A method for controlling high-speed lollipop production under high humidity and heat conditions
By constructing a correlation model between temperature and ambient temperature and humidity in lollipop production control, and combining PID control and dynamic correction models, dynamic process parameter regulation was achieved in high-humidity and hot environments. This solved the problems of temperature control delay and environmental changes in candy production, ensuring candy quality and reducing costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANTONG WEALTH MASCH TECH CO LTD
- Filing Date
- 2025-07-29
- Publication Date
- 2026-07-31
AI Technical Summary
Existing confectionery production control systems cannot achieve dynamic adjustment of production process parameters in high humidity and heat environments, leading to confectionery quality problems. Furthermore, traditional methods are either costly or have slow response times.
A theoretical model of the correlation between temperature and ambient temperature and humidity in lollipop production control is constructed. A temperature control model and a dynamic correction model are established by combining PID control. Dynamic regulation is carried out through real-time environmental data, and the temperature of the heating unit is adjusted by using a PID controller.
It enables dynamic control of production process parameters based on environmental changes in high humidity and heat environments, with low cost and fast response speed, avoiding the problems of temperature control delay and environmental mismatch, and ensuring candy quality.
Smart Images
Figure CN120780077B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of confectionery production technology, and in particular to a method for controlling the production of high-speed lollipops under high humidity and heat conditions. Background Technology
[0002] The sensitivity of confectionery production to environmental humidity extends throughout the entire process, from raw material processing to finished product packaging. Humidity control in confectionery workshops requires differentiated standards based on different production stages and confectionery types. Extruded semi-finished confectionery exposed to high humidity and heat is prone to absorbing moisture and melting or crystallizing, affecting the shape and transparency of the confectionery. Furthermore, the confectionery workshop itself generates heat and a large amount of steam during the cooking and extrusion processes, resulting in a constantly changing environment in terms of temperature and humidity, sometimes even experiencing sudden spikes in localized temperature and humidity. This necessitates corresponding adjustments to the process parameters in various areas of the extruder to prevent excessively high temperatures from causing caramelization or crystallization, which would ultimately affect the quality of the extruded confectionery.
[0003] Currently, there are two main control systems or methods for candy production. One method involves selecting appropriate process parameters based on the temperature and humidity at a specific moment in the candy workshop. However, when the ambient temperature and humidity change naturally or abruptly, the fixed process parameters cannot dynamically compensate for the temperature of the heating zone in real time. The other method involves directly using air conditioning and dehumidifiers to dynamically regulate the temperature and humidity of the entire production workshop. This control method is not only costly but also has a slow response time.
[0004] Therefore, this invention proposes a high-speed lollipop production control method under high humidity and heat environment to solve the above problems. Summary of the Invention
[0005] The technical problem to be solved by the present invention is to provide a high-speed lollipop production control method under high temperature and humidity environment, which realizes dynamic adjustment of production process parameters according to changes in ambient temperature and humidity, with low cost and fast response speed.
[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is: a method for controlling high-speed lollipop production under high humidity and heat conditions, the innovation of which lies in: including the following steps: Step 1: Based on experimental analysis and empirical design principles, construct a theoretical model of the correlation between the production control temperature of lollipops and ambient temperature and humidity, using ambient temperature (T) and ambient humidity (RH) as independent variables, and the production control temperature inside the lollipop extruder as the control temperature. As the dependent variable, the objective function is obtained through regression analysis, and its expression is as follows:
[0007] in, Reference temperature; Step 2: Establish a temperature control model for the heating unit of the lollipop extruder based on PID control. The expression is:
[0008]
[0009] in, This represents the temperature inside the lollipop extruder after the nth adjustment, where n≥1; This indicates the magnitude of the temperature change during the nth adjustment; The heating unit of the lollipop extruder is composed of Adjustment to Response time; Step 3: Based on the on-site measured data, establish a two-dimensional coordinate system, plot the trend lines of ambient temperature T and ambient humidity RH with respect to time t, and obtain the corresponding trend fitting equation. and RH(t); Step 4: Fit the trends of ambient temperature and humidity to the equation. Substituting RH(t) into the correlation theory model, the control temperature is obtained. Dynamic correction model for time t:
[0010] Step 5: Combining the temperature control model and the dynamic correction model, establish constraint formulas for controlling the temperature. Solve the problem and obtain the solution that satisfies the control conditions. After the value is set, the lollipop extruder uses The value is used as the target value for temperature control, and the PID controller is used to control the temperature of the heating unit.
[0011] Furthermore, the temperature change amplitude during the nth adjustment in step 2 The expression is:
[0012]
[0013] in, The coefficient of performance is the heat transfer efficiency. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; To reach the response time The measured temperature value at that moment; For integration time; for The measured temperature value at that moment; The rate of change of temperature with response time.
[0014] Furthermore, the method for obtaining the trend fitting equation in step 3 is as follows: Step 3.1: Establish a set of trend prediction models; Step 3.2: Establish a trend fitting model using maximizing the R-squared value as the objective function; Step 3.3: After establishing the two-dimensional coordinates, draw a scatter plot based on the field measurement data, use the regression analysis model to draw the trend curve of the scatter plot, obtain the corresponding trend fitting equation, and calculate the R-squared value of the corresponding trend curve, which are then used as the current solution. Step 3.4: Iterate through the entire trend prediction model set, compare the calculated solution with the current solution, and replace and update the current solution if the calculated solution is greater than the current solution. Step 3.5: After traversing the set of trend prediction models, the trend fitting equation corresponding to the current solution is taken as the optimal trend fitting equation.
[0015] Furthermore, the set of trend prediction models is {L, X, O, P, W, M, G, S}; Where L is the regression model, X is the exponential model, O is the logarithmic model, P is the multinomial model, W is the power function model, M is the modified exponential model, G is the Gompertz model, and S is the logistic curve model.
[0016] Furthermore, the constraint formula in step 5 is:
[0017]
[0018] in, This represents the time corresponding to the nth adjustment.
[0019] Furthermore, the control conditions in step 5 are as follows:
[0020] in, This is the preset allowable temperature deviation value.
[0021] Furthermore, in the process of using a PID controller to regulate the temperature of the heating unit in the lollipop extruder, the newly solved... Compare with the current temperature control target value for the nth temperature control operation; If the control conditions are not met, continue with the nth temperature control. If the control conditions are met, the current control is stopped, and the temperature reached at the current stop time is taken as the control temperature. ,renew The current stopping time is used as ,renew And will update and Substitute the constraint formulas back into the solution.
[0022] The advantages of this invention are: The high-speed lollipop production control method of this invention transforms empirical parameters into a quantifiable static correlation theory model. By correlating on-site measured data into the correlation theory model, a dynamic correction model is obtained that can dynamically adjust based on real-time environmental data as the temperature changes over time. When solving the problem using the temperature control model and the dynamic correction model, the method fully considers the situation where the on-site environmental measured data has changed after the temperature control is completed due to the response delay of the production system temperature regulation. A constraint formula is established so that the solved temperature regulation target value is the on-site environmental fitting data after the response time. This solves the problem that the temperature regulation cannot match the actual environment due to response delay and dynamic changes in the on-site environment. Thus, it realizes dynamic adjustment of production process parameters according to changes in ambient temperature and humidity, which is low-cost and has a fast response speed. Attached Figure Description
[0023] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0024] Figure 1 This is a graph showing the trend of ambient temperature T with respect to time t in an embodiment of the present invention.
[0025] Figure 2 This is a graph showing the trend of ambient humidity (RH) with respect to time t in an embodiment of the present invention. Detailed Implementation
[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.
[0027] Example This embodiment provides a method for controlling high-speed lollipop production under high humidity and heat conditions, including the following steps: Step 1: Based on experimental analysis and empirical design principles, construct a theoretical model of the correlation between the production control temperature of lollipops and ambient temperature and humidity, using ambient temperature (T) and ambient humidity (RH) as independent variables, and the production control temperature inside the lollipop extruder as the control temperature. As the dependent variable, the objective function is obtained through regression analysis, and its expression is as follows:
[0028] in, This is the reference temperature.
[0029] In this embodiment, based on experimental analysis and empirical design principles, it was found that the production temperature control inside the lollipop extruder is crucial. It has a linear relationship with ambient temperature T and ambient humidity RH, and the objective function expression is:
[0030] in, The ambient temperature coefficient is dimensionless and represents the intensity of the influence of ambient temperature on the production control temperature inside the extruder. The ambient humidity coefficient, expressed in °C / %, indicates the intensity of the influence of ambient humidity on the production control temperature inside the extruder.
[0031] In this embodiment, The value is -0.5, meaning that for every 1°C increase in ambient temperature, the temperature inside the extruder needs to be reduced by 0.5°C to prevent overheating from causing coking or sand return. The value is -0.2, which means that for every 10% increase in ambient humidity, the temperature inside the extruder needs to be reduced by 2°C to prevent excessive softening due to moisture absorption. If the value is 150℃, then the expression for the objective function is: Equation (1):
[0032] Step 2: Establish a temperature control model for the heating unit of the lollipop extruder based on PID control. The expression is:
[0033]
[0034] in, This represents the temperature inside the lollipop extruder after the nth adjustment, where n≥1; This indicates the magnitude of the temperature change during the nth adjustment; The heating unit of the lollipop extruder is composed of Adjustment to Response time; In this embodiment, The expression is: Equation (5):
[0035] in, The coefficient of performance is the heat transfer efficiency. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; To reach the response time The measured temperature value at that moment; For integration time; for The measured temperature value at that moment; The rate of change of temperature with response time.
[0036] Step 3: Based on the on-site measured data, establish a two-dimensional coordinate system, plot the trend lines of ambient temperature T and ambient humidity RH with respect to time t, and obtain the corresponding trend fitting equation. And RH(t), the detailed process is as follows: Step 3.1: Establish a trend prediction model set {L, X, O, P, W, M, G, S}, where L is the regression analysis model, X is the exponential model, O is the logarithmic model, P is the multinomial model, and the multinomial model adopts binary, ternary, and quaternary multinomial regression, W is the power function model, M is the modified exponential model, G is the Gompertz model, and S is the logistic curve model. Step 3.2: Establish a trend fitting model using maximizing the R-squared value as the objective function; Step 3.3: After establishing the two-dimensional coordinates, draw a scatter plot based on the field measurement data, use the regression analysis model to draw the trend curve of the scatter plot, obtain the corresponding trend fitting equation, and calculate the R-squared value of the corresponding trend curve (0≤R-squared value≤1), and use them as the current solutions in turn. Step 3.4: Iterate through the entire trend prediction model set, compare the calculated solution with the current solution, and replace and update the current solution if the calculated solution is greater than the current solution. Step 3.5: After traversing the set of trend prediction models, the trend fitting equation corresponding to the current solution is taken as the optimal trend fitting equation.
[0037] In this embodiment, as Figure 1 and Figure 2 As shown, based on the on-site measured temperature and humidity data, the quaternary polynomial model yielded the largest R-squared value. The trend fitting equations for a detection time of 180s are as follows: Equation (2):
[0038] Equation (3):
[0039] The above trend fitting equation , As the detection time progresses, it changes dynamically in real time.
[0040] Step 4: Fit the trends of ambient temperature and humidity to the equation. Substituting RH(t) into the correlation theory model, the control temperature is obtained. Dynamic correction model for time t:
[0041] In this embodiment, the obtained trend fitting equations (2) and (3) are substituted into the correlation theory model (1) to obtain the dynamic correction model: Equation (4):
[0042] The dynamic correction model fits the equation as the trend changes. , It changes dynamically in real time due to changes.
[0043] Step 5: Combining the temperature control model and the dynamic correction model, establish constraint formulas for controlling the temperature. Solve the problem and obtain the solution that satisfies the control conditions. After the value is set, the lollipop extruder uses The value is used as the target value for temperature control, and the PID controller is used to control the temperature of the heating unit. The constraint formula is:
[0044]
[0045] in, This represents the time corresponding to the nth adjustment. This refers to the temperature inside the lollipop extruder after the last (n-1)th adjustment. This corresponds to the time of the last adjustment, i.e., the (n-1)th adjustment. Therefore, in solving for... During the process, as well as All of these are known constants.
[0046] The control conditions are:
[0047] in, The preset allowable temperature deviation is preferably 0.5~1.0℃.
[0048] In this embodiment, when solving, equations (4) and (5) are substituted into the constraint formula to solve. Each new solution obtained is evaluated according to the control conditions. If the control conditions are met, the new solution is used as the temperature control target value, and the PID controller is used to complete the temperature control of the heating unit.
[0049] In the process of using a PID controller to regulate the temperature of the heating unit in a lollipop extruder, the newly solved... Compare with the current temperature control target value for the nth temperature control operation; If the control conditions are not met, continue with the nth temperature control. If the control conditions are met, the current control is stopped, and the temperature reached at the current stop time is taken as the control temperature. ,renew The current stopping time is used as ,renew And will update and Substitute the constraint formulas back into the solution.
[0050] The high-speed lollipop production control method of this invention transforms empirical parameters into a quantifiable static correlation theory model. By correlating on-site measured data into the correlation theory model, a dynamic correction model is obtained that can dynamically adjust based on real-time environmental data as the temperature changes over time. When solving the problem using the temperature control model and the dynamic correction model, the method fully considers the situation where the on-site environmental measured data has changed after the temperature control is completed due to the response delay of the production system temperature regulation. A constraint formula is established so that the solved temperature regulation target value is the on-site environmental fitting data after the response time. This solves the problem that the temperature regulation cannot match the actual environment due to response delay and dynamic changes in the on-site environment. Thus, it realizes dynamic adjustment of production process parameters according to changes in ambient temperature and humidity, which is low-cost and has a fast response speed.
[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A method for controlling high-speed lollipop production under high humidity and heat conditions, characterized in that: Includes the following steps: Step 1: Based on experimental analysis and empirical design principles, construct a theoretical model of the correlation between the production control temperature of lollipops and ambient temperature and humidity, using ambient temperature (T) and ambient humidity (RH) as independent variables, and the production control temperature inside the lollipop extruder as the control temperature. As the dependent variable, the objective function is obtained through regression analysis, and its expression is as follows: in, Reference temperature; Step 2: Establish a temperature control model for the heating unit of the lollipop extruder based on PID control. The expression is: in, This represents the temperature inside the lollipop extruder after the nth adjustment, where n≥1; This indicates the magnitude of the temperature change during the nth adjustment. The heating unit of the lollipop extruder is composed of Adjustment to Response time; Temperature change amplitude during the nth regulation The expression is: in, The coefficient of performance is the heat transfer efficiency. This is the proportional gain coefficient; This is the integral gain coefficient; The differential gain coefficient; To reach the response time The measured temperature value at that moment; For integration time; for The measured temperature value at that moment; The rate of change of temperature with response time; Step 3: Based on the on-site measured data, establish a two-dimensional coordinate system, plot the trend lines of ambient temperature T and ambient humidity RH with respect to time t, and obtain the corresponding trend fitting equation. and RH(t); Step 4: Fit the trends of ambient temperature and humidity to the equation. Substituting RH(t) into the correlation theory model, the control temperature is obtained. Dynamic correction model for time t: Step 5: Combining the temperature control model and the dynamic correction model, establish constraint formulas for controlling the temperature. Perform the solution until the control conditions are met. After the value is set, the lollipop extruder uses The value is used as the target value for temperature control, and the PID controller is used to control the temperature of the heating unit.
2. The high-speed lollipop production control method under high humidity and heat environment according to claim 1, characterized in that: The method for obtaining the trend fitting equation in step 3 is as follows: Step 3.1: Establish a set of trend prediction models; Step 3.2: Establish a trend fitting model using maximizing the R-squared value as the objective function; Step 3.3: After establishing the two-dimensional coordinates, draw a scatter plot based on the field measurement data, use the regression analysis model to draw the trend curve of the scatter plot, obtain the corresponding trend fitting equation, and calculate the R-squared value of the corresponding trend curve, which are then used as the current solution. Step 3.4: Iterate through the entire trend prediction model set, compare the calculated solution with the current solution, and replace and update the current solution if the calculated solution is greater than the current solution. Step 3.5: After traversing the set of trend prediction models, the trend fitting equation corresponding to the current solution is taken as the optimal trend fitting equation.
3. The high-speed lollipop production control method under high humidity and heat environment according to claim 2, characterized in that: The set of trend prediction models is {L, X, O, P, W, M, G, S}; Where L is the regression model, X is the exponential model, O is the logarithmic model, P is the multinomial model, W is the power function model, M is the modified exponential model, G is the Gompertz model, and S is the logistic curve model.
4. The high-speed lollipop production control method under high humidity and heat environment according to claim 1, characterized in that: The constraint formula in step 5 is: in, This represents the time corresponding to the nth adjustment.
5. The high-speed lollipop production control method under high humidity and heat environment according to claim 4, characterized in that: The control conditions in step 5 are as follows: in, This is the preset allowable temperature deviation value.
6. The high-speed lollipop production control method under high humidity and heat environment according to claim 5, characterized in that: In the process of using a PID controller to regulate the temperature of the heating unit in the lollipop extruder, the newly solved... Compare with the current temperature control target value for the nth temperature control operation; If the control conditions are not met, continue with the nth temperature control. If the control conditions are met, the current control is stopped, and the temperature reached at the current stop time is taken as the control temperature. ,renew The current stopping time is used as ,renew And will update and Substitute the constraint formulas back into the solution.