CN107766648A - Drink sterilization node temperature sets optimization method - Google Patents

Abstract

The invention relates to a method for optimizing the temperature setting of beverage sterilization nodes. On the premise of ensuring the sterilization conditions, by adjusting the flow rate Q of 35°C blended liquid, the flow rate Q ₂ of circulating water, the flow rate Q ₁ of ice water at 10°C and the heating current I, After the system reaches a stable production state, record the temperature T _y0 after the first preheating of the beverage, the temperature T _y1 after the homogenization of the beverage, the stable temperature T _y2 , the temperature T _y3 after the third preheating of the beverage, and the temperature T after ultra-high temperature heating. _g . Beverage sterilization maintenance temperature T, temperature T ₁ after the first cooling of the beverage, T ₂ after the second cooling of the beverage, and T ₀ of the finished beverage. At the same time, the beverage quality score D is given according to the beverage evaluation system. By establishing a simultaneous equation, Find the causal relationship or numerical correlation between the beverage quality score D and the temperature of each node, so as to determine the optimal temperature value of each node in the beverage sterilization process. The invention adopts an adaptive method for determining the optimal temperature setting value, which can reduce the influence of external environmental factors such as season and climate on the beverage quality.

Description

Optimization method for temperature setting of beverage sterilization node

technical field

The invention relates to a method for optimizing the temperature setting of a beverage sterilization node, in particular to a method for setting a preset reference value of a temperature parameter in each link of a beverage sterilization process detected by multiple points and sensors.

Background technique

Beverage liquid sterilization generally adopts physical sterilization, ultra-high temperature instantaneous sterilization, referred to as UHT, which is a common sterilization method for beverage liquid sterilization. The pre-treated beverage liquid is quickly heated to above 135°C before filling it into the packaging container. After 2S-15S, it is rapidly cooled to the filling temperature (below 90°C). During this process, the microorganisms and spores in the beverage liquid are killed and the biological enzymes are inactivated. There are many manufacturers of ultra-high temperature instantaneous sterilization equipment in China. Many research institutions and enterprises have been optimizing and improving ultra-high temperature instantaneous sterilization equipment. will have a significant impact. When the ambient temperature is uncertain, open-loop flow and heating control cannot ensure product quality, while closed-loop control needs to determine the target parameters of the system. How to select these target parameters and their preset reference values is the key issue. However, the quality parameters of beverages cannot be detected online, and they cannot be used as target parameters in production engineering. Therefore, it is urgent to find target parameters that are convenient for online detection and can effectively reflect the quality of beverages, so as to realize the optimal operation of the beverage liquid sterilization production process.

Contents of the invention

The purpose of the present invention is to solve the problem of fully automatic control of the sterilization system of the beverage material liquid, find out the correlation between the beverage quality and the temperature and flow rate of each node in the sterilization process through the analysis of the operation data of the beverage sterilization system, and aim at different beverage products. According to the different evaluation indexes of different beverage sterilization processes, the theoretical optimal value of temperature in each link of different beverage sterilization processes is calculated, and provided to the control system as a reference for system parameter setting. That is, under the premise of ensuring the sterilization conditions, by adjusting the 35 ℃ blending liquid flow Q, the circulating water flow Q ₂ , the 10 ℃ ice water flow Q ₁ and the heating current I, after the system reaches a stable production state, record the beverage for the first time. Temperature after preheating T _y0 , temperature after beverage homogenization T _y1 , stable temperature T _y2 , temperature after the third preheating of beverage T _y3 , temperature T _g after ultra-high temperature heating, beverage sterilization maintenance temperature T, beverage first cooling The final temperature T ₁ , the temperature T ₂ after the second cooling of the beverage, and the temperature T ₀ of the finished beverage. At the same time, according to the beverage evaluation system, the beverage quality is scored D. By establishing a simultaneous equation, the relationship between the beverage quality score D and the temperature of each node is found. The causal relationship or numerical correlation between them can determine the optimal temperature of each node in the beverage sterilization process.

The technical solution adopted by the present invention to solve its technical problems is:

A method for optimizing the temperature setting of beverage sterilization nodes, comprising the following steps:

(1) Adjust the heating current I, the flow rate Q of the blended solution at 35°C, the flow rate Q ₂ of circulating water, and the flow rate Q ₁ of ice water at 10°C, so that these four variables are fixed at the set values, and the sterilization system runs for a long enough time. After the system reaches a stable state, observe the finished beverage temperature T ₀ , the beverage temperature T ₁ after the first cooling, the beverage sterilization maintenance temperature T, the ultra-high temperature heating temperature T _g , the stable maintenance temperature T _y2 , and the beverage temperature T ₂ after the second cooling , the temperature T _y3 after the third preheating of the beverage, the temperature T _y1 after the homogenization of the beverage, and the temperature T _y0 after the first preheating of the beverage. Sampling of beverages, evaluate and record its quality score D through the quality evaluation system, record all the above parameter values in the data table, and complete a set of data sampling;

(2) On the premise of ensuring the sterilization conditions, select three flow levels to adjust the flow Q of the 35°C blended liquid, three flow levels to adjust the flow rate of circulating water Q ₂ , three flow levels to adjust the flow rate of 10°C ice water Q ₁ , and three flow levels to adjust the flow rate of 10°C ice water. Flow level adjustment heating current I, a total of 81 adjustment states;

(3) According to the sampling method of step (1), complete the sampling of the 81 sets of data mentioned in step (2), and select the five sets of data with the best quality score D to establish the beverage quality score D and the temperature of each node Corresponding relationship between beverage finished product temperature T ₀ , beverage first cooling temperature T ₁ , beverage sterilization holding temperature T, ultra-high temperature heating temperature T _g , stable holding temperature T _y2 , beverage second cooling temperature T ₂ , The average value of the 81 sets of data of nine temperature values, T _y3 after the third preheating of the beverage, T _y1 after homogenization of the beverage, and T _y0 after the first preheating of the beverage, is taken as the average value for each temperature in the beverage sterilization process. Optimal temperature setpoint for the node.

Further, the aforementioned data sampling method is used to sample at 8 time points every day, and the optimal temperature setting value is adjusted 8 times a day. The optimal temperature setting value is taken from the two sets of data with the best quality in a consecutive week at the corresponding time point take the average.

According to the beverage evaluation system, the present invention scores the beverage quality score D, and finds the causal relationship or numerical correlation between the beverage quality score D and the temperature of each node by establishing simultaneous equations, thereby determining the temperature of each node in the beverage sterilization process. optimal temperature. Temperature T _y0 after the first preheating, T _y1 after beverage homogenization, stable temperature T _y2 , beverage temperature T _y3 after the third preheating, temperature T _g after ultra-high temperature heating, beverage sterilization maintenance temperature T, beverage The temperature T ₁ after the first cooling, the temperature T ₂ after the second cooling of the beverage, the temperature T ₀ of the finished beverage, and the scoring value D of the beverage quality.

The causal relationship between the beverage quality score D and the temperature of each node is expressed by weighting coefficients K ₀ , K ₁ , K ₂ , K ₃ , K ₄ , K ₅ , K ₆ , K ₇ , K ₈ , and K ₉ . The formula is as follows:

K ₀ T ₀ +K ₁ T ₁ +K ₂ T ₂ +K ₃ T _y0 +K ₄ T _y1 +K ₅ T _y2 +K ₆ T _y3 +K ₇ T _g +K ₈ T＝D

Substituting the nine sets of data with the best beverage quality actually measured into the above formula to establish a simultaneous equation:

Thus, weighting coefficients K ₀ , K ₁ , K ₂ , K ₃ , K ₄ , K ₅ , K ₆ , K ₇ , K ₈ , and K ₉ are calculated.

The beneficial effects of the present invention are:

The present invention adopts an adaptive method for determining the optimal temperature setting value. Data sampling is performed at 8 time points every day, and the optimal temperature setting value is adjusted 8 times a day. The optimal temperature setting value is taken from the corresponding time point. The average value of the two sets of data with the best quality for one consecutive week is used to reduce the influence of external environmental factors such as seasons and climate on the beverage quality.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Fig. 1 is a schematic diagram of a beverage sterilization distributed temperature setting optimization model of the present invention;

In the figure: 1. Beverage product temperature T ₀ , 2. Circulating water flow rate Q ₂ , 3. Beverage temperature T ₁ after the first cooling, 4. Beverage sterilization maintenance temperature T, 5. Temperature T _g after ultra-high temperature heating, 6. Stable maintenance of temperature T _y2 , 7.35°C blended liquid flow Q, 8.10°C ice water flow Q ₁ , 9. temperature T ₂ after the second cooling of the beverage, 10. heating current I, 11. temperature T _y3 after the third preheating of the beverage , 12. The temperature T _y1 after the beverage is homogenized, 13. The temperature T _y0 after the beverage is preheated for the first time.

Detailed ways

All features disclosed in this specification, or steps in all methods or processes disclosed, may be combined in any manner, except for mutually exclusive features and/or steps.

Any feature disclosed in this specification (including any appended claims, abstract and drawings), unless expressly stated otherwise, may be replaced by alternative features which are equivalent or serve a similar purpose. That is, unless expressly stated otherwise, each feature is one example only of a series of equivalent or similar features.

As shown in Figure 1, the change of the flow rate Q of the blended liquid at 35°C will affect the sterilization time, the temperature after the first preheating T _y0 13, the temperature after homogenization of the beverage T _y1 12, the stable temperature T _y2 6, the third time of the beverage Preheated temperature T _y3 11, beverage sterilization temperature T 4, beverage first cooling temperature T ₁ 3, beverage second cooling temperature T ₂ 9, beverage finished product temperature T ₀ 1; circulating water flow Q ₂ 2 The change will affect the temperature after the first preheating T _y0 13, the temperature after homogenization of the beverage T _y1 12, the stable temperature T _y2 6, the temperature after the third preheating of the beverage T _y3 11, the beverage sterilization maintenance temperature T 4, The temperature T ₁ 3 after the first cooling of the beverage, the temperature T ₂ 9 after the second cooling of the beverage, and the temperature T ₀ 1 of the finished beverage will have an impact; the change of the flow rate Q ₁ 8 of 10°C ice water will have an impact on the temperature T ₀ 1 of the finished beverage; The change of adjusting the heating current I 10 will affect the temperature after the first preheating T _y0 13, the temperature after homogenization of the beverage T _y1 12, the stable temperature T _y2 6, the temperature after the third preheating of the beverage T _y3 11, and the ultra-high temperature The temperature after heating T _g 5, the temperature T 4 of beverage sterilization, the temperature T ₁ 3 after the first cooling of the beverage, the temperature T ₂ 9 after the second cooling of the beverage, and the temperature T ₀ 1 of the finished beverage have an impact.

Increasing the 35°C blending liquid flow rate Q 7 will reduce the temperature of all nodes. Increase circulating water flow rate Q ₂ 2, temperature T _y0 13 after first preheating, temperature T _y1 12 after beverage homogenization, stable temperature T _y2 6, temperature T _y3 11 after third preheating of beverage will increase , the temperature T ₁ 3 after the first cooling of the beverage, the temperature T ₂ 9 after the second cooling of the beverage, and the temperature T ₀ 1 of the finished beverage will decrease, but the circulating water flow Q ₂ 2 usually has an inflection point, that is, the circulating water flow Q ₂ 2 When it is too fast, it may cause unsatisfactory preheating and cooling effects due to insufficient heat exchange. Therefore, the circulating water flow Q ₂ 2 must first set a reasonable interval, and the adjustment control cannot exceed this interval. Increase the flow rate Q ₁ 8 of 10°C ice water, the temperature T ₀ 1 of the finished beverage will drop significantly, and the flow rate Q ₁ 8 of 10°C ice water should generally operate within a set range. The adjustment of the heating current I10 mainly ensures the control of the sterilization temperature, and the increase of the heating current I will cause the temperature of all nodes to increase.

The temperature of each node depends on the degree of heat exchange, and the temperature variables of the entire sterilization system change nodes include heating current I 10, 35°C blending liquid flow Q 7, circulating water flow Q _{2 2} , 10°C ice water flow Q ₁ 8, in order To achieve the ideal sterilization effect, the value range of these four variables is very limited, so in the previous experiment, three values can be selected for each variable, and a total of 81 sets of data can complete the determination of the optimal quality node temperature setting value . The method is: for each operating state, after the system reaches a steady state, record the temperature of each node, and at the same time sample the product and give the beverage quality D through the beverage evaluation system. After all the 81 sets of data are collected, considering the error of the quality evaluation system, the 5 sets of data with the best quality are used to establish the corresponding relationship between the beverage quality score D and the temperature of each node, and the temperature of each node is obtained by calculation. The average value is used as the optimal temperature setting value. During fully automatic self-adaptive operation, by adjusting the 35°C blending liquid flow rate Q 7, the circulating water flow rate Q _{2 2} , the 10°C ice water flow rate Q ₁ 8, and the heating current I 10, the temperature value of each node tends to the optimal temperature setting Fixed value, so as to realize the optimal control of quality.

The sampling process of each group of data is: according to the requirements set in the experiment, respectively adjust the heating current I 10, the flow rate of 35°C preparation solution Q 7, the flow rate of circulating water Q ₂ 2, and the flow rate of 10°C ice water Q ₁ 8, so that the four Variable fixed value on setpoint. Start up and run for a long enough time, observe the finished beverage temperature T ₀ 1, the temperature after the first cooling of the beverage T ₁ 3, the beverage sterilization maintenance temperature T 4, the temperature after ultra-high temperature heating T _g 5, the stable maintenance temperature T _y2 6, the beverage second Temperature after cooling T ₂ 9, temperature after the third preheating of the beverage T _y3 11, temperature after homogenization of the beverage T _y1 12, temperature after the first preheating of the beverage T _y0 13, when these temperature values remain basically constant Record these temperature values. At the same time, sample the sterilized beverage, evaluate and record its quality score D through the quality evaluation system, record all the above parameter values in the data table, and complete a set of data sampling.

The method of determining the optimal temperature setting value of each node in the beverage sterilization process: according to the above sampling method, complete the sampling of 81 sets of data, select the five sets of data with the best quality score D, and determine the temperature of the finished beverage T ₀ 1, After the first cooling of the beverage, the temperature T ₁ 3, the beverage sterilization maintenance temperature T 4, the ultra-high temperature heating temperature T _g 5, the stable maintenance temperature T _y2 6, the beverage second cooling temperature T ₂ 9, the beverage after the third preheating The nine temperature values of temperature T _y3 11, temperature T _y1 12 after beverage homogenization, and temperature T _y0 13 after the first preheating of the beverage are respectively averaged as the optimal temperature setting value for each node in the beverage sterilization process .

In the actual production process of the present invention, in order to reduce the influence of external environmental factors such as season and climate on the beverage quality, an adaptive method for determining the optimal temperature setting value is adopted. The aforementioned data sampling method is used to sample at 8 time points every day, and the optimal temperature setting value is adjusted 8 times a day. The optimal temperature setting value is taken from the average of the two sets of data with the best quality for one consecutive week at the corresponding time point value, adjust the optimum temperature setting value once a week.

The invention directly judges the quality of the beverage by measuring the temperature of each link in the beverage sterilization process, and then controls the flow rate and heating current of each part according to the temperature value of the best quality to ensure the quality of the beverage.

The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the present invention. The present invention extends to any new feature or any new combination disclosed in this specification, and any new method or process step or any new combination disclosed.

Claims (1)

1. a kind of drink sterilization node temperature sets optimization method, it is characterised in that：Comprise the following steps：

(1) heated current I, 35 DEG C of allotment flow quantity Q, circulating water flow Q are adjusted respectively₂, 10 DEG C of frozen water flow Q₁, make this four Variable is fixed in setting value, disinfection system operation, after system reaches stable state, observes beverage product temperature T₀, beverage Temperature T after first cooling₁, drink sterilization keeping temperature T, superhigh temperature temperature after heating T_g, stable keeping temperature T_y2, beverage second Temperature T after cooling₂, beverage third time preheat after temperature T_y3, temperature T after beverage homogeneous_y1, beverage for the first time preheat after temperature T_y0, These temperature values are recorded when these temperature values are held essentially constant constant, while the beverage after sterilization is sampled, pass through quality Appraisement system carries out evaluation and test and records its quality score value D, and above-mentioned all values of consult volume recorded in tables of data, one group of data of completion Sampling；

(2) on the premise of sterilization conditions are ensured, three flow grades is chosen and adjust 35 DEG C of allotment flow quantity Q, three flows etc. Level regulation circulating water flow Q₂, three flow grades adjust 10 DEG C of frozen water flow Q₁, three flow grade regulation heated current I, always Totally 81 kinds of adjustment states；

(3) according to the sample mode of step (1), the sampling of 81 groups of data mentioned in step (2) is completed, selects quality score value D Five groups of best data are used to establish corresponding relation between beverage quality score value D and each node temperature, to beverage product temperature T₀、 Temperature T after beverage first cools down₁, drink sterilization keeping temperature T, superhigh temperature temperature after heating T_g, stable keeping temperature T_y2, beverage Temperature T after second cooling₂, beverage third time preheat after temperature T_y3, temperature T after beverage homogeneous_y1, beverage for the first time preheat after temperature T_y081 groups of data of this nine temperature values are averaged respectively, and the optimum temperature as each node in drink sterilization technical process is set Definite value.

Drink sterilization node temperature according to claim 1 sets optimization method, it is characterised in that：Per 8 times of natural gift Point is respectively adopted foregoing data sampling method and sampled, and adjusts 8 suboptimum desired temperatures, optimum temperature setting value daily The continuous one week best in quality 2 group data for being derived from corresponding time point are averaged.