Detailed Description
The following detailed description of embodiments of the application is, therefore, to be taken in conjunction with the accompanying drawings, and it is to be understood that the scope of the application is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the term "comprise" or variations thereof such as "comprises" or "comprising", etc. will be understood to include the stated element or component without excluding other elements or components.
The technical scheme provided by the embodiment of the application can be applied to various sewage treatment scenes, in which sewage is treated firstly and then the treated sewage is further detected, for example: detecting the contaminant to obtain a contaminant detection result. And, in different sewage treatment scenarios, the detection result of the treated sewage may have different applications, for example, in some scenarios, the treated sewage may be reused according to the detection result of the contaminant; in other scenarios, the treated wastewater may be discharged based on the contaminant detection results.
In an embodiment of the present application, the sewage treatment scenario may be a sewage treatment scenario in a real environment, for example, a factory sewage treatment scenario. The sewage treatment scenario may also be a sewage treatment scenario in a simulated environment, e.g., an experimental sewage treatment scenario, etc.
In the related art, for detecting pollutants, a fixed detection strategy is generally adopted, or a detection strategy is formulated by a manual site, so that the detection efficiency is low; and, the obtained detection result is also poor in applicability.
Based on the above, the embodiment of the application provides a technical scheme, which takes factors such as a sewage treatment strategy, observation parameters, water quantity, a requirement identifier and the like of treated sewage into consideration to prepare a reasonable treated sewage detection strategy; and further, the detection efficiency of the treated sewage can be improved, so that the applicability of the detection result is improved.
Referring to fig. 1, an embodiment of the present application provides a sewage treatment system, the sewage treatment system includes: sewage treatment device and terminal equipment.
In some embodiments, the wastewater treatment device may be a hardware device that implements wastewater treatment, such as the following wastewater treatment devices:
Buried sewage treatment device: the device comprises a base and a barrel assembly, wherein a pushing mechanism is fixedly connected to the left side of the barrel assembly, the lower end face of the pushing mechanism is fixedly connected with the base, a feeding pipe is fixedly connected to the left side of the upper end face of the barrel assembly, and a pretreatment device is fixedly connected to the upper end face of the feeding pipe. The device is generally applied to the treatment of dispersed sewage in rural areas and tourist attractions.
Domestic sewage treatment device: comprises a grid, a grit chamber, an adjusting tank, an aeration tank, a sedimentation tank, a disinfection device and other process units, which are used for treating daily sewage.
An industrial sewage treatment device: the device for treating sewage generated in the industrial production process may comprise different treatment units and processes according to the characteristics of different industrial sewage.
An integrated domestic sewage treatment device: the toilet flushing water is suitable for single families, and the working principle is that toilet flushing water firstly passes through a separation tank, solid-liquid separation is carried out by using grids, feces are left upstream after separation, are solidified by further treatment, and then are manually drawn out to be used as organic fertilizer. The liquid with a small amount of excrement continues to flow downwards, is further intercepted at the outlet of the separation tank, fully removes impurities, and then enters the anaerobic tank for biochemical reaction.
In addition, there are also hospital sewage treatment devices, integrated wastewater treatment devices, and the like.
In the embodiment of the application, a plurality of sewage treatment devices can be configured, and each sewage treatment device is used for executing different sewage treatment strategies; or configuring an integrated apparatus integrating a plurality of sewage treatment apparatuses so that the integrated apparatus can perform a plurality of sewage treatment strategies.
The terminal equipment, it can with sewage treatment plant communication connection, can transmit data each other between the two, for example: issuing an instruction, acquiring processing data, detection data and the like.
For the terminal equipment, according to the data transmitted by the sewage treatment device and some information configured locally, a detection strategy of the treated sewage is formulated and fed back to the relevant users, so that the relevant users can detect according to the detection strategy.
In some embodiments, the detection strategy may be performed by a detection device, in particular, the detection strategy may be implemented by a relevant person according to the detection strategy, using the detection device to obtain the detection result.
The detection means may be a hardware device capable of performing contaminant detection, for example:
Portable water contaminant detection device: the method can rapidly detect the indexes of COD, ammonia nitrogen, total phosphorus, total nitrogen and the like in water, and is suitable for various industries such as environmental protection, scientific institutions, third party detection, petrochemical industry, photoelectric energy, aquaculture, sheet metal electroplating, ink coating, aquaculture, food processing, bio-pharmaceuticals, engineering and the like.
Water quality analyzer: the method is used for detecting various parameters in water quality, such as pH value, dissolved oxygen, turbidity and the like, and is widely applied to the fields of water treatment, environment monitoring and the like.
In the embodiment of the application, a plurality of detection devices can be configured, and each detection device is used for detecting different detection items; or an integrated device integrating a plurality of detection devices is configured so that the integrated device can detect a plurality of detection items.
In the embodiment of the application, a plurality of detection devices can be configured, the detection devices can be arranged at different positions, and the treated sewage can be introduced into the corresponding detection devices, so that the detection devices can detect the treated sewage.
Referring to fig. 2, an embodiment of the present application provides a sewage treatment flow chart, which includes:
The sewage treatment device carries out sewage treatment to obtain treated sewage; the terminal equipment determines a detection strategy for the treated sewage; detecting sewage after treatment by a detection personnel according to a detection strategy through a detection device to obtain a detection result; and according to the detection result, applying the treated sewage.
For example, if the detection result indicates that the treated sewage meets the discharge standard, discharging the treated sewage; and if the detection result represents that the treated sewage can be used, the treated sewage is used, and the like.
It can be understood that the technical solution of the embodiment of the present application is mainly aimed at determining the process of the detection strategy, and as for the sewage treatment and the specific execution mode of the detection strategy, reference may be made to the mature technology in the field.
Referring to fig. 3, an embodiment of the present application provides a flowchart of a method for determining a detection strategy for treated sewage, where the method for detecting pollutants includes:
Step 301, determining a first influencing parameter according to the type of the sewage treatment strategy corresponding to the treated sewage.
Step 302, determining a second influencing parameter according to the observed parameter of the treated sewage and the water quantity of the treated sewage.
Step 303, determining at least one detection item corresponding to the treated sewage according to the requirement identification of the treated sewage and a preset detection item comparison table.
Step 304, determining a detection strategy of the treated sewage according to the first influence parameter, the second influence parameter and at least one treated sewage detection item corresponding to the treated sewage.
In step 301, the wastewater treatment strategy types include: physical type, biological type, and chemical type.
In some embodiments, for the type of wastewater treatment strategy, for example:
Physical type: the physical effect is utilized to separate insoluble substances in the sewage, and the chemical property is not changed in the treatment process. Commonly used are gravity separation, centrifugal separation, reverse osmosis, air flotation, etc.
Biological type: organic matters in a dissolved or colloid state in the sewage are decomposed and oxidized into stable inorganic matters by utilizing the metabolism function of microorganisms, so that the sewage is purified. Commonly used are activated sludge process and biofilm process.
Chemical type: the use of chemical reactions to treat or recover dissolved or colloidal substances of sewage is often used in industrial wastewater. Examples of the conventional methods include coagulation, neutralization, redox, and ion exchange.
It will be appreciated that some types of wastewater treatment strategies may have an impact on the detection of other treated wastewater. For example: biological type sewage treatment strategies, because microorganisms are introduced, the detection coverage is wider when detecting the sewage after treatment. Generally, if sewage treatment and post-treatment sewage detection are separate and independent operation processes, they do not affect each other; but will affect each other if the sewage treatment and the post-treatment sewage detection are continuous processes.
As an alternative embodiment, the first influencing parameter comprises: influence factors and influence values; step 301 comprises: if the type of the sewage treatment strategy is a physical type, determining the influence factors as a type of influence factors, and determining an influence value according to the sewage treatment time and the detection time corresponding to the treated sewage; if the sewage treatment strategy type is a chemical type or a biological type, determining an influence factor as a second type of influence factor, and determining a first preset influence value as an influence value; if the sewage treatment strategy type comprises at least two types, determining the influence factors as three types of influence factors, and determining the second preset influence value as an influence value, wherein the second preset influence value is larger than the first preset influence value.
In some embodiments, the first preset influence value may be a value between 0 and 0.5 and the second preset influence value may be a value between 0.9 and 1, assuming that the influence value range is 0 to 1.
It can be understood that, as the sewage treatment strategy with single chemical type or biological type does not have great influence on the subsequent detection flow, the corresponding influence value is smaller; the complexity of the sewage treatment strategies is higher, and the subsequent detection process is possibly influenced, so that the corresponding influence value is larger.
Further, determining an influence value according to a sewage treatment time and a detection time corresponding to the treated sewage, including: if the time interval between the sewage treatment time and the detection time is greater than or equal to the preset time interval, determining an influence value as 0; if the time interval between the sewage treatment time and the detection time is smaller than the preset time interval, determining a third preset influence value as an influence value, wherein the third preset influence value is larger than the first preset influence value and smaller than the second preset influence value.
In some embodiments, the third preset influence value may be any value between the first preset influence value and the second preset influence value. The preset time interval may be a value between 0.5 and 1 h.
It can be understood that the physical type sewage treatment strategy is greatly influenced by time; if the interval time is longer, the subsequent detection flow is not influenced; if the interval time is shorter, the subsequent detection flow is affected to a moderate degree.
In step 302, the observed parameter of the treated sewage may be a parameter obtained by observing the treated sewage; it may be acquired by an observation device, such as: an image, a video acquisition device, etc. The water quantity of the treated sewage can be the measured result by a corresponding measuring instrument.
As an alternative embodiment, the observation parameters include: turbidity, visibility, and color depth.
In some embodiments, the turbidity may be determined by the distribution of particular pixels (with greater differences in pixel values from other pixels) of the collected processed wastewater image, the denser the distribution, the higher the turbidity. Visibility can be determined by the mean value of the pixel values of the collected processed sewage image, and the higher the mean value of the pixel values, the lower the visibility. The color depth can be determined by the distribution condition of the pixel values of the collected processed sewage image, and if the distribution condition of the pixel values is uniform, the depth is higher.
In some embodiments, a parameter identification model may be pre-trained, the image identified using the parameter identification model, and the observed parameters output.
In some embodiments, the second influencing parameter comprises: offset factor and offset value.
As an alternative embodiment, step 302 includes: if the water quantity of the treated sewage is larger than or equal to the preset water quantity, determining the offset factor as a type of offset factor, and determining an offset value according to turbidity, visibility and color depth; if the water quantity of the treated sewage is smaller than the preset water quantity, determining the offset factor as a second class offset factor, and determining an offset value according to turbidity and visibility.
In some embodiments, the preset amount of water may be determined based on a single maximum sewage treatment, for example: the preset water quantity is half of the single maximum sewage treatment capacity.
As an alternative embodiment, determining the offset value from the turbidity, the visibility, and the color depth includes: determining an offset value according to a first offset value calculation formula, turbidity, visibility and color depth; the first offset value calculation formula is: ; wherein P represents a first offset value,Representing the offset value corresponding to the color depth,Represents the offset value corresponding to the turbidity,Representing the offset value corresponding to the visibility,Representing the first coefficient of offset and,Representing a second offset coefficient, the sum of the first offset coefficient and the second offset coefficient being 1.
In some embodiments, the determination of the offset value for each observed parameter may include: and converting the observed parameter into an offset value in the range according to the range of the preset offset value and the range of the observed parameter. For example, assume that the range of the preset offset value is 0-1, and the range of the preset observation parameter is 0-100; then, if the observed parameter is proportional to the extent of influence of the treated wastewater detection, then, for example: the observed parameter was 10 and the offset was 0.1. Whereas if the observed parameter is inversely proportional to the extent of influence of the post-treatment wastewater detection, then, for example: the observed parameter was 10 and the offset was 0.9.
In some embodiments, an offset value determination model, which may correspond to a formula, may also be preconfigured, which is capable of determining an offset value based on turbidity, visibility, and color depth.
As an alternative embodiment, determining the offset value from the turbidity and the visibility includes: determining an offset value according to the second offset value calculation formula, the turbidity and the visibility; the second offset value calculation formula is:
; wherein b represents a second offset coefficient, the second offset coefficient being less than 1,Represents the offset value corresponding to the turbidity,Represents the corresponding offset value of the visibility, and K represents the preset offset value.
In the above embodiment, the first offset coefficient may be a value between 0.5 and 1, and the second offset coefficient may be a value between 0 and 0.5.
In this embodiment, an offset value is preset, which can be used as a reference offset value, and turbidity and visibility are compared with the preset offset value respectively, and different offset value integration modes are adopted according to different magnitude relations. For example, the preset offset value may be a value corresponding to one third or one half of the range of the aforementioned preset offset values, for example: the preset offset value ranges from 0 to 100, and may be 50 or 30.
In step 303, the preset detection item comparison table includes: the plurality of requirement identifiers and the plurality of requirement identifiers respectively correspond to the treated sewage detection items, and each requirement identifier corresponds to at least one treated sewage detection item.
In some embodiments, a demand identification is configured for each treated wastewater; the demand identification may characterize the application demand of the treated wastewater. For example: assuming that the treated sewage has three application modes (such as discharge, use, processing, etc.), three kinds of requirement identifiers can be configured: 01A;02B;03C.
Further, the treated sewage detection items corresponding to each requirement identifier are pre-configured. For example: if it is an emission, the detection term is related to the emission; if so, detecting that the item is related to the use; if it is a process, the test item is related to the process.
Some test items are exemplified as follows:
Chemical oxygen demand: high chemical oxygen demand means that water contains a significant amount of restorative materials, mainly organic pollutants. The higher the chemical oxygen demand, the more serious the organic pollution.
Biochemical oxygen demand: the aerobic microorganisms in the water synthesize organic matters in the water into inorganic matters at a certain temperature. The amount of dissolved oxygen required for oxidation during this particular time is a comprehensive indicator of the level of organics and other aerobic contaminants in the water.
Suspension: solid materials suspended in water, including inorganic materials that are insoluble in water; organic matter and silt, clay, living things, etc. The suspended matter content in water is one of the indicators that trade off the water pollution level.
Total phosphorus: after digestion of the water sample, various forms of phosphorus are converted to orthophosphate, measured as milligrams of phosphorus per liter of water sample.
Coliform group: it refers to a group of bacteria with certain characteristics associated with fecal contamination.
PH value: pH it is a scale of the activity of hydrogen ions in solution, a trade-off specification for the pH of a solution in general.
Ammonia nitrogen: the nitrogen content of animal organic compounds is generally higher than that of plant organic compounds, and meanwhile, the nitrogen organic compounds in human and animal excreta are very unstable and are easy to synthesize ammonia. Thus, when the ammonia nitrogen content in water increases, it refers to complex ammonia that exists as ammonia or ammonium ions.
Phosphate: phosphate is one of the natural ingredients of all foods and is commonly used as an important food ingredient and functional additive in food processing. Phosphate is the major component of the total dissolved solids content under contamination problems.
Therefore, a preset detection item comparison table can be configured according to the actual application scene; in the case where the demand identification is known, the treated wastewater detection term can be determined.
It can be appreciated that some of the pre-configured treated wastewater detection items may not have detection significance; such as: the presence of this sewage detection item has been excluded by sewage treatment. Therefore, it is necessary to combine multiple information items to determine the final detection scheme.
Further, in step 304, the detection strategy of the treated wastewater includes: a target detection item and a detection strategy for the target detection item.
Wherein the number of target detection items may be one or more; the detection policy of the target detection item may be a predetermined detection policy for the target detection item.
In connection with the description of the foregoing embodiments, the first influencing parameters include: an influence factor and an influence value, the second influence parameter comprising an offset factor and an offset value.
As an alternative embodiment, step 304 includes: determining a detection item determining rule and a detection strategy determining rule according to the first influence parameter and the second influence parameter; determining a target detection item from at least one treated sewage detection item according to a detection item determination rule; determining a target detection strategy from at least one detection strategy corresponding to the target detection item according to the detection strategy determination rule; and determining the detection strategy of the treated sewage according to the target detection item and the target detection strategy.
In some embodiments, determining the detection item determination rule and the detection policy determination rule based on the first influence parameter and the second influence parameter comprises: if the influence factors are one type of influence factors and the offset factors are one type of offset factors or two types of offset factors, determining a detection item determining rule and a detection strategy determining rule according to the average value of the influence values and the offset values and a preset reference value; if the influence factors are the second-class influence factors and the offset factors are the second-class offset factors, determining a detection item determining rule and a detection strategy determining rule according to the difference value between the influence values and the offset values and a preset reference value; if the influence factors are three kinds of influence factors and the offset factors are one kind of offset factors, determining a detection item determining rule and a detection strategy determining rule according to a weighted sum value of the influence values and the offset values and a preset reference value; the influence degree of the three kinds of influence factors on the detection item is larger than that of the one kind of influence factors on the detection item, the influence degree of the one kind of influence factors on the detection item is larger than that of the two kinds of influence factors on the detection item, and the influence degree of the one kind of offset factors on the detection item is larger than that of the two kinds of offset factors on the detection item.
In the embodiment of the application, the detection item determining rule is one of the following rules: randomly selecting a preset number of detection items from at least one treated sewage detection item; selecting a detection item with a correlation from at least one treated sewage detection item, the correlation being used to characterize whether a derivative relationship exists between contaminants; and selecting a designated detection item from at least one treated sewage detection item, wherein the designated detection item is determined according to a sewage source corresponding to the treated sewage.
Wherein, the preset quantity can be half of the total quantity of the treated sewage detection items. It will be appreciated that each detection item is used to detect a different contaminant, and therefore, for a relevant detection item, there may be a derivative relationship between the corresponding contaminants. For example: there may be a derivative relationship between the suspension and the coliform and phosphate, etc.
In some embodiments, different detection terms may correspond to a specified sewage source, such as: the sewage source of the coliform group may be a septic tank; the sewage source of phosphate may be a waste pool or the like.
The detection policy determines that the rule is one of the following: selecting a detection strategy with highest precision from at least one detection strategy; selecting a detection strategy with the precision being greater than a preset precision and the predicted detection duration being less than a preset duration from at least one detection strategy; the detection strategy is randomly selected from at least one detection strategy.
In some embodiments, each detection policy is preset with information such as detection accuracy and detection duration, and according to the information, the detection policy may be selected. For example: the detection can be realized more quickly by adopting a chemical type detection strategy aiming at chemical oxygen demand. For suspended matters, a physical type detection strategy is adopted, so that detection can be realized more quickly. The preset time period may be 10 minutes.
Example: the pH value can be detected by pH test paper, pH reagent and the like; the coliform group can be detected by sampling the treated sewage and observing the sampled sewage by adopting devices such as a microscope, an observation mirror and the like.
Thus, there may be multiple detection strategies for the same detection item; and, different detection strategies may have different advantages. For example: some have high detection accuracy, but the detection time is long.
In some embodiments, the preset reference values include a plurality of preset reference value ranges, each corresponding to one of the detection item determination rules and the detection policy determination rules. For example, the three detection item determining rules sequentially correspond to a first preset reference value range, a second preset reference value range, and a third preset reference value range, where any reference value in the first preset reference value range is greater than any reference value in the second preset reference value range, and any reference value in the second preset reference value range is greater than any reference value in the third preset reference value range. For example: assuming that the reference value is within the range of 0-1, the first preset reference value range may be 0.8-1, the second preset reference value range may be 0.5-0.8, and the third preset reference value range may be 0-0.5.
And, the three detection policy determining rules may correspond to each other in sequence: the third preset reference value range, the second preset reference value range and the first preset reference value range.
Therefore, in the embodiment of the application, if the reference value is higher, more detection items can be detected, and the accuracy of the detection strategy is not limited; if the reference value is low, fewer detection items can be detected, and the accuracy of the detection strategy or the detection duration needs to be limited.
In the embodiment of the application, a first influence parameter is determined according to the type of the sewage treatment strategy corresponding to the treated sewage, a second influence parameter is determined according to the observation parameter of the treated sewage and the water quantity of the treated sewage, and at least one treated sewage detection item is determined according to the requirement identification of the treated sewage and a preset detection item comparison table; furthermore, by combining the first influence parameter, the second influence parameter and at least one treated sewage detection item corresponding to the treated sewage, the detection strategy of the treated sewage can be determined. The determination mode of the detection strategy does not need to manually make the detection strategy or adopt a fixed detection strategy, and the detection strategy capable of improving the detection efficiency of the treated sewage can be determined.
Referring to fig. 4, an embodiment of the present application further provides a determining apparatus 400 for a detection strategy of treated sewage, including:
A first determining module 401, configured to: determining a first influence parameter according to the type of the sewage treatment strategy corresponding to the treated sewage; determining a second influence parameter according to the observed parameter of the treated sewage and the water quantity of the treated sewage; determining at least one detection item corresponding to the treated sewage according to the requirement identification of the treated sewage and a preset detection item comparison table, wherein the preset detection item comparison table comprises: the plurality of requirement identifiers and the treated sewage detection items respectively corresponding to the plurality of requirement identifiers, and each requirement identifier corresponds to at least one treated sewage detection item.
A second determining module 402, configured to determine a detection policy of the treated sewage according to the first influencing parameter, the second influencing parameter, and at least one treated sewage detection item corresponding to the treated sewage, where the detection policy of the treated sewage includes: a target detection item and a detection strategy of the target detection item.
Referring to fig. 5, an embodiment of the present application further provides a terminal device, where: comprising a processor 501 and a memory 502, the processor 501 being communicatively coupled to the memory 502. The terminal device can be used as an execution main body of the corresponding steps of the pollutant detection method after sewage treatment.
The processor 501 and the memory 502 are electrically connected directly or indirectly to each other to realize transmission or interaction of data. For example, electrical connections may be made between these elements through one or more communication buses or signal buses. The foregoing modules or method steps performed by the respective interactive side each include at least one software functional module that may be stored in the memory 502 in the form of software or firmware (firmware).
The processor 501 may be an integrated circuit chip having signal processing capabilities. The processor 501 may be a general-purpose processor including a CPU (Central Processing Unit ), NP (Network Processor, network processor), etc.; but may be a digital signal processor, an application specific integrated circuit, an off-the-shelf programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. Which may implement or perform the disclosed methods, steps, and logic blocks in embodiments of the invention. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Memory 502 may store various software programs and modules. The processor 501 executes various functional applications and data processing by running software programs and modules stored in the memory 502, i.e., implements the various steps of embodiments of the application.
Memory 502 may include, but is not limited to, RAM (Random Access Memory ), ROM (Read Only Memory), PROM (Programmable Read-Only Memory, programmable Read Only Memory), EPROM (Erasable Programmable Read-Only Memory, erasable Read Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), and the like.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative, and that the terminal device may also include more or fewer components than shown in fig. 5, or have a different configuration than shown in fig. 5.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present application are presented for purposes of illustration and description. It is not intended to limit the application to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the application and its practical application to thereby enable one skilled in the art to make and utilize the application in various exemplary embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the application be defined by the claims and their equivalents.