Patent Application
20250092784
The technical field generally relates to oil sands mining operations, and more particularly to monitoring and control of oil sands mining that employs trucks and shovels for enhanced performance.
Oil sands mining operations can include the use of trucks and shovels to retrieve oil sands ore from the mine face and transport it to extraction facilities. Deployment, monitoring and operation of mining equipment and associated workforce involves notable challenges.
Various techniques are described herein for truck and shovel monitoring and control in oil sands mining operations.
In some implementations, there is provided a process for oil sands mining comprising: utilizing trucks and shovels to mine oil sands ore, wherein the trucks and the shovels are operated by a workforce comprising shift teams that include equipment operators and wherein the mined oil sands ore is input into an oil sands extraction operation to extract bitumen from mineral solids and produce a bitumen product; monitoring key mining performance indicators (KMPI) comprising: truck operating parameters related to the trucks; shovel operating parameters related to the shovels; and workforce performance parameters related to the workforce; and wherein the process also includes controlling the trucks and the shovels based on a pre-developed model comprising the KMPI.
In some implementations, the workforce performance parameters comprise: shift team performance parameters of the shift teams that make up the workforce; and operator performance parameters of the operators who make up the shift teams. In some implementations, the truck operating parameters comprise truck productivity, truck availability, and truck cycle indicators. In some implementations, the truck cycle indicators comprise loading, hauling, emptying, dumping, waiting and spotting. In some implementations, the shovel operating parameters comprise truck wait times at the shovels, under-loading conditions, over-loading conditions, and shovel productivity. In some implementations, the truck operating parameters and the shovel operating parameters each include key performance metrics and key performance thresholds. In some implementations, the shift team performance parameters comprise shift change time. In some implementations, the operator performance parameters comprise: individual operator performance parameters and group norm parameters; truck operator performance parameters comprising truck travelling speed, truck productivity, truck operator lunch break times, truck operator break times, and truck operator shift change parameters; and shovel operator performance parameters comprising under-loading metrics, over-loading metrics, shovel productivity, shovel operator break times, and shovel operator shift change parameters. In some implementations, the controlling of the trucks and the shovels comprises tactical decision making to impact in-shift performance. In some implementations, the tactical decision making comprises: in response to a long truck-wait at shovels or dumps corresponding to over-truck conditions, adjustment made by truck dispatchers to reduce the truck wait times; in response to truck bunching, adjustment made by truck dispatchers to reduce the truck bunching; in response to excessive truck overloading, adjustment made by shovel operators to reduce the truck overloading; in response to a disruption of tonnage during shift change, investigation of actual cause thereof and adjustment of approach in shift change in terms of time of shift change on individual trucks, order and/or location where shift change occurs; in response to excessive switch-out times corresponding to switching operators in shift on a given truck, determination of root cause and devising of alternative approach to switch-outs; and/or in response to ore blending upset, adjustments made by truck dispatchers to ensure that characteristics of ore blends return to expected levels in terms of ranges of bitumen and fines content. In some implementations, the controlling of the trucks and the shovels comprises strategic decision making to impact mining performance. In some implementations, the strategic decision making comprises: assessing truck inventory requirements including one or more of new truck purchases, truck rentals, truck fleet size, type of trucks, truck equipment features, and truck equipment size, optionally in 5 to 10 years; and/or assessing shovel inventory requirements including one or more of new shovel purchases, shovel rentals, shovel fleet size, type of shovels, shovel equipment features, and shovel equipment size, optionally in 5 to 10 years. In some implementations, the pre-developed model is further based on extraction facility parameters such that the controlling of the trucks and the shovels is performed based on monitored extraction facility parameters related to the oil sands extraction operation. In some implementations, the monitoring comprises displaying the KMPI and information derived therefrom on a dashboard and/or a scorecard to enable a user to evaluate performance based on the displayed KMPI.
In some implementations, there is provided a method for monitoring truck and shovel performance in an oil sands mining operation, the method comprising: providing a model comprising a plurality of databases housing information acquired from the oil sands mining operation; determining key mining performance indicators (KMPI) comprising: truck operating parameters related to the trucks; shovel operating parameters related to the shovels; and workforce performance parameters related to the workforce; and displaying the KMPI on interactive digital displays comprising dashboards and scorecards for assessment by a user, the digital displays showing KMPIs for both in-shift and past-shift time intervals
In some implementations, the truck operating parameters comprise truck productivity, truck availability, and truck cycle indicators, and wherein the truck cycle indicators comprise loading, hauling, emptying, dumping, waiting and spotting; the shovel operating parameters comprise truck wait times at the shovels, under-loading conditions, over-loading conditions, and shovel productivity; the workforce performance parameters comprise shift team performance parameters of the shift teams that make up the workforce, and operator performance parameters of the operators who make up the shift teams; wherein the workforce performance parameters comprise shift change time; and the digital displays comprise an overall mining scorecard comprising KMPI and qualitative information based on pre-determined codes, a truck performance dashboard including time-based truck performance monitoring; a payload performance dashboard; an overall team performance dashboard; a shovel performance dashboard; and shift change dashboard.
In some implementations, there is provided a method of monitoring shift change performance of trucks used in oil sands mining operations, the method comprising: receiving truck performance data from a truck fleet, the truck performance data comprising operator-input codes that include a shift-change code indicating when a shift change is occurring and stationary-state codes indicating states during which the trucks are stationary; determining a base shift change duration based on the shift-change codes; determining a supplementary shift change duration based on a selection of the stationary-state codes; and providing an adjusted shift change duration based on the base shift change duration and the supplementary shift change duration.
In some implementations, the selected stationary-state codes comprise an operator maintenance code, miscellaneous code, truck breakdown code, a hauling stopped code and/or an empty stopped code. In some implementations, the selected stationary-state codes exclude stationary-state codes corresponding to truck operation of dumping and loading. In some implementations, the selected stationary-state codes are used in determining the supplementary shift change duration when the codes are input within a pre-determined time interval proximate the shift change. In some implementations, the pre-determined time interval is within at most 30 minutes of the shift change. In some implementations, an average shift change duration is determined by dividing the adjusted shift change duration by a number of trucks in the corresponding truck fleet. In some implementations, the method also includes displaying a plurality of the adjusted shift change durations via a digital dashboard over time to provide in-shift and past-shift information.
In some implementations, there is provided a method of monitoring shift change performance of trucks used in oil sands mining operations, the process comprising: receiving truck performance data from a truck fleet, the truck performance data comprising truck cycle times; for each truck in the truck fleet: determining truck cycle times during a pre-determined time interval spanning the corresponding shift change, each truck cycle including loading, hauling, dumping, and dispatching; determining a longest truck cycle; determining an average truck cycle which excludes at least the longest truck cycle; and determining the shift change duration based on a difference between the longest truck cycle and the average truck cycle; and wherein the method also includes determining an average shift change duration for the truck fleet based on the shift change durations determined for the respective trucks.
In some implementations, if a given truck lacks cycle data, determining the shift change duration of the given truck is performed using the steps of: receiving truck performance data that comprise operator-input codes that include a shift-change code indicating when a shift change is occurring and stationary-state codes indicating states during which the trucks are stationary; determining a base shift change duration based on the shift-change codes; determining a supplementary shift change duration based on a selection of the stationary-state codes; and providing an adjusted shift change duration based on the base shift change duration and the supplementary shift change duration. In some implementations, determining the average truck cycle is based on truck cycles that are comparable in total distance travelled by the truck within at most 10 km. In some implementations, the method also includes displaying a plurality of the adjusted shift change durations via a digital dashboard over time to provide in-shift and past-shift information.
The attached figures illustrate various features, aspects and implementations of the technology described herein. Certain figures intentionally include redactions.
The present description relates to methods and systems for monitoring and controlling mining operations, notably oil sands mining that involves trucks and shovels. The oil sands mining method can include utilizing trucks and shovels to mine oil sands ore, the trucks and the shovels being operated by a workforce including shift teams that include equipment operators and the mined oil sands ore being input into an oil sands extraction operation to extract bitumen from mineral solids and produce a bitumen product.
The methods can include monitoring key mining performance indicators (KMPI) that include truck operating parameters, shovel operating parameters, and workforce performance parameters; and controlling operation of the trucks and the shovels based on a pre-developed model comprising the KMPI for enhanced performance. Various truck and shovel operating parameters can be used (e.g., truck productivity, truck availability, and truck cycle indicators that include loading, hauling, emptying, dumping, waiting and spotting; truck wait times at the shovels, under-loading conditions, over-loading conditions, and shovel productivity; truck switch-out; and shift change time and shift change throughput). The controlling of the trucks and the shovels can include strategic decision making to impact mining performance as well as tactical decision making to impact in-shift performance. The pre-developed model can also be based on extraction facility parameters such that controlling the trucks and shovels takes into account the monitored extraction facility parameters.
The methods can also include monitoring shift change performance of trucks used in oil sands mining operations. In one implementation, the shift-change monitoring method includes receiving truck performance data from a truck fleet, the truck performance data comprising operator-input codes that include a shift-change code indicating when a shift change is occurring and stationary-state codes indicating states during which the trucks are stationary; determining a base shift change duration based on the shift-change codes; determining a supplementary shift change duration based on a selection of the stationary-state codes; and providing an adjusted shift change duration based on the base shift change duration and the supplementary shift change duration. In another implementation, the shift-change monitoring method includes receiving truck performance data from a truck fleet, the truck performance data comprising truck cycle times; for each truck in the truck fleet: determining truck cycle times during a pre-determined time interval spanning the corresponding shift change, each truck cycle including loading, hauling, dumping, and dispatching; determining a longest truck cycle; determining an average truck cycle which excludes the longest truck cycle; and determining the shift change duration based on a difference between the longest truck cycle and the average truck cycle; and the method then includes determining an average shift change duration for the truck fleet based on the shift change durations determined for the respective trucks.
In terms of additional context for the present technology, mining is a complex and expensive operation with a goal to mine and deliver ore to extraction facilities in an effective and efficient manner. Achieving and sustaining an optimal mining operation is difficult for various reasons, including because the operation involves work from different groups and is subject to many constraints. Key processes among these groups are the operation of trucks and shovels, which have goals including to mine and move material (both ore and waste) in the most effective and efficient manner. Stewardship of key mine performance is often done daily (e.g., last 24 hours) and appropriate decisions are taken to address arising performance issues. If performance shortfalls are detected early and during shift, production staff can take timely actions to eliminate or mitigate negative outcomes. Another important limitation in the context of monitoring mining operations is the availability of mining performance information and the form in which it is available to production staff. Key performance information should be consistent and widely available to operations, not being limited to mining staff alone, but also easily visible to downstream processes such as extraction and tailings operations.
Oil sands operations can monitor mining performance using tools developed by suppliers and while these tools are sufficient in monitoring equipment and processes in mining, there are challenges and drawbacks in terms of the ability to allow staff to monitor process performance beyond the tool domain. For example, in this context, decision making often involves access to information available in downstream processes, such as extraction and tailings; and there are certain unique characteristics in each mining operation that are not directly addressed by current supplier tools that are available.
It has been found that decision support methods and systems described herein to manage mining performance has had beneficial results. Oil sands mining staff can rely on such techniques to monitor performance of trucks and shovels in real-time, to make tactical (e.g., in-shift) and strategic (e.g., long-term) decisions using captured structured data. In addition, staff at the extraction facilitates can also use this technology to investigate past recovery upsets and improve future performance.
As mentioned above, the technology described herein relates to performance monitoring strategies that allow operations to monitor and manage truck and shovel performance. In some implementations, the method starts with development of a framework, an example of which is shown in
Mining performance assessment is possible during shifts via online monitoring of key performance metrics, allowing timely decisions to correct detected shortfalls. These metrics can be consistent whether they are derived during a short interval or are aggregated in a full shift, full day, or longer time period. Therefore, the performance information can be consistent for either tactical or strategic decision making.
Furthermore, the developed framework can be provided so as to be supplier-neutral, meaning that its methodology can be adapted to any commercial truck dispatch tool to provide production staff with consistent information. The framework is provided with the ability to integrate data which come from systems beyond truck-dispatch systems in mining, e.g., data that is provided from downstream extraction facilitates. A KPI database that is provided as part of the system can host all KPIs including those KPI's well known in the oil sands mining industry and others that may be specific to individual company operators. KPIs can be derived based on information from industry practice and local technical expertise in the mining staff using structured approaches.
In general, relevant performance elements in the mine monitoring framework can include the following. First, an overall mining scorecard can be provided and include all performance metrics for truck and shovel operation. The overall mining scorecard can capture codified performance levels and can include operator/leader comments on certain aspects of mining performance. The overall mining scorecard can be prepared to encapsulate and summarize key truck and shovel metrics. Second, truck operation performance can include various metrics including truck payload performance, truck wait times at shovel and dump points, productivity monitoring in a current shift and a historical shift, effect of shift change, and monitoring of truck operator performance. Third, shovel operation performance can include various metrics including shovel throughput and stewardship compared to the mining plan, shovel productivity and efficiency in deployment, shovel stewardship compared to the mining plan elevation per load (in-shift and past shifts). Fourth, team performance can be tracked, where each team can include group of individuals that are on a particular shift, truck, shovel, mine area, or the like.
Mining production staff can rely on the overall mining scorecard, an example of which is shown in
Still referring to
The scorecard can be provided with drill-down functionalities that enable staff to obtain further detailed information regarding performance of a given truck or shovel. Detailed information regarding each of the KPIs listed on the scorecard can be easily accessed by a user to further assess or investigate operational upsets or other information.
Referring to
Referring to
Periodic generation of summary reports regarding the coded problems and their causes can be automated and made available for access by certain users, leaders or managers, which may be done in real time, allowing management to make pertinent decisions in a timely manner. For example, the system and summary reports can help assess and implement equipment maintenance or replacement.
Truck performance can be monitored and displayed in various ways, some of which are described below. In some implementations, mining staff monitors truck performance based on truck cycle times and individual cycle component, and can detect when a performance deviation occurs. As shown in
In
In addition, section B (see
Truck performance can also be filtered down based on one or more of the following: all shovels, a specific group of shovels, and/or a given shovel; all dumps and/or a specific dump; and/or a specific truck.
Payload performance monitoring is related to both truck performance and shovel performance. Payload can be considered part of the truck performance as it affects the reliability of truck equipment and/or truck warranty, but it can also be related to the performance of shovel operators because it depends on how the operators work with loading ore material onto trucks. Thus, payload can be viewed as being associated with truck performance and the responses/actions related to shovel operators.
Various KPIs related to payload can be monitored, displayed and used to provide enhanced performance in mining operations. Certain display functionalities of the system will be further described below.
Payload monitoring is a relevant aspect of performance monitoring in a mining operation. Under-loading of trucks can correspond to inefficient usage while heavy overloading can lead to premature mechanical truck failures. Staff can routinely monitor truck payload during shifts and/or assess performance after each day of operation to guard against severe overloading and too much under-loading. It is advantageous that these conditions be detected early so that pertinent actions can be taken to avoid future repeated problems.
Referring to
In some implementations, in reference to
Another operating parameter that can be considered and monitored is truck Switch-out, which generally corresponds to the time period (typically measured in minutes) during which operators are switched out. Target durations for truck switch-out can include one for ore trucks and another for waste trucks, and are used to measure the effectiveness of operator switching activity.
Various KPIs related to workforce team performance can be monitored, displayed and used to provide enhanced performance in mining operations. Certain display functionalities of the system will be further described below.
As shown in
Performance of a shovel is assessed based on its throughput contribution and the efficiency with which it operates. Staff frequently observes whether shovels meet their throughput demand and their time efficiency in terms of loading and waiting times. Staff's objective is to detect any deviations early in shift so that they can take appropriate timely actions to mitigate its effect.
Referring to
In oil sands mining, shift change performance can be a relevant factor for steady ore throughput and accurate shift change monitoring can provide enhanced operations. Poor shift changes can have a negative impact on maintaining a steady ore throughput. Potential tonnage shortages during shift change have been seen to occur in both ore and overburden hauling. While ore shortage during shift change can affect ore supply to extraction facilitates for a short time for each event, such shortages can add up to a large reduction in bitumen produced each year. Improvement of shift change helps maintain a steady ore throughput, reduce truck resources required, and ultimately leads to reductions in overall truck operating costs.
Operations leaders have evaluated shift change performance by monitoring average shift change duration, which can be determined based on time durations when trucks are in a designated state. The designated state can be linked to a pre-determined “state code” that is entered by a truck operator at the end of their shift and then deactivated by the truck operator of the subsequent shift. However, it has been found that this state code, which is entered manually by truck operators, is not captured in a consistent manner. Therefore, this performance indicator does not correctly reflect shift change performance.
There is thus a need for a new metric to monitor the process while carrying out initiatives to improve shift-change performance. The below section describes novel approaches to determining shift change duration and quantifies the negative impact on tonnage during shift change. This information can be valuable in studies to determine appropriate actions that can be taken to improve shift change performance.
A project was initiated in a mining operation to reduce the average tonnage loss during shift change. It was found that the average shift change duration calculated based on a single designated truck code is not a consistently true measure of shift change performance. Work was performed to develop new approaches to determine average shift change using additional data and to integrate shift change data into new dashboards that help truck leaders monitor shift change performance in an effective manner. Dashboard users can monitor shift change and analyze data in such a way as to help identify key areas where actions can be taken to improve shift change performance.
Two new methods, labeled methods A and B below, were developed as alternatives in determining and reporting average truck shift changes. Method A was given an appropriate upper threshold and integrated into mining scorecards. New dashboards were also developed to allow truck leaders to monitor current shift changes and view how shift changes develop over time. More regarding methods A and B is described below.
Overall truck performance is monitored based on how efficiently its resource is deployed. Each truck is equipped with a WENCO™ hardware unit that allows operations to capture data on when and for how long a truck is in a given state.
Wireless beacons, which are installed along truck routes and around shovels and dumps, allow automatic capture of truck state data such as those involving hauling, emptying, loading, dumping. However, there are also other states that require operators' input. These states include those associated with operation delays, breakdown codes, and standby/idle. Truck shift change data is captured via a specific state code, which is one of the operation delay codes. The shift change code will be referred to as code “SCC” herein. When a shift change begins, a truck operator manually inputs the truck in shift change mode. The number of minutes a truck is in this state will be captured as the shift change duration for this truck. Shift change durations are gathered for all trucks during a shift change and will be used to determine an average value, which will denote shift change for the shift. It has been found that the collected shift change data have been lower than the actual durations.
Excessively long shift changes result in tonnage shortage, which can be overcome by adding more truck resources. This solution is not optimal since it can lead to higher truck costs. It is likely not feasible to eliminate the negative impact of shift change entirely. A more practical approach is to minimize the effect of tonnage shortage and hold it within an acceptable limit. For example, a percentage between 5-15% for ore and 10-25% for waste could be used as upper thresholds of shortage of tonnage, but this can be adapted depending on various factors that may be specific to the given mine or facility.
Shift change duration determination methodologies were developed and will now be described:
Truck cycles are queried with detailed truck times during a period from 2 hours prior to 2 hours after shift change (or another pre-determined short time period). Truck cycle data is queried over a period of 4 hours to ensure that sufficient data is included: (a) to determine an expected hourly tonnage if shift change poses no tonne shortage for purpose of comparison, and (b) to ensure that sufficient truck time data is available for the calculation time duration.
In the “SCC” Method, shift change duration is simply derived from averaging all trucks' shift changes based on truck code SCC. A drawback of this approach is that it requires manual entry of specific code SCC by the operator to put a truck into shift change mode. Entries of shift change have not been consistent amongst operators and shifts, leading to inaccuracy in reporting of shift change data.
It has been observed that actual durations of shift change occur over many truck codes, other than SCC. Some trucks have also gone through shift changes without being put into code SCC at all. Method A proposes that shift changes are tallied when a truck is put into the following codes around shift change time: Shift Change (SCC), if it exists, in addition to other pre-determined state codes that can include operation delay code, breakdown codes, and standby/idle codes. For example, the additional codes can include that include operation delay codes: operation maintenance code, miscellaneous delay code, standby cod, breakdown code, hauling-stopped code(s), and empty-stopped code(s). Thus, the shift change data used to determine shift change duration includes the shift change code as well as other pre-determined codes of truck states where the truck is not moving around the time of a shift change and limited to a pre-determined time limit that would correspond to an approximate shift change. Certain additional codes, such as breakdown code, will only be captured within the shift change picture if its duration approximates a shift change duration, and thus longer breakdown durations will not be captured for shift change determination. This method helps to account for mistakes, forgetfulness, etc., of the operators and captures a more fulsome shift change picture.
As noted above, a time duration associated with each of these codes should be less than a pre-determined duration (e.g., 25 minutes) for it to be included in the shift change determination. This constraint is used to exclude long durations, which are used for the original intention of the code and not associated with shift changes. Average shift change is then obtained by dividing the accumulated minutes by truck count.
The following steps are followed for method B:
In the event that a shift change cannot be calculated with method B for one or more given trucks due to lack of cycle data, method A will be used for these trucks to calculate shift changes.
In terms of displaying the shift change duration information, dashborads can be equipped with the ability to view the data determined based on any one of the methods. For example, the average shift change data for the above three methods (i.e. the SCC method, method A, method B) can be calculated and saved for each shift over a period of time. They can then be made available for subsequent queries in several new dashboards, one of which is shown in
Referring to
The new dashboards can report shift change data based on three approaches for comparison. Method SCC is unrealistically short because not all truck operators use the SCC code consistently. As a result, method A is proposed because the shift change calculation takes into account specific non-SCC codes during shift change. However, method B goes beyond method A in that it does not depend on non-SCC codes, but relies on a comparison between the longest cycle and an average cycle among equivalent cycles of a given truck. It has been generally found that with method A, shift change data are more realistic than with the SCC-based method, but not as long as those calculated with method B.
In conclusion, the new shift change metrics appear to reflect a truer measure of shift change durations. The newly developed dashboards have been used by leaders effectively to monitor shift change performance. Users can also rely on these new dashboards to continually evaluate the appropriateness of shift change calculations and make the necessary changes to improve the metric.
An architecture was developed that includes database objects, such as tables or views, and stored procedures that are invoked by external programs to either insert or update data into the databases and provide the information to the dashboards. The system can implement a batch process, which can be scheduled to run once per shift following a shift change period. The batch process is responsible for capturing and inserting shift change data into the database. Shift change data is captured and maintained in the databases to allow truck leaders to monitor current shift changes and to observe its trend over a given period. Shift change data is pre-determined and stored in the database ahead of users' queries such that users do not incur a slow response when querying the data for visualisation on the dashboards.
Various types of decisions can be made in response to the monitored KPI's regarding truck and shovel operation. For example, tactical decisions can include (a) in response to a disruption of tonnage during shift change, investigation of actual cause thereof and adjustment of approach in shift change in terms of time of shift change on individual trucks, order and/or location where shift change occurs; (b) in response to excessive switch-out times corresponding to switching operators in shift on a given truck, determination of root cause and devising of alternative approach to switch-outs; and/or (c) in response to ore blending upset, adjustments made by truck dispatchers to ensure that characteristics of ore blends return to expected levels in terms of ranges of bitumen and fines content. Strategic decisions cam be made with a more longer term view, such as decisions for operations that will occur between 5 to 10 years. For example, strategic decisions can include assessing truck inventory requirements including one or more of new truck purchases, truck rentals, truck fleet size, type of trucks, truck equipment features, and truck equipment size, optionally in 5 to 10 years; and assessing shovel inventory requirements including one or more of new shovel purchases, shovel rentals, shovel fleet size, type of shovels, shovel equipment features, and shovel equipment size, optionally in 5 to 10 years. Based on monitored truck and shovel productivity, planners can make strategic decisions regarding whether there are enough trucks currently in the fleet for the mine plan in 5 to 10 years and whether new trucks should be bought or rented. In addition, the type of trucks and certain features can be strategically determined based in part on the monitored KPIs; for example, based on certain KPIs it can be determined what type of tires and what size of trucks would be optimal for the mine plan in 5 to 10 years. For instance, if the mine plan includes opening a new area of the mine with longer hauling routes, a strategic decision may be taken to use enhanced tires on the trucks that will be used for that new mine area. Decisions can thus be planned for and made regarding various truck and shovel parts and functions that may be optimal for future truck and shovel operations. Additional decisions can be made in relation to extraction operations based on the truck and shovel KPIs as well. For instance, the extraction process could be controlled to blend certain ores together, increase or reduce chemical (e.g., caustic) dosing, and the like, based on monitored truck and shovel data. For example, if the trucks are providing a higher quantity of poor ore with higher fines content, extraction could be modified by adding more caustic for primary separation to separate the hydrotransport slurry into bitumen froth and primary tailings. It is also possible to monitor extraction variables and use that data to adapt truck and shovel operations.
Several alternative implementations and examples have been described and illustrated herein. The implementations of the technology described above are intended to be exemplary only. A person of ordinary skill in the art would appreciate the features of the individual implementations, and the possible combinations and variations of the components. A person of ordinary skill in the art would further appreciate that any of the implementations could be provided in any combination with the other implementations disclosed herein. It is understood that the technology may be embodied in other specific forms without departing from the central characteristics thereof. The present implementations and examples, therefore, are to be considered in all respects as illustrative and not restrictive, and the technology is not to be limited to the details given herein. Accordingly, while the specific implementations have been illustrated and described, numerous modifications come to mind.
