Template-based crystallization process model generating apparatus and method

Abstract

The present application provides a method for operating a crystallization process model generating device, the method including the steps of selecting a solubility template, a particle size distribution template, a crystallizer template, a process time template, and a concentration factor template provided in a specific order, combining the selected templates to model a crystallization process model, and, once the crystallization process model is constructed, receiving conditions for an injection liquid, inputting the injection liquid into the crystallization process model, and performing a simulation to derive a crystal suspension.

Description

[Technical field]

[0001] Regarding the generation of template-based crystallization process models. [Background technology]

[0002] With the Fourth Industrial Revolution, the demand for digital-based process modeling and simulation technology is rapidly increasing in the chemical and bio industries. In general, digital process modeling is difficult for ordinary chemical and bio basic researchers who do not have specialized knowledge or experience related to modeling to use because it uses a programming language for modeling or a specialized process modeling and simulation tool. Therefore, it is expensive and time-consuming to request an expert to create the digital process model, and it is difficult to modify and manage the created process model and simulation process.

[0003] In addition, modeling and simulation related to industrial equipment applied to actual industrial sites requires equipment elements and specialized knowledge for each case applied to the actual industrial site in addition to the modeling construction work, so there are limitations to the creation of accurate models. For example, in the crystallization process of chemical processes, a solid phase with a particle size distribution exists, and it is necessary to copy the population balance equation for this, and to copy the settling / floating phenomenon due to the particle size distribution according to the geometric structure of the crystallization equipment.

[0004] Therefore, there is a demand for a technology that provides crystallization process modeling and simulation that allows general basic researchers without specialized knowledge or experience to easily and inexpensively provide various crystallization properties and process templates.

[0005] As a related prior art document, Korean Patent Registration No. 1180057, "Apparatus and method for modeling inter-substance mixing phenomenon," is disclosed. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Korean Patent No. 1180057 Summary of the Invention [Problem to be solved by the invention]

[0007] One object of the present application is to provide a template-based crystallization process model generation apparatus and method for generating and simulating a crystallization process model using a selected template using templates constructed for solubility, particle size distribution, crystallization equipment, process time and concentration factor.

[0008] In addition to the above objects, the embodiments of the present disclosure can be used to achieve other objects not specifically mentioned. [Means for solving the problem]

[0009] An operating method of a crystallization process model generating device according to one embodiment of the present disclosure includes the steps of selecting a solubility template, a particle size distribution template, a crystallization equipment template, a process time template, and a concentration factor template, which are provided in a certain order, respectively, combining the selected templates to model a crystallization process model, and, once the crystallization process model is constructed, receiving conditions for an injection solution, inputting the injection solution into the crystallization process model, and performing a simulation to derive a crystal suspension.

[0010] If a parameter value different from the basic value of the parameter set in the template is input, the selected step temporarily stores the template changed to the input parameter value, and the parameter may indicate one or more variables among a solubility parameter, a nucleation rate model parameter, a crystal growth rate model parameter, an inter-compartment unit flow rate flow, a settling / floating cutoff size, a temperature over time, and a concentration process condition.

[0011] The method further includes a step of constructing templates for crystallization properties and crystallization processes, and the constructing step can classify the constituent unit sections based on the geometric structure of the crystallizer, determine whether the constituent unit sections are circulated, and construct them as multiple crystallizer templates based on the crystallizer.

[0012] In the constructing step, model substance systems representative of a range of solubility are matched to construct a plurality of solubility templates, and the model substance systems of the solubility templates are used as parameters for predicting the yield.

[0013] In the constructing step, model material systems representative of the particle size distribution range are matched to construct a plurality of particle size distribution templates, and the model material systems of the particle size distribution templates are used as parameters for simulating the population balance equation.

[0014] The method may further include a step of analyzing the results of the simulation to optimize the parameters of the template applied to the crystallization process model based on the conditions for the injection solution and the crystal suspension, and providing the analysis data.

[0015] A computing device according to one embodiment of the present disclosure includes a memory including instructions, and at least one processor that executes the instructions to generate a crystallization process model, and the processor models the crystallization process model by combining crystallization property templates provided in a certain order, and if one or more templates are selected for the process template, the selected templates.

[0016] The processor selects one template each from the solubility template, particle size distribution template, crystallization apparatus template, process time template, and concentration factor template, and if parameters for a particular template are input, the processor temporarily stores a template to which the input parameters are applied, and if no parameters are input, the processor temporarily stores a template to which a value set as the base value of the template is applied.

[0017] The processor may receive one or more of the following parameters: solubility parameters, nucleation rate model parameters, crystal growth rate model parameters, inter-compartment unit mass flow, settling / floating cutoff size, temperature over time, and concentration process conditions.

[0018] Once the crystallization process model is constructed, the processor can receive conditions for the injectate and input the injectate into the crystallization process model to perform a simulation to derive a crystal suspension.

[0019] When one solubility template is selected from a plurality of solubility templates constructed by matching model substance systems representative of solubility ranges, the processor can use the model substance system of the selected solubility template as a parameter for predicting the yield.

[0020] When one particle size distribution template is selected from a plurality of particle size distribution templates constructed by matching a model material system representative of the range of particle size distribution, the processor can use the model material system of the particle size distribution template as a parameter for simulating the population balance equation.

[0021] The processor can analyze the results of the simulation run to optimize parameters of the template applied to the crystallization process model based on the conditions for the injectate and the crystal suspension, and provide analysis data.

[0022] The processor can modify the crystallizer template applied to the crystallization process model to generate a comparative crystallization process model, analyze the results of simulating the comparative crystallization process model, and recommend an optimal crystallizer based on the conditions for the injection solution and the crystal suspension. Effect of the Invention

[0023] According to the present disclosure, a crystallization model can be easily constructed and a simulation can be performed based on the input template information, enabling even general basic researchers to easily and inexpensively model and simulate the crystallization process.

[0024] In addition, by providing a template containing structural information on crystallization equipment used in actual industrial sites, it is possible to replicate a crystallization process that is similar to that in actual industrial sites even without actual case-specific equipment elements and specialized knowledge. [Brief description of the drawings]

[0025] [Figure 1] FIG. 2 is a configuration diagram of a crystallization process model generating device according to an embodiment. [Diagram 2] FIG. 2 is a model diagram of a crystallizer according to an embodiment. [Diagram 3] FIG. 2 is an exemplary view showing a crystallizer according to an embodiment. [Figure 4] 1 is a flowchart of a method for generating a crystallization process model according to an embodiment. [Diagram 5] 1 is a flow chart of a crystallizer recommendation method according to one embodiment. [Figure 6] FIG. 2 is a hardware configuration diagram of a computing device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Hereinafter, with reference to the accompanying drawings, an embodiment of the present disclosure will be described in detail so that a person having ordinary skill in the art to which the present disclosure pertains can easily carry out the present disclosure. However, the present disclosure may be realized in various different forms and is not limited to the embodiments described herein. In addition, in order to clearly explain the present disclosure in the drawings, parts that are not necessary for the explanation are omitted, and similar parts are designated by similar reference numerals throughout the specification.

[0027] In this disclosure, when a part "comprises" a certain element, this means that it can further include other elements, but not excluding other elements, unless specifically stated to the contrary.

[0028] The device described in the present disclosure is configured with hardware including at least one processor, a memory device, a communication device, etc., and a program that is executed in combination with the hardware is stored in a specified location. The hardware has a configuration and performance that allows it to execute the method of the present disclosure. The program includes instructions that realize the operating method of the present disclosure described with reference to the drawings, and executes the present disclosure in combination with hardware such as a processor and a memory device.

[0029] In this disclosure, "transmitting or providing" can include not only direct transmitting or providing, but also indirect transmitting or providing via other devices or using a roundabout path.

[0030] In this disclosure, expressions described in the singular are to be interpreted as singular or plural, unless expressly indicated otherwise by the use of expressions such as "one" or "single."

[0031] In this disclosure, like drawing numbers refer to like elements regardless of the drawings, and "and / or" includes each and every combination of one or more of the referenced elements.

[0032] In the present disclosure, terms including ordinal numbers such as first, second, etc. may be used to describe various components, but the components are not limited by the terms. The terms are used only to distinguish one component from another. For example, a first component may be named a second component, and similarly, the second component may be named a first component, without departing from the scope of the present disclosure.

[0033] In the flow charts described with reference to the figures in this disclosure, the order of operations may be changed, various operations may be combined, certain operations may be split, and certain operations may not be performed.

[0034] FIG. 1 is a configuration diagram of a crystallization process model generating device according to one embodiment.

[0035] Referring to FIG. 1, a crystallization process model generating apparatus 100 includes a control module 110 , a template setting module 120 , a model generating module 130 , a simulation module 140 , and an analysis module 150 .

[0036] The control module 110, the template setting module 120, the model generation module 130, the simulation module 140, and the analysis module 150 can be operated by at least one processor. Some of the modules constituting the crystallization process model generation device 100 may be realized separately, but for convenience of explanation, they are described as being realized as an integrated module. For example, the simulation module 140 executes the crystallization process model generated by the model generation module 130, and since it is only necessary to link the generated crystallization process model, the model generation module 130 and the simulation module 140 do not always need to be realized together.

[0037] The control module 110 plays a role in controlling the operation of each of the template setting module 120, the model generation module 130, the simulation module 140, and the analysis module 150. The control module 110 provides a user with an interface for selecting one template from among a plurality of templates using a check box, a list, a table image, etc., and for inputting additional parameters, environmental conditions, etc., and can transmit the input values ​​to the template setting module 120, the model generation module 130, and the simulation module 140, respectively.

[0038] The template setting module 120 provides one or more templates to the user for the preset templates, and one of the provided templates is selected. Here, the crystallization process model generating device 100 can generate the templates individually, or can search a database in which pre-constructed templates are stored to select one or more templates and set the parameters of each template. In more detail, a plurality of templates are realized based on certain criteria for solubility, particle size distribution, crystallization device, process time, concentration factor, etc.

[0039] First, a solubility template is constructed according to standards commonly used in the crystallization industry, as shown in Table 1 below (JW Mullin, Crystallization, 2001).

[0040] [Table 1]

[0041] Table 1 shows the criteria (solubility range) of the constructed templates and model substance systems. For example, the non-soluble template is a template that corresponds to CaCO3, among substance systems with a solubility value of 0.5 or less. The model substance system is generally a substance that is well researched, developed, and produced and has a representative value in relation to solubility, but is not necessarily limited to this. The model substance system is used in the process of selecting parameters of a solid-liquid equilibrium model for predicting the yield in a crystallization model. The solid-liquid equilibrium model is a model that calculates the composition of a mixture when an equilibrium state is reached for a mixture in which an arbitrary amount of x solid and an arbitrary amount of y liquid are mixed. For example, when it is divided into a dissolution progress stage (first stage), a solubility stage (second stage), a post-solubility stage (third stage), a dissolution progress stage under different conditions after the solubility state (fourth stage), and a solidified stage under different conditions in the solubility state (fifth stage), the amount of x solid, the amount of y liquid, and the conditions (temperature, etc.) for each stage are different, and the composition ratio at the equilibrium state is different accordingly. As a result, the solid-liquid phase equilibrium model is a model for calculating the solubility of a substance according to its composition and temperature, and is included in the solubility template.

[0042] Also, the model material system is used to determine the initial concentration of the crystallization process. In particular, if one of the model material systems corresponding to the solubility of the material is selected, it is applied to the solid-liquid phase equilibrium model based on the parameters of the selected material system. Thus, by calculating the composition ratio at equilibrium using the solid-liquid phase equilibrium model, the composition ratio at equilibrium (initial concentration value) is determined when the user inputs the amount of material, the amount of water, and the temperature. For example, the solubility parameters, which are parameters of the solid-liquid phase equilibrium model, include heat of fusion, melting point, activity coefficient, etc.

[0043] A particle size distribution template is constructed according to standards commonly accepted in the crystallization industry (JW Mullin, Crystallization, 2001), as shown in Table 2 below.

[0044] [Table 2]

[0045] Table 2 shows the criteria (particle size distribution range) and model material system of the constructed template. Here, the model material system indicates a material that is representative in relation to particle sizes that are generally well studied, developed, and produced, but is not necessarily limited to this. The model material system set in the particle size distribution template is then used to select parameters for the nucleation rate model and the crystal growth rate model, which are the main elements for simulating the population balance equation for predicting particle size distribution. The equations that model the nucleation rate and the crystal growth rate have the form of a power function, and can be simplified as the nucleation rate (B=k b S b ), crystal growth rate (G=k g S g ) can be shown as k b is the nucleation rate constant, b is the nucleation rate order, k g is the crystal growth rate constant, g is the crystal growth rate order, and S is the degree of supersaturation. The nucleation rate and crystal growth rate are determined using the degree of supersaturation calculated by the solid-liquid equilibrium model described above as the driving force. For example, if the nucleation rate is high and the crystal growth rate is low under the same degree of supersaturation, crystal nuclei are generated quickly, but growth is insufficient and crystals with a low average grain size are generated. Conversely, if the nucleation rate is low and the crystal growth rate is high, the generated crystal nuclei can grow sufficiently, and crystals with a high average grain size are generated.

[0046] Thus, by providing parameters of nucleation rate and crystal growth rate for each general average grain size together with a representative model material system, when a model material system including the average grain size of the material to be calculated is selected, the parameters of the model material system are automatically matched and provided. Here, the parameters are values ​​extracted under general crystallization process conditions, and the parameters themselves do not mean the average grain size, but are used to calculate the average grain size of crystals according to the crystallization device, process time, concentration factor, etc., which are determined from the template selected in a later stage.

[0047] The crystallizer template is constructed based on currently available crystallizers such as MSMPR (mixed suspension mixed product removal) crystallizer, forced circulation crystallizer, DTB (draft tube baffled) crystallizer, Oslo crystallizer, etc.

[0048] Each crystallizer can be modeled using compartment modeling techniques in advance using computational fluid dynamics simulations, and is configured with not only the crystallization compartment required for the crystallization process, but also the settling / floating compartment, fine particle dissolution compartment, and evaporation compartment.

[0049] The crystallizer template may be as shown below in FIG. 3, but is not necessarily limited to this as modifications and additions to the template for the crystallizer are possible.

[0050] [Table 3]

[0051] Table 3 shows the criteria for the constructed templates (type of crystallizer) and the unit sections configured by them. The parameters applied in the crystallizer template include unit flow rate between sections, settling / floating cutoff size, etc., and the internal flow is determined by the parameters. Among the types of crystallizers (MSMPR, forced circulation crystallizer, DTB crystallizer, Oslo crystallizer), the unit flow rate between sections can be modified in the DTB crystallizer template and the Oslo crystallizer template, which require discharge distribution between sections. When the DTB crystallizer template and the Oslo crystallizer template are selected, the basic values ​​corresponding to each crystallizer are basically provided, and the unit flow rate between sections can be modified later in the process of adjusting the detailed conditions.

[0052] In addition, when modifying the template itself, adding a new crystallizer template, or directly designing, if it is necessary to distribute the discharge in the sections of the crystallizer, the unit flow rate can be modified separately for each section. For example, the basic principle of the DTB crystallizer is that the large crystals accumulate at the bottom (settling / floating section) due to the flow rate generated by the lower impeller and the terminal velocity according to the crystal particle size, and the fine particles dissolve during the circulation process, and the small crystals grow (fine particle dissolution section).

[0053] At this time, since the inside of the DTB crystallizer is in a low pressure state, the water inside evaporates at a low temperature, and the section becomes an evaporation crystallization section. Heat is continuously supplied to the process liquid from a heat exchanger (not shown) in the circulation line, and the degree of supersaturation generated by the evaporated water becomes the driving force for crystallization.

[0054] The unit flow rate must be determined during the transition from the crystallization section to the settling / flotation section, where solids below the settling / flotation cutoff size are transferred to the fine particle dissolution section, and the remaining particles are returned to the crystallization section. Therefore, the settling / flotation cutoff size can be changed in the DTB crystallizer template and the Oslo crystallizer template with settling / flotation section.

[0055] The crystallization process time template is constructed based on the process times as shown in Table 4 below.

[0056] [Table 4]

[0057] The enrichment factor template is constructed based on a fixed crystallization enrichment factor, as shown in Table 5 below.

[0058] [Table 5]

[0059] The constructed templates are stored in the linked database with the parameters set to the basic values. The parameters for each template are the same as those in the table described above, and the inherent parameters are as follows. In other words, the inherent parameters for the solubility template include the heat of fusion, melting point, activity coefficient, etc. of each representative material system, and can be modified by the user when modifying detailed conditions.

[0060] The inherent parameters of the particle size distribution template are the nucleation rate constants (k b ), nucleation rate order (b), crystal growth rate constant (k g ), crystal growth rate order (g), etc., which can be modified by the user when modifying detailed conditions.

[0061] The parameters of the crystallization device template are determined for each device selected corresponding to the crystallization device. In particular, in the case of a crystallization device template having compartments that require inter-compartment discharge distribution, the user can modify the unit flow rate between compartments, the settling / floating cutoff size, etc., when modifying detailed conditions.

[0062] The parameters of the crystallization process time template and the crystallization concentration factor template are the same as those in Tables 4 and 5, respectively, and can be modified by the user when modifying detailed conditions.

[0063] In this way, the template setting module 120 provides the constructed templates to the user in a certain stage, and one template is selected for each template type, for example, one each of the solubility template, particle size distribution template, crystallizer template, process time template, and concentration factor template.

[0064] The template setting module 120 can set the parameters of the selected template differently based on the input parameters. If no separate parameter value is input, the default value is set. Meanwhile, the user selects the template model material system, crystallization device, crystallization process time, and crystallization process multiple to modify the detailed conditions. If no separate parameter modification is made, the parameters provided by default in the template can be used.

[0065] If the parameters of the selected template are separately input by the user, the template setting module 120 changes the solubility parameters for the solubility template, and the solubility template with the changed solubility parameters is temporarily stored in the database. In other words, the template setting module 120 selects the template and parameter values ​​selected by the user to apply to the crystallization process model, and transmits them to the model generation module 130.

[0066] The model generation module 130 generates a crystallization process model based on the selected template. The model generation module 130 collects the stored model formulas based on the selected template, and finally generates the crystallization process model using the parameters of the determined model formulas.

[0067] The simulation module 140 simulates a process in which a crystal suspension is extracted from an injectate set for the constructed crystallization process model. The simulation module 140 can repeatedly perform simulations by changing environmental conditions applied to the crystallization process model, and the results of the simulation can be provided to an associated display device (not shown) or stored in a separate database. Here, the environmental conditions include the injectate, the injectate concentration, the injectate temperature, etc.

[0068] The analysis module 150 provides crystal recovery rate, crystal size, etc. based on information about the crystal suspension derived from the results of the simulation. In addition, the analysis module 150 analyzes the results of the simulation to check whether the parameters included in the constructed crystallization process model are optimized. In other words, the analysis module 150 can check whether the crystallization process model generated based on the user's input is optimized and generate and output feedback information.

[0069] The analysis module 150 can determine whether optimization has been achieved based on criteria such as the crystal recovery rate (ratio of solid crystals in the suspension) and the crystal size. For example, if the crystal recovery rate has the highest value in a simulation or is equal to or greater than a reference value, the analysis module 150 can be estimated to have been optimized. Alternatively, if the crystal size has the largest value in a simulation or is equal to or greater than a reference value, the analysis module 150 can be estimated to have been optimized. However, since the crystallization result has different goals depending on the situation, the present invention is not necessarily limited to this, and the criteria for determining whether optimization has been achieved can be easily changed and set later by the user.

[0070] Meanwhile, the analysis module 150 can use a machine learning model that has completed learning in the process of generating feedback information based on input values. For example, the analysis module 150 can use a machine learning model that has been trained to select a crystallizer having the highest crystal recovery rate or the largest crystal size based on the input values. Here, the machine learning model can perform reinforcement learning to select an optimal action for a given state.

[0071] Alternatively, the analysis module 150 can use machine learning models trained to select parameters to be changed to satisfy optimization criteria, such as solubility / particle size distribution / crystallization equipment / process time / concentration factor template type, etc.

[0072] In addition, the analysis module 150 can generate a comparative crystallization process model so as to select an optimized crystallizer based on the feedback information, and can request the control module 110 to execute a simulation. In other words, the analysis module 150 can generate a comparative crystallization process model by arbitrarily changing a crystallizer template in the generated crystallization process model, and can analyze the results of executing simulations of a plurality of comparative crystallization process models.

[0073] Thus, the analysis module 150 can select the crystallization process model with the highest analyzed execution result and recommend a crystallizer included in the selected crystallization process model. The analysis module 150 can provide only the optimized crystallization process model or the most optimized crystallizer depending on the result of the comparison analysis, and can also provide the results of the comparison analysis for selecting the crystallization process model or the crystallizer.

[0074] FIG. 2 is a model diagram of a crystallizer according to an embodiment, and FIG. 3 is an exemplary diagram showing a crystallizer according to an embodiment.

[0075] Figure 2 (a) shows a unit cell model of an MSMPR (mixed suspension mixed product re moval) crystallizer, (b) shows a unit cell model of a forced circulation crystallizer, (c) shows a unit cell model of a DTB (draft tube baffled) crystallizer, and (d) shows a unit cell model of an Oslo crystallizer.

[0076] FIG. 3(a) is a simplified schematic diagram of an MSMPR crystallizer, (b) is a forced circulation crystallizer, (c) is a DTB crystallizer, and (d) is an Oslo crystallizer.

[0077] 2 and 3, the unit section model is divided based on the equipment characteristics of the four crystallizers. For reference, the division by crystallization section number in FIG. 2 is made for convenience to easily divide by flow, and the area for all crystallization sections is shown separately from the crystallization section number.

[0078] MSMPR crystallizer is an abbreviation of Mixed-Suspension, Mixed-Product Removal, and is a theoretical crystallizer that uses an external pump to forcibly circulate the process liquid inside the crystallizer to mix it. For example, in MSMPR crystallizers, the phenomenon of evaporation crystallization (water evaporates) occurs throughout the entire process liquid, but the evaporation phenomenon in the actual process occurs due to the gas-liquid equilibrium between the surface of the upper part of the process liquid and the gas. The advantage of such MSMPR crystallizers is that they are assumed to be homogeneous, so calculations can be easily made using theoretical formulas, making it easy to calculate the nucleation rate / crystal growth rate. In the MSMPR crystallizer, the injected liquid passes through the evaporation crystallization section and is extracted as a crystal suspension.

[0079] A forced circulation crystallizer uses an external pump to forcibly circulate the process liquid inside the crystallizer to mix it, and when circulation is sufficient, it has characteristics similar to an MSMPR crystallizer. In a forced circulation crystallizer, evaporation occurs at the process liquid / gas phase surface due to gas-liquid equilibrium, and crystallization occurs in the remaining part due to supersaturation caused by evaporation in the evaporation crystallization section. In this forced circulation crystallizer, the injected liquid passes through crystallization section 1, crystallization section 2, evaporation crystallization section, and crystallization section 3, and is discharged as a crystal suspension. At this time, it can be connected again from crystallization section 3 to crystallization section 1 and circulated again through the crystallization section and evaporation crystallization section.

[0080] In the DTB crystallizer, the flow rate generated by the lower impeller and the terminal velocity depending on the crystal particle size cause large crystals to accumulate at the bottom (settling / floating section) and small crystals and fine particles to circulate. In this circulation process, fine particles dissolve and small crystals grow (fine particle dissolution section).

[0081] The DTB crystallizer can be shown as Figure 2(c) if the crystallization sections, excluding certain sections (evaporative crystallization section, settling / floating section, etc.) that require discharge distribution between sections, are temporarily divided according to the flow of the process liquid through a computer fluid dynamics simulation performed in advance. Therefore, in the DTB crystallizer, the injected liquid passes through crystallization section 1, evaporative crystallization section, crystallization section 2, and crystallization section 3, and is discharged as a crystal suspension. At this time, it can be connected again from crystallization section 3 to crystallization section 4 to circulate through the crystallization section and the evaporative crystallization section once again, or it can be connected again from the fine particle dissolving section through the settling / floating section to crystallization section 4 to circulate through the crystallization section and the evaporative crystallization section once again.

[0082] In the Oslo crystallizer, the injectant is fed into the evaporator, and a flow is formed by the circulation line and pump connected to the top of the crystallizer body. At this time, the difference in the flow rate of the formed flow and the terminal velocity due to the crystal grain size causes the crystals to settle / float, and the small crystals that rise to the surface in the wide crystallizer body slowly stay and grow. Therefore, in the Oslo crystallizer, the injectant is discharged as a crystal suspension through crystallization section 1, evaporation crystallization section, crystallization section 2, and crystallization section 3. At this time, it passes through the settling / floating section from crystallization section 3 and is connected to crystallization section 1 again, and can circulate through the crystallization section and evaporation crystallization section again.

[0083] Meanwhile, the conditions for separating the crystal suspension are determined by the concentration factor template, and the concentration factor is (volume of injected liquid / volume of suspension), and in the case of a continuous crystallizer, the volume of the internal process liquid can be kept constant only when the balance of (flow rate injected per unit time) = (flow rate discharged per unit time) is formed. Therefore, when the concentration factor is determined by the template, the volume of the suspension is automatically calculated and the volume of the evaporated water is determined.

[0084] The process of generating a crystallization process model and providing a simulation will now be described.

[0085] FIG. 4 is a flow chart of a method for generating a crystallization process model according to one embodiment.

[0086] 4, the crystallization process model generating device 100 selects one template from the solubility templates (S110). The crystallization process model generating device 100 provides a plurality of templates constructed based on the solubility range, and one template is selected and a solubility parameter is input separately. When one solubility template is selected, the crystallization process model generating device 100 can temporarily store the selected solubility template in a storage location corresponding to the user's unique ID.

[0087] The crystallization process model generating device 100 selects one template from the particle size distribution templates (S120). The crystallization process model generating device 100 provides a plurality of templates constructed based on the particle size distribution, and one template is selected, and nucleation rate model parameters or crystal growth rate model parameters are separately input.

[0088] The crystallization process model generating device 100 selects one template from the crystallization device templates (S130). The crystallization process model generating device 100 provides a plurality of templates constructed based on crystallization devices, and one template is selected and the unit flow rate between compartments or the settling / floating cutoff size is separately input. At this time, the crystallization process model generating device 100 can separately provide further explanations regarding the crystallization device. For example, a description of when each crystallization device is frequently used or in which process it is an equipment for deriving a crystal suspension can be provided in the form of text or images.

[0089] The crystallization process model generating device 100 selects one template from the process time templates (S140). At this time, the crystallization process model generating device 100 separately inputs conditions for temperature or concentration over time based on the selected template.

[0090] The crystallization process model generating device 100 selects one template from the concentration factor templates (S150). At this time, the crystallization process model generating device 100 separately inputs the conditions for temperature or concentration over time based on the selected template.

[0091] The crystallization process model generating device 100 generates a crystallization process model (S160). The crystallization process model generating device 100 can generate a crystallization process model based on parameters input separately from the selected template. However, if there are no parameters input separately, parameters set as a base for each template are input.

[0092] The crystallization process model generating device 100 performs a simulation of the generated crystallization process model (S170). The crystallization process model generating device 100 performs a simulation by applying the input environmental conditions to the generated crystallization process model.

[0093] FIG. 5 is a flow chart of a crystallizer recommendation method according to one embodiment.

[0094] 5, the crystallization process model generating device 100 analyzes the results of a simulation for the generated crystallization process model (S210). The crystallization process model generating device 100 checks whether the generated crystallization process model satisfies an optimization criterion and analyzes the results.

[0095] The crystallization process model generating device 100 generates a comparative crystallization process model by changing at least one of the parameters and templates for optimizing the crystallization process model (S220). For example, if the optimization criterion is not satisfied, the crystallization process model generating device 100 selects parameters, such as solubility / particle size distribution / crystallization device / process time / concentration factor template type, that should be changed to satisfy the optimization criterion. Then, the selected change condition is applied to the crystallization process model to generate a comparative crystallization process model. At this time, multiple comparative crystallization process models can be generated by changing each change condition individually or in combination.

[0096] The crystallization process model generating device 100 performs a simulation for the generated comparative crystallization process model and compares and analyzes the results of the performed simulation (S230). The crystallization process model generating device 100 compares and analyzes the results of the simulation between the generated crystallization process model and the comparative crystallization process model.

[0097] The crystallization process model generating device 100 provides feedback information including an optimized crystallizer and an optimized crystallization process model for the injection solution and the suspension (S240). The crystallization process model generating device 100 can provide only the optimized crystallization process model according to the results of the comparative analysis, or can selectively provide the most optimized crystallizer. Here, the feedback information can include the results of the comparative analysis in addition to information on the crystallizer and the crystallization process model. In other words, the crystallization process model generating device 100 can provide an analysis result that allows for the selection of an optimized crystallizer and a crystallization process model.

[0098] FIG. 6 is a hardware configuration diagram of a computing device according to one embodiment.

[0099] 6, the crystallization process model generating apparatus 100 is realized by a computing device 200 operated by at least one processor. The computing device 200 may include one or more processors 210, a memory 220 for loading a computer program executed by the processor 210, a storage device 230 for storing the computer program and various data, a communication interface 240, and a bus connecting them. In addition, the computing device 200 may further include various other components.

[0100] The processor 210 is a device that controls the operation of the computing device 200 and may be any of various types of processors that process instructions contained in a computer program, and may include at least one of a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any type of processor well known in the technical field of the present disclosure.

[0101] The memory 220 stores various data, instructions, and / or information. The memory 220 can load computer programs from the storage device 230 so that instructions written to perform the operations of the present disclosure are processed by the processor 210. The memory 220 can be, for example, a read only memory (ROM), a random access memory (RAM), etc.

[0102] The storage device 230 can non-temporarily store computer programs and various data. The storage device 230 includes a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or any other type of computer-readable recording medium well known in the technical field to which the present disclosure pertains.

[0103] The communication interface 240 may be a wired / wireless communication module supporting wired / wireless communication. The bus provides communication between the components of the computing device 200.

[0104] The computer program includes instructions executed by the processor 210 and is stored in a non-transitory computer readable storage medium, the instructions causing the processor 210 to perform the operations of the present disclosure. The computer program may be downloaded over a network or sold in product form. The template may be embodied in the computer program executed by the processor 210.

[0105] Thus, according to the embodiment, a crystallization model can be easily constructed and a simulation can be performed based on the input template information, and even general basic researchers can easily and inexpensively perform crystallization process modeling and simulation.

[0106] By inputting various parameter values ​​and environmental conditions obtained through theory or experiments, the system can automatically calculate the predicted crystallization yield and crystal size, which can be useful for selecting an actual process later.

[0107] In addition, by providing a template containing structural information on crystallization equipment used in actual industrial sites, it is possible to replicate a crystallization process that is similar to that in actual industrial sites even without actual case-specific equipment elements and specialized knowledge.

[0108] The embodiments of the present disclosure described above may be realized not only by an apparatus and a method, but also via a program that realizes functions corresponding to the configuration of an embodiment of the present disclosure or a recording medium on which the program is recorded.

[0109] Although the embodiments of the present disclosure have been described in detail above, the scope of the present disclosure is not limited thereto, and various modifications and improvements made by those skilled in the art using the basic concept of the present disclosure defined in the claims below also fall within the scope of the present disclosure.

Claims

1. A method for operating a crystallization process model generating device, comprising: selecting a solubility template, a particle size distribution template, a crystallizer template, a process time template, and a concentration factor template in a specific order; combining the selected templates to model a crystallization process model; Once the crystallization process model is constructed, receiving conditions for an injectate and inputting the injectate into the crystallization process model to perform a simulation to derive a crystal suspension.

2. The selecting step includes temporarily storing a template modified to the input parameter value if a parameter value different from a basic value of the parameter set in the template is input, 2. The method of claim 1, wherein the parameters represent one or more of the following variables: solubility parameters, nucleation rate model parameters, crystal growth rate model parameters, inter-compartment unit mass flow, settling / floating cutoff size, temperature over time, and concentration process conditions.

3. Further comprising the step of constructing a template for crystallization properties and processes; The method of claim 1, wherein the constructing step classifies constituent unit sections based on the geometric structure of the crystallization apparatus, determines whether or not the constituent unit sections are circular, and constructs the crystallization apparatus as a plurality of crystallization apparatus templates based on the crystallization apparatus.

4. The constructing step includes constructing a plurality of solubility templates by matching model substance systems represented based on a solubility range; The method of claim 3 , wherein the model substance system of the solubility template is used as a parameter to predict yield.

5. The constructing step includes constructing a plurality of particle size distribution templates by matching model material systems representative of particle size distribution ranges; The method of claim 3 , wherein the model material system of the particle size distribution template is used as parameters to simulate a population balance equation.

6. The method of claim 1, further comprising: analyzing the results of the simulation run to optimize template parameters applied to the crystallization process model based on conditions for the injectate and the crystal suspension, and providing analytical data.

7. 1. A computing device comprising: a memory containing instructions; and at least one processor for executing the instructions to generate a crystallization process model; The processor is a computing device that, when a solubility template, a particle size distribution template, a crystallization apparatus template, a process time template, and a concentration factor template provided in a certain order are selected, models the crystallization process model by combining the selected templates, and when the crystallization process model is constructed, receives conditions for an injection solution and inputs the injection solution into the crystallization process model to perform a simulation to derive a crystal suspension.

8. The processor, 8. The computing device of claim 7, further comprising: if a parameter for a particular template is input, a template to which the input parameter is applied is temporarily stored; and if a parameter is not input, a template to which a value set as a base value of the template is applied is temporarily stored.

9. 8. The computing device of claim 7, wherein the processor receives one or more of the following parameters: solubility parameters, nucleation rate model parameters, crystal growth rate model parameters, inter-compartment unit mass flow, settling / floating cutoff size, temperature over time, and concentration process conditions.

10. The processor selects one solubility template from a plurality of solubility templates constructed by matching model substance systems represented by a solubility range, 10. The computing device of claim 7, wherein the model substance system of the selected solubility template is used as a parameter to predict yield.

11. the processor selects one particle size distribution template from a plurality of particle size distribution templates constructed by matching a model material system represented based on a particle size distribution range, The computing device of claim 7 , wherein the model material system of the particle size distribution template is used as a parameter for simulating a population balance equation.

12. 8. The computing device of claim 7, wherein the processor analyzes results of the simulation run to optimize template parameters applied to the crystallization process model based on conditions for the injectate and the crystal suspension, and provides analysis data.

13. 10. The computing device of claim 7, wherein the processor generates a comparative crystallization process model by modifying a crystallizer template applied to the crystallization process model, and analyzes results of simulating the comparative crystallization process model to recommend an optimal crystallizer based on conditions for the injectate and crystal suspension.

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