Filtration system, method for predicting the maintenance status of a filtration system, and method for predicting the recovery status of a filtration system - Patents.com

Abstract

A method for predicting a maintenance condition at a predetermined time for a filtration system of a diagnostic or laboratory analyzer is performed by performing the following steps: first, measuring a plurality of consecutive raw permeability values ​​of the permeability of a fluid through a filtration device over a specified measurement period; then, a smoothed permeability value is determined by a data processing method to reduce the variability of the raw permeability values ​​over time; then, a regression analysis function is applied through the consecutive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function fits the measured permeability values; finally, determining when the regression analysis function crosses a predetermined threshold, and determining the threshold crossing as a maintenance condition.

Description

[Technical Field]

[0001] The present invention relates to a filtration system, a method for predicting the maintenance status of a filtration system, and a method for predicting the recovery status of a filtration system. [Background technology]

[0002] The laboratory diagnostics industry uses reagents containing small particles, e.g., nanoparticles and / or microparticles, with diameters smaller than 5 mm. Wastewater generated in this industry contains these small particles, which can be harmful to the environment due to their source material (e.g., microplastics). They also tend to be highly adsorptive, potentially transporting contaminants to and from the environment and thus potentially causing damage. These particles differ from particulate waste generated in other industries in that they have a well-defined composition and a very uniform particle size. Therefore, it is difficult to efficiently remove particles from laboratory analyzer wastewater.

[0003] Filtration techniques are often used to remove particles from liquids. Cross-flow filtration (also called tangential flow filtration) is a type of filtration in which the liquid to be filtered passes across a filter membrane rather than directly into the filter as in dead-end filtration. Substances in the liquid that are smaller than the pore size of the filter membrane pass through the membrane, producing a permeate. The liquid passes across the filter membrane at a positive pressure relative to the permeate side. It is this positive pressure that provides the primary driving force for the cross-flow filtration process. The pressure difference between the two sides of the filter membrane (called the feed / retentate side and the permeate side) is measured as the transmembrane pressure (TMP).

[0004] Many crossflow filtration processes involve turbulent liquid flow (e.g., Reynolds numbers above about 2000) over a filter membrane. In such processes, the turbulent flow of liquid near the surface of the filter membrane prevents particulates from settling on the surface and forming a filter cake that blocks the membrane pores. The turbulent liquid flow in crossflow filtration is typically generated in filtration systems with very high liquid flow rates. Such flow rates are typically generated by high-power pumps. These pumps tend to be large in size and have high energy consumption.

[0005] When crossflow filtration is performed at low liquid flow rates, laminar flow can occur over the surface of the filter membrane. Because laminar flow results in low tangential flow near the membrane surface, the membrane can easily become clogged with particulates, for example, when the particulates aggregate and form a filter cake on the membrane surface. In such cases, filtration must be stopped and the clogged membrane replaced with an unclogged one. To reuse the membrane, the membrane must be cleared of any obstructions, but the process of clearing the obstructions can damage it due to its delicate nature. This is undesirable, as membranes are an expensive component of crossflow filtration systems.

[0006] Therefore, attempts are made to delay such replacement procedures or maintenance work as long as possible.

[0007] However, the longer such maintenance work is delayed, the greater the chance of the filtration system suddenly failing.

[0008] To perform these maintenance tasks, the filtration system must be turned off again, which slows down the filtration of the wastewater, which can lead to delays in analysis and increase production costs.

[0009] Therefore, it is important to assess the condition of the filtration system to minimize such downtime.

[0010] Traditionally, filtration systems are maintained at regular intervals. The condition of the filtration system is then only inspected during the maintenance. If in doubt, the components that are likely to be serviced are replaced without even knowing the condition of the old components. This approach leads to frequent downtime and unnecessary costs due to unnecessary replacement of components such as filter membranes. Summary of the Invention

[0011] It is therefore an object of the present invention to provide a method for predicting the maintenance state of a filter system, which allows the maintenance state to be reliably predicted in a simple manner.

[0012] A further object of the present invention is to provide a method for predicting the regeneration status of a filter system, which makes it possible to reliably predict in a simple manner when regeneration of the filter system is necessary.

[0013] One or more objects are solved by the features of the independent claims. Advantageous further developments and preferred embodiments form the subject matter of the dependent claims.

[0014] A method for predicting a maintenance condition at a predetermined time for a filtration system of a diagnostic or laboratory analyzer is performed by performing the following steps: first, measuring a plurality of consecutive raw permeability values ​​of the permeability of a fluid through a filtration device over a specified measurement period; second, a smoothed permeability value is determined by a data processing method to reduce fluctuations in the raw permeability values ​​over time; then, a regression analysis function is applied through the consecutive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function fits the measured permeability values; and finally, determining when the regression analysis function crosses a predetermined threshold, which is deemed a maintenance condition.

[0015] Smoothing the raw permeability values ​​is a core step in the method for determining maintenance status. Filtration systems are often used in a discontinuous manner. Outages can occur, such as overnight or on holidays, when no filtration is performed. It is known that during such outages the membrane permeability recovers and the permeability value increases. This causes an unstable change in the raw permeability values. There are also further statistical effects that lead to an unstable progression of the raw values. By smoothing the progression of the raw values, a steady-state function can be fitted to the raw permeability values.

[0016] Membrane rejuvenation during shutdown is used in pulse cycle mode to achieve improved membrane performance. Permeability decreases during each active phase of each cycle. Because the membrane rejuvenates during shutdown, the average permeability value in pulse cycle mode is higher than the average permeability value of the same system running in continuous mode.

[0017] The drawback is that the transmittance over time has a sawtooth profile, which makes it difficult to apply a regression analysis function.

[0018] By smoothing the permeability values, individual fluctuations caused by the filter system itself, for example due to cycling, are smoothed out and decreases over longer periods, i.e., over several cycles in the case of a cyclic filter machine, are made more apparent.

[0019] Smoothing eliminates sawtooth profile characteristics and other variations in the raw permeability values.

[0020] This allows the maintenance state to be reliably determined even in non-continuous, and especially pulsed, systems, and therefore the method can be used in high throughput modes.

[0021] Furthermore, determining a maintenance state based on only a single cycle will lead to a point well before the machine actually fails, which is too early to perform maintenance.

[0022] Regression analysis can be used to determine the progression of permeability over time. This progression in the form of a mathematical function can be used to determine when the mathematical function falls below a predetermined threshold. A fall below the threshold indicates a maintenance condition.

[0023] A maintenance condition is a condition that means that maintenance should be performed within a predetermined time period, otherwise the filter system may fail within a certain period of time.

[0024] The threshold and the time of possible failure are interdependent. The lower the threshold, the more likely the system is to fail. The higher the threshold, the less likely the system is to fail. However, because these are only averages of known system failures, there is no guarantee that at a particular threshold, the system will take a given amount of time to fail. Temporary fluctuations can occur. However, it can give a probability that the system is likely to fail to a certain percentage in a particular time interval.

[0025] The raw permeability value is the measured permeability value of the filtration system. However, the raw permeability value itself may also be calculated from, for example, pressure measurements, weight cell measurements, pump power consumption measurements, and / or optical sensors.

[0026] As used herein, the term "diagnostic apparatus" includes any such device or apparatus for performing a diagnostic function that generates wastewater containing nanoparticles and / or microparticles. Diagnostic apparatus are used, particularly in the medical field, to identify the nature or cause of a particular phenomenon, and the information provided by the apparatus helps a clinician make a diagnosis about a patient's health condition. The diagnostic apparatus is preferably a medical diagnostic apparatus.

[0027] As used herein, the term "laboratory analyzer" includes any device or apparatus (typically automated) for use in a laboratory to qualitatively identify the presence or quantitatively determine the amount (e.g., typically the concentration) of a chemical or substance in a sample, and which generates wastewater containing nanoparticles and / or microparticles. The laboratory analyzer is preferably a medical laboratory analyzer (e.g., a medical laboratory analyzer). The sample may be, for example, serum, plasma, urine, or other bodily fluids from a human or animal patient. Analytes or analytes identified using the device or apparatus may include proteins, metabolites, electrolytes, or drugs. The laboratory analyzer may perform heterogeneous immunoassays. Examples of laboratory analyzers include the cobas® ELISA kit manufactured by the applicant. TM e 801 module and cobas TM There is a c 701 module.

[0028] Preferably, the smoothed permeability value is a moving average of the two or more most recent measured raw permeability values, covering a duration of at least 1 / 1000, preferably at least 1 / 100, and preferably at least 1 / 10 of the average time the filtration system will operate before it must be maintained.

[0029] A moving average of the last values ​​may be particularly suitable here for calculating the smoothed transmittance values, as these can be calculated quickly and easily, without requiring large computing power.

[0030] In principle, other methods are also conceivable, such as a moving low-pass filter.

[0031] Specifying the number of values ​​to be averaged depends on the respective field filtration system. The longer the field filtration system operates without maintenance, the shorter the period that can be selected. Selecting a longer period will result in a more accurate prediction. However, the longer the period, the greater the computational effort required for large amounts of data, which may cause delays in processing the data.

[0032] Optionally, the smoothed permeability value is a moving average of the two or more most recent measured raw permeability values, which may cover a duration of at least one minute, at least one day, or at least one week.

[0033] The period selected should not be too short, otherwise the regression analysis will be inaccurate.

[0034] Advantageously, at least the last two steps of applying the regression analysis function and determining the maintenance state are carried out when the permeability value is less than 90%, preferably less than 70%, preferably less than 40%.

[0035] If the smoothed permeability values ​​are lower than these values, it may be beneficial to run the filtration system in pulse cycle mode.

[0036] In the following description of the method for predicting maintenance conditions, unless it is explicitly stated that the permeability values ​​are raw permeability values, the permeability values ​​refer to smoothed permeability values.

[0037] Surprisingly, it has been found that membrane clogging can be reduced or prevented during cross-flow filtration by flowing wastewater across the surface of the membrane in pulsed cycles. This significantly improves the performance and efficiency of cross-flow filtration technology. Cross-flow filtration can be run for longer periods while maintaining a high permeate flow rate through the membrane. This reduces the need to periodically stop filtration to unclog and clean the membrane. It may also extend the membrane's lifespan.

[0038] For high permeability values, such as above 90%, predictions have been shown to be too uncertain.

[0039] It has been shown that when permeate flow rates are reduced below these values, especially below 40%, the data become more stable and the slope becomes flatter.

[0040] Optionally, at least the last two steps of applying the regression analysis function and determining the maintenance state are performed only if the smoothed permeability value is greater than 5%, preferably greater than 10%, preferably greater than 20%.

[0041] On the other hand, if the smoothed transparency values ​​are too low, especially if they are less than 5%, 10%, or 20%, the expected time correction when a maintenance condition exists cannot be expected. The analysis here will only consume unnecessary resources through computation.

[0042] Furthermore, the method may be configured such that the regression analysis function is the fitting function.

[0043] The fitting function is defined as a specific embodiment of a regression analysis function, characterized in that it is based on the least squares method. Such functions are already included in many software tools, and the use of such methods is also generally known. Implementing such methods in the system here is particularly easy.

[0044] However, in principle, other features of regression analysis are also possible, such as Bayesian methods, percentage regression, least absolute deviation, non-parametric regression, scenario optimization, or distance metric learning.

[0045] In an embodiment, the regression analysis function is the following function: - exponential function, preferably: f(t)=a*exp(-b*t)+c, - logarithmic function, preferably: f(t)=-a*ln(-b*t)+c, - a linear function, preferably: f(t)=-a*t, - quadratic function, preferably: f(t)=-a*t^2, -preferably a Taylor polynomial function of at least second order, at least third order, or at least fourth order; where t is time and a, b, and c are fitting parameters.

[0046] These functions are particularly well suited to trending permeability values ​​over time, and because they represent trends in permeability values ​​particularly well, the functions herein are also well suited to predicting future trends and therefore reliably indicating when a maintenance condition exists.

[0047] A method for predicting a recovery state of a filtration system of a diagnostic or laboratory analyzer at a given time point is performed by performing the following steps: First, a plurality of consecutive permeability values ​​of the permeability of a fluid through a filtration device are measured over a specified measurement cycle; Next, a regression analysis function is applied through the consecutive permeability values, the regression analysis function including fitting parameters adapted to fit the fitting function to the measured permeability values; Finally, the fitting parameters determine whether a recovery state exists.

[0048] The Recover status indicates that recovery of the filtration system, particularly the membrane, is recommended. Ideally, the filtration system will perform recovery automatically.

[0049] During the filtration process, particles can form a film on the membrane and block it. The more particles that accumulate on the filter's membrane, the greater the decay in permeability over time. The average permeability decreases.

[0050] Permanent damage to the membrane can be caused by fouling processes. When there is a fouling process acting on the membrane and the particle concentration is high, a particle cake forms on the membrane, which leads to rapid blockage. As the fouling process progresses, the rate at which the membrane becomes blocked increases.

[0051] Recovery can be achieved by reversing the fluid flow.

[0052] Restoration increases permeate flow, potentially extending filter life and increasing the time between maintenance appointments.

[0053] During the recovery process, filtration systems cannot be used to filter, so it is important to minimize recovery rates and perform them when they are most efficient.

[0054] The fitting parameters provide information about the general shape of the trend of the permeability values ​​over time. This shape can be used to draw conclusions about the need for restoration. The shape can be described by the fitting parameters.

[0055] If one or more of these parameters are within a predetermined range, this is an indication that the function describing the permeability indicates that recovery is necessary.

[0056] It has been shown that depending on the regression analysis function selected, one or more fitting parameters may correspond to the saturation of the filter membrane.

[0057] Thus, the behavior of one or more fitting parameters indicates the state of the membrane. A recovery state exists when the parameter or parameters reach a predetermined value or values. Analysis of the fitting parameters can determine whether the parameter value is within a predetermined range of values ​​or below a threshold.

[0058] The analysis of fitting parameter values ​​can be automated, and thus the method described herein for determining when a recovery state exists can be automated, allowing recovery to be initiated without user interaction, and predicting the moment when recovery will be most effective.

[0059] Preferably, the determination of recovery status is based primarily on a particular slope of the fitting function being above a particular threshold slope.

[0060] The recovery state is a predetermined indication that the filtration system is performing too poorly and recovery is necessary. For example, in a pulsed filtration system, the permeability value may be acceptable at the beginning of each cycle, but the permeability value decreases within each cycle. This decrease may be so rapid that the average permeability value over one cycle becomes too low, indicating that the filtration system is filtering poorly.

[0061] The further back in time the last recovery is, the greater the slope.

[0062] Optionally, the regression analysis function is a fitting function.

[0063] A fitting function is defined herein as a specific embodiment of a regression analysis function, characterized in that it is based on the least-squares method. Such functions are already included in many programs, and the use of such methods is also generally known. Implementing such methods in the system here is particularly easy.

[0064] However, in principle, other functions of regression analysis are also possible, such as Bayesian methods, percentage regression, least absolute deviation, non-parametric regression, scenario optimization, or distance metric learning. Advantageously, the regression analysis function is the following function: - exponential function, preferably: f(t)=a*exp(-b*t)+c, - logarithmic function, preferably: f(t)=-a*ln(-b*t)+c, - a linear function, preferably: f(t)=-a*t, - quadratic function, preferably: f(t)=-a*t^2, -preferably a Taylor polynomial function of at least second order, at least third order, or at least fourth order; where t is time and a, b, and c are fitting parameters.

[0065] These functions are particularly well suited to representing trends in permeability values ​​over time. Because they represent trends in permeability values ​​particularly well, the functions herein are also well suited to predicting future trends and therefore reliably indicating when recovery conditions exist.

[0066] Preferably, a regression analysis function is fitted to the continuous permeability values ​​by an optimization method.

[0067] The mathematical optimization method attempts to identify the optimal parameters of a regression analysis function to best match this function to the permeability values.

[0068] Optionally, the regression analysis function is an exponential function f(t)=a*exp(-b*t)+c, and the determination of whether a recovery state exists is determined solely by the fitting parameter b.

[0069] Although both parameters a and b affect the slope of the permeability function, parameter b alone has been shown to be sufficient to indicate whether the filtration system needs to be restored, as will be explained in more detail in the embodiments below.

[0070] Advantageously, the first three steps are repeated at predetermined intervals.

[0071] In particular, these predetermined intervals are predetermined by the periodic cycle applied to the periodically operating filtration system.

[0072] It can be used repeatedly to periodically check if a recovery condition exists.

[0073] Preferably, the measurement cycle determined in the first step is assigned to an active period of a pulse cycle.

[0074] When the active phase is used, permeability can be measured in a meaningful way.

[0075] The method can be used such that wastewater is passed across the surface of the filter membrane in pulsed cycles, with the or each pulsed cycle having an active phase and an inactive phase. The advantages provided by the first aspect of the invention relate to the use of this combination of active and inactive phases.

[0076] It is hypothesized that nanoparticles and / or microparticles on or inside the filter membrane can be redispersed into the wastewater during the inactive period. During the inactive period, there is little or no wastewater flow over the surface of the filter membrane, so the particles redisperse into the wastewater. The pulsing cycle may also create localized turbulence on the surface of the filter membrane, which may prevent particles from settling on or inside the filter membrane and / or break up the filter cake formed on or inside the filter membrane.

[0077] A pulse cycle includes an active period. Preferably, a pulse cycle includes a single active period.

[0078] Optionally, the predetermined interval lasts up to 3600 seconds, preferably 1200 seconds or less, more preferably 600 seconds or less.

[0079] The active period or periods have a duration that is greater than 50% of the corresponding pulse cycle. On the other hand, the inactive periods can reduce or prevent the formation of filter cake on the filter membrane, keeping the membrane surface clean. On the other hand, the inventors have also found that a longer active period and a shorter inactive period in a pulse cycle increases the effective transport time per pulse, thereby enabling greater throughput. In other words, if the duration of the active period in a pulse cycle is longer than the duration of the inactive period, more permeate can be obtained at the same pressure. Thus, by lengthening the active period of a pulse cycle compared to the inactive period, the efficiency of the filtering process is improved and energy consumption is reduced overall.

[0080] This effect arises from the following relationship: during the inactive phase, a portion of the particles forming the filter cake go back into solution. The longer the inactive phase, the more particles dissolve in the liquid. The dissolved particles are then washed away during the active phase. Laminar flow makes it possible for most of the dissolved particles to be washed away, with only a small portion being reintroduced into the filter cake. Therefore, the inactive phase should not be arbitrarily short.

[0081] Advantageously, the predetermined interval lasts for at least 5 seconds, preferably at least 30 seconds or more, more preferably at least 100 seconds or more.

[0082] Crossflow filtration systems can be used. During continuous use of a crossflow filtration system, the duty pressure and / or transmembrane pressure force particles against the filter membrane. These particles can accumulate on or within the filter membrane, forming a particle filter cake. This filter cake impedes the flow of liquid into or through the filter membrane, causing the filtration rate to exponentially decrease until a plateau is reached. At this point, filtration reaches a steady state, and the filtration rate stabilizes at the plateau level. This is the asymptotic value of steady state, described below. Once the filtration system reaches steady state, the same amount of energy is required as in the initial stages of filtration, but less liquid is filtered, resulting in reduced energy efficiency.

[0083] Therefore, the active period is preferably shorter than the time it takes for the permeate flow throughput to decrease from the maximum permeate flow throughput during the active phase to the steady-state permeate flow throughput. The active period is preferably 80% or less of the time required to reach steady state, preferably 60% or less, more preferably 50% or less, and even more preferably 40% or less of the time required to reach steady state. Steady state is an asymptotic value that is never achieved in practice. In the present invention, steady state is reached when the flow throughput is within 5% of the asymptotic value of the steady state.

[0084] This adjustment of the active phase with respect to the steady state can also be applied to other processes of cross-flow filtering of liquids by pulsating flow through membranes, particularly hollow fiber membranes.

[0085] The active period is preferably 5 to 3600 seconds, for example, 10 to 2800 seconds, preferably 30 to 1800 seconds, more preferably 60 to 1200 seconds, for example, 90 to 900 seconds, and further preferably 100 to 600 seconds.

[0086] The or each active period has a duration that is greater than 50% of the corresponding pulse cycle, and therefore the active period is greater than 50% of the period of the pulse cycle.

[0087] The inventors have found that if the active period is longer than the inactive period, the permeate throughput is improved while maintaining the energy efficiency of cross-flow filtration. For the avoidance of doubt, the inactive period cannot be zero in order for pulsed cycles to exist.

[0088] The ratio of the duration of the active phase to the total duration of the pulse cycle can be expressed as a parameter called the duty cycle. If the active period is greater than 50% of the period of the pulse cycle, this corresponds to a duty cycle greater than 50%.

[0089] The duty cycle may be 51% or more, preferably 55% or more, more preferably 57% or more, such as 60% or more, even more preferably 65% ​​or more, preferably 70% or more, and most preferably a duty cycle of 75% or more.

[0090] The duration of the active phase does not exceed 99% of the corresponding pulse cycle, preferably does not exceed 95%, more preferably does not exceed 90%, and even more preferably does not exceed 80%. In other words, the duty cycle does not exceed 99%, preferably does not exceed 95%. More preferably, the duty cycle is 85% or less, more preferably 83% or less, for example 82% or less, even more preferably 81% or less, and most preferably a duty cycle of 80% or less.

[0091] Any lower duty cycle limit may be combined with any upper duty cycle limit.

[0092] The duty cycle may be greater than 50% and not exceed 99%, preferably between 51% and 95%, more preferably between 55% and 90%, in particular between 55% and 85%, for example between 57% and 83%, in particular between 57% and 82%, even more preferably between 60% and 81%, in particular between 65% and 80%, even more preferably between 70% and 80%, most preferably a duty cycle (D) of between 75% and 80%.

[0093] The optimal duty cycle to provide maximum permeate throughput while minimizing the energy required to operate the pump depends on several factors, including the type of filter membrane, the wastewater flow rate, the TMP, and the nature of the microparticles and / or nanoparticles.

[0094] Optionally, at least two, preferably three, more preferably four fitting parameters of the regression analysis function are determined.

[0095] In principle, each parameter increases the degrees of freedom with which the regression function is fitted to the transparency values, thereby providing greater opportunity for achieving precision in fitting the function to the transparency values.

[0096] Optionally, the method for predicting a maintenance state of a filtration system and the method for predicting a recovery state of a filtration system are performed simultaneously according to any of the methods for predicting a recovery state described above.

[0097] By implementing both methods simultaneously, both the recovery and maintenance states can be monitored, which allows the filtration system to operate for particularly long periods of time, in that recovery can be successful in partially restoring the original initial performance, while predicting the maintenance state can produce the longest possible operation of the pump.

[0098] Recovery has been shown to have no significant effect on the time that filtration systems must be maintained. Recovery extends the time between maintenance.

[0099] Advantageously, the fluid is water, preferably wastewater, more preferably wastewater from a diagnostic device.

[0100] As used herein, the term "wastewater" refers to an aqueous solution containing nanoparticles and / or microparticles. Wastewater is waste obtained directly from a diagnostic device or laboratory analyzer. The nanoparticles and / or microparticles are used or unused reagents obtained from a diagnostic or laboratory analysis performed by the diagnostic device or laboratory analyzer. Wastewater may contain other waste or by-products resulting from the diagnostic or laboratory analysis, such as chemicals or substances being analyzed.

[0101] As used herein, the terms "nanoparticle" and "microparticle" generally refer to particles having a size of 5 mm or less. Microparticles have a particle size of 5 mm or less (preferably 1 mm or less) and 1.0 μm or more. Nanoparticles have a particle size of less than 1.0 μm (e.g., 999 nm or less) and 1 nm or more. Generally, nanoparticles and / or microparticles are reagents used in diagnostic devices or laboratory analyzers.

[0102] For the avoidance of doubt, all parameters relating to wastewater flow or permeate throughput relate to the temperature at which the method according to the first and / or second aspect of the invention is carried out or the temperature at which the system of the invention is operated. Typically, the invention is carried out or operated at room temperature (e.g., 20°C). Pressure figures refer to pressures above atmospheric pressure unless the context requires otherwise.

[0103] According to a further development, the filtration system is a cross-flow filtration system.

[0104] The method specifically relates to cross-flow filtration of a liquid (in this case, wastewater from a diagnostic device or laboratory analyzer), in which the liquid is passed or transported across the surface of a filter membrane in a laminar flow. Nanoparticles and / or microparticles are removed from the filter membrane by size exclusion. In other words, the pore size of the filter membrane must be small enough to prevent nanoparticles and / or microparticles from entering the pores.

[0105] In a first aspect of the invention, the method comprises flowing wastewater across the surface of a filter membrane at a flow rate such that the flow is laminar with a Reynolds number (Re) of less than 500. Preferably, the flow is laminar with a Reynolds number (Re) of less than 250, more preferably less than 150, and even more preferably with a Reynolds number (Re) of 100 or less, especially 75 or less. The Reynolds number is determined by the temperature of the wastewater at which the cross-flow filtration is carried out.

[0106] Typically, the flow of wastewater is a laminar flow with a Reynolds number (Re) of 1 to less than 500. The flow of wastewater is preferably a laminar flow with a Reynolds number (Re) of 5 to 250, more preferably 10 to 150, and even more preferably 15 to 100, particularly 20 to 75 or 10 to 75.

[0107] The filtration system of the diagnostic device or laboratory analyzer is designed to perform at least one of the above methods.

[0108] In further embodiments, the present invention relates to the following aspects:

[0109] 1. The maintenance status of the filtration system of a diagnostic device or laboratory analyzer at a given point in time: The following steps, a) measuring a plurality of consecutive raw permeability values ​​of the permeability of a fluid through a filtration device over a specified measurement period; b) determining smoothed permeability values ​​by data processing methods to reduce fluctuations in the raw permeability values ​​over time; c) applying a regression analysis function through the successive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function is fitted to the measured permeability values; and d) determining when the regression analysis function crosses a predetermined threshold, the crossing of the threshold being determined as a maintenance condition; A method for predicting by performing

[0110] 2. The method of embodiment 1, wherein the smoothed permeability value is a moving average of two or more of the most recent measured raw permeability values ​​covering a duration of at least 1 / 1000, optionally at least 1 / 100, and optionally at least 1 / 10 of the average time the filtration system will operate before maintenance must be performed.

[0111] 3. The method of aspect 1 or 2, wherein the smoothed permeability value is a moving average of two or more of the most recent measured raw permeability values ​​covering a duration of at least one minute, at least one day, or at least one week.

[0112] 4. The method of any one of aspects 1 to 3, wherein at least steps c) and d) are performed when the smoothed transmittance value is less than 90%, optionally less than 70%, and optionally less than 40%.

[0113] 5. The method of any one of aspects 1 to 4, wherein at least steps c) and d) are performed only if the smoothed transmittance value is greater than 5%, optionally greater than 10%, and optionally greater than 20%.

[0114] 6. The method according to any one of aspects 1 to 5, wherein the regression analysis function is a fitting function.

[0115] 7. The regression analysis function is: - exponential function, preferably: f(t)=a*exp(-b*t)+c, - logarithmic function, preferably: f(t)=-a*ln(-b*t)+c, - a linear function, preferably: f(t)=-a*t, - quadratic function, preferably: f(t)=-a*t^2, - optionally a Taylor polynomial function of at least degree 2, at least degree 3, or at least degree 4; 7. The method according to any one of aspects 1 to 6, wherein t is time, and a, b, and c are fitting parameters.

[0116] 8. The recovery status of the filtration system of a diagnostic device or laboratory analyzer at a given time point: The following steps, a) measuring a plurality of successive permeability values ​​of the permeability of a fluid through a filtration device over a designated measurement cycle; b) applying a regression analysis function through the successive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function is fitted to the measured permeability values; and c) determining whether a recovery state exists by fitting parameters; A method for predicting by performing

[0117] 9. The method of embodiment 8, wherein the determination of the recovery state is based primarily on a particular slope of the fitting function being above a particular threshold slope.

[0118] 10. The method according to aspect 8 or 9, wherein the regression analysis function is a fitting function.

[0119] 11. The regression analysis function is: - exponential function, preferably: f(t)=a*exp(-b*t)+c, - logarithmic function, preferably: f(t)=-a*ln(-b*t)+c, - a linear function, preferably: f(t)=-a*t, - quadratic function, preferably: f(t)=-a*t^2, - optionally a Taylor polynomial function of at least degree 2, at least degree 3, or at least degree 4; 11. The method according to any one of aspects 8 to 10, wherein t is time, and a, b, and c are fitting parameters.

[0120] 12. The method according to aspect 10 or 11, wherein a regression analysis function is fitted to the successive permeability values ​​by an optimization method.

[0121] 13. The method according to aspect 11 or 12, wherein the regression analysis function is an exponential function f(t)=a*exp(-b*t)+c, and the determination of whether a recovery state exists is determined solely by the fitting parameter b.

[0122] 14. The method according to any one of aspects 8 to 13, wherein steps a) to c) are repeated at predetermined intervals.

[0123] 15. The method according to any one of aspects 8 to 14, characterized in that the measurement cycle determined in step a) is assigned to an active phase of a pulse cycle.

[0124] 16. The method of aspect 14 or 15, wherein the predetermined interval lasts up to 3600 seconds, optionally not more than 1200 seconds, and optionally not more than 600 seconds.

[0125] 17. The method according to any one of aspects 14 to 16, wherein the predetermined interval lasts for at least 5 seconds, optionally at least 30 seconds or more, and optionally at least 100 seconds or more.

[0126] 18. The method according to any one of aspects 11 to 17, characterized in that at least two, optionally three, and optionally four fitting parameters of the regression analysis function are determined.

[0127] 19. A method according to any one of aspects 1 to 7, characterized in that the method according to any one of claims 8 to 18 is carried out simultaneously.

[0128] 20. The method according to any one of aspects 1 to 19, characterized in that the fluid is water, optionally wastewater, and optionally wastewater from a diagnostic device.

[0129] 21. The method according to any one of aspects 1 to 20, wherein the filtration system is a cross-flow filtration system.

[0130] 22. A filtration system of a diagnostic device or laboratory analyzer designed to carry out the method of any one of aspects 1 to 21.

[0131] The invention is explained in more detail below by means of exemplary embodiments shown in the drawings. [Brief explanation of the drawings]

[0132] [Figure 1] Cross-flow filtration system. [Figure 2] Illustrative permeability of cross-flow filtration systems in continuous and pulsed modes. [Figure 3] FIG. 1 is a block diagram for the operation of a crossflow filtration system. [Figure 4] 1 is a block diagram for a method for predicting the maintenance status of a filtration system. [Figure 5] Illustration of fitting functions for predicting permeability and maintenance status. [Figure 6] Diagram of permeability over time. [Figure 7] Zoomed view of transparency over time. [Figure 8]Further zoomed view of the permeability over time of the cross-flow filtration system in a pulsed mode. [Figure 9] The fitting function of the transmittance over time is shown in Figure 8 . [Figure 10a] Zoomed-in view of a section of a hollow fiber in a cross-flow filtration system with a particle layer at the beginning of the duty cycle. [Figure 10b] Zoomed-in view of a section of a hollow fiber of a cross-flow filtration system with a particle layer at the end of a duty cycle. [Figure 10c] Zoomed-in view of a section of a hollow fiber from a cross-flow filtration system with a particle layer that forms an irreversible filtration resistance, accompanied by permanent damage such as an irreversible fouling process. [Figure 11] Diagram of the exponential a parameter of the fitting function over time. [Figure 12] Diagram of the exponential b parameter of the fitting function over time. [Figure 13] 1 is a block diagram for a method for predicting the recovery state of a filtration system method. DETAILED DESCRIPTION OF THE INVENTION

[0133] In the following, a filtration system 1 for carrying out a method for predicting the maintenance state of a filtration system and a method for predicting the recovery state of a filtration system is described (FIG. 1).

[0134] The filtration system 1 comprises a filtration module 2 and an analysis module 3 .

[0135] The filtration module 2 is described in detail in unpublished European patent application 22157842.0, which is incorporated herein by reference.

[0136] The filtration module 2 comprises a sensor 4 for detecting overflow, a sensor 5 for detecting maximum fill level, a sensor 6 for detecting minimum feed, a wastewater feed conduit 7, a pump 8, a permeate conduit 9, a filter module 10, a filter membrane 11, a flow restrictor such as a pressure limiter 12, a retentate conduit 13, a vessel 14, a pre-filter 15, a flow sensor 16, a permeate vessel 17, a sensor 18 for detecting the wastewater flow rate such as a flow sensor, and a controller 19 such as an electronic microcontroller.

[0137] The pressure source is pump 8. Pump 8 draws wastewater (e.g., feed) through conduit 7 from container 14 through pre-filter 15. The operation of pump 8 is controlled by electronic microcontroller 19 via electrical coupling. The flow rate of wastewater entering filter module 10 is measured using pressure sensor 18 electrically coupled to electronic microcontroller 19. Wastewater from pump 8 is forced or conveyed into filter module 10, which has filter membrane 11.

[0138] If the filter membrane is not clogged, permeate is produced and enters conduit 9. The flow rate of the permeate in conduit 9 is measured using flow sensor 16. Flow sensor 16 is electrically connected to an electronic microcontroller 19 and provides information regarding the amount of permeate produced during filtration. The permeate passes through flow sensor 16 and may be collected in permeate container 17.

[0139] Any wastewater from the feed that does not pass through filter membrane 11 becomes retentate. The retentate exits filter module 10 in conduit 13 and flows into a flow restrictor, in this case pressure limiter 12. Pressure limiter 12 is used to control the pressure of the feed / retentate on the feed / retentate side of filter membrane 11. From pressure limiter 12, the retentate is returned to vessel 14 and reused as part of the liquid feed.

[0140] A signal is sent via the electrical coupling to the pump 8 to sequentially turn the pump on and off to generate a pulse cycle. The on-off switching of the pump controls the length of the active and inactive periods of the pulse cycle. The magnitude of the duty pressure is determined in part by the flow rate output from the pump 8.

[0141] The pressure within the system is monitored using pressure sensor 18 and adjusted using pressure limiter 12. Pressure limiter 12 can also be used to adjust the active period and magnitude of the pulse cycle. The active period or duty cycle of the pulse cycle can be varied until flow sensor 16 detects permeate with a flow rate that meets some minimum threshold. At this point, the parameters for the pulse cycle can be fixed and crossflow filtration can be implemented. If flow sensor 16 detects a decrease in permeate flow rate below some minimum threshold, the pulse cycle can be adjusted until the permeate flow rate again exceeds the threshold.

[0142] The vessel 14 has a sensor 4 for detecting wastewater overflow from the vessel 14. The sensor 4 is electrically connected to an electronic microcontroller 19. When the sensor 4 detects that the vessel 14 is full, the electronic microcontroller 19 may sound an alarm.

[0143] The vessel 14 has a sensor 5 for detecting the maximum amount of wastewater in the vessel 14. The sensor 5 is electrically connected to an electronic microcontroller 19. When the sensor 5 detects that the amount of wastewater has reached the maximum level, the electronic microcontroller 19 may issue a notification. The notification may ask the end user whether to start the cross-filtration process or whether to start filtration automatically.

[0144] The vessel 14 has a sensor 6 for detecting a minimum amount of wastewater in the vessel 14. The sensor 6 is electrically connected to an electronic microcontroller 19. When the sensor 6 detects that the minimum amount of wastewater has been reached, the electronic microcontroller 80 may sound an alarm and / or switch off the pump 10.

[0145] In this embodiment, the analysis module 3 is embodied as a computer on which software is stored and executable to carry out the procedures for determining the maintenance status and / or the procedures for determining the recovery status.

[0146] A computer may be understood herein as a system having a processor and a memory. For example, a computer may be a mobile terminal such as a PC, a laptop, a mobile phone, a tablet, or a laptop, but a computer may also represent a server or be implemented as a microcontroller. A computer may also be accessible as a cloud application via the Internet.

[0147] The software is composed of several modules that exchange data through channels. A channel is a logical data connection between individual modules. The modules can be software modules as well as hardware modules.

[0148] The analysis module 3 is connected to the sensors of the filtration module 2 via a data link 20 .

[0149] The analysis module 3 also has a display device (not shown) and a data input device (not shown) to which corresponding commands can be input and results, predictions, can be output. In the simplest case, the data output is a monitor.

[0150] In the following, a process is described that roughly describes a maintenance cycle of the above-mentioned filtration system, which here should be understood as the period between two maintenance operations.

[0151] The process begins at step S1 (FIG. 3).

[0152] Subsequently, in step S2, the filtration system 1 is in a continuous pumping process, which means that the wastewater is continuously filtered (FIG. 2).

[0153] During this process, the permeability of filtration system 1 is also measured. In this example embodiment, flow sensor 16 and sensor 18 measure values ​​from which a permeability value can be calculated. The sensor values ​​are passed to analysis module 3 for this purpose, which then performs the calculation.

[0154] The permeability was also converted into a percentage value, with 100% corresponding to the permeability performance immediately after maintenance and 0% corresponding to occlusion, i.e., no filtering at all.

[0155] Step S3 now checks whether the percentage transparency value is less than 50%. If not, the system continues filtering continuously and after a predefined time, the relative transparency is queried again.

[0156] If the relative permeability is less than 50%, a pulsation protocol is applied to the filtration system in step S4 (Figure 6).

[0157] This process is described in detail in European Patent Application 22157842.0, which is incorporated herein by reference.

[0158] In the next step S5, it is checked whether the relative permeability is less than 40% and whether the ratio of active time to total time of the cycle is greater than 79%. If not, the pulsation protocol is applied in step S4.

[0159] On the other hand, if the request is answered in the affirmative, data analysis begins at step S6, which is described in more detail below. Step S7 follows, and the next maintenance time is determined, a process which is also described below.

[0160] Step S8 queries whether the relative permeability is less than 20%. If not, data analysis continues according to steps S6 and S7.

[0161] However, if the query is answered in the affirmative, data acquisition stops at step S9.

[0162] The process ends at step S10.

[0163] The following describes the process of analyzing the data according to step S6 in order to determine the next maintenance state in advance according to step S7.

[0164] The process starts at step S11 (FIG. 4).

[0165] In step S12, the raw permeability value is measured. To this end, the pressure of the fluid is measured by sensors 16 and 18 in order to determine the raw permeability value therefrom. In other embodiments, other parameters such as the flow rate or weight of permeate vessel 17 can also be measured.

[0166] In this exemplary embodiment, a new value is determined every 10 seconds.

[0167] According to step S13, a smoothed permeability value is then determined from the raw permeability values. For this purpose, the data is smoothed using a moving average algorithm. A time frame of 800 seconds is selected for the algorithm. It is important that the averaging window is greater than the duration of one cycle, preferably greater than the duration of three cycles, and more preferably greater than the duration of ten cycles. Only then can outliers or deviations resulting from the cyclical operation of the field control system be compensated for. For example, very high permeability values, especially at the beginning of a cycle, result from cyclical operation, while a permeability value of 0 is calculated during inactive periods.

[0168] In step S14, a regression analysis function is applied to the smoothed transmittance values.

[0169] From this, the usual known process of mathematical optimization is applied.

[0170] The functions assumed here are exponential or decaying functions. f(t)=a*exp(-b*t)+c where t is time and a, b, and c are fitting parameters (Figure 7).

[0171] In step S15, the next maintenance state is determined.

[0172] For this purpose, a predetermined function of the corresponding parameters found is considered to determine the time to reach 5% permeability.

[0173] 5% is a predetermined value, and other predetermined values ​​that define the maintenance state as a threshold can also be specified.

[0174] Also, for example, Darcy or m 2 It is also possible that the absolute transparency value specified in is specified as a threshold.

[0175] The determined time, ideally in the future, can then be communicated to the user accordingly.

[0176] Appropriate measures can therefore be taken to ensure that maintenance can be carried out at a corresponding time, such that the corresponding downtime is minimised and / or the maintenance is carried out in a period that is deemed acceptable to the user, which may include, for example, appropriate holiday periods or overnight hours.

[0177] The process ends at step S16.

[0178] The process for determining recovery status is described below.

[0179] The process begins at step S17 (FIG. 13).

[0180] In step S18, the raw transmittance values ​​are determined, which is done in the same way as described in step S12.

[0181] In the following step S19, a regression analysis function is applied from the measured raw permeability values. Essentially, the application of this function corresponds to step S14, in which the raw permeability values ​​are used.

[0182] According to this exemplary embodiment, only data from a single active cycle from the pulsating operation of the filtration system is used here.

[0183] The function here also corresponds to the exponential function described in step S14.

[0184] The corresponding function is applied to the data via a mathematical optimization algorithm.

[0185] This gives us the a and b parameter values, both of which are related to the slope of the function. Figure 8 shows some slopes for different cycles. Figure 9 shows the corresponding fitting function with the resulting a and b parameter values.

[0186] The c parameter is a constant and is given as a value in this example embodiment.

[0187] The inventors recognize that there are physical principles associated with both the a and b parameter values.

[0188] Plotting the resulting a-parameter values ​​over several cycles shows that the a-parameter values ​​steadily decrease with time.

[0189] Thus, the a parameter value is associated with a steadily deteriorating filtration system that cannot be repaired by restoration (see Figure 11).

[0190] On the other hand, the b parameter value (see Figure 12) decreases as the distance to the last recovery increases (negative b parameter values ​​are plotted in the figure). With each new recovery, the b parameter value decreases again, resulting in the zigzag behavior shown in Figure 12.

[0191] This can be explained by looking at how different particles adhere to the membrane 11. In the case of a membrane that has been refreshed immediately after maintenance and recovery, no or very few particles are found on the membrane 11 (see Figure 10a). These light particles form a thin, removable film 21 through which wastewater can still flow.

[0192] The removable film 21 can be dissolved again by recovery.

[0193] However, if more recovery cycles are performed and no maintenance is performed for a long period of time, a thick film 21 will continue to build up (see FIG. 10B).

[0194] However, at some point, recovery is not sufficient to remove all of the removable film 21 from the filter membrane 11. A permanent, irreversible film 22 then builds up on the membrane 11. This can result in permanent damage and fouling to the membrane 11.

[0195] The inventors have found that irreversible films can also result from degradation processes, for example from pore particles, and a measure of these irreversible processes is given by the a parameter value.

[0196] The b parameter value refers to the thickness of the removable film 21 .

[0197] In step S20, it is determined whether a recovery condition exists.

[0198] To this end, a recovery state is determined when the b parameter value falls below a predetermined threshold, here preferably −0.01 in the exemplary embodiment.

[0199] The process ends at step S21.

[0200] In an alternative embodiment, a maintenance state may also be determined by the process, which is used to determine the recovery state by considering when the a parameter value is or will be below a predetermined threshold.

[0201] Here, additional adjustment of the trend of the a parameter value over several cycles is required. From additional fitted functions for several a parameter values, it is possible to determine when the a parameter value falls below a certain threshold.

[0202] Here, an exponential function is also advantageous as the fitted function. [Explanation of symbols]

[0203] 1. Filtration system 2 Filtration Module 3. Analysis Module 4 Overflow Sensor 5 Maximum filling level sensor 6 Sensor to detect minimum amount of feed 7 Wastewater feed pipe 8. Pump 9 Permeate conduit 10 Filter Module 11 Filter membrane 12 Pressure Limiter 13 Retentate conduit 14 Container 15 Pre-filter 16 Flow Sensor 17 Permeate container 18 Sensor for detecting wastewater flow rate 19 Controller 20 Data Links 21 Thin Removable Film 22 Persistent and irreversible resistance

Claims

1. The maintenance status of the filtration system of a diagnostic device or laboratory analyzer at a given point in time: The following steps, a) measuring a plurality of consecutive raw permeability values ​​of the permeability of a fluid through said filtration device over a designated measurement period; b) determining smoothed permeability values ​​by data processing methods to reduce fluctuations in the raw permeability values ​​over time; c) applying a regression analysis function through the successive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function is fitted to the measured permeability values; and d) determining when the regression analysis function crosses a predetermined threshold, wherein crossing the threshold is determined as a maintenance condition; A method for predicting by performing

2. 2. The method of claim 1, wherein the smoothed permeability value is a moving average of two or more of the most recent measured raw permeability values ​​covering a duration of at least 1 / 1000, optionally at least 1 / 100, and optionally at least 1 / 10 of the average time the filtration system will operate before maintenance must be performed.

3. 3. The method of claim 1 or 2, wherein the smoothed permeability value is a moving average of two or more of the most recent measured raw permeability values ​​covering a duration of at least one minute, at least one day, or at least one week.

4. 4. The method according to claim 1, wherein at least steps c) and d) are performed when the smoothed transmittance value is less than 90%, optionally less than 70%, and optionally less than 40%.

5. 5. The method according to any one of claims 1 to 4, characterized in that at least steps c) and d) are only performed if the smoothed transmittance value is greater than 5%, optionally greater than 10%, optionally greater than 20%.

6. The method according to any one of claims 1 to 5, characterized in that the regression analysis function is a fitting function.

7. The regression analysis function is the following function: - exponential function, preferably: f(t)=a*exp(-b*t)+c, - logarithmic function, preferably: f(t)=-a*ln(-b*t)+c, - a linear function, preferably: f(t)=-a*t, - quadratic function, preferably: f(t)=-a*t^2, - optionally a Taylor polynomial function of at least degree 2, at least degree 3, or at least degree 4, 7. The method according to claim 1, wherein t is the time and a, b, and c are the fitting parameters.

8. The recovery status of a diagnostic device or laboratory analyzer filtration system at a given point in time is The following steps, a) measuring a plurality of successive permeability values ​​of the permeability of a fluid through said filtration device over a designated measurement cycle; b) applying a regression analysis function through the successive permeability values, the regression analysis function including fitting parameters adapted such that the fitting function is fitted to the measured permeability values; and c) determining whether a recovery state exists according to the fitting parameters; A method for predicting by performing

9. 9. The method of claim 8, wherein the determination of recovery status is based primarily on a particular slope of the fitting function exceeding a particular threshold slope.

10. 10. The method of claim 8 or 9, wherein the regression analysis function is the exponential function f(t)=a*exp(-b*t)+c, and the determination of whether the recovery state exists is determined solely by the fitting parameter b.

11. Method according to any one of claims 8 to 10, characterized in that the measurement cycle determined in step a) is assigned to an active phase of a pulse cycle.

12. Method according to any one of claims 1 to 7, characterized in that the methods according to any one of claims 8 to 11 are carried out simultaneously.

13. A method according to any one of claims 1 to 12, characterized in that the fluid is wastewater from a diagnostic device.

14. The method according to any one of claims 1 to 13, characterized in that the filtration system is a cross-flow filtration system.

15. A filtration system of a diagnostic device or laboratory analyzer designed to carry out the method according to any one of claims 1 to 14.

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