Method and apparatus for determining sequestration parameters

Abstract

The present invention relates to a computer-implemented method and an apparatus for determining optimized operating parameters of an installation for the sequestration of carbon dioxide in a storage material, wherein, in a test sequestration device (4), multiple sequestration tests are carried out on each test sample (2), with each sequestration test being carried out using a specified test parameter set, and wherein a prediction model is applied to the prediction data record by means of a computing unit (7), as a result of which the sequestration parameters are optimized.

Description

[0001] Method and device for determining sequestration parameters

[0002] The present invention relates, among other things, to a method, a device, and a machine-readable storage medium according to the independent patent claims. In particular, the present invention relates to the technical field of carbon dioxide sequestration.

[0003] Carbon dioxide sequestration is generally understood as a process that aims to capture carbon dioxide (CO2) from exhaust gases or remove it from the atmosphere and, if possible, permanently store it in a storage medium. Due to the greenhouse effect of CO2, carbon dioxide sequestration has gained significant importance in recent years.

[0004] In principle, any material containing at least one carbonatable phase can be used to sequester carbon dioxide, with solids being preferred. The storage media available are therefore diverse, and in addition to industrial waste, naturally occurring minerals or other materials can also be used. The sequestration potential and optimal process parameters for the sequestration process vary depending on the material.

[0005] State-of-the-art processes and devices are typically unable to respond quickly to changes in materials, which can lead to suboptimal utilization of the sequestration potential. If the starting material changes, reductions in the utilization of the sequestration potential often have to be accepted. Furthermore, a prior estimation of the practically usable sequestration potential of a specific starting material is often not possible.

[0006] Within the scope of the present invention, a computer-implemented method and a device are now proposed which can at least partially overcome these and other disadvantages of the prior art.

[0007] One object of the invention can therefore be seen as creating a method and a device capable of determining optimized operating parameters of a sequestration process with the least possible effort. In particular, the determination should be possible on a laboratory scale, such as in a mobile system.

[0008] In general, the method or device can be used to derive various input parameters by examining a storage material. These input parameters are then subjected to processing steps, such as data processing in a computer-aided self-learning model and / or a regression model, to provide a parameter set with optimized sequestration parameters. The result, i.e., the determined parameter set, can subsequently be used to operate an industrial-scale sequestration plant with these optimized parameters.

[0009] The term storage material within the meaning of the present description generally encompasses all materials that have at least one carbonatable phase. In one embodiment, the storage material is a solid, in particular a mineral solid. The storage material can, for example, be a residue from an industrial primary process, such as slag. At least one material parameter of the storage material, also referred to as the starting material, can be determined in one step. The material parameter is, in particular, an inherent or unchangeable material parameter, such as the chemical composition or the mineralogical composition. The material parameter can be determined using an analysis device. The analysis device can be arranged or carried out spatially away from other parts of the device or steps of the method.For example, the analysis can be performed directly at the source of the storage material, while other steps are performed at another location.

[0010] Advantageously, the determined material parameter is representative of the entirety or at least of a certain portion of the storage material.

[0011] The analysis device can, for example, use one or more of the following analysis techniques: X-ray fluorescence analysis (XRF); optical emission spectroscopy (OES), in particular inductively coupled plasma optical emission spectroscopy (ICP-OES); mass spectrometry (MS), in particular inductively coupled plasma mass spectrometry (ICP-MS). If different material parameters are to be determined, the analysis device can also comprise several, possibly independent, analysis devices.

[0012] The analysis device can be configured to determine the desired material parameter directly from a solid (e.g. XRF) or from a processed, for example dissolved, form of the storage material (e.g. ICP-OES).

[0013] In a further step, test samples of the storage material can be subjected to several sequestration tests. The test samples are designed to be representative of the overall composition of the storage material. In a sequestration test, carbon dioxide is sequestered in a test sample, particularly on a small scale, for example, at a laboratory scale. A sequestration test is particularly indicative of the behavior of the storage material in an industrial-scale sequestration process.

[0014] As mentioned, sequestration experiments can be conducted on a laboratory scale. A sequestration experiment can be conducted in a sequestration reactor, for example, a laboratory sequestration reactor.

[0015] A sequestration test is based on a set of test parameters. A test parameter set includes, in particular, the conditions or parameters to which a test sample is subjected in a given sequestration test.

[0016] The test parameter set may, for example, include one or more of the following parameters during sequestration: pressure; temperature; water content (e.g. wet / dry); gas composition; aggregates; gas conditioning; gas flow rate; in-situ treatments, such as ultrasound or pressure surge; treatment time.

[0017] The test parameter set may alternatively or additionally include parameters or properties present in the test sample itself. For example, these may be one or more of the following parameters: grain size; surface area; moisture content. These parameters can be adjusted, if necessary, by pretreating the test sample, for example, by grinding or drying. However, inherent, unchanging material parameters, such as elemental composition, are not considered parameters in a test parameter set.

[0018] During a sequestration test, at least one sequestration parameter is determined using a measuring device. The sequestration parameter can be a parameter that is determined, in particular several times, during the sequestration test (e.g., online or inline) and / or that is determined once, in particular after completion of the sequestration test.

[0019] A sequestration parameter may be selected from one or more of the following parameters determined during the sequestration test: mass increase of the test sample; exhaust gas composition; pH value; temperature; viscosity; agglomeration behavior of the test sample; conductivity.

[0020] A sequestration parameter may additionally or alternatively be selected from one or more of the following parameters, which are determined after completion of the sequestration test: mass increase of the test sample; binding stability for CO2; eluate behavior of heavy metals; chemical composition; dimensional stability; strength.

[0021] The measuring device intended to determine a sequestration parameter can be located at or spatially remote from the location where the sequestration tests are carried out.

[0022] The determined sequestration parameter can subsequently be used to determine at least one target parameter. If necessary, several sequestration parameters can be combined into a single target parameter. The number of target parameters can be equal to, greater than, or less than the number of determined sequestration parameters. To determine the target parameter(s) from the sequestration parameter(s), a data processing algorithm can be applied to the respective sequestration parameter(s). A target parameter is typically indicative of a sequestration property of the storage material, for example, the CO2 storage potential.

[0023] The conversion of sequestration parameter to target value can be carried out in a computing unit, which in turn can be part of a computer.

[0024] A prediction data set can be stored in a storage unit, which can also be part of a computer. The prediction data set can include at least the material parameter(s), the sequestration parameter(s), and the target variable(s). In the prediction data set, the target variables are assigned, in particular, to the sequestration parameters of the test parameter sets.

[0025] Where appropriate, the prediction dataset may include, in addition to the results obtained in the sequestration experiments, data obtained from chemical and / or physical simulations with regard to the storage material.

[0026] A prediction model can then be applied to the prediction dataset, designed to optimize the sequestration parameters with respect to the target variable. The optimization can be performed from various perspectives, for example, with regard to the greatest economic efficiency of the sequestration process or the absolute sequestration of carbon dioxide. The prediction model can also be applied in a processing unit, which may be part of a computer.

[0027] The prediction model can be a model that was created or trained based on one or more training datasets. Training datasets typically contain data obtained from different storage media. In one embodiment, the prediction model is a self-learning model, so that prediction datasets are simultaneously used to further develop or train this prediction model. The prediction model can also be a regression model or another mathematical calculation model.

[0028] If necessary, sequestration parameters from an industrial sequestration plant can also be used to train the model.

[0029] Finally, a parameter set can be output or obtained as a result, which contains or consists of optimized sequestration parameters with regard to the target variable. With this parameter set, a sequestration plant can be operated on an industrial scale. If necessary, adjustments can be made to the parameter set to adapt the parameters to the specific conditions of the respective sequestration plant. In connection with this description, the following designations may be used for the parameters and other values ​​mentioned here, unless otherwise stated or otherwise apparent from the context:

[0030] - material parameters Mi , M2, ... , Mk, where k is the number of determined material parameters;

[0031] - experimental parameter set Pi, P2, Pm, where m is the number of different experimental parameter sets; in particular, each experimental parameter set comprises one or more predetermined sequestration parameters;

[0032] - Sequestration parameter Seq-i, Seq2, Seq n , where n is the number of sequestration parameters determined;

[0033] - Target variable Z1 , Z2, ... , Z P , where p is the number of target variables determined from the sequestration parameters;

[0034] - Prediction dataset V;

[0035] - Training dataset Ti , T2, ... , Tq, where q is the number of training datasets;

[0036] - Parameter set with optimized target variable P op t

[0037] In general, methods and devices are disclosed that are implemented or configured centrally or decentrally. For example, a method can be implemented using a computing unit that is part of a network system, i.e., is not physically located at the location where the method is applied. Furthermore, the analysis device or the measuring device, or a part thereof, can also be located separately from the other components of the device.

[0038] In a specific embodiment, the sequestration tests are carried out in a sequestration unit which is in particular self-contained and, for example, mobile.

[0039] A computer-implemented method for determining optimized operating parameters of a system for the sequestration of carbon dioxide in a storage material is described. Optionally, the method comprises one or more of the following steps: a) determining, by means of an analysis device, at least one material parameter Mi, M2, ..., Mk of the storage material, b) providing test samples of the storage material, c) carrying out, in a test sequestration device, a plurality of sequestration tests on one test sample each, wherein each sequestration test is carried out with a predetermined test parameter set Pi, P2, ..., Pm, and wherein the test parameter sets Pi, P2, ..., Pm of the sequestration tests differ from one another in at least one sequestration parameter, d) determining, by means of a measuring device, a plurality of sequestration parameters Seqi, Seq2, ..., Seq n, from the test samples, e) forwarding of the sequestration parameters Seqi, Seq2, ... , Seq n to a computing unit and determination of at least one target variable Z1, Z2, ... , Z P from the sequestration parameters Seqi, Seq2, ... , Seq n , f) storing, in a storage unit, a prediction data set V specific to the storage material, wherein the prediction data set V contains material parameters Mi, M2, ... , Mk, sequestration parameters of the test parameter sets Pi, P2, ... , Pm and target variable Z1, Z2, ... , Z P comprises, g) applying, by means of the computing unit, a prediction model to the prediction data set and optimizing the sequestration parameters with regard to the target variable Z1, Z2, ..., Z P , h) Output, by an electronic output unit, of a parameter set Po P t with optimized sequestration parameters.

[0040] Where appropriate, the prediction model is intended to be a self-learning model and / or a regression model trained with training data sets Ti, T2, ... , Tq, wherein the training data sets Ti, T2, ... , Tq were obtained from different storage materials.

[0041] Optionally, it is provided that the prediction dataset V is used to further train the self-learning model and / or the regression model. Optionally, it is provided that the material parameter Mi, M2, ..., Mk is a parameter inherent to the material, wherein the material parameter Mi, M2, ..., Mk is selected in particular from one or more of the following parameters: chemical elemental composition, mineralogical composition, content of carbonatable phases, porosity, material history.

[0042] Where appropriate, it is provided that the sequestration parameters specified by the test parameter sets Pi, P2, ... , Pm are selected from variable material parameters of the storage material and / or process parameters of the sequestration.

[0043] Optionally, variable material parameters are selected from one or more of the following parameters: grain size of the storage material, water content of the storage material, surface area of ​​the storage material.

[0044] Where appropriate, sequestration process parameters are selected from one or more of the following parameters: additive addition, temperature, pressure, flow conditions, gas composition, gas conditioning, gas volume flow, treatment during sequestration, treatment time.

[0045] If necessary, it is provided that a test sample is treated in a pre-processing device, for example, ground and / or dried or moistened, in order to adjust a variable material parameter.

[0046] Where appropriate, it is intended that the sequestration parameters Seqi, Seq2, ... , Seq n are selected from: parameters determined during the sequestration tests and / or parameters determined after the sequestration tests.

[0047] Where appropriate, parameters determined during the sequestration tests are selected from one or more of the following parameters: mass, gas composition, viscosity, temperature, pH, conductivity. Where appropriate, parameters determined following the sequestration tests are selected in particular from one or more of the following parameters: CO2 binding stability, heavy metal elution behavior, chemical composition, mechanical properties.

[0048] Where appropriate, it is provided that steps c) to h) are carried out using at least one set of parameters P op t be repeated at least once with optimized sequestration parameters as a test parameter set.

[0049] Where appropriate, it is provided that several sequestration tests are carried out simultaneously and / or that in step c) more than two, in particular between 5 and 50, sequestration tests are carried out with different test parameter sets Pi, P2, ... , Pm.

[0050] If necessary, it is provided that in step e) the target variable Z1, Z2, ... , Z P by applying an algorithm from the sequestration parameters Seqi, Seq2, ... , Seq n is determined.

[0051] If necessary, it is provided that in step e) the target variable Z1, Z2, ... , Z P directly a sequestration parameter Seqi, Seq2, ... , Seq n corresponds.

[0052] Optionally, it is provided that the storage material comprises at least one carbonatable phase and / or that the storage material is a residue of an industrial process.

[0053] Where appropriate, a machine-readable storage medium is also described which comprises computer-executable instructions for carrying out a method described herein.

[0054] Optionally, a device for determining optimized operating parameters of a system for the sequestration of carbon dioxide in a storage material is also described. Optionally, the device comprises one or more of the following components: a) an analysis device for determining at least one material parameter Mi, M2, Mk of the storage material, b) a test sequestration device for carrying out a plurality of sequestration tests on a test sample of the storage material, wherein each sequestration test is carried out with a predetermined test parameter set Pi, P2, ..., Pm, and wherein the test parameter sets Pi, P2, ..., Pm of the sequestration tests differ from one another in at least one sequestration parameter, c) a measuring device for determining a plurality of sequestration parameters Seqi, Seq2, ..., Seq n, from the test samples, d) a computing unit for determining at least one target value Z1 , Z2, ... , Z P from the sequestration parameters Seqi, Seq2, ... , Seq n , e) a storage unit for storing a prediction data set specific to the storage material, wherein the prediction data set contains material parameters Mi, M2, ... , Mk, sequestration parameters of the test parameter sets Pi, P2, ... , Pm and target variable Z1, Z2, ... , Z P comprises, f) an electronic output unit,

[0055] Where appropriate, the computing unit applies a prediction model to the prediction data set and adjusts the sequestration parameters with respect to the target variable Z1 , Z2, ... , Z P optimized, whereby the electronic output unit has a parameter set P op t with optimized sequestration parameters.

[0056] Where appropriate, the analysis device may comprise one or more of the following devices: a spectroscopic device, in particular an X-ray spectroscopic device; a mass spectrometric device; an emission spectrometric device.

[0057] Optionally, the measuring device comprises one or more of the following devices: a temperature sensor; a pressure sensor; a device for measuring the concentration of at least one gas, in particular carbon dioxide; a flow meter; a balance; a viscometer, a pH sensor; a device for thermogravimetric analysis (TGA).

[0058] Optionally, it is provided that the device comprises a pre-processing device for treating a test sample to adjust a variable material parameter, wherein the pre-processing device comprises, for example, a grinding device and / or a drying or moistening device.

[0059] Where appropriate, the computing unit, the storage unit and the electronic output unit are part of a computer.

[0060] Where appropriate, it is provided that the device is a mobile device and / or that the components of the device are accommodated in a common housing.

[0061] Optionally, the invention also relates to a method for operating a plant for the sequestration of carbon dioxide in a storage material, wherein the plant is operated with an optimized parameter set P op t obtained by a process described here.

[0062] If necessary, the optimized parameter set Popt is transmitted to the system via the electronic output unit.

[0063] Further features emerge from the patent claims, the description of the embodiment, and the figure. It should be noted that features described with reference to a method are equally applicable to a device, and vice versa.

[0064] The present invention is explained in detail below using an exemplary embodiment. This embodiment serves to illustrate advantageous aspects of the invention and is not intended to limit the scope of the patent claims. Fig. 1 shows a schematic representation of the sequence of a method according to an exemplary embodiment.

[0065] Unless otherwise indicated, the following features are shown in Fig. 1: analysis device 1; test sample 2; storage material 3; test sequestration device 4; electronic output unit 5; measuring device 6; computing unit 7; storage unit 8; pre-processing device 9; computer 10; industrial sequestration plant 11.

[0066] In the exemplary embodiment, the storage material 3, which is slag from an industrial process, is analyzed using an analysis device 1. The analysis device 1 is designed as an X-ray fluorescence analysis device and is configured to determine the chemical composition of the storage material 3. From this, the contents of carbonatable phases can be determined. For example, in this exemplary embodiment, the analysis device 1 determines the contents of CaO, MgO, and Fe2O3 in the storage material. These contents form the material parameters Mi, M2, and M3, which, as illustrated in Fig. 1, are transmitted to the computing unit 7 and temporarily stored in a prediction data set V in the storage unit 8 until further processing.

[0067] Five test samples 2 are formed from the storage material 3, each of which is subjected to a sequestration test in the test sequestration device 4. Each sequestration test is assigned a test parameter set P1, P2, P3, P4, P5, with the test parameter sets differing from each other in at least one sequestration parameter. The test parameter sets, along with their sequestration parameters, are transmitted to the computing unit 7 and temporarily stored in the prediction data set V.

[0068] The sequestration parameters considered in the present embodiment are: temperature T; pressure p; grain size of the storage material d; carbon dioxide content in the gas c(CO2). For the test parameter sets P1 and P2, a mill is provided as the preprocessing device 9 to adjust, i.e., reduce, the sequestration parameter grain size of the storage material d. For the other test parameter sets, the sequestration parameter grain size of the storage material d is left in its initial state.

[0069] During the sequestration tests, the mass increase of the test samples 2 is continuously monitored using measuring device 6, which comprises several scales, and the temporal progression of the measured values ​​is recorded. The temporal progression of the mass represents the first sequestration parameter Seqi. After the sequestration tests are completed, further tests are carried out on the sequestered test samples using measuring device 6. Further sequestration parameters determined in measuring device 6 include CO2 binding stability (Seq2), the eluate behavior of heavy metals (Seqs), and the mechanical strength (Seq4).

[0070] The obtained results for the sequestration parameters are forwarded to the computing unit 7, where they are summarized into two target variables Z1 and Z2 using a predefined calculation algorithm. Target variables Z1 and Z2 are indicative of the CO2 storage potential and the reusability of the storage material 3. The target variables are stored in the prediction data set V.

[0071] The prediction data set V stored in the storage unit 8 thus comprises the material parameters Mi, M2 and M3, the test parameter sets Pi, P2, P3, P4 each with four sequestration parameters (T1-4; p-1-4; di-4; c(CO2)i-4) as well as the target variables Z1 and Z2 for each test parameter set.

[0072] A prediction model is then applied to the prediction data set V in the computing unit 7, in which the sequestration parameters are optimized with regard to the target variables Z1 and Z2. In this exemplary embodiment, the prediction model is a self-learning model that was trained using a plurality of training data sets T. The training data sets T, like the prediction data set V, include the material parameters Mi, M2 and M3, a plurality of test parameter sets Pi, P2, ..., Pm, each with four sequestration parameters (Ti- m ; pi-m; di- m ; c(C02)im) as well as the target variables Z1 and Z2 for each experimental parameter set. Each training dataset refers to a different storage material.

[0073] The result output via an electronic output unit 5 consists of an optimized parameter set P op t, which contains the optimized sequestration parameters temperature Topt; pressure p opt; grain size of the storage material dopt; carbon dioxide content in the gas c(CO2)o P t contains.

[0074] In this embodiment, the electronic output unit 5 is designed as an interface to an industrial sequestration system 11, to which the parameter set P op t is passed on. By using the parameter set Popt, an optimized sequestration process can be carried out on the storage material 3 in the industrial sequestration plant 11. The carbon dioxide introduced into this sequestration process can, for example, originate from another industrial plant.

[0075] The data of the prediction dataset V can be used as training dataset T in the self-learning model.

[0076] In this embodiment, the electronic output unit 5, the computing unit 7 and the storage unit 8 are part of a computer 10.

[0077] In an embodiment not described in detail, the optimization is performed with one iteration. In comparison to the embodiment described above, five optimized parameter sets P op ti-5, which are used as experimental parameter sets in a further round of sequestration experiments. The obtained results are processed as described in the above example to obtain an optimized parameter set Popt after one iteration.

[0078] The method or device described here can generally achieve one or more of the following objectives:

[0079] - indicative derivations of the theoretically possible and practically achievable sequestration potential based on a small number of input parameters or input variables;

[0080] - Selection of optimized parameter sets for sequestration in the context of the analysis of a new storage material;

[0081] - the derivation of further useful parameter configurations for evaluation in additional test runs;

[0082] - the design and optimization of sequestration processes on an industrial scale;

[0083] - optimal process control of industrial-scale plants by recording real process data;

[0084] - Derivations for changing the management of the primary process to optimize the CO2 storage potential of the produced residue.

Claims

Patent claims 1. A computer-implemented method for determining optimized operating parameters of a system for the sequestration of carbon dioxide in a storage material, the method comprising the following steps: a) determining, by means of an analysis device (1), at least one material parameter Mi, M2, ... , Mk of the storage material, b) providing test samples (2) of the storage material (3), c) carrying out, in a test sequestration device (4), a plurality of sequestration tests on a test sample (2), each sequestration test being carried out with a predetermined test parameter set Pi, P2, ... , Pm, and the test parameter sets Pi, P2, ... , Pm of the sequestration tests differing from one another in at least one sequestration parameter, d) determining, by means of a measuring device (6), a plurality of sequestration parameters Seqi, Seq2, ... , Seq n, from the test samples (2), e) forwarding of the sequestration parameters Seqi, Seq2, ... , Seq n to a computing unit (7) and determining at least one target variable Z1, Z2, ... , Z P from the sequestration parameters Seqi, Seq2, ... , Seq n , f) storing, in a storage unit (8), a prediction data set V specific to the storage material, wherein the prediction data set V contains material parameters Mi, M2, ... , Mk, sequestration parameters of the test parameter sets Pi, P2, ... , Pm and target variable Z1, Z2, ... , Z P comprises, g) applying, by means of the computing unit (7), a prediction model to the prediction data set and optimizing the sequestration parameters with regard to the target variable Z1, Z2, ... , Z P , h) Output, by an electronic output unit (5), of a parameter set P op t with optimized sequestration parameters.

2. Method according to claim 1, characterized in that the prediction model is a self-learning model and / or a regression model which has been trained with training data sets Ti, T2, ... , Tq, wherein the training data sets Ti, T2, ... , Tq have been obtained by carrying out steps a) to e) on different storage materials.

3. The method according to claim 2, characterized in that the prediction data set V is used for further training of the self-learning model and / or the regression model.

4. Method according to claims 1 to 3, characterized in that the material parameter Mi, M2, ... , Mk is a parameter inherent in the material, wherein the material parameter Mi, M2, ... , Mk is selected in particular from one or more of the following parameters: chemical elemental composition, mineralogical composition, content of carbonatable phases, porosity, material history.

5. Method according to one of claims 1 to 4, characterized in that the sequestration parameters specified by the test parameter sets Pi, P2, ... , Pm are selected from variable material parameters of the storage material and / or process parameters of the sequestration, - wherein variable material parameters are in particular selected from one or more of the following parameters: grain size of the storage material, water content of the storage material, surface of the storage material, and - wherein process parameters of the sequestration are in particular selected from one or more of the following parameters: additive addition, temperature, pressure, flow conditions, gas composition, gas conditioning, gas volume flow, treatment during sequestration, treatment time.

6. Method according to claim 5, characterized in that a test sample (2) for setting a variable material parameter in a Pre-processing device (9) is treated, for example ground and / or dried or moistened.

7. Method according to one of claims 1 to 6, characterized in that the sequestration parameters Seqi, Seq2, ... , Seq n are selected from: parameters determined during the sequestration tests and / or parameters determined after the sequestration tests, - wherein parameters determined during the sequestration tests are in particular selected from one or more of the following parameters: mass, gas composition, viscosity, temperature, pH value, conductivity, and - whereby parameters determined following the sequestration tests are in particular selected from one or more of the following parameters: CO2 binding stability, heavy metal elution behaviour, chemical composition, mechanical properties.

8. Method according to one of claims 1 to 7, characterized in that steps c) to h) are carried out using at least one parameter set P op t with optimized sequestration parameters as a test parameter set should be repeated at least once.

9. Method according to one of claims 1 to 8, characterized in that several sequestration tests are carried out simultaneously, and / or that in step c) more than two, in particular between 5 and 50, sequestration tests are carried out with different test parameter sets Pi, P2, ..., Pm.

10. Method according to one of claims 1 to 9, characterized in that - that in step e) the target variable Z1 , Z2, ... , Z P by applying an algorithm from the sequestration parameters Seqi, Seq2, ... , Seq n is determined, or - that in step e) the target variable Z1 , Z2, ... , Z Pdirectly to a sequestration parameter Seqi, Seq2, ... , Seq n corresponds.

11. Method according to one of claims 1 to 10, characterized in that the storage material comprises at least one carbonatable phase, and / or that the storage material is a residue of an industrial process.

12. A machine-readable storage medium comprising computer-executable instructions for carrying out a method according to any one of claims 1 to 11.

13. Device for determining optimized operating parameters of a system for the sequestration of carbon dioxide in a storage material, the device comprising: a) an analysis device (1) for determining at least one material parameter Mi, M2, ... , Mk of the storage material, b) a test sequestration device (4) for carrying out a plurality of sequestration tests on a test sample (2) of the storage material, each sequestration test being carried out with a predetermined test parameter set Pi, P2, ... , Pm, and the test parameter sets Pi, P2, ... , Pm of the sequestration tests differing from one another in at least one sequestration parameter, c) a measuring device (6) for determining a plurality of sequestration parameters Seqi, Seq2, ... , Seq n , from the test samples (2), d) a computing unit (7) for determining at least one target value Z1, Z2, ..., Z Pfrom the sequestration parameters Seqi, Seq2, ... , Seq n , and e) a storage unit (8) for storing a prediction data set specific to the storage material, wherein the prediction data set contains material parameters Mi, M2, ... , Mk, sequestration parameters of the test parameter sets Pi, P2, ... , Pm and target variable Z1, Z2, ... , Z P and f) an electronic output unit (5), wherein the computing unit (7) applies a prediction model to the prediction data set and the sequestration parameters with respect to the target variable Zi , Z2, , Z P optimized, and wherein the electronic output unit outputs a parameter set Popt with optimized sequestration parameters.

14. Device according to claim 13, characterized in that the analysis device (1) comprises one or more of the following devices: a spectroscopic device, in particular an X-ray spectroscopic device; a mass spectrometric device; an emission spectrometric device.

15. Device according to claim 13 or 14, characterized in that the measuring device (6) comprises one or more of the following devices: a temperature sensor; a pressure sensor; a device for measuring the concentration of at least one gas, in particular carbon dioxide; a flow meter; a balance; a viscometer, a pH sensor; a device for thermogravimetric analysis (TGA).

16. Device according to one of claims 13 to 15, characterized in that the device comprises a pre-processing device (9) for treating a test sample (2) to adjust a variable material parameter, wherein the pre-processing device (9) comprises, for example, a grinding device and / or a drying or moistening device.

17. Device according to one of claims 13 to 16, characterized in that the computing unit (7), the storage unit (8) and the electronic output unit (5) are part of a computer.

18. Device according to one of claims 13 to 17, characterized in that the device is a mobile device and / or that the components of the device are accommodated in a common housing.

19. A method for operating a plant for the sequestration of carbon dioxide in a storage material, wherein the plant is operated with an optimized parameter set Popt obtained in a method according to any one of claims 1 to 11.

20. Method according to claim 19, characterized in that the optimized parameter set Popt is transmitted to the system via the electronic output unit (5).

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