Method for controlling a particle-forming fluidization process proceeding in a fluidizer - Patents.com

Abstract

The present invention relates to a method for controlling a particle-forming fluidization process proceeding in a fluidizer with respect to at least one product characteristic (w) of the process product.

Description

[Technical field]

[0001] The present invention relates to a method for controlling a particle-forming fluidization process proceeding in a fluidizer with respect to at least one product characteristic of the process product. [Background technology]

[0002] According to the prior art, which is usually not documented in the literature, particle-forming fluidization processes are driven by a set of process parameters, a so-called "recipe", which comprises process parameters that lead to the desired product properties when a predefined sequence of process parameters is adhered to, so that the fluidization process always follows the same time flow.

[0003] Furthermore, typical product properties, such as the absolute moisture content of the particles, can be adjusted by controlling process parameters, such as the drying gas temperature or the volumetric flow rate of the drying gas. A corresponding method for treating particulate process products in a fluidizer and an associated fluidizer are disclosed in German patent application DE 10 10 2004 13 2004.

[0004] Although known methods approach the desired target value for the product characteristic to be controlled, they still have deviations therefrom which have a negative effect on the product quality of the process product. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] German Patent Invention No. 102020208204 Summary of the Invention [Problem to be solved by the invention]

[0006] The object of the present invention is therefore to develop an improved method for controlling a particle-forming fluidization process taking place in a fluidizer, which further optimizes the product properties of the process product with respect to target values. [Means for solving the problem]

[0007] The object of the present invention is achieved by a method according to the above-mentioned aspect, in which a method cycle includes determining a number of process parameters of a fluidization process at a first time point, transmitting the process parameters as actual process parameter values ​​to a control device having control functions, calculating in the control device process model product characteristic values ​​for a second time point subsequent to the first time point with the process parameter actual values ​​on the basis of a process model stored for at least one product characteristic, forming in the control device a number of optimized variable sets from the provided number of optimized process parameter values, calculating a number of optimized forecast values ​​at a third time point by means of the optimization model using the optimized variable sets, applying a correction value to each of the optimized forecast values ​​in order to form a corrected optimized forecast value, calculating an optimized difference value at the third time point from a comparison of each of the corrected optimized forecast values ​​with a forecast set value for at least one product characteristic determined at the third time point from a set value function stored in the control device, and then outputting the optimized process parameter value of the optimized variable set which corresponds to the smallest absolute value of the optimized difference values ​​as a reference variable for the second time point subsequent to the first time point. Advantageously, the method for controlling a particle formation fluidization process proceeding in a fluidizer determines suitable reference variables for achieving at least one product characteristic of the process product to be controlled without user presettings, whereby the product quality of the process product is clearly improved while the reproducibility of the results is high.

[0008] In this respect, multiple method cycles are carried out one after the other, with the second point in time of each method cycle forming the first point in time of the following method cycle, whereby, for example, the value of the second point in time in a first method cycle forms the value of the first point in time of a second method cycle.

[0009] The process parameters are determined expediently by measuring the process parameters or by simulation, whereby it is advantageous if the process parameters can be provided in various ways, which can lead to savings in measurement technology, for example, when simulating the process parameters.

[0010] According to another development of the method, the measurement of the process parameters is carried out as an in-line measurement and / or an at-line measurement and / or an online measurement, and the process parameters are measured at a process parameter sampling frequency. In the method for controlling the particle-forming fluidization process proceeding in the fluidizer with respect to at least one product characteristic of the process product, the current process parameters are always provided for control.

[0011] According to another development of the method, the process parameter actual values ​​determined at the first time point form a process parameter variable set, as a result of which the transfer of the process parameter actual values ​​to the control device is facilitated, in this regard, each optimization variable set is formed from a number of process parameter optimized values ​​corresponding to a number of process parameter actual values ​​of the process parameter variable set, in which at least one process parameter optimized value replaces the corresponding process parameter actual value.

[0012] Furthermore, each of the process parameter optimization values ​​can take any optimization value, which can preferably be selected from a preset number of optimization values. This limits the number of optimization variable sets resulting from any combination of the process parameters and the preset number of optimization values ​​of the respective process parameters. When the preset number of optimization values ​​for each process parameter optimization value is purposefully the same, the maximum number of optimization variable sets is "the number of process parameter optimization values ​​to the power of the number of the preset number of optimization values". In principle, the number of the preset number of optimization values ​​is arbitrary, and the number of optimization variable sets, and thus also the number of optimization values, is purposefully determined by the calculation time required for the optimization model in the control device. The higher the performance of the control device, the more optimization values ​​and process parameter optimization values ​​can be used. Furthermore, a high number of optimization values ​​and process parameter optimization values ​​accordingly also affects the accuracy of the control of the fluidization process.

[0013] Advantageously, the preset optimization values ​​are based on the actual values ​​of the respective process parameters. The optimization values ​​then map as large a value range as possible that is within the technical boundaries of the fluidization process. The optimization values ​​that lead to instability of the fluidization process are then no longer within the technical boundaries of the fluidization process. The technical and product-specific boundaries of the fluidization process for the respective process parameters are usually determined in a preliminary experiment associated with the fluidization process.

[0014] According to an additional development of the method, a first time span with at least one time step is located between the first time point and the second time point, and a second time span with at least one time step is located between the second time point and the third time point. In this regard, the first time span and the second time span each have a different number of time steps, the first time span having a single time step. In preliminary experiments in this regard, it has been found to be advantageous for the first time span to have one time step and the second time span to have 19 time steps. Thereby, 20 time steps are located between the first time point and the third time point. The time span between the first time point and the third time point may, however, be directed further into the future and may have, for example, 30, 40, 50 or more time steps. The second time span should be adjusted accordingly. Expediently, the number of time steps of the first and second time spans is adapted to one another, but the number of time steps of the first and second time spans remains freely selectable for each individual fluidization process.

[0015] According to one development of the method, for each product characteristic to be controlled, a setpoint function is stored in the control device. Expediently, the setpoint function stored in the control device for each product characteristic to be controlled maps the targeted progression of the respective product characteristic to be controlled over time. If the selected product characteristic to be controlled is the grain size, the setpoint function maps, for example, the grain growth over time.

[0016] According to another development of the method, the setpoint function for at least one product characteristic to be controlled is formed from experimental data or from a setpoint process model, whereby the setpoint function is adapted to the physicochemical basis applied to the particle formation fluidization process.Furthermore, the method has the possibility that the setpoint function is any function that is preset by an operator and can be stored in the control device.

[0017] In this respect, the target value process model is expediently based on a kinetic model of at least one product characteristic. Kinetic model refers to the mathematical representation of the course of at least one product characteristic of all the product characteristics to be controlled in the fluidization process in relation to various process parameters, for example the growth kinetics of particle size. Preferably, in this method, the at least one product characteristic is particle size and / or particle moisture and / or particle composition. The use of kinetic model also makes the target value function fit the physicochemical basis applied to the particle formation fluidization process.

[0018] According to another development of the method, at least one product characteristic to be controlled is determined as a product characteristic measured at a first time point and transmitted to the control device as a product characteristic actual value. In this regard, the product characteristic actual value is smoothed by a mathematical smoothing method, expediently by the Whittaker-Henderson method. Furthermore, the product characteristic actual values ​​form a product characteristic variable set. This facilitates the transfer of the product characteristic actual values ​​to the control device.

[0019] Expediently, the correction value at the first time point is calculated by subtracting the process model product characteristic value of the at least one product characteristic calculated for the at least one product characteristic at the first time point from the at least one product characteristic actual value detected at the first time point. By such calculation of the correction value, an error calculated by the process model at each time step and accumulating over time is corrected. Preferably, the correction value at the first time point is "0" since in the first method cycle no product characteristic actual value at the first time point has been detected.

[0020] Preferably, the detection of the product characteristic is carried out as an in-line measurement and / or an at-line measurement and / or an online measurement, and the product characteristic is detected at a product characteristic sampling frequency. In the method for controlling the particle-forming fluidization process proceeding in the fluidization device with respect to at least one product characteristic of the process product, the current product characteristic is always provided for control.

[0021] In a further preferred development of the method, the process parameter sampling frequency and the product characteristic sampling frequency have the same value, which ensures that in a method cycle or at a point in time, respectively, the current process parameter actual value is provided and at the same time the current product characteristic actual value is also provided.

[0022] Furthermore, a product characteristic variable set is formed from the number of product characteristics to be controlled from two, and a prioritization of the product characteristics to be controlled is performed with respect to preferential control of one of said product characteristics. The prioritization of the product characteristics to be controlled (also called weighting of the product characteristics to be controlled) is performed according to the importance of each product characteristic in the fluidization process and / or with respect to product quality and / or user wishes. For example, when in a fluidization process the particle humidity to be achieved in the process product is more important than the particle size of the process product, the particle humidity should be prioritized or weighted accordingly. The prioritization causes that the optimized difference value of the prioritized product characteristic can be preferentially achieved compared to the lower prioritized product characteristics.

[0023] For this purpose, for example, a number of optimized difference values ​​of the product characteristic to be controlled of the set of optimized variables are added, and when added, each optimized difference value is multiplied by a weighting factor depending on its priority, i.e. weighted. The sum of the optimized difference values ​​can then be divided by the number of optimized difference values. In one example, the lowest sum or lowest average process parameter optimized value of the set of optimized variables belonging to the optimized difference values ​​is then output as the reference variable.

[0024] According to one development of the method, the optimization model is based on the process model, in particular the optimization model corresponds to the process model. The optimization model corresponding to the process model ensures that a number of optimization forecast values ​​calculated by the optimization model at the third time point have the same basis as the process model product characteristic values ​​calculated by the process model at the first time point. This makes it possible to achieve an improved control of at least one product characteristic to be controlled.

[0025] According to a further development of the method, the process model for the calculation of the process model product characteristic value is based on a linear or non-linear process model of the fluidization process to be controlled, and as the non-linear process model, an artificial neural network is used suitably. In this respect, the artificial neural network is in particular formed as a multilayer perceptron or as a simple recurrent network, for example an ELMAN network, or as a non-linear autoregressive exogenous network, for example a NARX network. The artificial neural network is suitably trained by experiments carried out in a fluidizer before carrying out the method for controlling a particle-forming fluidization process proceeding in the fluidizer with respect to at least one product characteristic of the process product, during which various process parameters are respectively changed taking into account at least one product characteristic to be controlled.

[0026] As process parameters, preferably one or more process parameters are used which are selected from the group of the atomizing gas pressure and / or the atomizing rate and / or the atomizing amount and / or the particle temperature and / or the drying gas temperature at the inlet of the fluidizer and / or the relative humidity of the drying gas at the outlet and / or the drying gas volumetric flow rate.

[0027] The present invention will now be described in detail with reference to the accompanying drawings. [Brief description of the drawings]

[0028] [Figure 1] 1 is a schematic diagram of a method for controlling a particle-forming fluidization process proceeding within a fluidizer with respect to at least one product characteristic of the process product. [Diagram 2] 1 is a graph including product properties plotted over time for a first process cycle and indications of detailed excerpts A and B. [Diagram 3] This is an enlarged view of detailed extract A. [Figure 4] FIG. 1 is an enlarged view of detailed extract B. [Diagram 5]1 is a graph including product properties plotted over time for a second process cycle and a display of detailed excerpts C and D. [Figure 6] This is an enlarged view of detailed extract C. [Figure 7] This is an enlarged view of detailed extract D. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Unless otherwise stated, the following description relates to all embodiments shown in the drawings of a method for controlling a particle-forming fluidization process proceeding in a fluidizer with respect to at least one product characteristic w of the process product.

[0030] The subscript n used in this description is any natural number, where n can take on various values ​​for various alphabets. For example, for on, n=3, and for o“n, n=27.

[0031] In a method cycle z, the flow of which is described below on the basis of the embodiment shown in Fig. 1, a number of process parameters p of the fluidization process are determined at a first time t1, where the first time t1 is to be understood as the current time t of the fluidization process. As process parameters p, inter alia the atomization gas pressure and / or the atomization rate and / or the atomization amount and / or the particle temperature and / or the drying gas temperature at the inlet of the fluidizer and / or the relative humidity of the drying gas at the outlet and / or the drying gas volume flow rate can be used.

[0032] The process parameters p are determined by simulation or by measurement. The measurement of the process parameters p is optionally carried out as in-line, at-line or online measurement using corresponding measurement techniques known to the person skilled in the art. Expediently, the process parameters p are measured with a process parameter sampling frequency fp. The determined process parameters p are transmitted as process parameter actual values ​​p' to a control device 1 having a control function. The process parameter actual values ​​p' determined at the first time t1 then preferably form a process parameter variable set p". In an embodiment not shown, some of the process parameters p are simulated and other parts of the process parameters p are determined by measurement technology.

[0033] In parallel to the measurement and / or simulation of the process parameter p, at a first time t1 at least one product characteristic wm is also measured with a product characteristic sampling frequency fw. Expediently, the measurement is selectively performed as an in-line, at-line or online measurement. The at least one product characteristic w to be controlled is determined as a product characteristic actual value w'm measured at the first time t1 and transmitted to the control device 1. Expediently, the product characteristic actual values ​​w'm form a product characteristic variable set w"m. As product characteristic, inter alia, particle size and / or particle moisture and / or particle composition are used.

[0034] Particularly preferably, the process parameter sampling frequency fp and the product characteristic sampling frequency fw have the same value, so that the process parameter actual value p' and the product characteristic actual value w'm are respectively present in the control device 1 at the same point in time.

[0035] In the control device 1, the delivered product characteristic actual values ​​w'm are smoothed by a mathematical smoothing method in a smoothing module 2 assigned to the control device 1. Expediently, this is carried out by a mathematical smoothing method, for example the Whittaker-Henderson method. The mathematically smoothed product characteristic values ​​w's then form, inter alia, a product characteristic variable set w"s.

[0036] The control device 1 further comprises a process model module 3 in which a process model product characteristic value w'c for a second time t2 subsequent to the first time t1 is calculated on the basis of a stored process model for at least one product characteristic w using the detected process parameter actual values ​​p', which preferably form a process parameter variable set p". The process model product characteristic values ​​w'c expediently form a process model product characteristic variable set w"c.

[0037] The process model for the calculation of the corresponding process model product characteristic value w'c is based on a linear or non-linear process model of the fluidization process to be controlled, and as the non-linear process model, an artificial neural network is used suitably. The artificial neural network is preferably formed in this case as a multilayer perceptron, as a simple recurrent network, or as a non-linear autoregressive exogenous network. The artificial neural network is suitably trained by experiments carried out in the fluidization device before carrying out the method for controlling the particle-forming fluidization process proceeding in the fluidization device with respect to at least one product characteristic of the process product, during which various process parameters p are respectively changed taking into account at least one product characteristic w to be controlled.

[0038] The control device 1 further comprises a correction module 4. In the correction module 4, a correction value k at the first time t1 is calculated. The correction value k is calculated for each product characteristic w to be controlled by subtracting a process model product characteristic value w'c of the at least one product characteristic w calculated for the at least one product characteristic w at the first time t1 from at least one product characteristic actual value w'm, preferably a mathematically smoothed product characteristic actual value w's, detected at the first time t1. The correction value k may also form a correction value variable set k". In the first method cycle z1, based on the missing process model product characteristic value w'c at the first time t1, the respective correction value k is set to the value "zero".

[0039] Furthermore, before the start of the method for controlling the particle-forming fluidization process proceeding in the fluidizer with respect to at least one product characteristic w of the process product, an optimization value v is preset based on a foreseeable effective range of the process parameter p. Effective range here means, for example, a certain effective range, i.e., for example, 75°C to 100°C, for the particle temperature used as the process parameter optimization value o, where the optimization value v divides the effective range in particular equally spaced intervals, for example, into portions having six values, here, i.e., v1=75°C, v2=80°C, v3=85°C, v4=90°C, v5=95°C and v6=100°C.

[0040] Generally speaking, each of the process parameter optimization values ​​o can take any optimization value v, which can be selected from a number of preset optimization values ​​v. The number of optimization values ​​v can range from two optimization values ​​to n optimization values. FIG. 1 shows for this purpose an optimization value vo1 for the process parameter optimization value o1 exemplarily for every optimization value v. In the illustrated embodiment, the process parameter optimization value o1 has six optimization values ​​vo1,1 to vo1,6. Purposefully, but not necessarily, all process parameter optimization values ​​o have the same number of optimization values ​​v.

[0041] The control device 1 further comprises an optimization module 5. In a next step, each of the optimization variable sets o″ is formed in a combination theory module 6 assigned to the optimization module 5 from a number of process parameter optimization values ​​o corresponding to a number of process parameter actual values ​​p′ of the process parameter variable set p″, in which at least one process parameter optimization value o replaces the corresponding process parameter actual value p′. In the combination theory module 6, therefore, n6 optimization variable sets o″ are formed for example n process parameter optimization values ​​o and six optimization values ​​vo1,1 to vo1,6. In particular, for example three process parameter optimization values ​​o each with six optimization values ​​v, 36=729 optimization variable sets o″ will result. The number of optimization values ​​v and the number of process parameter optimization values ​​o depend inter alia on the performance of the control device 1.

[0042] Additionally, the optimization module 5 has an optimization model module 7. Using the determined optimization variable set o″, a number of optimization forecast values ​​x for a third time point t3 are calculated by means of an optimization model which is stored in the control device 1, expediently in the optimization model module 7. The number of optimization forecast values ​​x in this case corresponds to the number of optimization variable sets o″. The optimization model is in this case preferably based on a process model, in particular the optimization model corresponds to a process model.

[0043] In a next step, a correction value k is applied to each of the optimized preview values ​​x to form a modified optimized preview value xk. In the illustrated embodiment of FIG. 1, the correction value k is subtracted from the optimized preview value x to form the modified optimized preview value xk.

[0044] Subsequently, in a comparison module 8 assigned to the control device 1, an optimized difference value Δ at the third time t3 is calculated from a comparison of each of the corrected optimized preview values ​​xk with a preview setpoint value xS for at least one product characteristic w, determined at the third time t3 from at least one setpoint function S stored in the control device 1. Preferably, for each product characteristic w to be controlled, a setpoint function S is stored in the control device 1. The setpoint function S for the at least one product characteristic w to be controlled is formed, in particular, from experimental data or from a setpoint process model. Appropriately, the kinetics of the product characteristic, for example the growth kinetics of the particle size, can be used to pre-set the setpoint value xS.

[0045] In the embodiment shown in Fig. 1, the optimization difference value Δ is formed as the absolute value of the subtraction of the modified optimization preview value xk and the target value xS. In the embodiment shown, respectively, a set of variables is considered.

[0046] The control device 1 further comprises an evaluation module 9 in which an evaluation of the absolute values ​​of the optimized difference values ​​Δ is performed and compared with one another. The optimized value o of the process parameter of the optimized variable set o″ which belongs to the smallest absolute value of the optimized difference values ​​Δ is then output as a reference variable r for a second time point t2 following the first time point t1, respectively. The respective reference variable r is then provided to and regulated by further control of the process parameter p (P, PI, PID control) in the fluidization process.

[0047] From the two product characteristics w to be controlled, a product characteristic variable set w″ is formed, and the product characteristics w to be controlled are prioritized for preferential control. The product characteristics w to be controlled are prioritized according to the importance of the respective product characteristic w in the fluidization process. For example, in a fluidization process, when the product characteristics of the particle size of the process product and the particle humidity of the process product are to be controlled, and the particle humidity to be achieved in the process product is more important than the particle size of the process product, the particle humidity should be prioritized or weighted accordingly. For this purpose, the control device 1 has a weighting module 10, in which the prioritization is performed.

[0048] The prioritization causes that prioritized product characteristics w can be achieved preferentially compared to lower prioritized product characteristics w. For this purpose, for example, a number of numerical values ​​of the optimized difference values ​​Δ of the product characteristics w to be controlled are added, and each absolute value of the optimized difference value Δ is weighted during the addition depending on its prioritization, for example multiplied by its weighting factor g. The sum of the optimized difference values ​​can then be divided by the number of optimized difference values. In one example, the process parameter optimized value o with the lowest sum of numerical values ​​of the optimized variable set o "belonging to the optimized difference values ​​Δ" is output as a reference variable.

[0049] Between the first time t1 and the second time t2 lies a first time span Δt1 having at least one time step d, and between the second time t2 and the third time t3 lies a second time span Δt2 having at least one time step d. The first time span Δt1 and the second time span Δt2 preferably each have a different number of time steps d, the first time span Δt1 expediently having a single time step d.

[0050] In the embodiment shown in Fig. 1, a time span Δt1 having one time step d is located between a first time t1 and a second time t2, and a time span Δt2 having 19 time steps d is located between the first time t2 and a third time t3, so that there are 20 time steps d between the first time t1 and the third time t3. The first and second time spans Δt1 and Δt2 may have other value combinations, and the ones listed represent preferred value combinations.

[0051] A number of method cycles z may proceed one after the other, with the second time point t2 of a preceding method cycle z respectively forming the first time point t1 of the following method cycle z+1.

[0052] FIG. 2 shows a graph including a product characteristic w plotted over time t for a first method cycle z1 and indications of detailed excerpts A and B.

[0053] In this embodiment, control is performed according to one product characteristic w, which is selected as the product characteristic w: particle size.

[0054] For the control itself, in the illustrated embodiment, three process parameters p were used: atomizing gas pressure as process parameter p1, atomization rate as process parameter p2, and particle temperature as process parameter p3, with each process parameter p being allowed to take only two optimized values ​​v.

[0055] Between the first time point t1 and the second time point t2, there is a time span Δt1 having one time step d, and between the first time point t2 and the third time point t3, there is a time span Δt2 having 19 time steps d, resulting in 20 time steps d between the first time point t1 and the third time point t3.

[0056] Additionally, a target value function S for particle size as a product characteristic w is shown.

[0057] In the combinatorial theory module 6, when there are three process parameter optimization values ​​o and two optimization values ​​v, 2 In particular, within the combinatorial theory module 6, nine optimization variable sets o″ are therefore formed. The nine functions resulting from the optimization model with the respective optimization variable sets o″ are indicated in the graph with F1 to F9.

[0058] The optimized preview values ​​x1 to x9 result as function values ​​of nine functions F1 to F9 at a third point in time t3.

[0059] Extract A of Fig. 3 shows the course of the measured product characteristic actual value w'm and the smoothed product characteristic actual value w's. The smoothed product characteristic actual value w's is additionally shown at a time t2. Furthermore, the product characteristic actual value w'c calculated by the process model at a second time t2 is shown.

[0060] 4 shows the functions F5 and F6 in an enlarged view and the optimized preview values ​​x5 to x6 at the time t3. Both functions F5 and F6 are located closest to the target value xS of the target value function S at the time t3.

[0061] The correction values ​​k are calculated for the product characteristic w to be controlled by subtracting the process model product characteristic values ​​w'c of the product characteristic w calculated for the product characteristic w at the first time point t1 from the mathematically smoothed product characteristic actual values ​​w's at the first time point t1. Since in the first method cycle z1 the respective correction values ​​k cannot be calculated due to the missing process model product characteristic values ​​w'c at the first time point t1, the correction values ​​k are set to the value "zero" in the first method cycle z1.

[0062] In the embodiment shown in FIG. 4, the optimized preview value x5 or x6 is subtracted from the target value xS to form optimized difference values ​​Δ5 and Δ6. A comparison of the absolute values ​​of the optimized difference values ​​Δ5 and Δ6 shows that the absolute value of the optimized difference value Δ6 is smaller than the absolute value of the optimized difference value Δ5. This results in outputting the optimized value o of the process parameter of the optimized variable set o“6 belonging to the optimized difference value Δ6 as the reference variable r for the next time point t2.

[0063] Figures 5 to 7 show the same as Figures 2 to 4, but for the second method cycle z2.

[0064] FIG. 5 shows a schematic diagram of a second method cycle z2 of an exemplary embodiment, including product characteristics w plotted over time t and excerpts C and D.

[0065] The product characteristic actual values ​​w's and the process model product characteristic values ​​w'c available at the second time point t2 of the method cycle z form the product characteristic actual values ​​w's and the process model product characteristic values ​​w'c at the first time point t1 in the following method cycle z+1.

[0066] Between the first instant t1 and the second instant t2 there is still a time span Δt1 with one time step d, and between the second instant t2 and the third instant t3 there is a time span Δt2 with 19 time steps d, so that, like in the first method cycle z1, between the first instant t1 and the third instant t3 there are 20 time steps d.

[0067] Additionally, a target value function S for particle size as a product characteristic w is shown.

[0068] Within the combinatorial theory module 6, for three process parameter optimization values ​​o and two optimization values ​​v respectively, 2In particular, within the combinatorial theory module 6, nine optimization variable sets o″ are therefore formed. The nine functions resulting from the optimization model with the respective optimization variable sets o″ are indicated in the graph with F1 to F9.

[0069] The optimized preview values ​​x1 to x9 result as function values ​​of nine functions F1 to F9 at a third point in time t3.

[0070] Extract C of Fig. 6 shows the course of the measured actual product characteristic value w'm and the smoothed actual product characteristic value w's. The smoothed actual product characteristic value w's is additionally shown at a time t2. Furthermore, the actual product characteristic value w'c calculated by the process model at a second time t2 is shown.

[0071] 7 shows the functions F6 and F7 in an enlarged view and the optimized preview values ​​x6 to x7 at the time t3. Both functions F6 and F7 are located closest to the target value xS of the target value function S at the time t3.

[0072] The correction value k is calculated for the product characteristic w to be controlled by subtracting the process model product characteristic value w'c(t1) calculated for the product characteristic w at the first time point t1 from the mathematically smoothed product characteristic actual value w's(t1) at the first time point t1.

[0073] In the illustrated embodiment of FIG. 7, the correction value k is subtracted from the optimized preview value x6 or x7, respectively, and then the target value xS is subtracted from the corrected optimized preview value x6,k or x7,k to form the optimized difference values ​​Δ6 and Δ7. A comparison of the absolute values ​​of both the optimized difference values ​​Δ6 and Δ7 shows that the absolute value of the optimized difference value Δ7 is smaller than the absolute value of the optimized difference value Δ6. As a result, the optimized value o of the process parameter of the optimized variable set o″7 belonging to the optimized difference value Δ7 is output as the reference variable r for the next time point t2.

Claims

1. 1. A method of controlling a particle-forming fluidization process proceeding in a fluidizer with respect to at least one product characteristic (w) of a process product, comprising the steps of: In a method cycle (z), a number of process parameters (p) of a fluidization process are determined at a first time point (t1); The process parameter (p) is transmitted as a process parameter actual value (p') to a control device (1) having a control function; Calculating in the control device (1) a process model product characteristic value (w'c) for a second time point (t2) subsequent to the first time point (t1) according to the actual process parameter value (p') based on a stored process model for at least one product characteristic (w), forming a number of optimized variable sets (o'') from a number of process parameter optimized values ​​(o) provided in the control device (1); Using the optimized variable set (o″), an optimization model calculates a number of optimized forecast values ​​(x) at a third time point (t3); applying a correction value (k) to each of the optimized preview values ​​(x) to form a corrected optimized preview value (xk); calculating an optimization difference value (Δ) at the third time point (t3) from a comparison between each of the corrected optimization preview values ​​(xk) and a preview target value (xS) for at least one product characteristic (w) determined at the third time point (t3) from a target value function (S) stored in the control device (1); Then, the process parameter optimization value (o) of the optimization variable set (o″) that belongs to the smallest absolute value of the optimization difference value (Δ) is output as a reference variable (r) for the second time point (t2) subsequent to the first time point (t1). A method comprising:

2. 2. The method according to claim 1, characterized in that a number of method cycles (z) proceed one after the other, and the second time point (t2) of a method cycle (z) respectively forms the first time point (t1) of the next succeeding method cycle (z+1).

3. 3. The method according to claim 1, wherein the process parameter (p) is determined by measurement or by simulation.

4. 4. The method according to claim 3, characterized in that the measurement of the process parameter (p) is performed as an in-line measurement and / or an at-line measurement and / or an online measurement, and that the process parameter (p) is measured at a process parameter sampling frequency (fp).

5. 5. The method according to claim 1, wherein the process parameter actual values ​​(p') determined at the first time point (t1) form a process parameter variation set (p'').

6. 6. The method of claim 5, wherein each of the optimization variable sets (o") is formed from a number of process parameter optimized values ​​(o) corresponding to a number of process parameter actual values ​​(p') of the process parameter variable set (p"), and in the optimization variable set (o"), at least one process parameter optimized value (o) replaces a corresponding process parameter actual value (p').

7. 7. The method according to claim 1, wherein each of the process parameter optimization values ​​(o) can take any optimization value (v), which is selectable from a number of preset optimization values ​​(v).

8. 8. The method according to claim 7, characterized in that the preset optimization values ​​(v) are based on the respective process parameter actual values ​​(p').

9. 9. The method according to claim 1, characterized in that between the first time point (t1) and the second time point (t2) there is a first time span (Δt1) having at least one time step (d), and between the second time point (t2) and the third time point (t3) there is a second time span (Δt2) having at least one time step (d).

10. 10. The method according to claim 9, characterized in that the first time span (Δt1) and the second time span (Δt2) each have a different number of time steps (d), the first time span (Δt1) expediently having a single time step (d).

11. 11. The method according to claim 1, wherein for each product characteristic (w) to be controlled, a setpoint function (S) is stored in the control device (1).

12. 12. The method according to claim 1, wherein the setpoint function (S) for at least one product characteristic (w) to be controlled is formed from experimental data or from a setpoint process model.

13. 13. The method of claim 12, wherein the set point process model is based on a kinetic model of at least one product characteristic (w).

14. 14. The method according to claim 1, wherein at least one product characteristic (w) to be controlled is determined as a product characteristic (p) measured at a first time point (t1) and transmitted to the control device (1) as a product characteristic actual value (p').

15. 15. The method according to claim 14, characterized in that the determined product characteristic actual values ​​(p') are smoothed by a smoothing method, expediently by the Whittaker-Henderson method.

16. 16. A method according to claim 14 or 15, characterized in that the product characteristic actual values ​​(p') form a product characteristic variable set (p'').

17. 17. The method according to claim 14, characterized in that the correction value (k) at the first time point (t1) is calculated by subtracting a process model product characteristic value (w'c) of at least one product characteristic (w) calculated for the at least one product characteristic (w) at the first time point (t1) from the at least one product characteristic actual value (w'm) detected at the first time point (t1).

18. 18. The method according to claim 14, characterized in that the detection of the product characteristic (w) is performed as an in-line measurement and / or an at-line measurement and / or an online measurement, and advantageously the product characteristic (w) is detected at a product characteristic sampling frequency (fw).

19. 19. The method according to claim 4 and claim 18, characterized in that the process parameter sampling frequency (fp) and the product characteristic sampling frequency (fw) have the same value.

20. 20. The method according to claim 1, further comprising forming a product characteristic variable set (w") from two product characteristics (w) to be controlled and performing a prioritization of the product characteristics (w) to be controlled with respect to a preferential control of one of said product characteristics (w).

21. 21. The method according to any one of the preceding claims, characterized in that at least one product characteristic (w) is particle size and / or particle moisture and / or particle composition.

22. 22. The method according to claim 1, wherein the optimization model is based on, in particular corresponds to, the process model.

23. 23. The method according to claim 1, characterized in that the process model for the calculation of the process model product characteristic value (w'c) is based on a linear or non-linear process model of the fluidization process to be controlled, whereby an artificial neural network is expediently used as the non-linear process model.

24. 23. The method according to claim 22, characterized in that the artificial neural network is formed as a multi-layer perceptron, or as a simple recurrent network, or as a non-linear autoregressive exogenous network.

25. 25. The method according to claim 1 , characterized in that as process parameter (p) one or more process parameters (p) are used which are selected from the group of the atomizing gas pressure and / or the atomizing rate and / or the atomizing amount and / or the particle temperature and / or the drying gas temperature at the inlet of the fluidizing device and / or the relative humidity of the drying gas at the outlet and / or the drying gas volumetric flow rate.

Images