are sufficiently large to require collection, or which have specific end-of-life requirements.

In embodiments, recycling options may be established as a ranked list. In embodiments, recycling options may be established by best cost at scheduled times. In embodiments, on selection of a recycling option, a recycling event is scheduled.

In a second aspect of the present disclosure, there is provided a computer-implemented system for recycling non-consumable items, the system comprising at least one processor and at least one memory, wherein the processor is programmed to: establish a lifetime measurement method for at least one non-consumable item; determine an end of life for the non-consumable item according to the lifetime measurement method; initiate a recycling process determined by proximity to the determined end of life for the non- consumable item; and on confirmation that the non-consumable item is to be recycled, establish recycling options for selection.

In embodiments, the system may be adapted to receive electrical usage data from a building, and to determine from the electrical usage data operational cycles for the non- consumable item, wherein the non-consumable item is an electrical appliance. The system may also be adapted to identify a plurality of non-consumable items, of which a plurality are electrical appliances, and wherein the system is further adapted to disaggregate the electrical data to determine operational cycles for each of the electrical appliances.

The system may further comprise a user interface for identifying non-consumable items and for selecting recycling options.

It will be appreciated that preferred and/or optional features of each aspect of the disclosure may be incorporated alone or in appropriate combination in the other aspects of the disclosure also.

BRIEF DESCRIPTION OF THE DRAWINGS

Examples of the disclosure will now be described with reference to the accompanying drawings, in which:

Figure 1 shows an exemplary context for an embodiment of the disclosure - specifically, Figure 1 shows a household environment situated in a wider infrastructure;

Figure 2 shows at a high level a recycling process according to an embodiment of the disclosure;

Figure 3 shows set-up and initialisation of the process of Figure 2;

Figure 4 shows establishment of appliance lifetimes in the process of Figure 2;

Figure 5 shows establishment of recycling options in the process of Figure 2;

Figure 6 shows management of appliance collection in the process of Figure 2;

Figure 7 shows a simplified schematic view of an electrical circuit that is formed by an electricity distribution apparatus used to distribute a supply of electrical power to a plurality of electrical loads in a building;

Figure 8 shows a schematic view of an exemplary sensor apparatus of the electricity distribution apparatus shown in Figure 7; Figure 9 shows the steps of an exemplary method of controlling the sensor apparatus of

Figure 8 to obtain usage information for electrical appliances; and

Figure 10 shows exemplary sub-steps of the method shown in Figure 9.

DETAILED DESCRIPTION

Embodiments of the disclosure relate to a process for determining and scheduling recycling operations. Figure 1 shows an environment in which such embodiments can be employed. A home environment 1 has an electrical power feed 3 which feeds one or more electrical circuits 10 through one or more circuit breakers 20 - as is described below, in embodiments circuit breakers 20 have sensing and processing capabilities and are used to obtain information about use of electrical loads (separate sensors, possibly at other points in the electrical system, may also be used). There are a plurality of appliances (here illustrated as refrigerator 11 , dryer 12 and dishwasher 13) powered through the electrical circuits 10. An occupier 14 here has a computer 15 and a mobile telephone 16, both of which can connect to a cloud service 17 supporting the process. A recycling facility 18 and a collection service 19 are also in communication with the cloud service.

A process according to the disclosure is illustrated in general form in Figure 2. A first part is to initialize 220 the process to identify recyclable items - in the primary embodiment described below, appliances - in the home environment and to establish how their usage can be measured. A second part is to determine 240 an end-of-life for these items - this may be by predicting a remaining useful lifetime for these appliances. A third part is to establish 260 when a recycling event should occur, and to identify recycling options. A fourth, optional, part is to manage 280 the transfer of the appliance for recycling according to the preferred recycling option.

Implementation of these four parts of the process of Figure 2 will now be described in more detail, part-by-part, with reference to Figures 3 to 6.

Figure 3 shows an initialisation process - in essence, this involves determining which appliances are to be considered for recycling, and to determine how their use can be measured. The first step is to establish 310 an arrangement in which one or more circuit breakers are able to acquire data on appliance usage. The Eaton EMCB (Energy Management Circuit

Breaker) range of products - described, for example, in

:-circuit-breaker/smart-breaker-;

;-sheet-TD003013EN.pdf (version dated

Effective June 2021) is particularly suitable for this purpose. There may be one circuit breaker for a home, or the data may be collected from a plurality of circuit breakers. This data will typically be in the form of energy or power usage by the appliance, in which case both current and voltage information may be required - a description of how such information may be captured is provided further below with reference to Figures 7 to 10. Once this arrangement is established, it is simply used for data collection 320 - the circuit breakers are continually sampled at an appropriate frequency (1kHz may be suitable - but higher and lower sample rates can also be used. The voltage and current information data obtained from the circuit breaker is then disaggregated 330 to determine contributions from individual appliances, each of which will have their own usage pattern (e.g. toaster, microwave, dishwasher - all of which have specific usage signatures, such as particular powers for particular periods of time). The disaggregation of the sampled data into individual appliance contributions has already been achieved by the present applicant and is not the subject of the present application - in addition to the discussion below with reference to Figures 7 to 10, approaches to disaggregation are described in the applicant’s existing patent application number PCT/EP2022/051405, and further teaching on disaggregation is provided in the applicant’s existing patent application number PCT/EP2022/025270. Both of these disclosures are incorporated by reference herein to the extent permitted by applicable law.

The initialisation activity requires the determination of which appliances are to have their usage measured, and how this will be done - this is effectively a separate track to the obtaining of circuit breaker data (which can start at any point). A variety of different approaches can be used to establish appliance data. First of all, the appliances themselves need to be identified - a suitable approach is simply to capture an image of an appliance or of its model number, product name, or barcode. This can be done through approaches already developed by the applicant - for example, as described in the applicant’s existing patent application number PCT/EP2021/077120 which is incorporated by reference herein to the extent permitted by applicable law - to identify objects with the assistance of character recognition to allow for recognition of an appliance type and model. If camera data acquisition 340 is available, it is used 350 to identify appliances - if it is not (or for cases where it is not), then a requirement 360 for manual data acquisition 370 may be identified. This may be used to identify additional appliances, for example through a suitable menu that leads the user through a decision tree to identify specific appliances. Typically some manual data will need to be entered to supplement recognised data (for example, the purchase date of an appliance). Potentially recyclable items are identified 380 and linked with captured and disaggregated data 330 from the circuit breaker, and all relevant data - such as appliance type, brand, date of purchase, electricity usage for the appliance, and usage patterns - are stored 390 in a database.

The next stage is to provide useful lifetime predictions for each of the appliances. Information for this may be obtained 410 from appropriate sources such as manufacturer websites to determine lifetime information - generally in the form of a number of cycles in the useful lifetime of the appliance - by use of web-scraping and natural language processing to find and recognise the information needed. This information is used to predict 420 the remaining useful lifetime (RUL) for each appliance - in addition to the total lifetime obtained in the previous step, use to date and rate of use is determined from purchase date, current date, and current usage patterns in terms of cycles or electricity usage (from circuit breaker information). Appropriate predictive techniques - for example, nonlinear or linear regression, or a deep learning technique - may be employed to make the lifetime prediction (the technique employed will typically be informed by the complexity of the data). The RUL for each appliance is then stored 430 in the database. This value may be stored as time - in which case it may be redetermined at appropriate intervals and modified for changes in usage - or as cycles, in which case it may simply be measured against cycles determined for the appliance from the disaggregated circuit breaker data.

The next stage involves determining that a recycling event is appropriate, and if a recycling event is agreed, determining the options available. There are two ways to trigger a recycling event according to this process. A first is user triggering 510 - a user decision that an item should be recycled (this may be triggered by breakdown of the appliance, or user realisation that a more suitable appliance has become available). The second is lifetime triggering 520 - this involves the RUL decreasing to below a threshold value. Such threshold values may be user determined, or for example, fixed for a particular class of appliances. Once this threshold is reached, the user is prompted 530 to initiate a recycling event. If they decide 535 to do so, the process continues to the next stage, but if not, there may then be reminders (which may continue up to the RUL and beyond - as the appliance may of course still be functioning at the end of the RUL, and the user may for example decide to maintain the appliance in use until it breaks down or requires expensive servicing, for example). At this stage, there may be a behavioural economics intervention 540 - gamification, peer comparison, highlighting of recycling benefits (prevention of landfill, efficient use of resources) and success indicators. The user may of course be prompted to initiate recycling by any of these reminders or interventions.

If a recycling action is triggered, the next step is to identify 550 whether there is a history (or in some cases, a sufficient history) of recycling of that appliance (this may be type of appliance, or specific appliance, depending on whether this in practice affects recycling options). If there is no such history, the next step is to web-scrape 560 to identify the nearest available recycling centres for the relevant items. If there is a history, then this is used 570. In either case, a ranking list is established 580 according to predetermined criteria and weightings. In either case, closeness of location to user and opening hours may be factors used in ranking. Price will typically be a significant factor - if there is no or minimal history, price may be a primary factor, but user reviews will also be a significant factor (possibly even the major factor) if there is a history. In either case, a ranked list of recycling options will result.

It should be noted that this ranking result should improve over time. In time, there should be a significant history for every appliance type, and the results from additional users allow well-tested ranking lists to be developed.

At this stage, the user is provided with a ranked list of recycling options, and the process can simply end at this point with the user taking a recycling decision from this list. Figure 6 shows an optional approach for managing collection as a final part to this overall process. This approach provides guided decision-making for the user, and requires some prepreparation - here this is represented by a database 610 from which transportation costs can be established. This will be informed by agreements with transportation companies with a presence local to the user - this may involve vehicle rental or collection from a recycling transportation service provider. The process uses this information to establish a cost calculation 620 for the recycling event - this will be informed by the number of items to be recycled and their size and weight (which may determine the number of vehicles required, for example). It is possible that this could be integrated with existing or expected recycling events, so that prices could be based on combining the user’s recycling event with the recycling events of other users - so date and time of collection may inform the options, as well as criteria such as distance to the recycling centre. Again, these options may be provided as an ordered list rather than simply one option - this may be provided as an ordering by cost. Another possibility is to show a calendar view - so the user can select a date and time - with the lowest cost option at that date and time presented by the calendar. If the user agrees 630 to a specific option, transport is booked 640 accordingly (if they do not agree, the process simply ends 650 at this point and the request to recycle is cancelled).

Other embodiments of this approach may be employed which do not use power usage determination by a circuit breaker or otherwise - this may apply to non-appliances, such as furniture or mattresses. For such items, the user will need to update the list of items (as before, this may be done by recognition or manually) but will also need to determine an “end-of-life” time to trigger the recycling event. While there is no calculation of remaining useful life for such items, when the recycling event is triggered, the approach is essentially the same as for appliances - the user can also simply trigger a recycling event themselves (as a result of changed circumstances) as for appliances. Consequently, recycling of appliances and non-appliances can be handled through the same overall process, simply with a different route being taken to reaching a triggering event in each case.

As noted in Figure 3, in embodiments of the disclosure electrical sensor apparatus may be used to obtain usage information for electrical appliances. Such sensor apparatus may be embodied in circuit breakers used in the home environment. Sensor apparatus of this kind is described below with reference to Figures 7 to 10, and in more detail in the applicant’s copending patent application PCT/EP2022/051405, which is incorporated by reference herein.

Figure 7 schematically illustrates an example electrical circuit 1 for supplying electrical power to a plurality of electrical loads 2a-c of a building. The electrical circuit 1 features a power line 3 and an electrical distribution apparatus 4, through which the power line 3 is connected to the plurality of electrical loads 2a-c.

The power line 3 provides an electrical power supply to the electrical loads 2a-c of the building and may, for example, take the form of a supply line from a power distribution network or power grid. The electricity distribution apparatus 4 is configured to distribute the electrical power supply to the electrical loads 2a-c and may, for example, take the form of a panel board. The electricity distribution apparatus 4 comprises a plurality of branch circuits 10a-c, arranged in parallel, that connect to the electrical loads 2a-c of the building. For the sake of simplicity, the plurality of branch circuits 10a-c includes a first branch circuit 10a, a second branch circuit 10b, and a third branch circuit 10c, in this example. Furthermore, each branch circuit 10a-c connects to a respective electrical load 2a-c of the building, in this example, as may be formed by a respective electrical appliance.

It shall be appreciated that the example electricity distribution apparatus 4 is not intended to be limiting on the scope of the disclosure though and, in other examples, the electricity distribution apparatus 4 may include any number of branch circuits and each branch circuit may connect to one or more electrical loads formed by respective electrical appliances, for example.

The electricity distribution apparatus 4 is here shown to include a circuit breaker 20 which comprises sensor apparatus for monitoring the power supply to the electrical loads 2a-c connected to the power line 3. Such sensor apparatus may be provided separately from a circuit breaker in principle, but in the embodiments shown here are incorporated within a circuit breaker (the terms “circuit breaker” and “sensor apparatus” are both used below for the same system element in consequence). The sensor apparatus 20 may, for example, take the form of an energy management circuit breaker that further includes a circuit breaker mechanism for selectively interrupting the power supply to the electrical loads 2a- c. In this example, the sensor apparatus 20 is connected to the power line 3, upstream of the branch circuits 10a-c, where the circuit breaker mechanism can act as a main circuit breaker for providing absolute control of the electrical power supply to the electrical loads 2a-c. Other sensor apparatuses may be included in branch circuits in alternative embodiments.

An exemplary sensor apparatus 20 is considered in more detail with reference to Figure 4, which illustrates a non-limiting example of the sensor apparatus 20. As shown in Figure 8, the sensor apparatus 20 may include a sensor module 22, a circuit breaker mechanism 24, a processor or control module 26, a feedback module 28 and a communications module 30. That is, in the described example five functional elements, units or modules are shown. Each of these units or modules may be provided, at least in part, by suitable software running on any suitable computing substrate using conventional or customer processors and memory.

The sensor module 22 is configured to acquire sensor measurements indicative of the power supply to the electrical loads 2a-c. For this purpose, the sensor module 22 may comprise, or connect to, one or more sensors (not shown) configured to measure the current, the temperature, and/or the voltage, of the power supply. For example, the sensor module 22 may connect to any positive integer, N, of sensors. The N sensors may include a temperature sensor for measuring the temperature of the power supply, a current sensor for measuring the current of the power supply, and/or a voltage sensor for measuring the voltage of the power supply. The temperature sensor, the current sensor, and the voltage sensor, may connect to the power line 3 upstream of the branch circuits 10a-c to allow sampling of the power supply to each of the electrical loads 2a-c simultaneously. However, in other examples, sensors may be arranged in each of the branch circuits 10a-c to allow sampling of the power supply to the electrical loads 2a-c individually.

The processor module 26 is configured to perform various data processing tasks associated with the sensor measurements acquired by the sensor module 22, including determining noise thresholds for the respective N sensors of the sensor module 22. In order to determine the noise threshold(s), the processor module 26 may include a data processing module 32, a memory storage module 34, and an event detection module 36, as shown in Figure 8.

The memory storage module 34 comprises a database of sensor measurements acquired from the sensor module 22, including sensor measurements indicative of the power supply during one or more events associated with the operation of the electrical loads 2a-c and sensor measurements indicative of noise measurements during a baseline condition of the electrical circuit 1 (during which there is negligible or substantially no power draw from the electrical loads 2a-c). The sensor measurements determined by the sensor module 22 may be added to the database continuously, or periodically, to establish, or update, the database. In particular, the database of sensor measurements may be updated or established upon receiving further sensor measurements from the sensor module, and/or upon activating any one or more of the N sensors.

The memory storage module 34 may interact with the communications module 30 of the sensor apparatus 20, which may connect to one or more external servers for providing updates, corrections, or additions to the memory storage module 34. For the purpose of receiving and/or storing such data, the memory storage module 34 may take the form of a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium).

The data processing module 32 is configured to operate on sensor measurements for any required purpose, including to determine a noise threshold for each of the N sensors of the sensor module 22.

The processor module 26 also includes an event detection module 36, as shown in Figure 8. The event detection module 36 is configured to analyse the frequency characteristics by determining the power spectral density (PSD) of the power supply, as captured in the acquired sensor measurements. The PSD is a measure of the power content of the power supply versus frequency. The PSD may therefore be determined based on an electric power signal formed by a series of the acquired sensor measurements. For example, a series of sensor measurement may be acquired by the sensor module 22 during an event, forming an electrical power signal indicative of the power supply during the event, and the event detection module 36 may be configured to determine the PSD of the electrical power signal to analyse the frequency characteristics of the power supply. For example, the data points (or sensor measurements) in the database of sensor measurements may be ordered, shaped and/or formatted to readily construct one or more electrical power signals from respective series of sensor measurements. Accordingly, the processor module 26 may be configured to compile a series of sensor measurements into an electrical power signal indicative of the power supply to the electrical loads 2a-c during a respective event. The series of sensor measurements may be stored accordingly as an electrical power signal in the database of sensor measurements, which may be used to analyse the powerfrequency spectrum of the power supply during respective events, as shall be described in more detail.

It shall be appreciated that the frequency characteristics associated with the operation of an electrical device are largely specific to the particular operation being performed by that appliance. Hence, the PSD generated by a particular electrical device operation will be largely repeatable, and therefore recognisable. For example, the operation of a heating element is known to introduce white noise into the PSD. A motor on the other hand has a complex impedance but notably, small arcs of current may exist between electrical contacts, which is known to add pink noise to the PSD. For this purpose, the event detection module 36 may include one or more spectrum analyser algorithms that are known in the art for determining the PSD of an electrical power signal.

In order to detect an event, i.e. a change in the operation of one or more of the electrical loads 2a-c, the event detection module 36 may be further configured to identify changes in the PSD. For example, the determined PSD may be compared to a reference PSD associated with a previous event, a baseline condition (where there is no power draw from the electrical loads 2a-c), and/or an average condition of the electrical loads 2a-c, in order to identify one or more changes in the determined PSD. The reference PSD may be stored in the memory storage module 34, for example. In order to compare the features of the determined PSD and the reference PSD, the event detection module 36 may include one or more data processing operations or algorithms that are known in the art. For example, the event detection module 36 may be configured to determine respective curve functions for the determined PSD and the reference PSD. For this purpose, the event detection module 36 may include one or more curve fitting functions that model different frequency ranges of a PSD with respective curve functions. For example, the one or more curve fitting functions may be configured to determine an average power curve, or a floor curve, for the determined PSD and the reference PSD for comparison to one another.

By way of example, for a baseline condition (in the absence of a power draw from the electrical loads 2a-c) a curve function for the PSD will have a characteristic 1/f line at low frequencies that flattens out at higher frequencies. However, when a heating element is operated, white noise is added to the determined PSD, producing a linear shift of the curve function. Accordingly, various features of the curve function for the determined PSD may be indicative of changes in the operations of the electrical loads 2a-c relative to the reference condition. Such features may include the slope and intersection point (i.e. the 1/f corner) of the curve function, amongst other features (such as duration of baselining, or a beta value for the function 1/(f^AP), for example where the beta value is 0 for white noise, 1 for pink noise, and 2 for Brownian noise).

In order to infer information associated with the operation of the electrical loads 2a-c, the event detection module 36 may be configured to determine the transformation operations between the respective curve functions of the reference PSD and the determined PSD, i.e. to determine how the curve function of the reference PSD is changed or what transformation operations are required to produce the curve function of the determined PSD.

The event detection module 36 may further include one or more schemes, rules, or methods, for classifying the operations of the electrical loads 2a-c based on the determined transformation operation(s). For example, as noted above, the operation of a heating element may produce a linear shift of the curve functions between the reference PSD and the determined PSD, and, upon determining the linear shift, the event detection module 36 may be configured to classify the event accordingly. In other examples, the event detection module 36 may be configured to classify events with reference to a database of classified events and associated transformation operations (relative to the reference PSD), or a look-up table, that may be stored in the memory storage module 34. Updates to the database may then be provided from one or more external servers via the communications module 30 of the circuit breaker 20, where offline analysis of the frequency characteristics can be carried out on the external servers.

The feedback module 28 is configured to control one or more feedback actions - these may involve adjusting noise thresholds, providing control signals to operate the circuit breaker mechanism 24, or to provide fault warnings.

The communications module 30 is configured to connect to one or more external servers, and/or the electrical loads 2a-c, for example to execute the feedback actions and/or to update the database of sensor measurements. For this purpose, the communications module 30 may, for example, include a wireless communication module configured to form a wireless connection to an external server or wireless network. The communications module 30 may therefore facilitate the offline analysis of noise thresholds and provide a means for updating the database of sensor measurements, as well as for providing event information to the user computer 15 or the cloud server 17.

The operation of the sensor apparatus 20 in the example electrical circuit 1 for providing appliance usage data shall now be described with reference to Figures 9 and 10. Before obtaining appliance usage data, system noise will be established so that events can be distinguished from noise for identification. This process is not described here as it is not directly relevant to the disclosure, but is described in the applicant’s earlier patent application PCT/EP2022/051405. A method 100 of operating the sensor apparatus 20 to obtain appliance usage data shall now be described with additional reference to Figures 9 and 10.

As shown in Figure 9, in step 130, the event detection module 36 may acquire an electrical power signal composed of a series of sensor measurements determined by the sensor module 22 during a respective event.

For example, the series of sensor measurement may be retrieved from the database of sensor measurements in the memory storage module 34 or otherwise determined by the sensor module 22. The acquired electrical power signal may therefore be based on a series of sensor measurements acquired by the current and voltage sensors, for example.

Advantageously, the circuit breaker 20 may therefore have filtered the series of sensor measurements using the noise threshold determined according to the steps 132 to 146 of the method 100 to remove extraneous sensor measurements. In this manner, the data processing can be carried out on a reduced set of useful data, such that sophisticated analysis can be carried out on-board the sensor apparatus 20.

In step 132, the event detection module 36 is configured to determine the PSD of the acquired electrical power signal. For this purpose, the event detection module 36 may use one or more of the spectrum analyser algorithms.

In step 134, the event detection module 36 is configured to compare the determined PSD to a reference PSD for the electrical circuit 3 in order to detect and/or classify the operations of the electrical loads 2a-c during the event.

For example, the reference PSD may be associated with a previous event, an average condition of the electrical circuit 3, or a baseline condition of the electrical circuit 3, during which there is no power draw from the electrical loads 2a-c. The determined PSD is compared to the reference PSD to identify changes that are indicative of respective operations, or states, of the electrical loads 2a-c. For example, the activation or deactivation of one of the electrical loads 2a-c will generate an identifiable shift in the determined PSD.

Accordingly, the event detection module 36 may apply one or more data processing operations or algorithms to compare the features of the determined PSD and the reference PSD, and thereby identify the differences that are indicative of the operations of the electrical loads 2a-c.

By way of example, Figure 10 illustrates example sub-steps 136 to 144 of the method 100 that may be executed by the event detection module 36 to compare the determined PSD to the reference PSD and classify the operations of the electrical loads 2a-c during the event.

In sub-step 136, the event detection module 36 may determine a first curve function representing the power distribution in the determined PSD. For example, the event detection module 36 may use one or more curve fitting functions to determine the first curve function, which may, for example, represent an average power curve or a floor of the determined PSD.

In sub-step 138, the event detection module 36 is configured to determine a second curve function representing the power distribution in the reference PSD. For example, the event detection module 36 may be configured to recall the second curve function from the memory storage module 34 of the circuit breaker 20, or otherwise use one or more curve fitting functions to determine the second curve function based on the reference PSD. It shall be appreciated that for effective comparison to the first curve function, the second curve function may similarly represent an average power curve or a floor of the reference PSD respectively. Moreover, each of the first and second curve functions may therefore include a 1/f^Ap curve function for a low frequency portion of the respective PSD and a further curve function for the broadband noise at a higher frequency portion of the respective PSD.

In sub-step 140, the event detection module 36 compares the first curve function to the second curved function to detect the event. For example, the event, i.e. a change in the operation of one or more of the electrical loads 2a-c, may be detected by a deviation of the first curve function from the second curve function.

In sub-step 142, in order to classify the detected event, the event detection module 36 may be further configured to determine one or more transformation operations between the first and second curve functions, where each transformation operation may be indicative of a change in the operation of the electrical loads 2a-c. For example, the event detection module 36 may use one or more methods that are known in the art for determining transformation operations from one curve function to another and may, for example, compare respective slopes and/or intersection points of the first and second curve functions to determine such transformations.

In sub-step 144, the event detection module 36 may be configured to classify the event based on the determined transformation(s). For example, the event detection module 36 may include one or more schemes, rules, or algorithms, that associate a determined transformation with a respective operation of the electrical loads 2a-c. For example, such rules, schemes, or algorithms, may be programmed into the event detection module 36, and/or stored in the memory storage module 34, such that the event detection module 36 is able to associate a linear shift of the intersection point with the introduction of white noise, for example due to a heating element being operated. Similarly, a change of the slope may be associated with the introduction of pink noise, for example where small arcs of current are being generated between electrical contacts of a motor. Such arcing is unwanted and so the safety of the electrical circuit 3 may be improved by communicating this feature to a user. In this manner, the event detection module 36 is able to classify the operations of the electrical loads 2a-c during the event by analysing the shift in the PSD of the electrical power signal relative to the reference PSD.

Returning to Figure 9, the method 100 further includes a step 146 of augmenting appliance usage data based on the detected or classified event. The events are used to determine the usage made of a specific appliance, and this information is used where the main process is operated - typically at user computer 15 or cloud service 17 - after export via the communications module. In principle, processing could take place at the sensor apparatus 20 itself if individual appliance data, including usage data, is stored in the memory storage module 34. As a result of the method 100, appliance usage information can be captured during normal operation of the home environment electrical system.

It will be appreciated that various changes and modifications can be made to the present disclosure without departing from the scope of the present application.

Claims

1 . A method of recycling non-consumable items, comprising: establishing a lifetime measurement method for at least one non-consumable item; determining an end of life for the non-consumable item according to the lifetime measurement method; initiating a recycling process determined by proximity to the determined end of life for the non-consumable item; and on confirmation that the non-consumable item is to be recycled, establishing recycling options for selection.

2. The method of claim 1 , wherein one or more of the non-consumable items is an electrical appliance, and wherein the lifetime measurement method for the electrical appliance comprises measurement of operational cycles performed by the electrical appliance.

3. The method of claim 2, wherein the remaining useful lifetime for the electrical appliance is determined as a number of operational cycles assessed to be remaining in the lifetime of the electrical appliance.

4. The method of claim 3, wherein the remaining useful lifetime for the electrical appliance is determined using a purchase date, a rate of usage and a predetermined operational lifetime for the electrical appliance.

5. The method of any of claims 2 to 4, wherein operational cycles are determined by measuring voltage and/or current in an electrical power supply to the electrical appliance.

6. The method of claim 5, wherein the voltage and current are measured in an electrical power supply to a building, wherein the measurements of voltage and current are disaggregated to determine whether an operational cycle has been performed by the electrical appliance.

7. The method of claim 6, wherein the voltage and/or current are measured in one or more circuit breakers of an electrical power system of the building.

8. The method of any preceding claim, wherein the method comprises identifying a plurality of non-consumable items and by establishing a lifetime measurement and determining an end of life for each of the plurality of non-consumable items.

9. The method of claim 8, wherein for some of the plurality of non-consumable items, identifying the item comprises photographing or scanning elements of the physical item and performing a recognition process to identify the non-consumable item

10. The method of claim 9, wherein the recognition process comprises determining a make and/or model of a non-consumable item and interrogating data sources relating to the make and/or model.

11. The method of any of claims 8 to 10, wherein in identifying the plurality of non- consumable items, a user is prompted to enter manually some or all data necessary to identify the non-consumable item and/or to establish its end of life.

12. The method of claim 11 , wherein user manual entry may be provided wholly or partly through a structured user interface.

13. The method of any preceding claim, wherein recycling options are established as a ranked list.

14. The method of any preceding claim, wherein recycling options are established by best cost at scheduled times.

15. The method of any preceding claim, wherein on selection of a recycling option, a recycling event is scheduled.

16. A computer-implemented system for recycling non-consumable items, the system comprising at least one processor and at least one memory, wherein the processor is programmed to: establish a lifetime measurement method for at least one non-consumable item; determine an end of life for the non-consumable item according to the lifetime measurement method; initiate a recycling process determined by proximity to the determined end of life for the non-consumable item; and on confirmation that the non-consumable item is to be recycled, establish recycling options for selection. The computer-implemented system of claim 16, wherein the system is adapted to receive electrical usage data from a building, and to determine from the electrical usage data operational cycles for the non-consumable item, wherein the non- consumable item is an electrical appliance. The computer-implemented system of claim 17, wherein the system is adapted to identify a plurality of non-consumable items, of which a plurality are electrical appliances, and wherein the system is further adapted to disaggregate the electrical data to determine operational cycles for each of the electrical appliances. The computer-implemented system of any of claims 16 to 18, further comprising a user interface for identifying non-consumable items and for selecting recycling options.