The present invention relates to the field of filtration of water from natural or outdoor sites. For example, the sites can be rivers, lakes, seas or oceans. The water is thus likely to incorporate impurities (algae, marine animals, leaves or dead branches and other waste, particularly organic). These elements can then clog rotary filters (for example drum filters) intended to filter the water before its use, for example but not exclusively upstream of steam generation circuits in electricity production facilities.
These elements, hereinafter called “clogging agents” (plants or living organisms) can indeed be drawn into any facility's pumping stations for supplying water. This may be a pumping station of a power plant, or of a drinking water production plant, or other facility. In any water pumping application of the above type, there is a risk of clogging the rotary filters (drum filters or chain filters) provided in the pumping station.
In certain applications of the above type, the risk of insufficient or zero flow downstream of the rotary filters cannot be allowed. Also, a solution is desired to optimize the design of the filters according to the minimum desired flow rate downstream of the filters, or at least the maximum tolerated pressure loss downstream of the filters, or at least a tolerable evolution over time of this pressure loss.
There is no precise quantitative technique for taking into account the effect of the arrival of these clogging agents, in the designing of mobile filtration systems of water pumping stations, with a quantitative definition of the design parameters of these filters (filter size, rotation speed, washing system).
The present invention improves this situation.
For this purpose, it proposes a method for managing a facility for pumping water originating from a natural environment likely to contain impurities, the facility being intended to make use of at least one rotary filter to purify the pumped water while at least some of the impurities are at least partially clogging the rotary filter.
In particular, the method comprises an estimation of an evolution over time of a pressure loss caused by the clogging of the rotary filter by impurities, based at least on:
Such an embodiment then makes it possible to anticipate the pressure loss over time, in order to optimize the operating conditions of the pumping facility, or even to design filters optimized for the requirements of this pumping facility.
In one embodiment, the rotary filter comprises at least one cylinder:
In this embodiment, the flow rate of water through the filter Qfiltre can be calculated at least as a function of the water level upstream of the filter Namont determined relative to the same reference as the water level Naval downstream of the filter, for:
as follows:
where:
The case (less problematic compared to pressure loss) where
gives a slightly different equation for Qfiltre presented below in the following detailed description.
In the above expression, the ratio f1 is given by f1=f0(1−Tc1), f0 being a constant relating to the porosity of the clean filter, and Tc1 being a clogging level of the filter at a current moment.
In an embodiment where the filter comprises a cross-wire sieve, the aforementioned Reynolds number is given by:
with δ=α0+δ0α0√{square root over (1−Tc1)}
In this embodiment, the velocity of the water upstream of the filter is calculated by:
with ξ being a pressure loss coefficient of the filter.
In one embodiment, ξ is the pressure loss coefficient of the submerged section of the filter, given by an Idel'chik correlation adapted to a fine mesh, and is expressed by:
In one embodiment, the evolution over time of the pressure loss is a function of at least one clogging level of the filter at a current moment and of the time derivative of this clogging level, and the method comprises a step of solving at least one differential equation associated with this clogging level and making use of said data relating to the natural environment.
In an embodiment where the rotary filter comprises one or more nozzles for washing at at least one given point of water jet impact on the inside periphery of the filter, the clogging level of the rotary filter is determined for three distinct types of filter portions:
Thus, the position of the impact of the jet from the nozzles on the inside peripheral wall of the filter plays a role in the pressure loss evolution calculations, as explained in detail further below and with reference to
In one embodiment, the first, second, and third clogging levels are linked by the differential equations:
VF, the rotation speed of the filter, is usually imposed by the operation of the filter according to the measured pressure loss thresholds.
In this embodiment, for a rotary filter of the drum type, of given radius RF, rotated about a given axis of height Naxe relative to a given reference, and of given width LF defined parallel to its axis of rotation:
where:
In an alternative embodiment where the rotary filter is of the chain filter type comprising an upper rotary cylinder and a lower rotary cylinder which are connected by a chain:
if
Namont>Naxe
and by
if Namont<Naxe
with Naxe<Naval and Namont<NFsup,
In one embodiment, the flow rate of water through the filter Qfiltre can be calculated as a function of a clogging level of the filter corresponding to said first clogging level Tcl in said first portion of the filter, mentioned above.
Thus, in one possible embodiment, the dimensions of the filter can be chosen as a function of the requirements of the facility in terms of the flow rate of drawn-in water, and in anticipation of clogging of the filter by impurities from the natural environment.
In an additional or alternative embodiment, the rotary filter can then be rotated at a variable speed as a function of an estimated anticipated pressure loss, given by the calculation of said evolution over time of the pressure loss, for a filter of given properties and dimensions.
In an additional or alternative embodiment, the flow rate of water drawn in downstream of the filter, for the requirements of the pumping facility, can be estimated in advance from the calculation of said evolution over time of the pressure loss, for a filter of given properties and dimensions.
The present invention also relates to a device comprising a processing circuit for implementing the method according to the invention.
It also relates to a computer program comprising instructions for implementing the method according to the invention, when this program is executed by a processor (and/or a storage medium storing the data of such a computer program, possibly in a non-transitory manner).
Other features and advantages of the invention will be apparent from reading the following detailed description of some exemplary embodiments and from examining the appended drawings in which:
The invention presented in the exemplary embodiment below proposes to model the evolution of the pressure loss at a rotary filter (or the evolution of the water level downstream of a rotary filter), this pressure loss being generated by an influx of clogging agents.
Different configurations for the operation of the filter and an analysis of the clogging kinetics for each configuration are proposed below.
In the following, it is assumed that a rotary filter operates as presented below with reference to
The water thus filtered inside the filter can be collected as illustrated in
In addition, two types of rotary filter are distinguished below:
The parameters appearing in the two
Table 1: Geometric Parameters of the Filter
The last parameter of Table 1 above relating to the position of the washing nozzles is explained below with reference to
The filter operating parameters are presented in the following Table 2.
Table 2: Filter Operating Parameters
Further defined are parameters characterizing the clogging agents:
The clogging agent concentration data is linked to knowledge of local hydrobiology. It may for example be estimated based on the biomass removed at the pumping station during prior clogging events. These data may be derived from an observation and may be measured. They can thus characterize a parameter presented in the equations below.
The principle of calculating the evolution of the pressure loss in the event of an influx of clogging agents is presented below.
The evolution of the level downstream of the rotary filter is calculated in particular, which depends on:
In addition, we adopt the hypothesis in which the concentration of clogging agents is uniformly distributed in the water column. This concentration may be constant or variable over time.
We also adopt the hypothesis that all clogging agents which reach the filter are retained thereon.
Next, it is advantageous to take into account transient phenomena in order to properly represent clogging dynamics: the flow rate through the filter is not necessarily equal to the drawn-in flow rate during clogging.
At the initial time, a clean filter and a steady state are assumed so that initially the flow rate through the filter corresponds to the drawn-in flow rate. Then the variations of the main variables between times t and t+dt in three sequences are calculated with a system of coupled equations, as follows:
The calculation notations are summarized in the table below.
Table 3: Calculation Variables
The evolution of the clogging level is calculated as presented below.
By definition, the clogging level Tc is the ratio of the volume of clogged fluid to the initial volume of fluid. Expressed differently, by denoting as f the porosity of a clogged filter represented as the ratio of the void volume for fluid to the total volume of the filter considered (more solid fluid), we have:
f0 being the porosity of the clean filter.
The solid volume corresponds to the sum of the volumes of the clean filter and of the accumulated clogging, as follows:
For a “two-dimensional” filter (grouping filters of the grille, drum filter or chain filter type), these volume ratios correspond to the ratios of the sections.
The sections corresponding to the physical dimensions in which the filters are designed are therefore used below to describe the numerical models.
We seek to compare in particular the clogged surface area and fluid surface area of the filter.
The fluid section is given by:
Sfluide=f0(Stot−Scolm)
The total clogged section is linked to the clogging level:
Scolm=TcStot
Furthermore, the clogging agent is characterized by the “clogging capacity” denoted P and such that:
Mc=PScolm=TcPStot, Mc being the mass of clogging agents (in kg) on the filter and associated with the clogged surface.
The surface area of the drum filter or of the bottom of the chain filter in contact with clogging agents is defined according to the three cases of
In
Sbal=S0+LFVFt, therefore corresponding to a swept surface area greater than S0 but less than the total external surface area of the drum Smax. In
We can more particularly consider three adjacent and complementary zones, relative to the swept surface area Sbal in the first rotation(s) of the rotary filter (first revolution(s) of the filter, typically before the establishment of a steady state after several rotations):
We define the masses exchanged between the zones, over the time interval dt:
In zone Z1 (section S0), the mass balance over time dt is written:
dMentrant+dM1−dM2=PdScolm1
We obtain a first relation:
In zone Z2 (section Sbal-S0) the mass balance over time dt is written:
dM2−dM3=PdScolm2
We obtain a second relation:
More precisely, zone Z2 is defined in more detail as a function of the position of the impact of the jet from the nozzles, and the above relation is valid as long as Sbal<Sαlav
On the other hand, for Sbal≥Sαlav, we have:
In zone Z3 (section Smax-Sbal), for Sbal≤Sαlav, the clogging level is zero and we write:
We obtain the third relation:
Next, the flow rate through the filter can be calculated as follows. It is composed of a submerged flow rate and a non-submerged flow rate, the first being described by the Idel'chik equation, the second by a law established from tests in channels using a perforated plate of known porosity (according to internal studies of the Applicant):
Qfiltre(t)=Qim(t)+Qem(t)
The submerged flow rate Qim(t) can be calculated by an Idel'chik law as a function of the pressure loss.
In the general case where the clogging level varies along the submerged upstream surface, the submerged flow rate is:
The local velocity upstream of the section dS is calculated as a function of the pressure loss of the filter by:
The coefficient ξ depends on the local clogging level in the section dS. If the clogging level is uniform over the submerged section, the coefficient ξ is uniform and we obtain:
the term Sam ultimately only representing the previously calculated section S0.
For the clogged mesh of the filter, the coefficient ξ is given by an Idel'chik correlation adapted to a fine mesh:
here in particular with f=f1=f0(1−Tc1), f0 being a constant relating to the porosity of the clean filter, and Tc1 the clogging level of the filter in the first zone Z1.
The coefficient kRe depends on the Reynolds number Re calculated by:
with ν the kinematic viscosity of water (constant and equal to 0.000001 m2/s at 25° C.) and δ the diameter of the wire of the clogged mesh such that:
δ=α0+δ0−α0√{square root over (2Tc)}
For Re<400: kRe=1+0.7e−0.0106 Re
For Re>400: kRe=1
Here Tc=Tc1.
For a drum filter, the upstream section is:
For a chain filter, the upstream section is:
The non-submerged flow rate is then calculated by a correlation established from tests in channels using a perforated plate of known porosity (based on internal results of the Applicant). According to these tests, the non-submerged flow rate is calculated as a function of the cross-sectional area for the passage of fluid (fSem) and of the pressure loss, by:
QemCdƒSem√{square root over (2g(Namont−Nαval))}
where the flow coefficient Cd is dependent on:
for x<0.65: Cd=−0.8x3+2.48x22.60x+1.51
for x>0.65: Cd=0.6
These values of Cd are thus calculated as a function of the parameter x as dependent on the pressure loss (Namont−Naval). These formulas were established by tests in channels conducted by the Applicant and it therefore has proved preferable to take account the two formulas depending on whether x>0.65 or x<0.65.
For a drum filter, the section of the non-submerged filter is:
Sem=LFRF(αNamont−αNaval)
whether for Namont or N=Naval
For a chain filter, in the most frequent case where Naxe<Naval and Namont<NFsup, the section of the non-submerged filter is:
In a chain filter, the washing nozzles are on the upper cylinder above level NFsup and the position of the jet impact is used in the calculations as for a drum filter, but with a slightly different formula.
The evolution of the downstream level is then determined by solving the mass balance equation on the downstream volume:
We denote as Vf(N) the volume downstream of the filter associated with a level N.
For a drum filter, this volume is equal to:
with:
Thus:
For a chain filter, this volume depends on HN and αF:
For Namont>Naxe, this volume is
and we have:
For Namont<Naxe, the downstream volume and its derivative are calculated as for a drum filter.
To summarize below, as illustrated in
In step S1, characteristics specific to the pumping site are also taken into account:
Furthermore, in step S2, the following variables are also taken into account, which are liable to change over time and which constitute input parameters for the calculations carried out thereafter:
In practice, a local measurement of the pressure loss is provided at different times by sensors for levels Namont and Naval. Based on this measurement, a drum filter is rotated at a lower or higher speed VF(t) depending on the periodically measured pressure loss.
Indeed, the usual operation of a filter (drum or chain) is to rotate according to the pressure loss measured by sensors (measuring upstream and downstream levels). More particularly, the rotation speed VF is dependent on the exceeding of a pressure loss threshold value, after obtaining the abovementioned pressure loss measurement. Thus, the rotation speeds can be successively triggered as a function of the respective pressure loss thresholds Dp successively reached.
In step S3, from this we deduce the following variables used in the calculations and specific to the data of the filters, as installed on site (case of a drum):
Next, in the initialization step S4, initial conditions are determined at time t=0, at which time it is assumed that the filter is clean and that the flow rate is in a steady state, as follows:
Then, in step S5, at each time increment (dt):
In step S6, we can then posit the system with three differential equations over time, relating to the evolution of clogging levels, as seen above:
In this equation system, the flow rate through the filter Qfiltre is initially given by Qfiltre(0)=Qasp(0) and it is this value Qasp(0) that it initially uses to calculate dTc1 at time t=0+dt, as well as dTc1, dTc2 and dTc3, and then from this to deduce new values of Tc1, Tc2 and Tc3.
Next, in step S7, the new flow rate through the filter is deduced as a function of the clogging rate Tc1 and of the pressure loss, as follows, here by way of example in the most common embodiment where the filter is a rotary drum such that:
with δ=α0+δ0α0√{square root over (1−Tc1)} and
The value of the flow rate through the filter found in this manner Qfiltre, can then be reinjected into the first equation
dt to determine dTc1 for the next time t+dt, as well as dTc1, dTc2 and dTc3, and from there, Tc1, Tc2 and Tc3 again.
Finally, the evolution over time of the downstream level as a function of the flow rate of the filter and the drawn-in flow rate can also be given in step S8 by:
The invention can be used for:
The method of
The advantages of this method are as follows:
This method, as well as the above associated calculations, can be implemented by a computer program for which the general algorithm follows the flowchart presented above with reference to
Number | Date | Country | Kind |
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17 61372 | Nov 2017 | FR | national |
Filing Document | Filing Date | Country | Kind |
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PCT/EP2018/080331 | 11/6/2018 | WO |
Publishing Document | Publishing Date | Country | Kind |
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WO2019/105690 | 6/6/2019 | WO | A |
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International Search Report, dated Jan. 17, 2019, from corresponding PCT application No. PCT/EP2018/080331. |
Number | Date | Country | |
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20200368651 A1 | Nov 2020 | US |