Method for selecting a metering device and determining operating parameters for metering a bulk material with unknown flow properties

Abstract

In a method for selecting a metering device (1) and determining operating parameters for metering a bulk material (2) with unknown flow properties using said metering device (1), an optical image of a specified quantity of the bulk material (2) is recorded in a reference environment (4). Geometric parameters of the bulk material (2) are determined on the basis of the optical image. A user is guided by a dialogue with an input module of an electronic data processing device. A characteristic variable for the flow properties of the bulk material (2) is determined using a machine learning method and on the basis of the determined geometric parameters and the responses of the user. The user inputs a desired conveying rate for the bulk material (2) via the input module and receives a suggestion for a suitable metering device (1) and for suitable operating parameters for metering the bulk material (2), the operating parameters being based on the determined characteristic variable for the flow properties and the desired conveying rate.

Description

[0001] 24012P-WO 1 Qlar Europe GmbH

[0002] Method for selecting a dosing device and determining operating parameters for dosing a bulk material with unknown flow properties

[0003] Description

[0004] Technical field

[0005] The present invention relates to a method for selecting a dosing device and for determining suitable operating parameters for dosing a bulk material with initially unknown flow properties. In particular, the invention relates to a method based on a combination of image-based analysis, user interaction, and machine learning to automatically determine the optimal dosing device and the corresponding operating parameters.

[0006] State of the art

[0007] Various methods for selecting dosing devices for bulk materials are known in industry. Traditionally, dosing devices such as screw conveyors, vibratory feeders, or belt conveyors are selected based on known physical properties of the bulk material, such as particle size, moisture content, density, and flow behavior. These physical properties usually have to be determined through time-consuming, manual laboratory tests before a suitable 24012P-WO 2 Qlar Europe GmbH

[0008] Dosing device selected and the corresponding

[0009] Operating parameters can be set.

[0010] However, a known problem with these methods is that it is often difficult for the user to determine the flow properties of a bulk material, especially if it is a novel or previously untested material. In many cases, users have to resort to empirical testing, manually trying out and adjusting various dosing devices until a suitable solution is found. This approach is time-consuming and resource-intensive, as it requires repeated adjustments of operating parameters and multiple test series.

[0011] Furthermore, the flow properties of bulk solids can be influenced by external factors such as humidity, temperature, and particle size distribution, making it even more difficult for the user to select the correct dosing device and the optimal operating parameters. Precise, user-friendly procedures that simplify the process of selecting and configuring a dosing device for a bulk solid with initially unknown properties are often lacking.

[0012] Known approaches to automating this process are generally based on extensive datasets with pre-determined flow properties of various bulk materials. However, these databases only offer added value if the physical properties of the bulk material are already known and there is an exact match with an existing sample from the database. In practice, however, it often turns out that such a match is not always given, especially if the bulk material exhibits deviations in composition or physical properties.

[0013] For the user, this means that they often have to rely on numerous experimental tests to find the appropriate dosing device and its operating parameters. This manual approach is not only time-consuming but also carries the risk of suboptimal settings, which can lead to inefficient processes or faulty dosing results.

[0014] It is also known from practice that the user obtains various pieces of information about a bulk material with unknown flow properties and, using an evaluation module of an electronic data processing device, determines an estimated characteristic value for the flow properties based on the previously entered information. Based on this, a suitable dosing device and suitable operating parameters for dosing the bulk material with the respective dosing device are then determined and specified. The information to be obtained by the user beforehand can, for example, also include a photograph of a given quantity of the bulk material in a reference environment. From this, geometric parameters of the bulk material can then be determined, which are also taken into account for determining the characteristic value for the flow properties.However, it has been shown that determining the characteristic value for the flow properties solely on the basis of a photograph leads in most cases to unsatisfactory results when specifying the dosing device and the operating parameters.

[0015] Therefore, further information is required, which the user must obtain and enter. The more detailed the information and the more specific or precise this information must be defined and entered by the user beforehand, the better the results; however, the effort required to obtain the information also increases, and the more error-prone the process becomes if individual pieces of information are incorrectly obtained or specified by the user.

[0016] Summary of the invention

[0017] It is therefore considered an object of the present invention to quickly and reliably determine the suitable dosing device and the optimal operating parameters for a bulk material with initially unknown flow properties.

[0018] This problem is solved according to the invention by a method for selecting a metering device and determining operating parameters for metering a bulk material with unknown flow properties, comprising the steps: creating an optical image of a predetermined quantity of the bulk material in a reference environment;

[0019] Determining geometric parameters of the bulk material based on optical imaging using a geometry evaluation module of an electronic data processing device;

[0020] Guiding a user through a dialogue with an input module of an electronic 24012P-WO 5 Qlar Europe GmbH

[0021] Data processing device, wherein the user answers a number of questions by means of a binary decision; Determining a characteristic value for the flow properties of the bulk material with a characteristic value evaluation module of an electronic data processing device using a machine learning procedure, based on the determined geometric parameters and the user's answers;

[0022] User input of a desired delivery rate via the input module;

[0023] Suggestions for a suitable dosing device and suitable operating parameters for this dosing device for dosing the bulk material, based on the determined characteristic value for the flow properties and the desired conveying rate.

[0024] An optical image can be created by a user without much effort. It is advantageous to use a predefined reference environment whose relevant properties are known to the geometry evaluation module or are provided to the geometry evaluation module. In the simplest case, the reference environment can be a flat reference surface with a uniform color that differs from the surroundings and whose size is known. For the most precise evaluation of the optical image, it can be advantageous for the reference environment to provide at least a one-dimensional scale, or a two-dimensional scale in a plane, and preferably a three-dimensional scale, which can be used by the geometry evaluation module to determine geometric parameters for individual particles of the bulk material as well as for an accumulation of the 24012P-WO 6 Qlar Europe GmbH

[0025] To determine bulk material as reliably and precisely as possible, the reference environment can be, for example, a planar reference surface on which reference markings, preferably with a scale division, are applied or displayed in two orthogonal coordinate directions. In addition to the planar reference surface, the reference environment can also have a side wall projecting from the planar reference surface along at least one edge, on which reference markings, preferably with a scale division, are also applied or displayed. Any reasonably planar and horizontally oriented surface can also be used as a reference environment, whereby the reference markings can be created by the user, for example, by adding a ruler or scale, which are visible in the optical representation.

[0026] The additional information the user must enter consists solely of a number of binary decisions in response to predefined questions, with the binary decisions being queried via a dialog. The dialog and the questions posed can be designed in such a way that the user can answer these questions very reliably without the use of additional aids such as measuring devices or experimental setups. The dialog can be completed by the user within a short time, for example, within a few minutes or even seconds.

[0027] The method according to the invention allows for the largely automated and cost-effective selection of not only a suitable dosing device for the bulk material in question, but also the operating parameters to be set for the desired dosing of the bulk material with this dosing device, without the otherwise necessary complex investigations. By providing a dialog in which only binary decisions from the user are expected and recorded, simple and intuitive acquisition of the information required for determining the dosing device and its operating parameters is made possible.Using a suitable predefined machine learning method, the characteristic value for the flow properties of the bulk material and, based on this, the proposal for the dosing device and its operating parameters can be determined with increasing precision, starting from just a few and easy-to-answer questions in the course of the dialogue, without increasing the effort required for the individual user.

[0028] An optical image encompasses any type of visual representation produced by optical means. This could include photographic images as well as other types of optical measurements or representations, such as images from infrared cameras, scanner images, or even holographic representations. Preferably, the optical image is a photographic image of the bulk material taken by a camera on a mobile device. Most users already have access to a mobile device with a sufficiently high-quality camera. With a suitably designed software program, which can be easily accessed and executed on a mobile device, the geometry evaluation module, the input module, and the...

[0029] A key performance indicator (KPI) evaluation module is implemented, and machine learning procedures are carried out using the 24012P-WO 8 Qlar Europe GmbH. Apart from the mobile device, no further tools are required by the user, so the procedure can be carried out almost anytime, anywhere, and without much effort.

[0030] The geometric parameters can include all parameters derivable from the optical image without additional user input, in particular the size and shape of individual particles of the bulk material, as well as the height of the accumulated bulk material, the volume of the accumulation, and / or the angles of the cone surface. These parameters are directly linked to the flow properties of the bulk material, which enables a precise determination of the appropriate dosing device.

[0031] The metering device is advantageously selected from a group of known metering devices, including screw conveyors, vibratory feeders, or belt conveyors. For this group of known metering devices, the individual operating parameters and their permissible ranges can be pre-defined and stored for easy retrieval. Selecting from a known group of metering devices accelerates the decision-making process and reduces complexity, allowing for good results even with just a few questions. The method is flexible and can be applied to various metering devices, enabling broad industrial application.

[0032] For some applications, multiple dosing devices can be used, each capable of conveying and dosing the respective bulk material at the desired conveying rate. This 24012P-WO 9 Qlar Europe GmbH

[0033] Dosing devices regularly differ with regard to several parameters and properties, as well as in their respective acquisition costs and the operating costs typically expected. In order to enable and achieve the dosing accuracy desired by the user, and to propose the most cost-effective dosing device from among the various available options, an advantageous embodiment of the invention provides that, in addition to the desired feed rate, the user enters a desired dosing accuracy via the input module, and that a suitable dosing device and appropriate operating parameters for this dosing device are proposed for dosing the bulk material based on the determined characteristic value for the flow properties, the desired feed rate, and the desired dosing accuracy.By entering and considering the desired dosing accuracy, individual user requirements can be better accommodated. Depending on the dosing device, the dosing accuracy can be specified either as a single value or a desired range, or as a combination of several independent parameters or characteristics that define the dosing accuracy, such as values ​​or ranges for dosing consistency and a maximum tolerable dosing error. In addition to or alongside the dosing accuracy, other parameters or properties of the available dosing devices can also be entered by the user and considered for the automated selection and specification of suitable operating parameters. 24012P-WO 10 Qlar Europe GmbH.

[0034] Optionally, the operating parameters of the dosing device can include conveying speed, dosing frequency, and / or tilt angle. These parameters can be adjusted to the specific properties of the bulk material to achieve optimal results. Precise definition of these parameters leads to efficient and uniform dosing of the bulk material. Additional operating parameters, potentially specific to individual dosing devices, can also be defined and determined using this method.

[0035] According to an advantageous embodiment of the invention, the questions in the dialogue relate to physical properties of the bulk material, such as particle size, moisture content, or surface roughness. During the dialogue, the user can also be asked about further properties, such as strength, shape, deformability, or three-dimensional surface structuring of individual particles of the bulk material. The user is guided by targeted questions, which simplifies the data acquisition process. By focusing on relevant physical properties, the accuracy of the analysis is increased.

[0036] According to a particularly advantageous embodiment of the invention, the dialogue can be conducted via a graphical user interface. The questions posed in the graphical user interface present the user with two images illustrating examples of contrasting properties of the bulk material (24012P-WO 11 Qlar Europe GmbH). The user then selects, by means of a binary decision, which image better describes the property of the bulk material they have provided, as exemplified by the two images. The individual questions can be formulated using descriptive terms and illustrated with intuitively understandable images, thus facilitating the user's decision-making process.A significant advantage of the method according to the invention is that the user only needs to make binary decisions to answer the questions during the dialogue, and then, based on these binary decisions, not only can a suitable dosing device be selected and suggested, but operating parameters can also be determined and specified, which need not be represented by binary values, but can assume any values ​​within a permissible range for the dosing device in question. Thus, the method according to the invention also performs a transformation of a data format from binary decisions and inputs to real values ​​within a predetermined range.

[0037] To ensure that the dialog process is as simple and intuitive as possible while still eliciting meaningful information from the user, it can be implemented that individual questions are repeated multiple times, each time accompanied by two different images as examples of contrasting properties. For example, the question "How solid is the bulk material?" can be repeated several times, with two different images being shown the first time. 24012P-WO 12 Qlar Europe GmbH

[0038] Images of hard grains and a soft gel material are shown, while the second time, for the same question, two images of raw and cooked pasta are shown. These examples illustrate that the images do not depict complex concepts, but rather, by comparing them to objects familiar to the user from everyday life, the binary decision for each individual question is significantly simplified, allowing each question to be answered reliably, quickly, and intuitively.In this way, even a user who is not an expert in bulk materials and their flow properties is easily able to quickly select a suitable dosing device for their desired application and obtain the appropriate operating parameters, so that they can convey and dose the bulk material they specify at the desired conveying rate with the dosing device in question without any further knowledge.

[0039] For this purpose, it may be provided, for example, that the opposite properties of the illustrations relate to physical characteristics of the bulk material that can be determined by the user without aids, such as granularity versus fineness, dryness versus moisture, or smooth versus rough surface.

[0040] Advantageously, the machine learning method optionally includes a neural network trained on a large number of bulk material samples with known flow properties. Neural networks can learn complex relationships between the various input information and flow properties, resulting in high predictive accuracy. The machine learning method is 24012P-WO 13 Qlar Europe GmbH.

[0041] The learning process can be continuously improved with new data to increase its performance. The machine learning process can have different modules and, for example, largely independently of each other, determine the geometric parameters of the bulk material within the geometry evaluation module, based on optical characteristics obtained from the optical recording of the bulk material. Simultaneously or subsequently, a separate module of the machine learning process can evaluate the user's binary decisions in order to then determine the relevant characteristic value, or possibly several characteristic values, of the flow properties in the characteristic value evaluation module together with the geometric parameters.The machine learning method can be continuously improved by increasing the number of process sequences carried out for the same or different bulk materials, and by subsequent evaluations of the dosing device determined by the method and its operating parameters.

[0042] Many manufacturers of dosing devices have already used complex measurement procedures to determine various parameters for the flow properties of numerous bulk material samples and have also verified the suitability of individual dosing devices for the respective bulk material sample and an associated conveying rate range. The results of these measurement procedures and verifications are usually stored in a database or can be transferred to a database.In order to utilize the measurements and verification results already carried out for the implementation of the inventive method and, in particular, for the fastest possible learning process of the machine learning method 24012P-WO 14 Qlar Europe GmbH, one embodiment of the invention provides that a database containing a large number of bulk material samples is used, for which the flow properties have been determined and measured by previous experiments, and the method further comprises: comparing the geometric parameters of the bulk material provided by the user with the bulk material samples stored in the database; selecting one or more bulk material samples from the database whose flow properties most closely match the determined geometric parameters of the bulk material; and using the flow properties of the selected bulk material samples to determine the characteristic value for the flow properties of the bulk material.

[0043] In this way, the training of the machine learning model, which is based on previously painstakingly determined flow properties of a number of bulk material samples already stored in the database, can be significantly accelerated and the results improved. This is achieved in the parameter evaluation module for determining the parameter based on the determined geometric parameters and the user's responses. By automatically comparing results obtained without comparison to the bulk material samples stored in the database and their determined flow properties with results obtained with this comparison, supervised learning and training of the machine learning model can be performed.In this way, the machine learning method in the key performance indicator (KPI) evaluation module can be continuously trained and improved very quickly and efficiently without additional effort.

[0044] The bulk material samples stored in the database can be used, for example, to determine the flow properties of the initially unknown bulk material by comparing the geometric parameters using a machine learning method. This method calculates the similarities between the geometric parameters of the bulk material provided by the user and the bulk material samples stored in the database. The comparison can be automated without manual intervention, thus accelerating the process. The machine learning method can improve the accuracy of the comparison by considering complex patterns and relationships.

[0045] It is also conceivable that the database contains information on a variety of physical properties of the bulk material samples, including particle size, density, moisture content and surface structure, and that this information is used to improve the accuracy in selecting the appropriate bulk material sample.

[0046] According to one embodiment of the invention, it can be provided that the bulk material samples stored in the database were created through experimental tests to determine the flow properties under various conditions, including different moisture levels, temperatures, and particle size distributions. Considering these different conditions makes the method more robust to varying environmental conditions. Furthermore, the user can be presented with and asked questions about the environmental conditions or other properties of the bulk material in order to more precisely determine the characteristic value for the flow property, taking into account the various data sets experimentally determined for the same bulk material under different conditions.The environmental conditions or other conditions can be presented as additional questions during the dialogue to elicit corresponding binary decisions from the user. It is also conceivable that the environmental conditions or other conditions, for example, could be entered by the user via the input module as real numerical values ​​within a respective assigned value range, just like the funding rate.

[0047] The bulk material samples stored in the database can be classified into groups based on similar flow properties, and a dosing device selected from the best-matching group can be suggested to the user. Classifying the bulk material samples reduces the effort required to determine the appropriate dosing device for the calculated flow properties without significantly compromising the quality of the results.

[0048] Optionally, the database can also include simulation results that model the dosing device under various operating conditions in order to calculate the best combination of the 24012P-WO 17 Qlar Europe GmbH dosing device and operating parameters for the bulk material provided by the user. The simulation results can also be stored in and retrieved from a separate simulation database.

[0049] To ensure the most user-friendly process possible, the flow properties of the selected bulk material samples from the database are used in real time to refine the determined parameters for the flow properties of the bulk material provided by the user. In this way, the user can receive a suggestion for a suitable dosing device and its operating parameters for dosing the initially unknown bulk material immediately after completing their input. If the user subsequently changes any of the parameters, the effects of these changes can be displayed immediately.

[0050] Optionally, the comparison of the geometric parameters of the user-supplied bulk material with the flow properties of the bulk material samples stored in the database can be performed by a machine learning model that has learned the relationships between geometric parameters and flow properties. The comparison between the geometric parameters of a bulk material and the flow properties stored in the database can, for example, be based on an indirect approach. The geometric parameters, such as the volume, the height of the pile, or the angle of the heap, provide clues to the physical properties of the bulk material, such as particle size, density, or cohesion, which in turn influence the flow properties. Machine learning methods can be used to establish relationships between the geometric parameters and the physical flow properties.A suitable parameter for the flow properties of the bulk material can then be determined by comparison with the bulk material samples stored in the database.

[0051] To facilitate the most efficient and accurate comparison of the bulk material with the bulk material samples stored in the database, it is optionally provided that the machine learning method has been trained to estimate physical properties of the bulk material based on geometric parameters. These estimates are then compared with the physical properties stored in the database to find the best possible match. In this way, the machine learning method integrates information from different sources to maximize prediction accuracy.

[0052] It is also conceivable that the geometric parameters are used to estimate specific flow properties of the bulk material, such as the bulk density, the angle of repose or the flow behavior, and that these flow properties are compared with the values ​​stored in the database for the bulk material samples.

[0053] According to an advantageous embodiment of the invention, the proposals for the dosing device and the operating parameters are calculated in real time based on the information entered by the user and the determined characteristic value. 24012P-WO 19 Qlar Europe GmbH

[0054] Real-time calculation enables immediate feedback to the user and rapid information about the appropriate dosing device and its operating parameters. If the process involves considering bulk material sample data stored in an external database, the electronic data processing equipment used to execute the process must be able to establish a data-transmitting connection to the database and retrieve the necessary information from the database in real time.The query for the required information from the database can be limited to data records that are even relevant based on an initial evaluation of the information entered by the user, so that not all data records from the database have to be retrieved every time the procedure is carried out, or have to be stored in an internal storage unit of the electronic data processing device after previous retrievals.

[0055] In many cases, a mobile radio can be a particularly suitable electronic data processing device for carrying out the procedure. Photographic images of the bulk material with sufficient image quality can often be produced using a mobile radio. Most mobile radios, with their graphical display capabilities, available storage capacity, and processing power, are readily capable of handling the procedure in a user-friendly manner. Furthermore, most mobile radios have interfaces for standardized communication and data transmission, enabling connection to a database and automated processing.

[0056] It is possible to query data records from the database without additional devices or components.

[0057] According to one embodiment of the invention, the operating parameters of the dosing device are set within a predetermined range of values, which is determined based on the calculated characteristic value for the flow properties of the bulk material. This predetermined range ensures that the operating parameters are varied only within safe and effective limits. Furthermore, the predetermined range of values ​​can be specified or limited depending on the specific dosing device.

[0058] Optionally, the operating parameters can include continuous values ​​such as a conveying speed, a dosing frequency, an inclination angle, or vibration settings, and a real numerical value within the specified range can be proposed for each operating parameter. By not restricting the operating parameters to single or discrete values, the method allows for the determination and proposal of optimal operating parameters without, for example, imposing limitations on the adoption of the proposed operating parameters for the operation of the proposed dosing device, which would otherwise influence the result.If necessary, it may be provided that, in addition to the operating parameters proposed as real numerical values, device-specific operating parameter specifications are also determined and displayed, in which the device-specific properties and 24012P-WO 21 Qlar Europe GmbH.

[0059] Restrictions on the input or specification of operating parameters are taken into account. This simplifies the operation of the dosing device for the user and ensures a reliable dosing process.

[0060] Optionally, it can be provided that the specified value range for the operating parameters is calculated on the basis of bulk material samples with similar flow properties stored in the database.

[0061] According to one embodiment of the invention considered advantageous, the graphical user interface displays the proposed operating parameters for the dosing device in an adjustable slider or input field, allowing the user to manually adjust the values. These adjustments are visualized in real time. The graphical representation helps the user better understand the relationships between the flow properties and the dosing parameters. Furthermore, this visualization facilitates the selection of the optimal operating parameters and improves the efficiency of the process flow.

[0062] After determining the characteristic value, the graphical user interface can also display a graphical representation of the flow properties and their effects on the conveying and dosing processes to the user, in order to facilitate the selection and adjustment of operating parameters as well as to verify the proposed dosing device and operating parameters. 24012P-WO 22 Qlar Europe GmbH

[0063] The data stored in the database can be used for the automated selection of the dosing device by considering characteristics specific to each dosing device.

[0064] Flow velocity ranges are stored and compared with the flow properties determined for the bulk material. The operating parameters for the selected dosing device can be optimized in real time by the system using a machine learning algorithm to calculate the optimal values ​​for conveying velocity, dosing frequency, and other parameters based on the bulk material samples and their operating parameters stored in the database. If the data stored in the database also includes physical test results for a bulk material sample, conducted for different dosing devices and possibly under different conditions, this information can be used to enable a particularly precise selection of the appropriate dosing device and operating parameters.

[0065] According to a particularly advantageous embodiment of the invention, the operating parameters determined from the calculated flow properties are automatically transmitted by the electronic data processing unit to a dosing device selected by the user and used for subsequent operation of the dosing device. In this way, the user-selected dosing device can be set up and prepared for operation with the bulk material without any additional user interaction, thus making the process very convenient. Furthermore, errors in the operation of the dosing device are eliminated, errors which could, for example, be attributed to careless mistakes or a lack of expertise in entering the operating parameters.

[0066] Optionally, the process can also include a dosing device operation monitoring step, during which real-time data on flow rate and conveying capacity are continuously recorded. A monitoring module then checks whether the originally specified operating parameters need to be adjusted to maintain or improve dosing efficiency. This allows for monitoring the ongoing operation of the dosing device with the bulk material, enabling the consequences of any changes to an operating parameter to be assessed based on previously recorded information used to determine the operating parameters, and potentially additional data sets. Based on this, changes to the operating parameters can also be automated.Furthermore, the actual consequences of an automated change to the operating parameters can be recorded and used to train a machine learning process, which can then determine and specify future automated changes to the operating parameters.

[0067] The following are further explanations of exemplary embodiments of the invention, which are schematically illustrated in the drawings. It shows: 24012P-WO 24 Qlar Europe GmbH

[0068] Fig. 1 shows a schematic flow diagram for a process flow according to the invention for selecting a metering device and determining operating parameters for metering a bulk material with unknown flow properties.

[0069] Fig. 2 shows a schematic representation of a system for selecting a dosing device and specifying operating parameters for using the dosing device to dose the bulk material.

[0070] Fig. 3 shows a schematic representation of a question within a dialogue in which a user is prompted to enter a binary decision via a graphical user interface, and

[0071] Fig. 4 shows an exemplary representation of several questions that are presented to the user in succession in the dialogue for answering with a binary decision.

[0072] An exemplary process flow shown in Fig. 1 for a method for selecting a metering device 1, schematically depicted in Fig. 2, and for determining operating parameters for metering a bulk material 2 with unknown flow properties using the metering device 1 begins in a first step 3 with the creation of an optical image of a predetermined quantity of the bulk material 2 in a reference environment 4. The reference environment 4 comprises a flat reference surface 5 with a uniform color that differs from the surroundings and whose size is known. Reference markings 6 with a scale division are visible on the reference surface 5 in the recorded optical image 24012P-WO 25 Qlar Europe GmbH.

[0073] In a subsequent process step 7, geometric parameters of the bulk material 2 are determined using a geometry evaluation module of an electronic data processing device, which is shown by way of example as a mobile radio 8 in Fig. 2, based on the previously acquired optical image. For a planar arrangement of individual particles of the bulk material, the geometric parameters can relate to the size, size distribution, and shape of the particles. For an accumulation of a quantity of particles (not shown in Fig. 2), the geometric parameters can also include, for example, the height and volume of the accumulation as well as the angle of the cone's surface.

[0074] In a further process step 9, a user is guided through a dialogue with an input module of the electronic data processing device. The user must answer a number of questions, each by making a binary decision.

[0075] Subsequently, in process step 10, a characteristic value for the flow properties of the bulk material 2 is determined using a characteristic value evaluation module of the electronic data processing device, employing a machine learning method and based on the determined geometric parameters and the user's responses. 24012P-WO 26 Qlar Europe GmbH

[0076] In a subsequent process step 11, the user can enter a desired conveying rate for the bulk material 2 via the input module.

[0077] In a process step 12, the appropriate metering device 1 and suitable operating parameters for this metering device 1 for metering the bulk material 2 are proposed, which are based on the determined characteristic value for the flow properties of the bulk material 2 and the desired conveying rate.

[0078] Optionally, if the user of the proposed dosing device 1 and the operating parameters proposed for it agrees, in a subsequent process step 13 the previously determined operating parameters can be automatically transmitted to a user-selected and identified instance of the dosing device 1, so that the relevant instance of the dosing device 1 can be operated with the operating parameters to convey and dose the bulk material 2.

[0079] If necessary, a dosing device operation monitoring step 14 can be performed during the operation of the dosing device 1, during which real-time data on flow rate and delivery capacity are continuously recorded and checked with a monitoring module to determine whether the originally specified operating parameters need to be adjusted in order to maintain or improve the dosing efficiency. 24012P-WO 27 Qlar Europe GmbH

[0080] During the process, a database containing numerous bulk material samples, for which the flow properties have been determined and measured through previous experiments, can be accessed, and the process can be improved using these experimentally determined flow properties. For this purpose, the process can include the following process steps. The relevant data sets for bulk material samples are queried from a database 15 and made available to the electronic data processing system. The geometric parameters of the user-supplied bulk material 2, determined in process step 7, are compared with the corresponding information of the bulk material samples stored in database 15. Subsequently, one or more bulk material samples are selected from the database whose flow properties most closely match the determined geometric parameters of the bulk material 2.The flow properties of the selected bulk material samples identified in this way are taken into account to determine the characteristic value for the flow properties of the bulk material 2.

[0081] Figure 3 schematically illustrates how, with the aid of a graphical user interface 16, for example from a mobile radio 8, a tablet, or other electronic data processing device, a question is displayed to the user during the dialogue and the user's answer, transmitted as a binary decision, is recorded. In addition to a short question text 17, for example the question "What is the shape of the bulk material?", two figures 18, 19 are displayed, showing two materials with different shapes. 24012P-WO 28 Qlar Europe GmbH

[0082] The user decides which of the two images better answers the question posed and submits this binary decision, for example by tapping the corresponding one.

[0083] Figure 18, 19 or by a swiping motion, or by swiping in the direction of what he considers more appropriate

[0084] Figures 18 and 19.

[0085] Figure 4 shows, from top to bottom, an example sequence of questions and the two associated figures 18 and 19. In the course of the sequence of questions, some questions may have the same question text 17, which, however, is combined with different figures in each case, so that, through several binary decisions by the user, extensive information about the property of the bulk material 2 queried by the question text 17 can be obtained. The respective inputs or binary decisions that a user has entered during the processing of the dialog are indicated by way of example in the sequence of questions.

Claims

24012P-WO 29 Qlar Europe GmbH Patent claims 1. Method for selecting a metering device ( 1 ) and determining operating parameters for metering a bulk material ( 2 ) with unknown flow properties, comprising the steps: - Creating an optical image of a given quantity of the bulk material ( 2 ) in a reference environment ( 4 ) ; - Determining geometric parameters of the bulk material ( 2 ) based on the optical imaging with a geometry evaluation module of an electronic data processing device; - Guiding a user through a dialogue with an input module of an electronic data processing device, whereby the user answers a number of questions by a binary decision; - Determining a characteristic value for the flow properties of the bulk material ( 2 ) using a characteristic value evaluation module of an electronic data processing device using a machine learning procedure, based on the determined geometric parameters and the user's responses; - User input of a desired delivery rate via the input module; - Proposals for a suitable dosing device ( 1 ) and suitable operating parameters for this dosing device ( 1 ) for dosing the bulk material ( 2 ), based on the 24012P-WO 30 Qlar Europe GmbH determined the key figure for the flow properties and the desired flow rate.

2. Method according to claim 1, characterized in that, in addition to the desired conveying rate, a desired dosing accuracy is entered by the user via the input module, and that a suitable dosing device (1) and suitable operating parameters for this dosing device (1) for dosing the bulk material (2) are proposed based on the determined characteristic value for the flow properties and the desired conveying rate and the desired dosing accuracy.

3. Method according to claim 1 or claim 2, characterized in that the questions of the dialogue relate to physical properties of the bulk material (2), such as particle size, moisture content or surface roughness.

4. Method according to one of the preceding claims, characterized in that the dialogue is carried out by means of a graphical user interface (16) and that the questions posed in the graphical user interface (16) show the user two images (18, 19) with opposite properties of the bulk material (2), and the user selects by a binary decision which image (18, 19) better describes the properties of the bulk material (2) provided by him.

5. Method according to claim 4, characterized in that the opposite properties shown in the figures (18, 19) are physical characteristics of the bulk material (2) 24012P-WO 31 Qlar Europe GmbH concerns properties that can be determined by the user without aids, such as graininess versus fineness, dryness versus moisture, or smooth versus rough surface.

6. Method according to one of the preceding claims, characterized in that the machine learning method comprises a neural network that has been trained on the basis of a plurality of bulk material samples with known flow properties.

7. Method according to one of the preceding claims, characterized in that a database (15) containing a plurality of bulk material samples is used for which the flow properties have been determined and measured by prior tests, and the method further comprises: - Comparison of the geometric parameters of the bulk material provided by the user (2) with the bulk material samples stored in the database (15); - Selection of one or more bulk material samples from the database (15) whose flow properties most closely match the determined geometric parameters of the bulk material (2); - Use of the flow properties of the selected bulk material samples to determine the parameter for the flow properties of the bulk material (2) .

8. Method according to claim 7, characterized in that the comparison of the geometric parameters is carried out by means of a machine learning method which determines the similarities between the geometric parameters of the 24012P-WO 32 Qlar Europe GmbH determined from user-provided bulk material (2) and the bulk material samples stored in the database (15).

9. Method according to claim 7 or 8, characterized in that the database (15) comprises information on a variety of physical properties of the bulk material samples, including particle size, density, moisture content and surface structure, and this information is used to improve the accuracy in selecting the appropriate bulk material sample.

10. Method according to one of claims 7 to 9, characterized in that the bulk material samples stored in the database (15) were created by experimental tests to determine the flow properties under different conditions, including different moisture levels, temperature conditions and particle size distributions.

11. Method according to one of claims 7 to 10, characterized in that the flow properties of the selected bulk material samples from the database (15) are used in real time to refine the determined parameter for the flow properties of the bulk material (2) provided by the user.

12. Method according to any one of the preceding claims 7 to 11, characterized in that the comparison of the geometric parameters of the bulk material (2) provided by the user with the flow properties of the bulk material samples stored in the database (15) is carried out by a 24012P-WO 33 Qlar Europe GmbH machine learning model that has learned relationships between geometric parameters and flow properties.

13. Method according to claim 12, characterized in that the machine learning method was trained to estimate physical properties of the bulk material (2) based on geometric parameters, which are then compared with the physical properties stored in the database (15) to find the best possible match.

14. Method according to one of claims 7 to 13, characterized in that the geometric parameters are used to estimate specific flow properties of the bulk material (2), such as the bulk density, the angle of repose or the flow behavior, and these flow properties are compared with the values ​​stored in the database (15).

15. Method according to one of the preceding claims, characterized in that the suggestions for the dosing device (1) and the operating parameters are calculated in real time based on the information entered by the user and the determined characteristic value.

16. Method according to one of the preceding claims, characterized in that the operating parameters of the metering device (1) are set within a predetermined range of values, which is determined on the basis of the determined characteristic value for the flow properties of the bulk material (2).

17. Method according to claim 16, characterized in that the operating parameters are continuous values ​​such as 24012P-WO 34 Qlar Europe GmbH, for example, a conveying speed, a Dosed frequency, a tilt angle or vibration settings are included, and a real numerical value within the specified range is suggested for each operating parameter.

18. Method according to one of the preceding claims, characterized in that the graphical user interface ( 16 ) displays the proposed operating parameters for the dosing device ( 1 ) in an adjustable slider or input field to allow the user to manually adjust the values, the adjustments of which are visualized in real time.

19. Method according to one of the preceding claims, characterized in that the graphical user interface ( 16 ) shows the user, after the determination of the characteristic value, a graphical representation of the flow properties and their effects on the conveying and dosing processes, in order to facilitate the selection and adjustment of the operating parameters.

20. Method according to one of the preceding claims 7 to 19, characterized in that the data stored in the database ( 15 ) are used for the automated selection of the metering device ( 1 ) by storing characteristic flow velocity ranges for each metering device ( 1 ) which are compared with the flow properties determined for the bulk material ( 2 ). 24012P-WO 35 Qlar Europe GmbH 21. Method according to claim 20, characterized in that the operating parameters for the selected dosing device ( 1 ) are optimized in real time by a machine learning method calculating the optimal values ​​for conveying speed, dosing frequency and other parameters on the basis of the bulk material samples stored in the database ( 15 ) and their operating parameters .

22. Method according to one of the preceding claims, characterized in that the operating parameters determined from the determined characteristic of the flow properties are automatically transmitted by the electronic data processing device to a dosing device ( 1 ) selected by the user and are used for subsequent operation of the dosing device ( 1 ).

23. Method according to one of the preceding claims, characterized in that the method also includes a metering device operation monitoring step (14) within which real-time data on flow rate and delivery capacity are continuously recorded and a monitoring module is used to check whether the originally specified operating parameters need to be adjusted in order to maintain or improve the metering efficiency.

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