Self-adaptive temperature control infrared hot air combined drying test method

Abstract

The invention discloses a self-adaptive temperature control infrared hot air combined drying test method which comprises the following steps: modeling a hot air constant temperature control object to obtain a transfer function of the controlled object; a corresponding fuzzy PID intelligent control algorithm is designed according to the hot air transfer function; simulation analysis is conducted on a fuzzy PID intelligent temperature control algorithm under hot air drying, compared with a conventional PID algorithm, different drying methods are adopted for different drying periods, the drying time can be effectively shortened, the drying quality can be effectively improved, the power of an electric heating pipe is adjusted according to the temperature of a rice grain layer through the self-adaptive fuzzy PID algorithm, and the drying efficiency is improved. Compared with the prior art, good response smoothness, robustness and adaptability are shown, the requirement of drying control can be met for an infrared drying control system by using self-adaptive fuzzy PID, the height from an infrared radiation heating pipe to a material and the thickness of a thin-layer material can be adjusted, a better drying process is obtained through a test, and the drying efficiency is improved. Therefore, the grain drying speed and drying quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of grain drying, and specifically to an adaptive temperature-controlled infrared hot air combined drying test method. Background Technique

[0002] In modern agricultural production, removing moisture through traditional sun-drying methods not only requires a large amount of space but also has low efficiency and is not suitable for large granary operations. For large granaries, grain dryers are generally used to dry the grain so that the grain has an appropriate moisture content before entering the warehouse. Hot air drying is a traditional drying method and is also the most widely used drying means at present. However, the hot air drying treatment method has a relatively high temperature, and the quality of the grain after drying is greatly affected. The combination of hot air drying with low energy consumption and efficient infrared drying results in far-infrared hot air drying, which has an obvious synergistic effect. The drying speed is better than that of single drying. Its drying effect is mainly related to the irradiation intensity of the heat source, irradiation temperature, distance between the irradiation plate and the material, and convective air humidity. Chinese Patent 202210321169.1 discloses a drying detection device and a drying detection method using the drying detection device. This patent provides for controlling the driving of the optical detection device according to the internal pressure of the chamber measured by the pressure measurement unit;

[0003] However, there is currently a lack of experimental research on infrared hot air combined drying of grains, and analyzing the water loss characteristics and mass transfer analysis problems of infrared hot air combined drying of grains through simulation cannot verify the accuracy of the data. Summary of the Invention

[0004] The present invention provides an adaptive temperature-controlled infrared hot air combined drying test method, which can effectively solve the problem that there is currently a lack of experimental research on infrared hot air combined drying of grains, and analyzing the water loss characteristics and mass transfer analysis problems of infrared hot air combined drying of grains through simulation cannot verify the accuracy of the data proposed in the above background technique.

[0005] To achieve the above object, the present invention provides the following technical solution: An adaptive temperature-controlled infrared hot air combined drying test method, for paddy rice in different drying periods, different drying methods are adopted through an infrared hot air combined drying test device. Hot air drying is adopted in the initial drying stage, and infrared drying is adopted in the later drying stage;

[0006] In the hot air drying stage, a hot air adaptive two-dimensional fuzzy PID algorithm is adopted. The control object is the temperature of the grain layer, the execution device is an electric heating tube, the temperature error and the error change rate are input, and the three Kp, Ki, and Kd of the PID are output;

[0007] In the infrared drying stage, the power of the infrared heating tube is controlled by the fuzzy PID algorithm to control the radiation wavelength of the infrared tube, and the heat of the infrared tube is controlled to dry the rice by adjusting the distance between the infrared tube and the grain layer; due to the continuous heating of the rice by the infrared wavelength and heat, the temperature range of the grain layer is controlled, and the GPWM method is used to control the infrared heating tube;

[0008] In the process of infrared hot air constant temperature control, the specific steps are as follows:

[0009] Step 1: Model the hot air constant temperature control object to obtain the transfer function of the controlled object;

[0010] Step 2: Design a corresponding fuzzy PID intelligent control algorithm for the hot air transfer function;

[0011] Step 3: Conduct a simulation analysis of the fuzzy PID intelligent temperature control algorithm under hot air drying and compare it with the conventional PID algorithm;

[0012] Step 4: Control and model the infrared temperature control to obtain the transfer function of the controlled object;

[0013] Step 5: Determine the corresponding incremental parameters of the fuzzy controller for the infrared temperature control transfer function;

[0014] Step 6: Conduct a simulation analysis of the fuzzy PID intelligent temperature control algorithm under infrared drying and compare it with the conventional PID algorithm.

[0015] According to the above technical solution, in step 1, it specifically refers to modeling the hot air constant temperature control object to obtain the transfer function of the controlled object. Among them, the hot air drying rice stage in the drying box body is divided into two heat transfer processes, and a mathematical model is established for the temperature change of the grain layer and the heat required for absorption and evaporation;

[0016] Specifically, it includes the following steps:

[0017] Step 1-1: Establish a mathematical model between the resistance temperature change T1 of the electric heating tube and the input voltage U;

[0018] Establish a model according to the thermodynamic equilibrium:

[0019]

[0020] where: ρ is the density of the heating tube material, with the unit of kg / m3, A is the cross-sectional area of the heating tube, with the unit of m2, c is the specific heat capacity of the heating tube material, with the unit of J / kg·K, T is the average temperature of the heating tube, with the unit of K, t is the time, with the unit of s, k is the thermal conductivity of the heating tube material, with the unit of W / m·K, x is the position along the length direction of the heating tube, with the unit of m, h is the convective heat transfer coefficient, with the unit of W / m2·K, P is the perimeter of the heating tube, with the unit of m, T fluid is the temperature of the surrounding fluid, with the unit of K, Q is the heat generated by the electric heating tube per unit time, with the unit of W;

[0021]

[0022] Step 1-2: Model the grain layer temperature and heat convection, and establish the transfer function model of the temperature control object:

[0023]

[0024] Q 0 = L 0 ρ 0 C 0 T 0 ;

[0025] Q 1 = L 1 ρ 1 C 2 T 2 ;

[0026] Q s = kF(T 2 - 1);

[0027] where, C 1 is the heat capacity of paddy, with the unit of J / K, T 1 is the temperature of paddy in the machine, with the unit of °C, t is the time, with the unit of s, is the integral of paddy temperature with respect to time, Q 0 is the heat of the heat medium entering the dryer, with the unit of J / s, Q 1 is the heat flowing out of the dryer in the dryer, with the unit of J / s, Qs is the heat loss, with the unit of J / s, L 0 is the air inflow, with the unit of m 3 / s, ρ 0 is the density of the heat medium, with the unit of kg / m 3 C 0 is the specific heat capacity of the heat medium, with the unit of J / (kg·°C), T 0 is the temperature of the heat medium, with the unit of °C, L 1 is the air outflow, with the unit of m 3 / s, ρ 1is the exhaust gas density, with the unit of kg / m 3 , C 1 is the specific heat capacity of the exhaust gas, with the unit of J / (kg·℃), T 2 is the temperature outside the dryer, with the unit of ℃, k is the heat dissipation coefficient, with the unit of J / (m 2 ·s·℃), F is the heat dissipation area, with the unit of m 2 ;

[0028]

[0029] Model the thin layer of rice for the infrared hot air combined drying system object, and obtain the transfer function of the temperature control system during hot air drying:

[0030]

[0031] According to the above technical solution, in step 2, the following steps are included:

[0032] Step 2-1: Design a hot air constant temperature fuzzy PID controller and an infrared radiation fuzzy PID controller. By the control law of the fuzzy controller and the control method of the classical PID, determine the relevant fuzzy rules and establish a fuzzy rule table;

[0033] Step 2-2: Use the fuzzy rules for fuzzy inference to obtain the corresponding K p , K i , K d , and send them into the PID controller.

[0034] According to the above technical solution, step 2-2 specifically includes the following steps:

[0035] Step 2-2-1: Divide the input error e and error change rate ec into 7 fuzzy sets, which are: NB, NM, NS, O, PS, PM, PB;

[0036] For the output K p , K i , K d divide them into 7 fuzzy sets, which are: NB, NM, NS, O, PS, PM, PB;

[0037] Step 2-2-2: For the universes of discourse of each input and output: the fuzzy universes of discourse of the input error e and error change rate ec are {-3, 3}, and for the output ΔK p the universe of discourse is {-3, 3}, for the output ΔK i the universe of discourse is {-3, 3}, for the output ΔK d the universe of discourse is {-3, 3}, determine the correction coefficient of the input variable: 0.8;

[0038] Step 2-2-3: After determining the fuzzy sets and universes of discourse of the error e and the error change rate ec, assign values to the fuzzy variables using triangular membership functions;

[0039] Step 2-2-4: Through the parameters K p , K i , K d 's influence on the system output characteristics, summarize the self-tuning regulations of different errors e and error change rates ec on K p , K i , K d ;

[0040] Step 2-2-5: Based on the above, establish the fuzzy control rules for the hot air drying ΔKp, ΔKi, and ΔKd;

[0041] Step 2-2-6: Build the Simulink structures of the hot air drying smith fuzzy PID and conventional PID control systems according to the working principle.

[0042] According to the above technical solution, in step 3, it specifically refers to performing simulation analysis on the fuzzy PID intelligent temperature control algorithm and comparing it with the conventional PID algorithm;

[0043] In step 3, the fuzzy PID control has a smaller overshoot, an extremely short settling time, better response smoothness, and better robustness and adaptability compared to the conventional PID, and can adjust the control parameters according to the real-time performance of the system.

[0044] According to the above technical solution, in step 4, model the control of the infrared temperature control to obtain the transfer function of the controlled object;

[0045] In step 4, for the heat generated by the infrared heating tube, heat transfer occurs through convection, radiation, and conduction, and the exchange of heat follows the law of conservation of energy:

[0046] Q = Q 1 + Q 2 + Q 3 + Q 4 ;

[0047] Q = KuU(t - τ);

[0048] Q 1 = Cpm d(T 1 - T 0 )dt;

[0049] Q 2 = h A(T air - T 3 );

[0050] Q 3 = KtA(T1 -T 0 );

[0051]

[0052] Among them, Q is the total energy received during infrared drying, with the unit of J. Q 1 is the energy consumed for temperature rise during infrared drying, with the unit of J. Q 2 is the heat lost by convection, with the unit of J. Q 3 is the energy consumed during the drying process, with the unit of J. Q 4 is the energy loss caused by thermal radiation, with the unit of J. U is the input effect of the infrared heater, Ku is the system input coefficient, m is the mass of the drying oven, τ is the time-delay parameter, Cp is the molar specific heat capacity of the drying oven material, h is the convective heat transfer coefficient, with the unit of W / (m2·K), A is the heat transfer area, with the unit of m2, T air is the temperature of the air, with the unit of K, T 3 is the temperature of the object to be heated, also with the unit of K. Kt is the heat dissipation coefficient of the drying oven, with the unit of W / m2℃, A is the heat dissipation area, with the unit of m2, T 1 、T 0 are the infrared heat source temperature and the initial temperature, with the unit of ℃. σ is the Boltzmann constant, σ = 5.67×10–12, α is the emissivity, and its value ranges from (0, 1):

[0053]

[0054] According to the above technical solution, in step 5, corresponding incremental parameters of the fuzzy controller are determined for the infrared temperature control transfer function;

[0055] In step 5, a fuzzy controller for hot air drying is adopted. By changing the corresponding gain, one fuzzy controller can achieve the optimization of two transfer functions. The initial values of KP, KI, and KD are appropriately selected according to the PID regulation of matlab;

[0056] KP is selected as 0.05426, KI as 0.002666, KD as 0.08714, and the corresponding gain variables are 0.05, 0.05, and 0.18 respectively.

[0057] According to the above technical solution, in step 6, a simulation analysis is carried out on the fuzzy PID intelligent temperature control algorithm under infrared drying, and it is compared with the conventional PID algorithm. The main comparisons include overshoot, settling time, flexibility of parameter adjustment, handling of nonlinearity and uncertainty, and smoothness of the transient process.

[0058] According to the above technical solution, the infrared hot air combined drying test device includes a drying housing, a heating device is connected to the bottom of the drying housing, the heating device is composed of three finned dry-burning electric heating tubes, and an air duct is connected to the air inlet end of the heating device. The air duct is composed of a uniform air duct, a heating duct, and a bend variable-diameter duct;

[0059] The air inlet end at the bottom of the drying housing is connected to a bend variable-diameter duct, and one end of the bend variable-diameter duct is connected to the heating duct. The heating duct is arranged on the outside of the heating device, and three finned dry-burning electric heating tubes are installed on the inside of the heating duct. The end of the heating duct away from the bend variable-diameter duct is connected to a uniform air duct, and the air inlet end of the uniform air duct is connected to a centrifugal fan that provides cold air and drives the gas to flow;

[0060] The drying housing includes a hot air duct interface, an exhaust valve, and a material support frame;

[0061] The air inlet end at the bottom of the drying housing is connected to a hot air duct interface, and the bottom of the hot air duct interface is connected to the air outlet end of the bend variable-diameter duct. The middle of the top of the drying housing is connected to an exhaust valve, and a material support frame for hanging test materials is arranged inside the drying housing.

[0062] According to the above technical solution, the material support frame includes a lead screw, a material tray, a material tray support, a rubber sealing strip, a support fixing piece, an infrared radiation heating tube, a ring screw, and a temperature sensor;

[0063] Multiple layers of material tray supports are arranged at the top inside the drying housing. A ring screw is installed in the middle of the top end of the material tray support at the topmost layer inside the drying housing. The bottom of the ring screw is connected to the material tray support through a weighing sensor. Lead screws are connected to the four side corners of the multiple layers of material tray supports through nuts. A material tray is arranged inside the material tray support. Infrared radiation heating tubes are arranged in a staggered manner above the material tray, and a temperature sensor for detecting the temperature of the grain layer and the infrared temperature is installed on the inner top surface of the material tray;

[0064] The material tray is placed on the top of the rubber sealing strip. A support fixing piece is arranged at the edge of the material tray. The rubber sealing strip is placed on the material tray support to form a uniform passage of hot air through the thin material layer.

[0065] Compared with the prior art, the beneficial effects of the present invention:

[0066] The present invention can effectively reduce the drying time and improve the drying quality by adopting different drying methods in different drying periods, and adjust the power of the electric heating tube according to the temperature of the rice grain layer through the adaptive fuzzy PID algorithm, showing good response smoothness, robustness and adaptability. For the infrared drying control system, the use of the conventional PID can meet the requirements of drying control. The height from the infrared radiation heating tube to the material and the thickness of the thin-layer material can be adjusted, and a better drying process can be obtained through experiments, so as to improve the drying rate and drying quality of grains, effectively improve the local overheating caused by hot air, and promote the application of infrared radiation heating in grain drying, and solve the problem of the water loss characteristics and mass transfer analysis of the current infrared hot air combined drying of grains. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention.

[0068] In the drawings:

[0069] Figure 1 is the step flow chart of the test method of the present invention;

[0070] Figure 2 is the schematic block diagram of the fuzzy PID temperature control algorithm for hot air drying of the present invention;

[0071] Figure 3 is the schematic surface diagram of the fuzzy rules of the present invention;

[0072] Figure 4 is the schematic diagram of the fuzzy control rules of ΔKp, ΔKi, and ΔKd for hot air drying of the present invention;

[0073] Figure 5 is the structural diagram of the Simulink of the smith fuzzy PID control system for hot air drying of the present invention;

[0074] Figure 6 is the simulation diagram of the Simulink of the smith fuzzy PID control system for hot air drying of the present invention;

[0075] Figure 7 is the structural diagram of the Simulink of the fuzzy PID control system for infrared drying of the present invention;

[0076] Figure 8 is the simulation diagram of the Simulink of the conventional PID control system for infrared drying of the present invention;

[0077] Figure 9 is the schematic diagram of the temperature control principle of the infrared hot air combined drying test device of the present invention;

[0078] Figure 10It is a schematic diagram of the infrared hot air combined drying test device of the present invention;

[0079] Figure 11 It is a schematic diagram of the structure of the material tray of the present invention;

[0080] Reference numerals in the figure: 1, drying housing; 1-2, hot air duct interface; 1-3, exhaust valve; 1-4, material support frame; 141, lead screw; 142, material tray; 143, material tray support; 144, rubber sealing strip; 145, support fixing piece; 146, infrared radiation heating tube; 147, eyebolt; 148, temperature sensor;

[0081] 2, ventilation duct; 21, air distribution duct; 22, heating duct; 23, bend and diameter-changing duct;

[0082] 3, heating device. Specific embodiments

[0083] The following is a description of the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0084] Embodiment: The present invention provides a technical solution, an adaptive temperature control infrared hot air combined drying test method. For different drying periods of paddy rice, different drying methods are adopted through the infrared hot air combined drying test device. Hot air drying is adopted in the initial stage of drying, and infrared drying is adopted in the later stage of drying;

[0085] In the hot air drying stage, a hot air adaptive two-dimensional fuzzy PID algorithm is adopted, the control object is the temperature of the grain layer, the actuator is the electric heating tube, the temperature error and the error change rate are input. Temperature error: actual value - set value, error change rate: error / sampling time, and the three Kp, Ki, and Kd of PID are output;

[0086] In the infrared drying stage, the power of the infrared heating tube is controlled by the fuzzy PID algorithm to control the radiation wavelength of the infrared tube, and the heat of the infrared tube is controlled to dry the rice by adjusting the distance between the infrared tube and the grain layer; Since the infrared wavelength and heat continuously heat the rice, the temperature range of the grain layer is controlled, and the infrared heating tube is controlled by the GPWM method;

[0087] As Figure 1 、 2 shown, in the process of infrared hot air constant temperature control, the following steps are specifically included:

[0088] Step 1: Model the hot air constant temperature control object to obtain the transfer function of the controlled object;

[0089] Step 2: Design a corresponding fuzzy PID intelligent control algorithm for the hot air transfer function;

[0090] Step 3: Conduct simulation analysis on the fuzzy PID intelligent temperature control algorithm under hot air drying, and compare it with the conventional PID algorithm;

[0091] Step 4: Control and model the infrared temperature control to obtain the transfer function of the controlled object;

[0092] Step 5: Determine the corresponding incremental parameters of the fuzzy controller according to the infrared temperature control transfer function;

[0093] Step 6: Conduct simulation analysis on the fuzzy PID intelligent temperature control algorithm under infrared drying, and compare it with the conventional PID algorithm.

[0094] Based on the above technical solution, in Step 1, specifically, it refers to modeling the hot air constant temperature control object to obtain the transfer function of the controlled object. Among them, the hot air drying rice stage in the drying box body is divided into two heat transfer processes, and a mathematical model is established for the temperature change of the grain layer and the heat required for absorption and evaporation;

[0095] Specifically, it includes the following steps:

[0096] Step 1-1: Establish a mathematical model between the resistance temperature change T1 of the electric heating tube and the input voltage U;

[0097] Establish a model according to the thermodynamic equilibrium:

[0098]

[0099] Where: ρ is the density of the heating tube material, with the unit of kg / m3, A is the cross-sectional area of the heating tube, with the unit of m2, c is the specific heat capacity of the heating tube material, with the unit of J / kg·K, T is the average temperature of the heating tube, with the unit of K, t is the time, with the unit of s, k is the thermal conductivity of the heating tube material, with the unit of W / m·K, x is the position along the length direction of the heating tube, with the unit of m, h is the convective heat transfer coefficient, with the unit of W / m2·K, P is the perimeter of the heating tube, with the unit of m, T fluid is the temperature of the surrounding fluid, with the unit of K, and Q is the heat generated by the electric heating tube per unit time, with the unit of W;

[0100]

[0101] Step 1-2: Model the grain layer temperature and heat convection to establish the transfer function model of the temperature control object:

[0102]

[0103] Q 0 =L 0 ρ 0 C 0 T0 ;

[0104] Q 1 = L 1 ρ 1 C 2 T 2 ;

[0105] Q s = kF(T 2 - 1);

[0106] where C 1 is the specific heat capacity of paddy, with the unit of J / K, T 1 is the temperature of paddy in the machine, with the unit of °C, t is the time, with the unit of s, is the integral of paddy temperature with respect to time, Q 0 is the heat of the heat medium entering the dryer, with the unit of J / s, Q 1 is the heat flowing out of the dryer to the outside of the dryer, with the unit of J / s, Qs is the heat loss, with the unit of J / s, L 0 is the air inflow, with the unit of m 3 / s, ρ 0 is the density of the heat medium, with the unit of kg / m 3 C 0 is the specific heat capacity of the heat medium, with the unit of J / (kg·°C), T 0 is the temperature of the heat medium, with the unit of °C, L 1 is the air outflow, with the unit of m 3 / s, ρ 1 is the density of the exhaust gas, with the unit of kg / m 3 C 1 is the specific heat capacity of the exhaust gas, with the unit of J / (kg·°C), T 2 is the temperature outside the dryer, with the unit of °C, k is the heat dissipation coefficient, with the unit of J / (m 2 ·s·°C), F is the heat dissipation area, with the unit of m 2 ;

[0107]

[0108] Model the object of the infrared hot air combined drying system for thin layers of rice, and obtain the transfer function of the temperature control system during hot air drying:

[0109]

[0110] Based on the above technical solution, step 2 includes the following steps:

[0111] Step 2-1: Design a hot air constant temperature fuzzy PID controller and an infrared radiation fuzzy PID controller. Determine the relevant fuzzy rules and establish a fuzzy rule table by combining the control rules of the fuzzy controller with the control method of the classical PID.

[0112] Step 2-2: Use the fuzzy rules for fuzzy inference to obtain the corresponding K p 、K i 、K d , and send them into the PID controller.

[0113] Based on the above technical solution, Step 2-2 specifically includes the following steps:

[0114] Step 2-2-1: Divide the input error e and error change rate ec into 7 fuzzy sets, namely: NB, NM, NS, O, PS, PM, PB;

[0115] Divide the output K p 、K i 、K d into 7 fuzzy sets, namely: NB, NM, NS, O, PS, PM, PB;

[0116] Step 2-2-2: The universes of discourse for each input and output are as follows: the fuzzy universes of discourse for the input error e and error change rate ec are {-3, 3}, the universe of discourse for the output ΔK p is {-3, 3}, the universe of discourse for the output ΔK i is {-3, 3}, the universe of discourse for the output ΔK d is {-3, 3}, and determine the correction coefficient of the input variable: 0.8;

[0117] As Figure 3 shown, Step 2-2-3: After determining the fuzzy sets and universes of discourse of the error e and error change rate ec, assign values to the fuzzy variables with triangular membership functions;

[0118] Step 2-2-4: Based on the influence of the parameters K p 、K i 、K d on the system output characteristics, summarize the self-tuning regulations of different errors e and error change rates ec on K p 、K i 、K d ;

[0119] Specifically: when the deviation between the measured temperature and the target temperature is large, select a larger Kp and smaller Ki, Kd;

[0120] When the temperature of the drying oven is continuously rising and the temperature deviation gradually decreases, select a smaller Kp and appropriate Ki, Kd;

[0121] After the heating curve continuously tends to be flat and the temperature difference gradually decreases, the values of Kp and Kd are relatively large at this time. If |ec| is small, then increase Kd. If |ec| is large, then moderately decrease Kd;

[0122] As Figure 4 shown, step 2-2-5: According to the above, establish the fuzzy control rules for ΔKp, ΔKi, and ΔKd of hot air drying;

[0123] As Figure 5 、 6 shown, step 2-2-6: Build the Simulink structure of the hot air drying smith fuzzy PID and conventional PID control systems according to the working principle.

[0124] Based on the above technical solution, in step 3, specifically, it refers to performing simulation analysis on the fuzzy PID intelligent temperature control algorithm and comparing it with the conventional PID algorithm;

[0125] In step 3, the overshoot of the fuzzy PID control is small, the stabilization time is extremely short, the response smoothness is better, and it has better robustness and adaptability compared to the conventional PID, and it can adjust the control parameters according to the real-time performance of the system;

[0126] Overshoot: The overshoot of the fuzzy PID control is small, and the system will not exceed the set value too much before reaching the steady state, thus reducing the instability of the system;

[0127] Stabilization time: The fuzzy PID control reaches the stable state in a short time, which indicates that the fuzzy PID controller can respond to system changes faster and reduce the time required to reach the steady state;

[0128] Response smoothness: The response of the red curve is smoother without violent fluctuations, which indicates that the fuzzy PID controller is more effective in dealing with system disturbances and can provide a more stable control effect;

[0129] Robustness: The fuzzy PID controller processes uncertainties and non-linearities through fuzzy logic, making the system have better robustness in the face of parameter changes and external disturbances;

[0130] Adaptability: The fuzzy PID controller can adjust the control parameters according to the real-time performance of the system, which makes it more effective in dealing with complex or changing system dynamics.

[0131] Based on the above technical solution, in step 4, model the control of infrared temperature control to obtain the transfer function of the controlled object;

[0132] In step 4, for the heat generated by the infrared heating tube, heat transfer occurs through convection, radiation, and conduction, and the exchange of heat follows the law of conservation of energy:

[0133] Q = Q 1 + Q 2 + Q 3 + Q 4 ;

[0134] Q = KuU(t - τ);

[0135] Q 1 = Cpm d(T 1 - T 0 )dt;

[0136] Q 2 = hA(T air - T 3 );

[0137] Q 3 = KtA(T 1 - T 0 );

[0138]

[0139] Among them, Q is the total energy received during infrared drying, with the unit of J. Q 1 is the energy consumed for the temperature rise during infrared drying, with the unit of J. Q 2 is the heat lost by convection, with the unit of J. Q 3 is the energy consumed during the drying process, with the unit of J. Q 4 is the energy loss caused by thermal radiation, with the unit of J. U is the input action of the infrared heater, Ku is the system input coefficient, m is the mass of the drying oven, τ is the time-delay parameter, Cp is the molar specific heat capacity of the drying oven material, h is the convective heat transfer coefficient, with the unit of W / (m2·K), A is the heat transfer area, with the unit of m2, T air is the temperature of the air, with the unit of K. T 3 is the temperature of the object to be heated, also with the unit of K. Kt is the heat dissipation coefficient of the drying oven, with the unit of W / m2℃, A is the heat dissipation area, with the unit of m2, T 1 、T 0 are the infrared heat source temperature and the initial temperature, with the unit of ℃. σ is the Boltzmann constant, σ = 5.67×10–12, α is the emissivity, and its value ranges from (0, 1):

[0140]

[0141] As Figure 7 shown, based on the above technical solution, in step 5, the corresponding incremental parameters of the fuzzy controller are determined for the infrared temperature control transfer function;

[0142] In step 5, a fuzzy controller for hot air drying is adopted. By changing the corresponding gains, one fuzzy controller can achieve the optimization of two transfer functions. Appropriately select the initial values of KP, KI, and KD according to the PID regulation in Matlab.

[0143] Select KP as 0.05426, KI as 0.002666, KD as 0.08714, and the corresponding gain variables are 0.05, 0.05, and 0.18 respectively.

[0144] Based on the above technical solution, in step 6, a simulation analysis is carried out on the fuzzy PID intelligent temperature control algorithm under infrared drying, and it is compared with the conventional PID algorithm. The main comparisons include overshoot, settling time, flexibility of parameter adjustment, handling of nonlinearity and uncertainty, and smoothness of the transient process.

[0145] As Figure 8 shown, the infrared fuzzy PID control has a very small overshoot, reaches the stable state in less time, has better response smoothness, has better flexibility in parameter adjustment compared with the conventional PID, and can adjust the control parameters according to the real-time performance of the system in handling nonlinearity and uncertainty.

[0146] Overshoot: As can be seen from the figure, the overshoot of the fuzzy PID is smaller than that of the conventional PID, which means that before reaching the stable state, the output value of the fuzzy PID controller exceeds the target value to a lesser extent.

[0147] Settling time: It can be seen from the fact that the red line approaches and remains near the target value faster that the fuzzy PID may reach the stable state in a shorter time.

[0148] Flexibility of parameter adjustment: The fuzzy PID controller can automatically adjust the PID parameters through fuzzy logic, which makes it more flexible in the face of complex or changing system dynamics.

[0149] Handling of nonlinearity and uncertainty: The fuzzy PID controller can better handle the nonlinear characteristics and uncertainties of the system.

[0150] Smoothness of the transient process: The response curve of the fuzzy PID controller may be smoother without obvious oscillations, which helps to reduce the mechanical stress of the system and improve the control quality.

[0151] As Figure 9 、 10 、shown in 11, based on the above technical solution, the infrared hot air combined drying test device includes a drying housing 1. The bottom of the drying housing 1 is connected with a heating device 3. The heating device 3 is composed of three finned dry-burning electric heating tubes, and the air inlet end of the heating device 3 is connected with a ventilation duct 2. The ventilation duct 2 is composed of a uniform air duct 21, a heating duct 22, and a curved variable-diameter duct 23.

[0152] The bottom air inlet end of the drying housing 1 is connected to a curved variable-diameter pipe 23, and one end of the curved variable-diameter pipe 23 is connected to a heating pipe 22. The heating pipe 22 is arranged outside the heating device 3. Three finned dry-burning electric heating tubes are installed inside the heating pipe 22. The end of the heating pipe 22 far from the curved variable-diameter pipe 23 is connected to a uniform air pipe 21. The air inlet end of the uniform air pipe 21 is connected to a centrifugal fan that provides cold air and drives the gas flow.

[0153] The drying housing 1 is composed of a hot air pipe interface 1-2, an exhaust valve 1-3, and a material support frame 1-4.

[0154] The bottom air inlet end of the drying housing 1 is connected to a hot air pipe interface 1-2, and the bottom of the hot air pipe interface 1-2 is connected to the air outlet end of the curved variable-diameter pipe 23. The middle part of the top of the drying housing 1 is connected to an exhaust valve 1-3, and a material support frame 1-4 for hanging test materials is arranged inside the drying housing 1.

[0155] Based on the above technical solution, the material support frame 1-4 includes a lead screw 141, a material tray 142, a material tray support 143, a rubber sealing strip 144, a support fixing piece 145, an infrared radiation heating tube 146, a ring screw 147, and a temperature sensor 148.

[0156] Multiple layers of material tray supports 143 are arranged at the inner top of the drying housing 1. The middle part of the top end of the material tray support 143 at the topmost layer inside the drying housing 1 is installed with a ring screw 147. The bottom of the ring screw 147 is connected to the material tray support 143 through a weighing sensor. The four side corners of the multiple layers of material tray supports 143 are all connected with lead screws 141 through nuts. A material tray 142 is arranged inside the material tray support 143. Infrared radiation heating tubes 146 are arranged in a staggered manner above the material tray 142. A temperature sensor 148 for detecting the grain layer temperature and infrared temperature is installed on the inner top surface of the material tray 142.

[0157] The material tray 142 is placed on the top of the rubber sealing strip 144. A support fixing piece 145 is arranged at the edge of the material tray 142. The rubber sealing strip 144 is placed on the material tray support 143 to form a uniform hot air passage through the thin material layer.

[0158] The working principle and usage process of the present invention: During the actual drying process of the infrared hot air combined drying test device, first, select rice samples with complete shapes, good growth, and no mildew, and measure the initial moisture content of the grains. Place the rice samples in the material trays, close the drying chamber door, set the hot air drying temperature T1, infrared temperature T2, wind speed V1, and initial moisture content W1 in the MCGS configuration and transmit the data to the PLC.

[0159] During the hot air drying process, the PLC activates the centrifugal fan to drive air through the air distribution duct 21 to make the air flow uniform. The uniformly flowing air takes away the heat of the finned dry-burning electric heating tube in the heating device 3, forming hot air that flows through the curved variable-diameter duct 23 to the drying chamber. After the hot air passes through the curve of the curved variable-diameter duct 23, to prevent too large a wind speed difference between the inner and outer layers, the diameter is changed at the bend outlet to effectively improve the uniformity of the hot air duct interface 1-2. The hot air exchanges heat with the surface of the material to cause the moisture of the material to diffuse, and the flowing air takes away the water vapor on the surface of the material;

[0160] When the temperature sensor 148 in the middle of the grain layer detects the temperature signal, it is converted by the PLC into the real-time temperature T3 value. Among them, the temperature signal refers to the average value of 20 signals taken per unit time, and the error is equal to the real-time temperature T3 minus the set temperature T1, and the error change rate is equal to the error divided by the unit time;

[0161] Fuzzy processing is carried out according to the magnitudes of the error and the error change rate, and the corresponding output ΔK p 、ΔK i 、ΔK d The fuzzy values are added to the initial values K p 、K i 、K d Thereby imported into the PID temperature controller to realize the real-time adjustment of the power of the electric heating tube, and then realize the adjustment of the grain layer temperature;

[0162] The weighing system is equipped with a weighing sensor to detect the average signal value of the weight of the material support frame 1-4. The change in the moisture content of the rice is indirectly measured according to the change in the signal value per unit time twice. When the moisture content of the rice reaches 16%, the infrared drying is switched and the hot air drying is stopped;

[0163] During the infrared drying process, the infrared radiation heating tube 146 can directly act on the center of the rice, making the heat transfer and mass transfer directions the same, thereby accelerating the moisture diffusion. The centrifugal fan drives the air flow to take away the water vapor on the surface of the rice, realizing the reduction of the rice drying time. When the weight measured by the weighing sensor reaches the set value, the hot air drying is stopped and the infrared drying is carried out. The temperature sensor 148 on the surface of the grain layer detects the infrared temperature signal, which is fed back to the PLC and subjected to fuzzy processing and output, and then the PID controller is controlled to fluctuate the infrared temperature within a certain range, thereby indirectly controlling the wavelength of the infrared radiation of the infrared heating tube;

[0164] When the weighing sensor detects that the weight reaches the set termination, the infrared radiation heating tube 146 and the centrifugal fan stop working, and the drying is completed.

[0165] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An adaptive temperature-controlled infrared hot air combined drying test method, characterized in that: According to the different drying periods of rice, different drying methods are adopted through the infrared hot air combined drying test device, hot air drying is used in the early stage of drying, and infrared drying is used in the later stage of drying; The hot air adaptive two-dimensional fuzzy PID algorithm is used in the hot air drying stage. The control object is the grain layer temperature. The actuator is the electric heating tube. The temperature error and error change rate are input, and the three Kp, Ki, and Kd of PID are output. In the infrared drying stage, the power of the infrared heating tube is controlled by the fuzzy PID algorithm to control the radiation wavelength of the infrared tube, and the heat of the infrared tube is controlled to dry the rice by adjusting the distance between the infrared tube and the grain layer. Since the infrared wavelength and heat continuously heat the rice, the temperature range of the grain layer is controlled, and the infrared heating tube is controlled by GPWM. The process of infrared hot air constant temperature control specifically includes the following steps: Step 1: Model the hot air constant temperature control object and obtain the transfer function of the controlled object; Step 2: Design the corresponding fuzzy PID intelligent control algorithm according to the hot air transfer function; Step 3: Simulate and analyze the fuzzy PID intelligent temperature control algorithm under hot air drying and compare it with the conventional PID algorithm; Step 4: Control and model the infrared temperature control to obtain the transfer function of the controlled object; Step 5: Determine the corresponding incremental parameters of the corresponding fuzzy controller according to the infrared temperature control transfer function; Step 6: Simulate and analyze the fuzzy PID intelligent temperature control algorithm under infrared drying and compare it with the conventional PID algorithm.

2. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: In the step 1, specifically, a hot air constant temperature control object is modeled to obtain a transfer function of the controlled object, wherein the hot air drying rice stage in the drying box is divided into two heat transfer processes, and a mathematical model is established for the temperature change of the grain layer and the heat required for absorption and evaporation; The specific steps include: Step 1-1: Establish a mathematical model between the resistance temperature change T and the input voltage U of the electric heating tube; The model is built based on thermodynamic equilibrium: Where: ρ is the density of the heating tube material, in kg / m3, A is the cross-sectional area of ​​the heating tube, in m2, c is the specific heat capacity of the heating tube material, in J / kg·K, T is the average temperature of the heating tube, in K, t is the time, in s, k is the thermal conductivity of the heating tube material, in W / m·K, x is the position along the length of the heating tube, in m, h is the convective heat transfer coefficient, in W / m2·K, P is the circumference of the heating tube, in m, T fluid is the temperature of the surrounding fluid, the unit is K, Q is the heat generated by the electric heating tube per unit time, the unit is W; Step 1-2: Model the grain layer temperature and thermal convection, and establish the transfer function model of the temperature control object: Q0=L0ρ0C0T0; Q1=L1ρ1C2T2; Q s =kF(T2-1); Among them, C1 is the heat capacity of rice, the unit is J / K, T1 is the temperature of rice in the machine, the unit is ℃, t is the time, the unit is is the integral of rice temperature over time, Q0 is the heat of heat medium entering the dryer, in J / s, Q1 is the heat flowing from the dryer to the outside of the dryer, in J / s, Qs is the heat loss, in J / s, L0 is the air volume, in m 3 / s, ρ0 is the density of the heat medium, the unit is kg / m 3 , C0 is the specific heat capacity of the heat medium, the unit is J / (kg·℃), T0 is the temperature of the heat medium, the unit is ℃, L1 is the air volume, the unit is m 3 / s, ρ1 is the exhaust gas density, the unit is kg / m 3 , C1 is the exhaust gas specific heat capacity, the unit is J / (kg·℃), T2 is the dryer external temperature, the unit is ℃, k is the heat dissipation coefficient, the unit is J / (m 2 ·s·℃), F is the heat dissipation area, the unit is m 2 ; The infrared hot air combined drying system object rice thin layer was modeled, and the temperature control system transfer function during hot air drying was obtained:

3. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: The step 2 includes the following steps: Step 2-1: Design hot air constant temperature fuzzy PID controller and infrared radiation fuzzy PID controller, determine the relevant fuzzy rules and establish a fuzzy rule table by comparing the control law of the fuzzy controller with the control method of the classic PID; Step 2-2: Use fuzzy rules to perform fuzzy reasoning and obtain the corresponding PID K p , K i , K d , and sent to the PID controller.

4. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 3, characterized in that: The step 2-2 specifically includes the following steps: Step 2-2-1: Divide the input error e and the error change rate ec into 7 fuzzy sets, namely: NB, NM, NS, O, PS, PM, PB; K for output p , K i , K d It is divided into 7 fuzzy sets, namely: NB, NM, NS, O, PS, PM, PB; Step 2-2-2: The domain of each input and output is: the fuzzy domain of the input error e and the error change rate ec is {-3, 3}, and the domain of the output ΔK p The domain is {-3, 3}, and the output ΔK i The domain is {-3, 3}, and the output ΔK d The domain is {-3, 3}, and the correction coefficient of the input variable is determined as 0.8; Step 2-2-3: After determining the fuzzy sets and domains of error e and error change rate ec, assign values ​​to fuzzy variables using triangular membership functions; Step 2-2-4: By parameter K p , K i , K d The influence of different errors e and error change rate ec on the system output characteristics is summarized. p , K i , K d Self-tuning adjustment; Step 2-2-5: Based on the above, establish the fuzzy control rules of hot air drying ΔKp, ΔKi, and ΔKd; Step 2-2-6: Build the Simulink structure of hot air drying Smith fuzzy PID and conventional PID control system according to the working principle.

5. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: In the step 3, specifically refers to the simulation analysis of the fuzzy PID intelligent temperature control algorithm and the comparison with the conventional PID algorithm; In step 3, the fuzzy PID control has smaller overshoot, less stabilization time, better response smoothness, better robustness and adaptability than conventional PID, and can adjust control parameters according to the real-time performance of the system.

6. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: In the step 4, the infrared temperature control is modeled to obtain a transfer function of the controlled object; In step 4, the heat generated by the infrared heating tube is transferred through convection, radiation and conduction, and the exchange of heat follows the law of conservation of energy: Q = Q1 + Q2 + Q3 + Q4; Q = KuU(t-τ); Q1 = Cpm d(T1-T0)dt; <h2 style=";text-align:left;direction:ltr">Q2 = h A(T)<h2 style=";text-align:left;direction:ltr"> air <h2 style=";text-align:left;direction:ltr"> -T3); Q3 = KtA (T1-T0); Among them, Q is the total energy received during infrared drying, the unit is J, Q1 is the energy consumed by temperature rise during infrared drying, the unit is J, Q2 is the heat lost by convection, the unit is J, Q3 is the energy consumed in the drying process, the unit is J, Q4 is the energy loss caused by thermal radiation, the unit is J, U is the input effect of the infrared heater, Ku is the system input coefficient, m is the mass of the drying box, τ is the time lag parameter, Cp is the molar specific heat capacity of the drying box material, h is the convective heat transfer coefficient, the unit is W / (m2·K), A is the heat exchange area, the unit is m2, T air is the air temperature in K, T3 is the temperature of the heated object in K, Kt is the heat dissipation coefficient of the drying box in W / m2℃, A is the heat dissipation area in m2, T1 and T0 are the infrared heat source temperature and initial temperature in ℃, σ is the Boltzmann constant, σ=5.67×10–12, α is the emissivity, which takes values ​​between (0, 1):

7. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: In step 5, the corresponding incremental parameters of the corresponding fuzzy controller are determined according to the infrared temperature control transfer function; In step 5, a fuzzy controller for hot air drying is used to achieve two optimizations of two transfer functions by changing the corresponding gains, and the initial values ​​of KP, KI, and KD are appropriately selected according to the PID adjustment of Matlab; Select KP as 0.05426, KI as 0.002666, and KD as 0.08714, and the corresponding gain variables are 0.05, 0.05, and 0.18 respectively.

8. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: In step 6, the fuzzy PID intelligent temperature control algorithm under infrared drying is simulated and analyzed, and compared with the conventional PID algorithm, mainly comparing the overshoot, stabilization time, flexibility of parameter adjustment, nonlinear and uncertainty processing, and smoothness of the transition process.

9. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 1, characterized in that: The infrared hot air combined drying test device comprises a drying shell (1), the bottom of the drying shell (1) is connected to a heating device (3), the heating device (3) is composed of three wing-shaped dry-burning electric heating tubes, and the air inlet end of the heating device (3) is connected to a ventilation duct (2), the ventilation duct (2) is composed of a uniform air duct (21), a heating duct (22), and a curved variable diameter duct (23); The bottom air inlet end of the drying shell (1) is connected to a curved reducing pipe (23), and one end of the curved reducing pipe (23) is connected to a heating pipe (22); the heating pipe (22) is arranged outside the heating device (3); three wing-shaped dry-burning electric heating pipes are installed inside the heating pipe (22); one end of the heating pipe (22) away from the curved reducing pipe (23) is connected to a uniform air pipe (21); and the air inlet end of the uniform air pipe (21) is connected to a centrifugal fan that provides cold air and drives the gas flow; The drying shell (1) comprises a hot air duct interface (1-2), an exhaust valve (1-3) and a material support frame (1-4); The bottom air inlet end of the drying shell (1) is connected to a hot air duct interface (1-2), and the bottom of the hot air duct interface (1-2) is connected to the air outlet end of the curved reducing duct (23); the top middle end of the drying shell (1) is connected to an exhaust valve (1-3), and a material support frame (1-4) for hanging test materials is provided on the inner side of the drying shell (1).

10. The adaptive temperature-controlled infrared hot air combined drying test method according to claim 9, characterized in that: The material support frame (1-4) comprises a screw rod (141), a material tray (142), a material tray bracket (143), a rubber sealing strip (144), a bracket fixing plate (145), an infrared radiation heating tube (146), a lifting eye screw (147) and a temperature sensor (148); A multi-layer material tray support (143) is arranged on the top of the inner side of the drying shell (1), a lifting eye screw (147) is installed at the middle of the top of the material tray support (143) on the inner side of the drying shell (1), the bottom of the lifting eye screw (147) is connected to the material tray support (143) through a weighing sensor, and screw rods (141) are connected to the four side corners of the multi-layer material tray support (143) through nuts, a material tray (142) is arranged on the inner side of the material tray support (143), and infrared radiation heating tubes (146) are arranged above the material tray (142) in a staggered manner, and a temperature sensor (148) for detecting grain layer temperature and infrared temperature is installed on the inner top surface of the material tray (142); The material tray (142) is placed on top of the rubber sealing strip (144), a bracket fixing piece (145) is provided on the edge of the material tray (142), and the rubber sealing strip (144) is placed on the material tray bracket (143), so that hot air evenly passes through a thin layer of the material.

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