A temperature and humidity PID control system for a non-alum wide powder curing process

Abstract

This invention relates to the field of automated control in food processing, specifically a temperature and humidity PID control system for the ripening process of alum-free wide rice noodles. The system includes: a sensing interface module, which receives environmental state data including air temperature and air humidity; physical characterization data including real-time sample weight and sample surface temperature; a kinetic analysis module, which, in conjunction with the sample surface temperature and the drying weight loss rate, calculates the humidity gradient index characterizing the difference between the center moisture content and surface moisture content of the wide rice noodle sample; a strategy decision module, which determines dynamic correction setpoints for air temperature and air humidity; and an execution control module, which drives the temperature and humidity adjustment mechanism to perform heating, humidification, or dehumidification actions, so that the actual temperature and humidity in the ripening chamber approach the dynamic correction setpoints. This invention effectively solves the problem of monitoring blind spots caused by neglecting the physical evolution of the sample.

Description

Technical Field

[0001] This invention relates to the field of automated control in food processing, specifically a temperature and humidity PID control system for the cooking process of alum-free wide rice noodles. Background Technology

[0002] In the current alum-free wide rice noodle processing environment, the curing process is the core link that determines the quality of the finished product. Existing technologies generally adopt a closed-loop control scheme with constant temperature and humidity. Through a preset standard process curve, the heating and humidification mechanism is driven by a PID controller to make the environmental parameters in the curing room approach the set target. This type of scheme mainly focuses on external environmental indicators such as air temperature and air humidity, and executes offline or fixed segmented control logic based on these.

[0003] Most existing systems only monitor ambient temperature and humidity, lacking real-time capture of the physical evolution of the wide rice noodle sample itself. Because they cannot directly perceive the internal moisture migration and stress accumulation of the wide rice noodle, the control system is in a passive adjustment state, making it difficult to predict quality risks within the sample. During the drying and ripening process of alum-free wide rice noodles, the surface evaporation rate and the internal moisture diffusion rate often exhibit nonlinear decoupling. Under a constant process curve, the phenomenon of excessively rapid surface evaporation without sufficient internal moisture replenishment easily occurs, leading to surface hardening, internal stress concentration, and consequently, physical damage such as product breakage and brittle edges. Existing control architectures mostly mechanically execute preset curves, lacking a powder-based feedback mechanism. When environmental disturbances or raw material differences cause humidity gradients to exceed limits, the system cannot dynamically correct the temperature and humidity setpoints, making it difficult to balance the contradiction between drying efficiency and structural integrity. Traditional ripening processes often neglect the specific low-temperature requirements of the starch retrogradation stage, and parameter abrupt changes occur during process stage switching, easily causing temperature and humidity fluctuations, affecting the chewiness and sensory quality of the wide rice noodles.

[0004] Therefore, how to introduce multidimensional sensing and dynamic analysis methods to capture the moisture distribution and stress state inside wide rice noodles in real time, and to construct a dynamic correction mechanism based on physiological feedback, so as to improve the timeliness and adaptability of the cooking process control and solve the problems of breakage, crisp edges and uneven quality of alum-free wide rice noodles during the cooking process, has become an urgent technical problem to be solved. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention provides a temperature and humidity PID control system for the maturation process of alum-free wide rice flour. Specifically, the technical solution of the present invention includes:

[0006] The sensing interface module is configured to acquire environmental status data and physical characterization data of wide rice noodle samples in the curing room. The environmental status data includes air temperature and air humidity; the physical characterization data includes real-time sample weight and sample surface temperature.

[0007] The kinetic analysis module is configured to calculate the drying weight loss rate based on the real-time weight of the sample, and combine the sample surface temperature and drying weight loss rate to calculate the humidity gradient index, which characterizes the difference between the center moisture content and the surface moisture content of the wide powder sample.

[0008] The strategy decision module is configured to determine dynamic correction settings for air temperature and air humidity based on the humidity gradient index and a preset stress threshold.

[0009] The execution control module is configured to drive the temperature and humidity regulation mechanism to perform heating, humidification, or dehumidification actions based on the dynamic correction set value, so that the actual temperature and humidity in the curing room are close to the dynamic correction set value.

[0010] Preferably, the kinetic analysis module calculates the drying weight loss rate based on the real-time weight of the sample, and combines the sample surface temperature and drying weight loss rate to calculate the humidity gradient index inside the wide powder, including:

[0011] The real-time weight of the sample is retrieved, and the first derivative of the real-time weight of the sample with respect to time is calculated to generate the drying weight loss rate.

[0012] Input the drying weight loss rate and sample surface temperature into the preset moisture diffusion simulation model;

[0013] The moisture content at the center and the surface of the wide rice noodle sample were estimated using a moisture diffusion extrapolation model.

[0014] Calculate the difference between the center moisture content and the surface moisture content, and define the difference as the humidity gradient index.

[0015] Preferably, the strategy decision module determines the dynamic correction settings for air temperature and air humidity based on the humidity gradient index and a preset stress threshold, and is configured to execute the following steps:

[0016] Call the humidity gradient index and the standard process setting curve for the current curing stage;

[0017] The humidity gradient index is compared with a preset stress threshold.

[0018] If the humidity gradient index is greater than the preset stress threshold, an evaporation suppression command is generated, and the standard process setting curve is compensated based on the evaporation suppression command to generate a dynamic correction setting value.

[0019] If the humidity gradient index is less than or equal to the preset stress threshold, the value corresponding to the standard process setting curve will be determined as the dynamic correction setting value.

[0020] Preferably, the strategy decision module performs compensation calculations on the standard process set curve based on the evaporation suppression command, generating dynamically corrected set values, including:

[0021] In response to the evaporation suppression command, the fuzzy control rule table for the current moment is extracted;

[0022] The humidity gradient index is introduced as an input variable into the fuzzy control rule table, and the positive humidity compensation amount is calculated based on the degree of deviation of the humidity gradient index from the stress threshold.

[0023] The positive humidity compensation is superimposed on the air humidity setting item in the standard process setting curve to generate a dynamic correction setting value, thereby creating a high humidity buffer environment in the execution control module.

[0024] Preferably, the execution control module drives the temperature and humidity regulating mechanism to perform heating, humidification, or dehumidification actions based on dynamically corrected set values, including:

[0025] Construct a temperature and humidity tracking PID control loop;

[0026] Use the dynamically corrected setpoint as the target input value for the PID control loop;

[0027] Calculate the deviation between environmental status data and dynamic correction setpoints;

[0028] Based on the deviation, the control quantity is calculated and output to the temperature and humidity regulating mechanism through the PID algorithm.

[0029] Preferably, the sensing interface module acquires physical characterization data of the wide powder sample, including:

[0030] The real-time weight of the sample is collected using a weighing sensor array at a preset sampling frequency.

[0031] The surface temperature of the sample is scanned and read in real time using an infrared non-contact temperature probe.

[0032] The collected real-time weight and surface temperature of the samples are filtered and noise-reduced to generate physical characterization data.

[0033] Preferably, the standard process set-line curve includes three non-linear control regions:

[0034] The high-temperature gelatinization and conditioning zone corresponds to the initial stage of the maturation process and is equipped with a first temperature and humidity reference.

[0035] The medium-temperature slow dehumidification zone corresponds to the middle stage of the maturation process and is equipped with a second temperature and humidity reference.

[0036] The low-temperature deep regeneration zone corresponds to the final stage of the maturation process and is equipped with a third temperature and humidity reference.

[0037] The strategy decision module automatically switches the reference data of the control area it calls based on the current running time.

[0038] Preferably, the moisture diffusion model is a numerical approximation model based on Fick's second law, and the model presupposes the moisture diffusion coefficient of the wide starch gel matrix at different temperatures.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. This invention breaks through the limitations of traditional constant temperature and humidity control that only focuses on environmental parameters. By introducing a sensing interface and a dynamic analysis module, a dual closed-loop system of an inner environmental control loop and an outer physiological feedback loop is constructed. Through non-contact sensors, the weight and temperature evolution of wide rice noodles are captured in real time. The system can see through the internal moisture distribution that cannot be directly measured, and shift the control logic from passively executing preset curves to actively sensing the internal stress state of wide rice noodles. This effectively solves the problem of monitoring blind spots caused by ignoring the physical evolution of the sample.

[0041] 2. This invention addresses the issues of breakage and brittle edges caused by the decoupling of evaporation and diffusion during the drying process of alum-free wide powder. The system calculates the humidity gradient index in real time and compares it with the critical threshold of material mechanics. Once the risk of excessive surface evaporation and stress accumulation is detected, the strategy decision module immediately generates an evaporation suppression command. By dynamically adjusting the temperature and humidity settings, the environment is forcibly mitigated, sacrificing temporary drying speed to ensure the integrity of the product structure. This significantly reduces the breakage rate of the finished product and improves the yield.

[0042] 3. In the compensation calculation for excessive stress, the system of this invention uses a fuzzy control algorithm to handle nonlinear temperature and humidity adjustment requirements, making the changes in compensation amount smoother and more in line with physical laws. By introducing unidirectional cutoff logic, it ensures that the system only performs precise humidification or cooling operations when the humidity gradient exceeds the limit, avoiding erroneous adjustments within the safe range. This flexible control method eliminates parameter oscillations common in traditional control, providing a stable high-humidity buffer environment for the ripening of wide rice noodles, and further improving the chewiness of the texture.

[0043] 4. This invention pre-defines a nonlinear control zone covering three stages: high-temperature gelatinization, medium-temperature dehumidification, and low-temperature retrogradation. This precisely matches the physicochemical requirements of starch throughout its entire life cycle, from water absorption and swelling to rearrangement and retrogradation. By automatically switching reference data through a built-in timer and using a slope limiter to eliminate parameter step shocks during stage switching, the system not only ensures the basic structural stability of wide rice noodles but also endows alum-free wide rice noodles with sensory qualities comparable to alum-containing products through low-temperature deep retrogradation, achieving an optimal balance between process efficiency and finished product quality. Attached Figure Description

[0044] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0045] Figure 1 This is a structural diagram of the system of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0047] Example 1:

[0048] Please see Figure 1 A temperature and humidity PID control system for the curing process of alum-free wide rice noodles includes: a sensing interface module configured to acquire environmental state data and physical characterization data of the wide rice noodle sample in the curing chamber; the environmental state data includes air temperature and air humidity; the physical characterization data includes real-time sample weight and sample surface temperature; and a kinetic analysis module configured to calculate the drying weight loss rate based on the real-time sample weight, and, in conjunction with the sample surface temperature and drying weight loss rate, calculate the humidity gradient index, which characterizes the difference between the center moisture content and the surface moisture content of the wide rice noodle sample.

[0049] The strategy decision module is configured to determine dynamic correction settings for air temperature and air humidity based on the humidity gradient index and a preset stress threshold.

[0050] The execution control module is configured to drive the temperature and humidity regulation mechanism to perform heating, humidification, or dehumidification actions based on the dynamic correction set value, so that the actual temperature and humidity in the curing room are close to the dynamic correction set value.

[0051] This embodiment constructs a dual closed-loop control architecture, where the inner loop is a conventional PID control for air temperature and humidity, and the outer loop is a physiological feedback control based on the internal moisture migration state of wide rice noodles. This aims to solve the problem of surface hardening or internal cracking caused by the accumulation of internal stress in wide rice noodles due to focusing only on environmental parameters in existing technologies. The sensing interface module constructs a digital mapping of the physical world. It not only collects environmental state data in the curing chamber, but more importantly, it obtains physical characterization data of wide rice noodle samples through non-contact and high-frequency sampling methods. These data are the basic inputs for subsequent kinetic analysis. The kinetic analysis module solves the hidden physical layer that cannot be directly measured, calculates the drying weight loss rate based on real-time weight, and infers the moisture distribution state inside the wide rice noodles by combining surface temperature, and solves the humidity gradient index.

[0052] The data is derived from the kinetic analysis module through numerical model calculations based on real-time data. The physical meaning is the difference between the center moisture content and the surface moisture content of the wide powder sample, and the unit is dimensionless.

[0053] The strategy decision module implements state follow-up control, compares the real-time calculated humidity gradient index with the preset stress threshold. When the humidity gradient index exceeds the threshold, it indicates that the surface of the wide powder evaporates too quickly and the internal moisture cannot be replenished in time, which poses a risk of cracking. The module will generate dynamic correction settings for air temperature and air humidity, rather than mechanically executing the preset standard process curve.

[0054] The execution control module receives dynamically corrected setpoints from the strategy decision module and drives the heater, humidifier, exhaust fan, or refrigeration unit to bring the actual environment inside the curing chamber closer to the dynamic target, thereby eliminating excessive stress inside the wide rice flour. The addition of the refrigeration unit is to meet the requirements of the low-temperature deep regeneration zone in Example 7. Temperature control requirements;

[0055] This embodiment introduces a sensing interface and dynamic analysis, enabling the system to perceive the drying stress state of wide rice noodles in real time. When the risk of external dryness and internal moisture is detected, the strategy decision module will actively intervene in the PID setting value to achieve adaptive adjustment based on the rice noodles. This effectively avoids the breakage and brittle edges of alum-free wide rice noodles caused by uneven starch retrogradation. While ensuring the health attributes of the alum-free process, it significantly improves the chewiness and yield of the finished product.

[0056] To verify the technical effectiveness of this system, a comparative experiment was conducted: an experimental group was set up, using this system, and a control group was set up, using the traditional constant temperature and humidity process. relative humidity Each processing Using the same batch of raw materials, the results showed that the fracture rate of the experimental group was... for Significantly lower than the control group Furthermore, texture analysis showed that the tensile strength of the experimental components was... On average, it increased This powerfully demonstrates the significant advantages of this system in improving yield and taste.

[0057] Example 2:

[0058] The kinetic analysis module calculates the drying weight loss rate based on the real-time sample weight and, combined with the sample surface temperature and drying weight loss rate, calculates the humidity gradient index inside the wide rice noodle. This includes: calling the real-time sample weight, calculating the first derivative of the real-time sample weight with respect to time to generate the drying weight loss rate; inputting the drying weight loss rate and sample surface temperature into a preset moisture diffusion simulation model; using the moisture diffusion simulation model to estimate the center moisture content and surface moisture content of the wide rice noodle sample; calculating the difference between the center moisture content and surface moisture content, and defining the difference as the humidity gradient index.

[0059] This embodiment details the specific algorithm flow for the kinetic analysis module to calculate the humidity gradient index. This module maps macroscopic weight changes to microscopic differences in moisture distribution. The system calls the sensing interface module to collect the real-time weight of the sample, calculates its first derivative with respect to time, and calculates the drying weight loss rate. The calculation formula is as follows:

[0060] ;

[0061] in, The current weight after filtering. The weight at the previous moment; To balance noise suppression and dynamic response, the step size for differential calculation is specified in this embodiment. The value is consistent with the sampling period in Example 6, that is correspond Alternatively, the sampling period can be taken as an integer multiple of the actual noise level; when the differential calculation step size Increase to the sampling period When the moving average filter window length is multiplied by 1, It needs to be adjusted simultaneously to To maintain the rate of weightlessness The physical equivalence between data smoothness and temporal resolution during the calculation process; The physical meaning is the mass of moisture evaporated from the surface of wide rice noodles per unit time, and the original unit of calculation is... ;

[0062] The drying weight loss rate and sample surface temperature are input into a pre-defined moisture diffusion model. This model is constructed based on the steady-state approximate solution of Fick's second law under plate geometry. To improve the real-time performance of engineering calculations, the infinite series solution is simplified to a linear gradient model, and a unit correction factor is introduced. To resolve conflicts in physical dimensions; the calculation formula is as follows:

[0063] ;

[0064] in, The rate of weight loss during drying. The geometric half-thickness of the wide powder The moisture diffusivity is temperature-dependent. Wet density, The total evaporation area, is a dimensionless conversion constant; this formula, based on the one-dimensional steady-state approximation of Fick's second law, characterizes the degree of mismatch between the intensity of water evaporation per unit area and the internal diffusion capacity, thus quantifying the moisture content deviation between the center and the surface; due to The engineering surveying unit is The other parameters in the formula use the International System of Units (SI), namely density. Units are diffusion coefficient Units are In order to offset the difference in dimensions, The calculation process is as follows:

[0065] ;

[0066] Its physical function is to The engineering unit g / min is converted to the standard SI unit kilogram / second, and the coefficient in the numerator is... The numerator is the dimension conversion constant from gram to kilogram, and the 60 in the denominator is the time conversion constant from minute to second, thus ensuring that the physical dimensions of the numerator and denominator in the formula completely cancel each other out.

[0067] The source is the production process specification parameters, and the value is... The physical meaning of is the geometric half-thickness of the wide powder sample, which is the characteristic distance of moisture migration from the center to the surface;

[0068] The source is the laboratory density measurement of gelatinized starch gel, with values ​​ranging from [value missing]. The physical meaning of is the bulk density of wide rice noodles in a moist state;

[0069] The source is the effective loading area of ​​the weighing sensor tray, and the value is... The physical meaning is the total evaporation surface area of ​​the wide powder sample that participated in the weight monitoring;

[0070] Based on this, the system completes the mathematical mapping from external characterization data to internal stress state through the above calculation process;

[0071] In this embodiment, through the above algorithm, the system can see through the interior of the wide powder and construct a humidity gradient index by using the ratio of the external evaporation rate to the internal diffusion coefficient. This accurately captures the physical essence of the decoupling between the evaporation rate and the diffusion rate, which allows the control system to no longer blindly pursue the drying speed, but to limit the drying process within the tolerance limit of the physical structure of the wide powder.

[0072] Example 3:

[0073] The strategy decision module determines the dynamic correction settings for air temperature and air humidity based on the humidity gradient index and the preset stress threshold. It is configured to perform the following steps: call the humidity gradient index and the standard process setting curve for the current maturation stage; compare the humidity gradient index with the preset stress threshold; if the humidity gradient index is greater than the preset stress threshold, generate an evaporation suppression command and perform compensation calculations on the standard process setting curve based on the evaporation suppression command to generate the dynamic correction setting value; if the humidity gradient index is less than or equal to the preset stress threshold, determine the value corresponding to the standard process setting curve as the dynamic correction setting value.

[0074] This embodiment describes in detail how the strategy decision-making module determines the dynamic correction setpoint based on the humidity gradient index, which is a key step for the system to shift from passive execution to active intervention; the module simultaneously reads the real-time calculated humidity gradient index and the standard process setting curve for the current curing stage;

[0075] The source is a pre-set sequence of target temperature and humidity values ​​under ideal conditions, with the physical meaning being historical best process experience data, and the units are degrees Celsius and percentages; the humidity gradient index is compared with the preset stress threshold;

[0076] The source is from materials mechanics testing experiments, and the specific determination method is as follows: Select Standard dimensions, i.e., length ,Width ,thick The wet wide powder sample, under constant wind speed Accelerated drying is carried out under low-field nuclear magnetic resonance technology every [time period]. Scanning the internal moisture distribution of the sample to calculate the gradient index, while simultaneously monitoring the sample surface using a high-powered microscope; when the first sample exceeding [a certain length] is observed... When a microcrack appears, the gradient exponent at that moment is recorded; the average value of the critical fracture gradient measured experimentally is... Set the safety factor to Finally, the preset stress threshold is determined. Its physical meaning is the critical humidity gradient value at which the wide powder gel matrix undergoes plastic deformation or fracture, and the unit is dimensionless.

[0077] The system executes branch decision logic. When the humidity gradient index is less than or equal to the stress threshold, it indicates that the internal moisture diffusion is keeping up with the surface evaporation and there is no risk of cracking. In this case, the standard process setting curve is directly determined as the dynamic correction setting value. When the humidity gradient index is greater than the stress threshold, it indicates that the risk of surface hardening is high, and the system generates an evaporation suppression command. Based on this, the standard process setting curve is compensated and calculated according to the command to generate a corrected dynamic correction setting value. This usually manifests as increasing the humidity setting value or decreasing the temperature setting value, forcing the environment to slow down and waiting for the internal moisture to diffuse.

[0078] This embodiment establishes a melting mechanism that predicts risks in advance by monitoring the humidity gradient index before the wide powder is physically damaged. Once the index exceeds the limit, the target value of the PID is immediately modified. By sacrificing the temporary drying speed to maintain the integrity of the product structure, the process pain point of easy cracking of alum-free wide powder during rapid heating is effectively solved.

[0079] Example 4:

[0080] The strategy decision module performs compensation calculations on the standard process setting curve based on the evaporation suppression command, generating a dynamic correction setpoint. This includes: in response to the evaporation suppression command, extracting the fuzzy control rule table at the current moment; introducing the humidity gradient index as an input variable into the fuzzy control rule table, and calculating the positive humidity compensation amount based on the degree of deviation of the humidity gradient index from the stress threshold; and superimposing the positive humidity compensation amount onto the air humidity setting item in the standard process setting curve to generate a dynamic correction setpoint, thereby creating a high-humidity buffer environment in the execution control module.

[0081] This embodiment details how fuzzy control logic generates a specific positive humidity compensation amount when generating an evaporation suppression command. In response to the evaporation suppression command, the system extracts the fuzzy control rule table for the current moment. Using the humidity gradient index as an input variable, the degree of deviation exceeding the stress threshold is calculated. To avoid confusion with the error sign in PID control, this degree of deviation is defined here as... The degree of this deviation is then fuzzified into linguistic variables such as slight, moderate, and severe, and the calculation formula is as follows:

[0082] ;

[0083] in, To indicate the degree of deviation, For gradient exponent, The threshold is used; the precise positive humidity compensation amount is calculated based on the rule table. This process uses a fuzzy inference function to handle nonlinear relationships, and its calculation formula is as follows:

[0084] ;

[0085] in, This is the humidity compensation amount. For fuzzy inference functions, For deviation, For rate of change; input variable Specifically defined as humidity gradient deviation The rate of change, i.e. It is used to characterize the trend of stress accumulation;

[0086] In specific implementation, the fuzzy inference function The Mamdani-type inference mechanism is adopted, with the following specific configuration: Input membership function: Input variable bias and rate of change ,Right now All use triangular membership functions, and the universe of discourse is normalized to... It is divided into five fuzzy subsets: {NB, NS, ZO, PS, PB}; the specific coordinates of the triangular membership function of each fuzzy subset are defined as follows: for , for , for , for , for The coordinates mentioned above correspond to the left base, vertex, and right base of the triangle, respectively. This coordinate set is used to represent the input physical deviation. Mapped to the membership values ​​of each subset; Fuzzy rule table: 25 preset control rules are used to construct a fuzzy associative memory matrix covering the entire universe of discourse, where rows correspond to... , column corresponding The matrix elements correspond to the output. The calculation formula is as follows:

[0087] ;

[0088] This matrix clarifies the full-domain control logic from rapid negative deviation to extremely rapid positive deterioration; output definition and defuzzification: defining output variables. Using a triangular membership function isomorphic to the input, the universe of discourse is ;

[0089] In this matrix, the row vectors correspond to the deviations in sequence. Fuzzy subset The column vectors correspond to the rates of change in sequence. Fuzzy subset ;when and All are positive values ​​(i.e.) When the humidity gradient is in a state of severe over-limit deterioration, the matrix outputs the strongest positive humidity compensation.

[0090] The centroid method is used for fuzzy de-fuzzing calculation, and the calculation formula is as follows: In the formula To output discrete values ​​in the universe of discourse, This represents the corresponding membership degree. To resolve the contradiction between the negative output value caused by the negative bias in fuzzy rule coverage (i.e., the safe zone) and the positive compensation target, a one-way cutoff logic is introduced, and its calculation formula is as follows:

[0091] ;

[0092] in, This is the humidity compensation amount. Gain factor For normalized output; The source is the system's maximum adjustment capability constraint, with a value of [value missing]. The physical meaning is the maximum positive humidity correction that the fuzzy controller is allowed to apply, expressed as a percentage.

[0093] Through the above Function processing, when It is a negative value, that is lead to When it is negative, the system forces an output. This ensures that humidification is only performed when the gradient index exceeds the limit, avoiding erroneous drying behavior caused by negative output; the positive humidity compensation is superimposed on the air humidity setting item in the standard process setting curve to generate the final dynamic correction setting value.

[0094] In addition, to synergistically suppress excessively rapid evaporation of surface moisture, the system simultaneously generates a positive temperature compensation quantity. Its calculation logic is isomorphic to that of humidity compensation, but its direction of action is opposite. The formula is as follows:

[0095] ;

[0096] in, This is the temperature adjustment gain factor, with a value of [value missing]. When the gradient exponent Exceeding the threshold This leads to normalized output At that time, through The function truncates negative values ​​to compensate for the cooling of the air temperature setting in the standard process curve; the final dynamically corrected setting includes the corrected temperature. and corrected humidity It should be noted that when the system triggers the evaporation suppression command, the aforementioned negative temperature compensation amount... The adjustment priority is higher than the step temperature rise command of the standard process setting curve at the corresponding stage, so as to ensure stress protection is given priority;

[0097] This embodiment employs fuzzy control instead of simple on / off control, making the adjustment of the compensation amount smoother and conforming to nonlinear laws. At the same time, by introducing unidirectional cutoff logic, the logical flaw of the full domain rule table outputting negative values ​​in the safe zone is corrected, ensuring that the control strategy always serves the single technical goal of suppressing evaporation, and realizing stress-driven fine and flexible control.

[0098] Example 5:

[0099] The execution control module drives the temperature and humidity regulating mechanism to perform heating, humidification, or dehumidification actions based on the dynamically corrected setpoint. This includes: constructing a temperature and humidity tracking PID control loop; using the dynamically corrected setpoint as the target input value of the PID control loop; calculating the deviation between the environmental state data and the dynamically corrected setpoint; and, based on the deviation, outputting a control quantity to the temperature and humidity regulating mechanism through a PID algorithm.

[0100] This embodiment details how the execution control module drives the hardware mechanism based on dynamically corrected setpoints; it constructs a temperature and humidity tracking PID control loop, using the dynamically corrected setpoints output by the strategy decision module as the target input value of the PID loop; and it calculates the deviation between the current environmental state data and the target input value using the following formula:

[0101] ;

[0102] in, For deviation, Target value; These are measured values;

[0103] Based on this deviation, a PID algorithm is used to calculate and output a control quantity to the temperature and humidity control mechanism. This mechanism includes an electric heater driven by a solid-state relay, a humidifier, an exhaust fan, and a compressor / refrigeration unit. When the system is in a low-temperature deep regeneration zone and the current temperature is higher than the set value, the PID control algorithm outputs a negative power signal, which drives the compressor / refrigeration unit to start via the relay, thereby achieving forced cooling. Given that temperature and humidity have different physical response characteristics, this embodiment constructs independent PID control loops for each:

[0104] Temperature PID control loop; the system adopts a positional PID algorithm, with the integral term using trapezoidal summation and the derivative term using backward difference. The calculation formula is as follows:

[0105] ;

[0106] in, and Each is the current number Sampling time and the previous one The deviation between the air temperature at the sampling time and the set value; and The temperature deviation at the corresponding historical moment in the integral term. At the current sampling time, To control the cycle; The source is a temperature system tuning experiment, and the value is... The physical meaning is the proportional adjustment coefficient, and the unit is... ; The source is to eliminate static temperature difference, and the value is taken as follows: The physical meaning is the integral adjustment coefficient, and the unit is . ; The source is temperature dynamic response optimization, and the value is... The physical meaning is the differential adjustment coefficient, and the unit is . ;

[0107] Humidity PID control loop; the system adopts control logic that is isomorphic to the temperature loop. Considering the large hysteresis characteristic of humidity, its positional PID algorithm output is as follows:

[0108] ;

[0109] in, For the first term in the integral term Humidity deviation at the time of sampling For the first term in the integral term Humidity deviation at the time of sampling This represents the humidity deviation at the previous moment in the differential term. This represents the deviation between the current air humidity and the dynamic correction setpoint.

[0110] To prevent integral saturation of the positional PID algorithm under long-term deviation, this embodiment introduces an anti-saturation limiting operator in the calculation process:

[0111] ;

[0112] in, This refers to the original control quantity calculated in the formula. and The corresponding upper limit of the power of the actuator and lower limit When the calculated value touches the boundary, the system automatically stops the trapezoidal integration term. The accumulation of these values ​​ensures that the system can quickly exit the saturation region when the actuator switches directions, avoiding significant overshoot in the wide-powder curing environment. It should be noted that the above description of the anti-saturation limiting operator... and These are general expressions, which in actual control execution correspond to the aforementioned temperature control quantities. Temperature deviation and humidity control amount Humidity deviation .

[0113] The source is a humidity system tuning experiment, and the value is... The physical meaning is the humidity ratio adjustment coefficient, and the unit is... ; The source is to eliminate static humidity difference, and the value is [value missing]. The physical meaning is the humidity integral adjustment coefficient, and the unit is... ; The source is humidity hysteresis compensation, with a value of The physical meaning is the humidity differential adjustment coefficient, and the unit is . ;

[0114] During this process, the system continuously monitors changes in deviation and adjusts control quantities in real time to ensure that environmental parameters closely follow the dynamically set trajectory.

[0115] This embodiment ensures that the actual physical environment inside the curing chamber can quickly and accurately follow the dynamic target determined by the physiological state of the wide rice flour. By distinguishing different PID parameters for temperature and humidity and adding cooling hardware, it solves the problem that single parameter control cannot take into account dual-variable characteristics, and provides the necessary hardware and algorithm support for the low-temperature regeneration process.

[0116] Example 6:

[0117] The sensing interface module acquires physical characterization data of the wide powder sample, including: collecting the real-time weight of the sample at a preset sampling frequency through a weighing sensor array; scanning and reading the sample surface temperature in real time through an infrared non-contact temperature probe; and filtering and reducing noise on the collected real-time weight and sample surface temperature to generate physical characterization data.

[0118] This embodiment details the specific hardware implementation and processing method for the sensing interface module to acquire physical characterization data. A weighing sensor array installed below the sample holder collects the real-time weight of the sample at a preset sampling frequency. Simultaneously, an infrared non-contact temperature probe installed directly above the sample scans and reads the sample surface temperature in real time. This non-contact measurement method aims to avoid damage to the surface structure of the wide powder caused by sensor contact. The collected raw data is filtered to generate usable physical characterization data. This embodiment uses a moving average filtering algorithm, the calculation formula of which is:

[0119] ;

[0120] in, This is the filtered data. This is historical raw data. For window length, For the current index, Offset index;

[0121] The physical meaning is the sampling point index of the time series, with a value range greater than... 0 natural numbers;

[0122] The physical meaning is the number of backtracking steps in the summation process, with a range of values ​​of [value missing]. to Integers;

[0123] The source is a calculated value based on the system sampling frequency and noise frequency characteristics, specifically taken as... The determination is based on the following: setting the system's preset sampling frequency. The frequency caused by the on-site wind turbine is approximately to Mechanical vibration noise; to ensure filtering effect, the window time length... At least need to be covered One oscillation cycle, that is According to the formula Calculation This value effectively filters out high-frequency fluctuations while ensuring that the data response delay to minute-level changes in drying rate does not exceed [a certain threshold]. ;

[0124] The filtered data is transmitted to the dynamic analysis module. This process effectively filters out weight reading fluctuations caused by fan vibration and temperature noise caused by environmental radiation.

[0125] This embodiment combines high-precision sensing hardware with filtering algorithms to provide a clean and reliable data source for kinetic analysis. In particular, the combination of infrared temperature measurement and weighing sensor array enables all-time monitoring without interfering with the ripening process, solving the shortcomings of traditional pin-type moisture measurement methods that can only measure at a single point and damage the sample.

[0126] Example 7:

[0127] The standard process setting curve includes three nonlinear control regions: a high-temperature gelatinization and conditioning region, corresponding to the initial stage of the maturation process, configured with a first temperature and humidity reference; a medium-temperature slow dehumidification region, corresponding to the middle stage of the maturation process, configured with a second temperature and humidity reference; and a low-temperature deep regeneration region, corresponding to the end stage of the maturation process, configured with a third temperature and humidity reference. The strategy decision module automatically switches the reference data of the control region according to the current running time.

[0128] This embodiment details the segmented control logic of the standard process setting curve. Considering the physicochemical properties of alum-free wide rice flour, the curve includes three nonlinear control zones, and specifies the exact temperature and humidity baseline values ​​for each zone for the strategy decision module to access. A high-temperature gelatinization and conditioning zone is set as the initial stage of the maturation process, with a time interval of [time range missing]. It is equipped with a primary temperature and humidity reference, specifically set to a constant value: air temperature air humidity The aim is to promote the water absorption, swelling, and gelatinization of starch granules, establishing the basic framework of wide rice noodles; a medium-temperature, slow dehumidification zone is set as the intermediate stage of the maturation process, with a time interval of [missing information]. It is equipped with a second temperature and humidity reference. This stage is the main drying range and also the key area for stress control. Its reference value is a linear function of time, and its calculation formula is:

[0129] ;

[0130] ;

[0131] in, To set the temperature, To set the humidity, Runtime;

[0132] This function controls the environment from Slowly transition to approximately The low-temperature deep regeneration zone is set as the final stage of the maturation process, with a time interval of [time range missing]. It is equipped with a third temperature and humidity reference, specifically set to a rapid cooling and constant humidity environment: air temperature air humidity This is designed to promote the rearrangement and retrogradation of amylose, giving wide rice noodles a unique chewy texture; the strategy decision module has a built-in timer that adjusts the timing based on the current running time. Automatically switch the reference data of the control area being called;

[0133] Considering the significant step change in the reference value across different control intervals, such as from Down to The strategy decision module activates the slope limiter at the moment of switching, ensuring that the set value is set at a fixed slope. The smoothing process achieves a linear transition to the target value, rather than a sudden change. This smoothing effectively eliminates the impact of PID differential terms caused by the instantaneous increase in error, protects the mechanical life of the heater and refrigeration unit, and maintains the stability of the flow field in the curing chamber.

[0134] This embodiment precisely matches the full life cycle requirements of starch from gelatinization to retrogradation through segmented control. In particular, it decouples dehumidification and retrogradation on the time axis. Combined with the aforementioned stress feedback control, this allows alum-free wide rice noodles to obtain the optimal physicochemical evolution environment at each stage, thereby achieving a quality comparable to alum-containing wide rice noodles.

[0135] Example 8:

[0136] The moisture diffusion extrapolation model is a numerical approximation model based on Fick's second law. The model presupposes the moisture diffusion coefficient of the wide starch gel matrix at different temperatures.

[0137] This embodiment details the construction method of the diffusion coefficient in the moisture diffusion extrapolation model. This model is based on Fick's second law, where the key parameter, the moisture diffusion coefficient, is preset as a function of temperature. The system establishes the correlation between the diffusion coefficient and temperature. This embodiment uses the Arrhenius equation to describe this relationship, and clarifies that the temperature variable in the equation refers to the physical temperature of the sample, not the ambient temperature. The calculation formula is as follows:

[0138] ;

[0139] in, The diffusion coefficient is... For frequency factors, For activation energy, The gas constant is Absolute temperature; The data is derived from laboratory measurements of the drying kinetics of wide rice noodles. The physical meaning is a physical quantity that characterizes the ease with which water migrates within the wide rice noodle gel matrix, and the unit is square meters per second.

[0140] To ensure the feasibility of the model, this embodiment is based on the use of wide sweet potato starch noodles. to Isothermal drying experimental data within the temperature range determined the specific parameter values ​​in the equation: frequency factor. Values ;activation energy Values Gas constant Standard constant ;

[0141] Based on this, the model calls the sensing interface module to collect the sample surface temperature in real time during runtime. And convert it to absolute temperature in Kelvin units. ,Right now ,in, The constant offset for converting Celsius to Kelvin units is substituted into the above equation to dynamically calculate the corresponding diffusion coefficient. Substitute this into the subsequent gradient exponent calculation process;

[0142] By introducing a temperature-dependent diffusion coefficient, this embodiment enables the model to dynamically adapt to the influence of temperature changes on moisture migration during the ripening process. This allows the calculated humidity gradient index to maintain extremely high physical accuracy in both high and low temperature stages, thereby significantly improving the robustness and scientific validity of the control system.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A temperature and humidity PID control system for alum-free wide rice noodle cooking process, characterized in that, include: The sensing interface module is configured to acquire environmental state data and physical characterization data of wide rice noodle samples in the curing room. The environmental state data includes air temperature and air humidity; the physical characterization data includes real-time sample weight and sample surface temperature. The kinetic analysis module is configured to calculate the drying weight loss rate based on the real-time weight of the sample, and, in conjunction with the sample surface temperature and the drying weight loss rate, calculate the humidity gradient index, which characterizes the difference between the center moisture content and the surface moisture content of the wide powder sample. The strategy decision module is configured to determine dynamic correction settings for the air temperature and the air humidity based on the humidity gradient index and a preset stress threshold. The execution control module is configured to drive the temperature and humidity adjustment mechanism to perform heating, humidification or dehumidification actions according to the dynamic correction set value, so that the actual temperature and humidity in the curing room are close to the dynamic correction set value. The kinetic analysis module calculates the drying weight loss rate based on the real-time weight of the sample, and, in conjunction with the sample surface temperature and the drying weight loss rate, calculates the humidity gradient index inside the wide powder, including: The real-time weight of the sample is retrieved, and the first derivative of the real-time weight of the sample with respect to time is calculated to generate the drying weight loss rate. The formula for calculating the rate of drying weight loss is as follows: ； in, The current weight after filtering. The weight at the previous moment; The step size is used for differential calculation; The drying weight loss rate and the sample surface temperature are input into a preset moisture diffusion simulation model; Using the moisture diffusion extrapolation model, the humidity gradient index, which characterizes the difference between the center moisture content and the surface moisture content of the wide rice noodle sample, is calculated. The formula for calculating the humidity gradient index is as follows: ； in, The rate of weight loss during drying. The geometric half-thickness of the wide powder The moisture diffusivity is temperature-dependent. Wet density, The total evaporation area, This is a dimensionless conversion constant; The strategy decision module determines dynamic correction settings for the air temperature and air humidity based on the humidity gradient index and a preset stress threshold, and is configured to execute the following steps: Call the humidity gradient index and the standard process setting curve for the current maturation stage; The humidity gradient index is compared with the preset stress threshold. If the humidity gradient index is greater than the preset stress threshold, an evaporation suppression command is generated, and the standard process setting curve is compensated based on the evaporation suppression command to generate the dynamic correction setting value. If the humidity gradient index is less than or equal to the preset stress threshold, the value corresponding to the standard process setting curve is determined as the dynamic correction setting value. The strategy decision module performs compensation calculations on the standard process setting curve based on the evaporation suppression command, and generates the dynamic correction setting value, including: In response to the evaporation suppression command, the fuzzy control rule table for the current moment is extracted; The humidity gradient index is introduced as an input variable into the fuzzy control rule table, and the positive humidity compensation amount is calculated based on the degree of deviation of the humidity gradient index from the stress threshold. The positive humidity compensation is superimposed on the air humidity setting item in the standard process setting curve to generate the dynamic correction setting value, so as to form a high humidity buffer environment in the execution control module.

2. The temperature and humidity PID control system for the alum-free wide rice noodle cooking process according to claim 1, characterized in that, The execution control module drives the temperature and humidity regulating mechanism to perform heating, humidification, or dehumidification actions based on the dynamically corrected set value, including: Construct a temperature and humidity tracking PID control loop; The dynamic correction setpoint is used as the target input value for the PID control loop; Calculate the deviation between the environmental state data and the dynamic correction setpoint; Based on the deviation, a control quantity is output to the temperature and humidity regulating mechanism through a PID algorithm.

3. The temperature and humidity PID control system for the alum-free wide rice noodle cooking process according to claim 1, characterized in that, The sensing interface module acquires physical characterization data of the wide rice noodle sample, including: The real-time weight of the sample is collected using a weighing sensor array at a preset sampling frequency; The surface temperature of the sample is scanned and read in real time using an infrared non-contact temperature probe. The collected real-time weight and surface temperature of the sample are filtered and noise-reduced to generate the physical characterization data.

4. The temperature and humidity PID control system for the alum-free wide rice noodle cooking process according to claim 1, characterized in that, The standard process setting curve includes three nonlinear control regions: The high-temperature gelatinization and conditioning zone corresponds to the initial stage of the maturation process and is equipped with a first temperature and humidity reference. The medium-temperature slow dehumidification zone corresponds to the middle stage of the maturation process and is equipped with a second temperature and humidity reference. The low-temperature deep regeneration zone corresponds to the final stage of the maturation process and is equipped with a third temperature and humidity reference. The strategy decision-making module automatically switches the reference data of the control area it calls based on the current running time.

5. The temperature and humidity PID control system for the alum-free wide rice noodle cooking process according to claim 1, characterized in that, The moisture diffusion model is a numerical approximation model based on Fick's second law, and the model presupposes the moisture diffusion coefficient of the wide starch gel matrix at different temperatures.

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