A digital control method for freezing with liquid nitrogen micro-ice crystals

Abstract

The present invention discloses a digital control method for freezing with liquid nitrogen micro ice crystals, including: collecting pre-input product freezing parameters; constructing a deep learning algorithm model, inputting the product freezing parameters into the deep learning algorithm model for training, and ending the training when the output result of the freezing temperature change value meets the requirements, obtaining the single spray amount of liquid nitrogen, the spray duration each time, and the spray time interval; setting the liquid nitrogen freezing process by using the liquid nitrogen spray parameters, and performing the spray freezing of liquid nitrogen according to this setting. Applying the liquid nitrogen freezing technology to the ice cream freezing process improves the forming effect and taste of the product. On the other hand, the present invention sets the parameters of liquid nitrogen freezing to obtain a relatively mild freezing method, which plays a role in controlling the extremely rapid temperature drop during liquid nitrogen freezing.

Description

Technical Field

[0001] The present invention relates to the technical field of food freezing optimization, and particularly to a digital control method for freezing with liquid nitrogen micro ice crystals. Background Art

[0002] During the freezing process of frozen products, traditional quick-freezing equipment and methods are prone to deformation, uneven crystallization, and certain damage to nutrients due to the long freezing process, resulting in poor taste and appearance. Therefore, it is considered to optimize the traditional quick-freezing methods and equipment.

[0003] In the field of ice cream foods with the highest freezing requirements, the application of liquid nitrogen quick-freezing technology is relatively blank, and the value improvement of this technology for high-end ice cream products is more significant than that of other ordinary frozen foods. Therefore, it is considered to creatively apply liquid nitrogen freezing technology to the ice cream processing technology to improve the product forming effect and taste.

[0004] Currently, in the process of creatively applying liquid nitrogen quick-freezing technology to ice cream, there are also the following problems: due to the extremely fast freezing speed of liquid nitrogen, a large instantaneous temperature difference will be generated between the surface and the center of the food, and the expansion pressure is large, resulting in low-temperature fracture and damage to the food tissue structure, which has an adverse impact on food quality. How to control liquid nitrogen freezing has become an urgent aspect to be solved in this technology. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the above problems existing in the current liquid nitrogen quick-freezing technology for ice cream, the present invention is proposed.

[0007] Therefore, the technical problem solved by the present invention is to solve the problem that in the existing liquid nitrogen quick-freezing technology for ice cream, due to the extremely fast freezing speed, a large instantaneous temperature difference is generated between the surface and the center of the food, the expansion pressure is large, resulting in low-temperature fracture and damage to the food tissue structure, which has an adverse impact on food quality.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: A digital control method for freezing liquid nitrogen microcrystals, comprising the following steps: collecting pre-input product freezing parameters; constructing a deep learning algorithm model, inputting the product freezing parameters into the deep learning algorithm model for training, and ending the training when the output result of the freezing temperature change value meets the requirements, obtaining the single-time liquid nitrogen spraying amount, the spraying duration each time, and the spraying time interval; setting the liquid nitrogen freezing process using the liquid nitrogen spraying parameters, and performing spraying and freezing of liquid nitrogen according to this setting.

[0009] As a preferred embodiment of the digital control method for freezing liquid nitrogen microcrystals according to the present invention, wherein: the product freezing parameters specifically include: the density of the product, the flow viscosity, and the freezing temperature.

[0010] As a preferred embodiment of the digital control method for freezing liquid nitrogen microcrystals according to the present invention, wherein: the constructed deep learning algorithm model includes a CNN layer and an encoding layer; the CNN layer includes a convolutional layer, a pooling layer, and a fully connected layer; the encoding layer includes a convolutional encoding layer and a convolutional decoding layer.

[0011] As a preferred embodiment of the digital control method for freezing liquid nitrogen microcrystals according to the present invention, wherein: the constructed deep learning algorithm model is:

[0012]

[0013] Wherein, E is a function of the freezing temperature change value; t is the freezing temperature, that is, the temperature difference between the established room temperature and the temperature at which the product is formed into a solid state; ρ is the density of the product; μ is the flow viscosity; t1 is the temperature difference of the first temperature drop at room temperature; t2 is the temperature difference of the second temperature drop based on the first temperature drop; t n is t n-1 the temperature difference of the subsequent temperature drop based on the previous temperature drop.

[0014] As a preferred embodiment of the digital control method for freezing liquid nitrogen microcrystals according to the present invention, wherein: it is defined that when the E value is in the interval (0, 1), the output E value meets the requirements and the training ends.

[0015] As a preferred embodiment of the digital control method for freezing liquid nitrogen microcrystals according to the present invention, wherein: obtaining the single-time liquid nitrogen spraying amount, the spraying duration each time, and the spraying time interval specifically is: obtaining the minimum temperature drop range among t1, t2...t n as the spraying reference value; sequentially obtaining t1, t2...t nThe ratio relative to the spray reference value; obtaining the spray volume and spray duration of the spray reference value; sequentially obtaining the liquid nitrogen spray volume and spray duration within each temperature period according to the obtained spray volume and spray duration of the spray reference value; wherein, the spray time interval for each temperature period is defined as 30s to 45s.

[0016] As a preferred embodiment of the digital control liquid nitrogen microcrystal freezing method of the present invention, wherein: the spray time interval is defined as 36s.

[0017] Advantages of the present invention: The present invention provides a digital control liquid nitrogen microcrystal freezing method, constructs a dedicated deep learning algorithm model, trains the collected product freezing parameters, obtains the liquid nitrogen spray parameters exclusive to the product freezing parameters, and performs liquid nitrogen spray freezing; on the one hand, the present invention creatively applies the liquid nitrogen freezing technology to the ice cream freezing process, improving the product forming effect and taste, and on the other hand, the present invention sets the parameters of liquid nitrogen freezing, obtains a relatively mild freezing method, controls the extremely rapid temperature drop of liquid nitrogen freezing, and solves the problem that during the liquid nitrogen freezing process, due to the extremely fast freezing speed of liquid nitrogen, a large instantaneous temperature difference will be generated between the surface and the center of the food, the expansion pressure is large, resulting in low-temperature fracture, damaging the tissue structure of the food, and having an adverse impact on the food quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0019] Figure 1 It is a method flow chart of the digital control liquid nitrogen microcrystal freezing method provided by the present invention.

[0020] Figure 2 It is a method flow chart of the present invention for obtaining the single liquid nitrogen spray volume, each spray duration and spray time interval.

[0021] Figure 3 It is a freezing curve of frozen food provided by the present invention.

[0022] Figure 4 It is a schematic diagram of the sub-smoke spray process provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0024] In ice cream and other foods with the highest freezing requirements, the application of liquid nitrogen quick-freezing technology is relatively blank, and the value improvement of this technology for high-end ice cream products is more significant than that of other ordinary frozen foods.

[0025] Therefore, please refer to Figure 3 , in the figure, A-B-C-D-E is the freezing process of traditional food freezing technology, and A1-B1-C1-D1-E1 is the food freezing process of liquid nitrogen freezing technology). The liquid nitrogen quick-freezing technology can enable the food to quickly pass through the ice crystal area (the shaded area C-D in the figure, generally -5 to -1 °C), achieving a quick-freezing effect with a finer texture and more stable form of the finished product. Therefore, it is considered to creatively apply the liquid nitrogen freezing technology to the ice cream processing technology to improve the product forming effect and taste.

[0026] Currently, in the process of creatively applying the liquid nitrogen quick-freezing technology for ice cream, there are the following problems: Since the freezing speed of liquid nitrogen is extremely fast, a large instantaneous temperature difference will be generated between the surface and the center of the food, and the expansion pressure is large, resulting in low-temperature fracture and damaging the tissue structure of the food, which has an adverse impact on the food quality. How to control the liquid nitrogen freezing has become an aspect that urgently needs to be solved in this technology.

[0027] Therefore, please refer to Figure 1. The present invention provides a digital control method for liquid nitrogen micro-ice crystal freezing, including the following steps:

[0028] Collect the pre-input product freezing parameters;

[0029] Build a deep learning algorithm model, input the product freezing parameters into the deep learning algorithm model for training, and end the training when the output result of the freezing temperature change value meets the requirements, and obtain the single liquid nitrogen spraying amount, the spraying duration each time, and the spraying time interval;

[0030] Use the liquid nitrogen spraying parameters to set the liquid nitrogen freezing process, and perform the spraying and freezing of liquid nitrogen according to this setting.

[0031] Specifically, the product freezing parameters specifically include: the density, flow viscosity, and freezing temperature of the product.

[0032] Among them, the products that need to be frozen (liquid nitrogen-solid state) are pre-measured for their density, flow viscosity, and freezing temperature to form a solid state.

[0033] Furthermore, the constructed deep learning algorithm model includes a CNN layer and an encoding layer;

[0034] The CNN layer includes a convolutional layer, a pooling layer, and a fully connected layer;

[0035] The encoding layer includes a convolutional encoding layer and a convolutional decoding layer.

[0036] It should be noted that the basic constituent technology of the deep learning algorithm model is the application of existing technology, and the basic constituent method and code will not be elaborated here.

[0037] Furthermore, the constructed deep learning algorithm model is:

[0038]

[0039] Among them, E is the function of the freezing temperature change value; t is the freezing temperature, that is, the temperature difference between the established room temperature and the temperature at which the product is initially formed into a solid state; ρ is the density of the product; μ is the flow viscosity; t1 is the temperature difference during the first cooling at room temperature; t2 is the temperature difference during the second cooling based on the first cooling; t n is t n-1 the temperature difference during the re-cooling based on the t-th cooling.

[0040] It should be noted that the established room temperature is set to 25 °C, and ρ and μ are the density and flow viscosity of the product at the current temperature during the temperature change process - which can be measured in real time during the temperature change process, and the measurement method uses a conventional chemical measurement method.

[0041] During the specific training process, the preferred training data set is:

[0042] t1 is t 0 / 2, t2 is t 0 / 3,..., t n is t 0 / (n + 1), where t 0 is the temperature difference when the product is frozen from the established room temperature to a solid state during the selection of the conventional freezing process;

[0043] t1 is t 0 / 3, t2 is t 0 / 4,..., t n is t 0 / (n + 2), where t 0 is the temperature difference when the product is frozen from the established room temperature to a solid state during the selection of the conventional freezing process;

[0044] t1 is t 0 / 4, t2 is t 0 / 5,..., t n is t 0 / (n + 3), where t 0 is the temperature difference when the product is frozen from a given room temperature to a solid state during the selection of a conventional freezing process;

[0045] t1 is t 0 / 5, t2 is t 0 / 6,..., t n is t 0 / (n + 4), where t 0 is the temperature difference when the product is frozen from a given room temperature to a solid state during the selection of a conventional freezing process;

[0046] ...

[0047] Among them, it is defined that when the E value is in the interval (0, 1), the output E value meets the requirements and the training ends.

[0048] When the E value is in the interval (0, 1), from the perspective of placing it in the freezing temperature table, it can be understood sideways that the freezing curve is at Y = X 2 In the curve graph, the derivative curve (instantaneous degree of temperature drop) obtained by the freezing curve is within the coverage range of a linear equation.

[0049] Specifically, please refer to Figure 2 to obtain the specific single - time liquid nitrogen spraying volume, spraying duration each time, and spraying time interval as follows:

[0050] Obtain t1, t2...t n the minimum temperature drop range as the spraying reference value;

[0051] Successively obtain the ratios of t1, t2...t n relative to the spraying reference value;

[0052] Obtain the spraying volume and spraying duration of the spraying reference value - having obtained the minimum temperature drop range, the spraying duration of a certain amount of liquid nitrogen can be obtained based on this drop range, or the quantitative spraying volume required for a certain spraying duration can be obtained;

[0053] Successively obtain the liquid nitrogen spraying volume and spraying duration in each temperature period according to the spraying volume and spraying duration of the obtained spraying reference value;

[0054] Among them, the spraying time interval in each temperature period is defined as 30s - 45s.

[0055] Specifically, the spraying time interval is defined as 36s.

[0056] Please refer to Figure 4, the process of the present invention finally controls the freezing time of a single batch of ice cream within 58 s. Only 0.163 L of liquid nitrogen is required to complete the freezing of each ice cream, and the freezing raw material cost is controlled within 0.1 yuan. The precise control of this parameter by the research and development core algorithm maximizes the energy efficiency ratio of liquid nitrogen spraying. Moreover, through multiple tests and data accumulation, the freezing process of fractional spraying can also be applied to improve the production efficiency of frozen foods and save energy on the production line.

[0057] The present invention provides a digital control method for liquid nitrogen microcrystal freezing, constructs a dedicated deep learning algorithm model, trains the collected product freezing parameters, obtains the liquid nitrogen spraying parameters exclusive to the product freezing parameters, and performs liquid nitrogen spraying and freezing. On the one hand, the present invention creatively applies the liquid nitrogen freezing technology to the ice cream freezing process, improving the forming effect and taste of the product. On the other hand, the present invention sets the parameters of liquid nitrogen freezing, obtains a relatively mild freezing method, controls the extremely rapid temperature drop of liquid nitrogen freezing, and solves the problem that during the liquid nitrogen freezing process, due to the extremely fast freezing speed of liquid nitrogen, a large instantaneous temperature difference will be generated between the surface and the center of the food, the expansion pressure is large, resulting in low-temperature fracture, damaging the tissue structure of the food, and having an adverse impact on the food quality.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A digital control method for freezing liquid nitrogen micro-ice crystals, characterized in that, Including the following steps: Collect the pre-input product freezing parameters; Construct a deep learning algorithm model, input the product freezing parameters into the deep learning algorithm model for training, and end the training when the output result of the freezing temperature change value meets the requirements, and obtain the single-time liquid nitrogen spraying amount, the spraying duration each time, and the spraying time interval; Use the liquid nitrogen spraying parameters to set the liquid nitrogen freezing process, and perform liquid nitrogen spraying and freezing according to this setting; Among them, the product freezing parameters specifically include: the density, flow viscosity, and freezing temperature of the product; Among them, the constructed deep learning algorithm model includes a CNN layer and an encoding layer; The CNN layer includes a convolutional layer, a pooling layer, and a fully connected layer; The encoding layer includes a convolutional encoding layer and a convolutional decoding layer; Among them, the constructed deep learning algorithm model is: Among them, E is the function of the freezing temperature change value; t is the freezing temperature, that is, the temperature difference between the established room temperature and the temperature at which the product is formed into a solid state; is the density of the product; μ is the flow viscosity; t1 is the temperature difference of the first temperature drop at room temperature; t2 is the temperature difference of the second temperature drop based on the first temperature drop; t n is t n-1 is the temperature difference of the temperature drop again based on the t-th temperature drop.

2. The digital control method for freezing liquid nitrogen micro-ice crystals according to claim 1, characterized in that: Define that when the E value is in the interval (0, 1), the output E value meets the requirements and the training ends.

3. The digital control method for freezing liquid nitrogen micro-ice crystals according to claim 2, characterized in that, Obtaining the single-time liquid nitrogen spraying amount, the spraying duration each time, and the spraying time interval specifically is: Obtain the minimum temperature drop range among t1, t2... t n as the spray reference value; Successively obtain t1, t2... t n The ratio relative to the spray reference value; Obtain the spraying amount and spraying duration of the spraying reference value; Successively obtain the liquid nitrogen spraying amount and spraying duration in each temperature period according to the obtained spraying amount and spraying duration of the spraying reference value; Among them, the spraying time interval in each temperature period is defined as 30s to 45s.

4. The digital control method for freezing liquid nitrogen micro-ice crystals according to claim 3, characterized in that: The spraying time interval is defined as 36s.

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