Twin tank water treatment system and method

Embodiments of the invention provide a twin tank water treatment system and method. The water treatment system includes first tank with a first set of sensors and a first resin bed, a second tank with a second set of sensors and a second resin bed, and a valve assembly with a flow meter and a controller in communication with the first set of sensors, the second set of sensors, and the flow meter. The method includes determining when the resin beds are exhausted based on input from the flow meter, the sensors, and a water hardness setting.

BACKGROUND

In water softener systems, multiple tanks provide an efficient and reliable means of providing continuous soft water. In single tank systems, a reserve capacity is often configured into the tank controller. The reserve capacity helps to ensure that hard water is not delivered (i.e., to ensure untreated water is not output) during periods of normal water usage until a regeneration can be performed. For example, if regeneration is configured to occur at 2:00 a.m., and non-reserved softening capacity becomes exhausted at 10:00 a.m., the reserve capacity can maintain soft water production until the scheduled regeneration time. Any reserve capacity not exhausted will be regenerated, thereby lowering the efficiency of the system.

SUMMARY

Some embodiments of the invention provide a water treatment system including a first tank with a first set of sensors and a first resin bed, and a second tank with a second set of sensors and a second resin bed. The water treatment system also includes a valve assembly coupled to the first tank and the second tank. The valve assembly includes a controller in communication with the first set of sensors, the second set of sensors, and a flow meter. Also, the controller determines that the first resin bed is exhausted based on input from the flow meter and the first set of sensors, and switches service operation from the first tank to the second tank when the first resin bed is exhausted.

Some embodiments of the invention provide a method for determining resin bed exhaustion of a water treatment system. The method includes measuring a volume of fluid that has flowed through the resin bed, retrieving a water hardness setting and a resin bed capacity, and measuring a hardness front location along the resin bed using at least one sensor. The method also includes calculating a new water hardness setting using the hardness front location, the measured volume of fluid that has flowed through the resin bed, and a placement of the at least one sensor, estimating a remaining fluid volume capacity using the new water hardness setting, the resin bed capacity, and the measured volume of fluid that has flowed through the resin bed, and determining resin bed exhaustion when the remaining fluid volume capacity has flowed through the resin bed.

Some embodiments of the invention provide a water treatment system including a first tank with a first set of sensors and a first resin bed. The water treatment system also includes a valve assembly coupled to the first tank. The valve assembly includes a flow meter and a controller in communication with the first set of sensors and the flow meter. The water treatment system further includes a user interface capable of retrieving an initial water hardness setting. The controller continuously adjusts the initial water hardness setting based on input from the flow meter and the first set of sensors, calculates a remaining volume capacity of fluid flow until the first resin bed is exhausted based on the adjusted water hardness setting, and initiates regeneration of the first resin bed when the remaining volume capacity of fluid flow has been measured by the flow meter.

DETAILED DESCRIPTION

FIG. 1illustrates a twin tank water treatment system10according to one embodiment of the invention. The system10can include a first tank12, a second tank14, a valve assembly16, and sensors18. In some embodiments, the system10can be used for substantially continuous residential or commercial water softening.

As shown inFIG. 2, the valve assembly16can include a system inlet20for receiving untreated fluid (e.g., “hard” water) and a system outlet22for supplying treated fluid (e.g., “soft” water). In some embodiments, the valve assembly16can include a bypass valve24which, when actuated, can allow fluid received at the system inlet20to bypass the system10and flow straight to the system outlet22. As a result, the bypass valve24can allow the system outlet22to supply untreated fluid if necessary, for example, during maintenance of the system10. When the bypass valve24is not actuated, the valve assembly16can direct the flow of untreated fluid from the system inlet20to a tank inlet26(as shown inFIG. 3) of either the first tank12or the second tank14for treatment. The valve assembly16can then receive treated fluid from a tank outlet28of either the first tank12or the second tank14and direct the treated fluid to the system outlet22.

In some embodiments, the valve assembly16can control which tank12,14is in service and can control a regeneration process of the tank14,12that is not in service. For example, the valve assembly16can allow the first tank12to be in service for treating fluid while the second tank14is out of service, and once the in-service first tank12has been exhausted and requires regeneration, the valve assembly16can switch fluid flow to the second tank14for fluid treatment and control regeneration of the first tank12. As a result, fluid treatment can be substantially continuous, without requiring down-time like conventional single-tank systems.

The valve assembly16can also control regeneration stages of both the first tank12and the second tank14. For example, each tank12,14can include a resin bed30(as shown inFIG. 3.) saturated with mono-positive ions, such as sodium ions. The mono-positive ions can bind to resin beads of the resin bed at binding sites. During treatment, untreated fluid can flow from the tank inlet26through the resin bed30and di-positive and/or tri-positive (e.g., calcium ions, magnesium ions, iron ions, aluminum ions, etc., hereinafter “hardness ions”) in the untreated fluid can replace the mono-positive ions at the binding sites of the resin beads. The treated fluid (i.e., the fluid substantially free of the hardness ions) can then be supplied through the tank outlet28. More specifically, as shown inFIG. 3, untreated water can be supplied through the tank inlet26near a top portion of the tank12,14, flow downward through the resin bed30to a bottom portion of the tank12,14, through a collector cup32into a distributor tube34, and back up to the tank outlet28through the distributor tube34.

When the untreated water reaches the resin bed30, hardness ions can bind to the first available binding sites. As a result, the top portion of resin bed30can be exhausted first. More specifically, an interface, or hardness front, between exhausted and unexhausted resin can begin at the top portion of the resin bed30and move downward through the resin bed30over time. Once the mono-positive ions from substantially all binding sites have been replaced with hardness ions (i.e., once the hardness front has reached or nearly reached the bottom of the tank12,14), the resin bed30can be considered exhausted and can require regeneration with, for example, a brine solution to re-saturate the resin bed30with mono-positive ions. In some embodiments, the valve assembly16can include a controller36in communication with the sensors18and at least one flow meter38(as shown inFIG. 2) to determine when the resin bed30of the tank12,14is exhausted, as described below. As shown inFIG. 2, the valve assembly16can include the flow meter38to measure a volume of fluid flow through the system outlet22. In some embodiments, the flow meter38can measure a volume of fluid flow through the system inlet20, one of the tank inlets26, and/or one of the tank outlets28.

As shown inFIGS. 1 and 2, each tank12,14can include two sensors18. In some embodiments, the sensors18can be conductivity probes and can extend into the tank12,14vertically displaced from one another. The conductivity of the resin bed30can be dependent on the ions occupying the binding sites. As a result, the conductivity measured by each of the sensors18can be higher when the binding sites contain mono-positive sodium ions and lower when the binding sites contain di-positive or tri-positive hardness ions, resulting in a different conductivity on each side of the hardness front. Due to varying water supplies, the conductivity of the fluid supplied to the system10may not be uniform. The controller36can use the ratio of two conductivity measurements (i.e., from the two sensors18), as described below, to determine a location of the hardness front. By using a ratio, the conductivity of the fluid can become a common mode signal so that the resulting ratio is dependent on the conductivity of the sodium ions and/or the conductivity of the hardness ions in the resin bed30.

Conductivity is also strongly influenced by temperature. Temperature compensation can be a multiplying factor. The conductivity ratio can be independent of temperature when the sensors18are at the same temperature. The sensors18can be at a different temperature when there is fluid flow until the resin bed30reaches thermal equilibrium. Since the time to reach thermal equilibrium can be substantially shorter than the movement of the hardness front, a digital low pass filter can be applied to the ratio to help remove temperature effects. In some embodiments, the type of resin in the resin bed30can also be a factor which affects the ratio and can be taken into consideration when the controller36determines the ratio.

Conductivity is also influenced by a “cell constant” of each sensor18. The cell constant can be the ratio of an effective length of a conducting path the two electrodes of each sensor18and a cross sectional area between the two electrodes of each sensor18. The cell constant is also controlled by the geometry of the electrodes. In one embodiment, the ratio measured by the two sensors18can be independent of the cell constants if both sensors18have the same cell constants. If the cell constants of the two sensor18are not the same, the ratio of the cell constants can be calculated when the ratio of the conductivities is known. For example, as explained below, the conductivity ratio can be known after regeneration and after a complete service cycle. When the ratio of the cell constants is known, it can be used to correct the calculated conductivity ratio.

After regeneration, the ratio can be about 1.0 since both sensors18are exposed to approximately the same concentration of sodium ions. As hardness ions replace the sodium ions, the resin bed30can progressively become less conductive starting at the top portion and working toward the bottom portion. This causes the ratio of the bottom sensor18conductance relative to the top sensor18conductance to increase, indicating a “leading edge” in the ratio. For example, the ratio can be between about 1.8 and about 2.4 when the hardness front is between the sensors18. A maximum value of the ratio can be a function of the ratio of conductance of sodium and hardness ions.

When the hardness front passes the bottom sensor18, the ratio can again approach about 1.0, indicating a “trailing edge” of the ratio, because both sensors18are exposed to approximately the same concentration of hardness ions. As a result, there may be no difference between the regenerated ratio and the exhausted ratio and only when the hardness front is between the sensors18may the ratio differ from about 1.0.

In some embodiments, the controller36can include a microcontroller or a microprocessor (not shown) which can execute algorithms for calculating the ratio using measurements sensed by the sensors18and retrieved by the controller36.

After regeneration, the controller36can be in a leading edge state until the leading edge occurs. The controller36can calculate the probability of a leading edge hardness front, P[le], and more specifically, when the leading edge occurs, using the following equation:

In one embodiment, while in the leading edge state, the controller36can use a present, calculated ratio rather than a maximum ratio. In addition, in some embodiments, the controller36can use an average of ratios calculated since the last regeneration occurred rather than the minimum ratio. This can allow the controller36to ignore decreased signals that may be present after regeneration. The controller36can switch from the leading edge state to a trailing edge state when P[le] is non-zero. In other words, the controller36can determine that the leading edge occurs when P[le] changes from zero to one. Once the leading edge has occurred, the controller36can switch from a leading edge state to a trailing edge state in order to determine when the trailing edge occurs.

The controller36can calculate the probability of a trailing edge hardness front, P[te], and more specifically, when the trailing edge occurs, using the following equation:

The controller36can also calculate P[le] when in the trailing edge state. The controller can then use P[le] and P[te] to determine or detect impending exhaustion of the resin bed30. For example, in one embodiment, the controller36can detect complete exhaustion when the product of P[le] and P[te] is greater than, for example, about 0.38 for four consecutive hours. In some embodiments, while checking for exhaustion, if P[le] is less than 0.45, P[le] can be set to 0.0. In another embodiment, the controller36can detect impending exhaustion of the resin bed30once the hardness front passes the lowermost sensor18(i.e., once the trailing edge is triggered) and calculate when the resin bed30will be completely exhausted, as described below.

In some embodiments, the controller36can retrieve conductivity measurements from the sensors18to calculate the ratios, for example, through external connections40, as shown inFIGS. 1 and 3. In one embodiment, the algorithms described below can be used by the controller36.

Using two digital output lines, the controller36can generate an eight level Walsh approximation to a sine wave. This signal can be applied to a Walsh sine wave generator to combine the terms at correct ratios. The output of the Walsh sine wave generator can be applied to a low pass filter in order to remove high order harmonics, resulting in an essentially pure sine wave. The output of the low pass filter can pass though a resistor to drive the electrodes of the sensors18. The resistor can increase the range of a conductivity signal, because the voltage applied to the sensors18decreases as the current increases. For any non-zero generator voltage, the ratio of the conductivities can be independent of the generator voltage.

The sensors18can be excited with the sine wave. In one embodiment, the sine wave can have a frequency of approximately 1,000 Hertz with a peak amplitude of approximately 100 millivolts. This low excitation voltage can help prevent chemical reduction from occurring at the electrodes of the sensors18. Also, the relatively high excitation frequency can help reduce the possible effects of electrode double layer capacitance.

Current from each sensor18can be applied to individual current-to-voltage converters. The current-to-voltage converters can transform the current through the sensors18into a voltage. In one embodiment, the current-to-voltage converters can have a low pass filter that attenuates frequencies above the Nyquist frequency. The low pass filtered voltages can then be applied to an A/D input of the controller36. The controller36can alternately read eight samples of one cycle for each sensor input. A Fourier transform can adaptively filter and calculate the magnitude of the currents using, for example, 4000 cycles of each current. After calculating the Fourier sums for the 4000 cycles, the controller36can calculate the ratio and apply the ratio to another low pass filter. In one embodiment, the controller36can calculate the low pass filtered ratio once every minute.

In some embodiments, the controller36can use adaptive algorithms to follow the exhaustion front through the resin bed30. These algorithms can compensate for changes measured by the sensors18caused, for example, by unequal contamination of the untreated water. For example, untreated water can include a hardness setting (e.g., in kilo-grains/gallon of water) depending on concentrations of hardness ions in the untreated water. Untreated water from different sources or from the same source can include varied actual hardness settings due to unequal contamination, or unequal concentrations of hardness ions.

In some embodiments, the controller36can determine a location of the hardness front and a volume of fluid treated until the hardness front has reached the determined location, and the controller36can adjust a hardness setting of the fluid based on these determinations. As a result, a more accurate hardness setting can be used to estimate the volume capacity available before the resin bed30is completely exhausted.

In one embodiment, the controller36can use an estimated initial hardness setting, for example, as input by a user, and can adjust the hardness setting based on measurements from the sensors18and the flow meter38. In one embodiment, when the controller36detects impending resin bed exhaustion (i.e., when the controller36detects the trailing edge), it can automatically adjust a new hardness setting using the following formulas in order to compensate for the varied actual hardness settings of the untreated water:
Adjust Ratio=((100−Sensor Placement)×(capacity)×1000)/((fluid volume used since last regeneration)×(Current Hardness))
New Hardness=Old Hardness×Adjust Ratio

More specifically, an “adjust ratio” can be calculated based on a known capacity of the resin bed (e.g., in grains, as input by the user), placement of the lowermost sensor18(e.g., as a unit-less number input by the user), the measured volume of fluid which has been treated since the last regeneration (e.g., in gallons, as measured by the flow meter38), and the old hardness setting (e.g., in kilo-grains/gallon, either as input by the user or as previously calculated by the controller36). The new hardness setting can be a product of the old hardness setting and the adjust ratio. In some embodiments, the adjust ratio can be limited to about +/−20%. In some embodiments, the user can input the information, such as capacity of the resin bed, placement of the sensor18, an initial hardness setting, or other information using a user interface41of the controller36, as shown inFIG. 1.

By using measurements from the sensors18as well as measurements from the flow meter38as described above to adjust the hardness setting during each cycle, a point of complete resin bed exhaustion can be more accurately measured. For example, using the new hardness setting and the known capacity of the resin, a total volume capacity of the resin bed30can be calculated. The measured volume of fluid which has been treated since the last regeneration can be subtracted from the total volume capacity to determine a remaining volume capacity available before the resin bed30is completely exhausted (i.e., for the hardness front to move from the lowermost sensor18down to the bottom portion of the resin bed30). As a result, substantially the entire resin bed30can be completely utilized, maximizing an efficiency of the system10. For example, conventional systems without conductivity sensors typically require a reserve capacity. Such systems may only use a flow sensor to determine when regeneration should occur (i.e., after a certain volume of fluid has been treated). Since a flow sensor cannot determine where the hardness front is located along the resin bed, most conventional systems must be configured with the reserve capacity (or safety capacity) to ensure that a regeneration occurs before hard water is delivered. In some embodiments of the invention, the reserve capacity may no longer be required in the system10because an estimated volume capacity of fluid that can be treated before the resin bed30is fully exhausted can be more accurately measured during the service cycle, and the second tank14can provide an immediate source of fluid to treat whenever the first tank requires regeneration.

The controller36can use an adaptive algorithm to follow the hardness front through the resin bed. The controller can also include physical memory, such as electronic erasable programmable read-only memory (EEPROM), flash memory, etc. in order to store measurement values, past ratio calculations, and other data. For example, historical information regarding minimum ratios and maximum ratios during previous service cycles can be stored in the memory. This information can be used after a power outage to estimate a location of the hardness front.

In some embodiments, the valve assembly16can be capable of controlling which tank12,14is in service and the regeneration process of the tank12,14that is not in service. As shown inFIGS. 2 and 4, the system10can include a second tank adapter42, adapter clips44, yokes46, and yoke mounts48to couple the second tank14to a valve body50of the valve assembly16. The adapter clips44can couple the yokes mounts48to the valve body50and the second tank adapter42, as shown inFIG. 4, via fasteners52, such a screws. O-rings54and retainer rings56can also be used as seals at the connection points between the yokes mounts48and the valve body50, as well as the yoke mounts48and the second tank adapter42. As shown inFIG. 4, the second tank adapter42can also include a distributor adapter kit58, a distributor retainer ring60, and o-rings62in order to substantially seal a connection between the second tank adapter42and the second tank14. In addition, as shown inFIG. 5, the valve body50can be coupled to the first tank12by a distributor adapter96and a retainer ring98.

FIG. 5illustrates a portion of the valve assembly16according to one embodiment of the invention. As shown inFIG. 5, the valve assembly16can include a first piston assembly64and a second piston assembly66to control fluid distribution in the first tank12and the second tank14, respectively. For example, the controller36can control a position of a piston68within a spacer assembly70to provide proper fluid movement for a service cycle or for different stages of the regeneration cycle (e.g., backwash, brine draw, rinse, brine fill etc.). In one embodiment, the valve assembly16can include a timer assembly and/or an optical encoder (not shown) to move a piston rod link72and control a position of the piston68in the spacer assembly70. The controller36can also monitor piston positions within the spacer assembly70to monitor the stages of the regeneration cycle. The single controller36designed to interface with the sensors18, the flow meter38, and both the piston assemblies64,66can offer a low complexity, high efficiency system10. In addition, as shown inFIG. 5, each piston assembly64,66can include washers74, screws76, an end plug78, seals80, a piston rod82, and a piston rod retainer84. The valve assembly16can control the flow of fluid to either the first tank14or the second tank16with a switch valve assembly86including a spacer assembly88and end plugs90, as shown inFIG. 5. The switch valve assembly86can be enclosed in the valve body50by an end plate92and screws94.

In some embodiments, the valve assembly16can use an injector assembly100and a brine valve assembly102, as shown inFIG. 5, during stages of regeneration. The injector assembly100can include an injector body104, an injector throat106, an injector screen108, an injector nozzle110, an injector cap112, screws114, o-rings116, washers118, spacers120, a drain line flow control retainer button122, and an air disperser124. The brine valve assembly102can include a brine valve seat126, a brine valve stem128, a brine valve spacer130, a brine valve cap132, a brine valve spring134, a brine line flow control retainer136, a brine line flow control adapter138, o-rings140, retainer rings142, and washers144. In one embodiment, the injector assembly100can be in fluid communication with a drain portion146of the valve assembly16and the brine valve assembly102can be coupled to the injector body104.

In some embodiments, the controller36can also be used to determine faults or failures in the system10. During regeneration, the controller36can measure a maximum value and a minimum value of the resistive (or real) part of the current of the one of the sensors18. At the end of regeneration, the controller36can calculate the probability that salt was present, P[salt], using the following equation:

If P[salt] is less then 0.43 the controller36can set a “No Salt” flag to indicate no or minimal salt was present during regeneration (i.e., from the brine solution). If P[salt] is not less then 0.43, the controller can clear the No Salt flag.

In some embodiments, when the controller36detects impending exhaustion (i.e., when the trailing edge is triggered), the controller36can calculate the running average of P[le] for the last four service cycles. The controller36can then compare this average to P[le] of the present service cycle. If P[le] of the present service cycle is less than a percentage (e.g., about 22.5%) of the average, a “Reduced Capacity” flag can be set to indicate reduced capacity of the resin bed30. The controller36can also set the Reduced Capacity flag if P[le] for the present service cycle is less than a value (e.g., about 0.55). If P[le] is not less than a percentage of the average and is greater than the value, the controller36can clear the Reduced Capacity flag.

In some embodiments, the controller36can also determine if the current ratio is within a range (e.g., about 0.111 to about 100). If the ratio is outside this range, a “Bad Probe” flag can be set, indicating a failure or issue with one of the sensors18. The Bad Probe flag can be cleared when the ratio is within the range.