Dynamic identification control method of closed heat pump drying system based on waste heat recovery

Abstract

The invention discloses a dynamic recognition control method of a closed heat pump drying system based on waste heat recovery. The method comprises the following steps that a dynamic model of a drying chamber is built based on a heat and humidity transfer and heat transfer mechanism; constructing a dynamic model of each auxiliary machine part of the closed heat pump drying system based on waste heat recovery, and simulating a heat pump circulation and drying process; performing double-input double-output transfer function model identification on the closed heat pump drying system mechanism model based on waste heat recovery; and performing model prediction control based on the identified transfer function model. Dynamic modeling and simulation analysis of the closed heat pump drying system based on waste heat recovery are achieved, and system control for dealing with unstable environment working conditions and drying chamber outlet temperature and humidity input is achieved. According to the method, the feed-forward regulation capability of the system is improved, and the system can realize more reasonable coordination between control targets, so that mutual interference between inputs is effectively relieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of heat pump drying and waste heat recovery, and in particular relates to a dynamic identification and control method of a closed heat pump drying system based on waste heat recovery. Background Art

[0002] Heat pump drying technology utilizes the heat pump principle to convert heat from the air into thermal energy for drying. Its primary components include an evaporator, compressor, condenser, and expansion valve, achieving efficient energy utilization through circulating compressed refrigerant. Compared to traditional resistance heating or gas heating, heat pump drying significantly reduces energy consumption and reliance on fossil fuels, helping to reduce CO2 emissions and environmental pollution. This technology has been widely used in industries such as food processing, textile production, and wood processing, and its application is expected to expand further in the future as technology advances and market demand grows.

[0003] Currently, heat pump drying technology primarily encompasses three types based on the circulation method: open, semi-open, and closed. Open heat pump drying systems are the most mature, but they suffer from waste heat from the exhaust air at the drying chamber outlet, a problem that becomes increasingly severe in the later stages of drying. Currently, semi-closed and closed circulation systems are often used to recover the waste heat from the warm and humid air at the drying chamber outlet. These systems utilize an evaporator or a regenerator to recover the waste heat, preheating the fresh air, increasing the temperature of the fresh air at the condenser inlet, reducing the heat required to reach the rated drying temperature, and lowering compressor power consumption. However, while these systems can further improve drying energy efficiency, they also increase the system's heat-humidity coupling characteristics. Furthermore, the present invention, while utilizing a regenerator to recover some of the waste heat from the warm and humid air at the drying chamber outlet, directly feeds the majority of the air at the drying chamber outlet into the condenser. This effectively recycles the humid air at the drying chamber outlet, but this further increases the system's coupling characteristics. Furthermore, the temperature and humidity at the drying chamber outlet are constantly changing, leaving the system in an unstable open-loop state. Furthermore, due to the characteristics of the air source, the system is also affected by ambient temperature and humidity. Therefore, heat pump drying systems that utilize two-stage waste heat recovery are subject to constant variable operating conditions, fluctuating environmental conditions, and severe thermal and moisture coupling throughout the system. Therefore, achieving precise temperature control is crucial for ensuring drying quality. Traditional PID control suffers from poor control accuracy when dealing with such a tightly coupled system with frequently variable operating conditions. Summary of the Invention

[0004] Purpose of the invention: In order to solve the control design problem when facing a fluctuating environment and drying chamber outlet temperature and humidity input, the present invention provides a dynamic identification control method for a closed heat pump drying system based on waste heat recovery, which can realize dynamic modeling and simulation analysis of a closed heat pump drying system based on waste heat recovery, and realize system control when dealing with non-steady-state environmental conditions and drying chamber outlet temperature and humidity input.

[0005] Technical solution: The present invention provides a dynamic identification and control method for a closed heat pump drying system based on waste heat recovery, comprising the following steps:

[0006] Step 1: Construct a dynamic model of the drying chamber based on heat and moisture transfer and heat transfer mechanisms;

[0007] Step 2: Based on the constructed dynamic model of the drying chamber, continue to build dynamic models of the auxiliary components of the closed heat pump drying system based on waste heat recovery and simulate the heat pump cycle and drying process;

[0008] Step 3: Identify the dual-input and dual-output transfer function model of the closed heat pump drying system mechanism model based on waste heat recovery;

[0009] Step 4: Perform model predictive control based on the identified transfer function model.

[0010] Furthermore, step 1 specifically includes the following steps:

[0011] Step 1.1: Based on the heat and moisture transfer law, a dynamic response model of heat and moisture transfer in the drying chamber is constructed;

[0012] Step 1.2: Based on the drying chamber structure, construct a one-dimensional heat transfer dynamic response model of the drying chamber.

[0013] Furthermore, step 1.1 is specifically as follows:

[0014] Calculate the drying rate of grain:

[0015] The drying rate is related to the moisture content of the grain and is calculated using the empirical equation:

[0016]

[0017] Where M is the moisture content of grain, M e It indicates the minimum moisture content of wet materials that can be achieved by the hot dry air at the inlet of the drying room, and K is the drying rate;

[0018] Calculate the humidity content of the wet air at the inlet and outlet of the drying chamber:

[0019] The humidity content of the wet air at the inlet and outlet of the drying chamber is expressed as:

[0020]

[0021] In the formula, d is the humidity content of moist air, x is the mass fraction of water vapor, q ma is the air flow rate, A is the contact area, ρ is the grain density, ΔL is the micro-thickness, subscripts in, out, a, g represent the inlet and outlet of the drying chamber, air and grain respectively, and t represents the drying time;

[0022] Calculate dehumidification capacity;

[0023] The dehumidification between grain and hot dry air is obtained from the drying rate equation and the moisture content and flow rate of the wet air at the inlet and outlet of the drying chamber, namely:

[0024]

[0025] Among them, M0 represents the initial moisture content of the grain before drying.

[0026] Furthermore, step 1.2 is specifically as follows: considering the convective heat transfer between grains and hot dry air and the evaporation of water into water vapor, the dynamic response model of heat transfer in the drying chamber is expressed as:

[0027]

[0028] Where T is temperature, c is constant pressure heat capacity, L a is the latent heat of vaporization, V is the volume of the substance, h is the convective heat transfer coefficient, and the subscripts cv, w, and l represent convective heat transfer, water vapor, and water, respectively.

[0029] Furthermore, step 2 specifically includes the following steps:

[0030] Step 2.1: Build dynamic models of the four core components of the heat pump:

[0031] Based on the first law of thermodynamics, mass conservation, and heat exchange mechanisms, dynamic models of the compressor, condenser, expansion valve, and evaporator are constructed. These models consider refrigerant flow, phase change heat transfer, and energy transfer processes, and incorporate simulation modeling based on changes in working fluid physical parameters to form a physical model of the closed-loop heat pump cycle.

[0032] Step 2.2: Build a dynamic model of the circulation fan and auxiliary components:

[0033] For auxiliary equipment such as fans and condensate drains, a mathematical model is established to describe their dynamic response characteristics. The model is based on the characteristics of system inertia and response delay and is described using first-order or second-order linear differential equations.

[0034] Step 2.3: Module integration and thermal and moisture interface coupling:

[0035] Based on the first law of thermodynamics, mass conservation, and heat exchange mechanisms, dynamic models of the compressor, condenser, expansion valve, and evaporator are constructed. These models consider refrigerant flow, phase change heat transfer, and energy transfer processes, and incorporate simulation modeling based on changes in working fluid physical parameters to form a physical model of the closed-loop heat pump cycle.

[0036] Step 2.4: Perform system-level dynamic simulation and verification:

[0037] The dynamic model of the drying chamber in step 1, the four major components of the heat pump model, and the auxiliary equipment model are integrated based on a unified hot and humid air fluid interface to construct a complete dynamic model of the closed heat pump drying system. The model adopts a modular modeling method to support the dynamic interaction of heat, moisture, and fluid among various components.

[0038] Furthermore, in step 2, the auxiliary components of the closed heat pump drying system include: a drying chamber and auxiliary equipment; the auxiliary equipment includes a condenser, an expansion valve, an evaporator, a compressor, a circulating fan, a regenerator, a dehumidification fan and an evaporator fan; wherein, the condenser, the expansion valve, the evaporator and the compressor constitute a heat pump cycle, and the dehumidification fan draws part of the wet air from the outlet of the direct drying chamber into the regenerator for preheating the supplementary fresh air, and then discharges it; the preheated fresh air is then mixed with the remaining wet air from the outlet of the direct drying chamber; driven by the circulating fan, the mixed air enters the condenser, absorbs heat and is heated to the target drying temperature; then, the heated wet air re-enters the direct dryer to continue drying the wet material.

[0039] Furthermore, step 3 is specifically as follows: adopting a data preprocessing method to remove time characteristics to reduce the impact of time characteristics on the input and output characteristics of the system. The removed time characteristic trends are as follows:

[0040]

[0041] In the identification design, two input variables are considered: compressor speed and dehumidification fan air volume. Three discrete state-space models of order, 6th, 6th, and 5th, are used to simulate the input-output characteristics of the system in the early, middle, and late stages of drying, respectively. The discrete state-space models for the three periods are shown below:

[0042]

[0043]

[0044] Where: u1 is the compressor speed, u2 is the air volume of the dehumidification fan, y1 is the drying temperature T d , y2 is the drying chamber outlet humidity RH out .

[0045] Furthermore, step 4 is specifically as follows: performing model predictive control based on the identification model; the model predictive control method is based on the multi-time scale analysis of the closed heat pump drying system based on waste heat recovery, taking into account the characteristics of rapid flow response and the combined impact of refrigerant and air flow on drying temperature. Through control design, the drying temperature and drying chamber outlet humidity are dynamically adjusted to reduce overshoot;

[0046] In the control design, for each working condition, set the constraint parameters, including: sampling period τ, modeling time domain N, prediction time domain P, control time domain M, the step response sequence of the output to the control quantity and disturbance quantity in the modeling time domain; set the constraint parameters including: control increment constraint Δu max , Δu min , control quantity constraint u max 、u min , output constraint y max 、y min ; where Δu max is the maximum value of the control increment, Δu min is the minimum value of the control increment, u max is the maximum value of the control quantity, u min is the minimum value of the control quantity, y max is the maximum output value, y min The minimum value of the output.

[0047] The present invention further discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0048] The present invention further discloses a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the method of the present invention when the computer program / instruction is executed by a processor.

[0049] The present invention further discloses a computer program product, comprising a computer program / instruction, which implements the steps of the method of the present invention when executed by a processor.

[0050] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0051] The present invention establishes a dynamic model of a closed heat pump drying system based on waste heat recovery, and performs data preprocessing, model identification and control design, thereby realizing efficient control of a closed heat pump drying system based on waste heat recovery that is in a variable operating condition process and strong thermal and moisture coupling for a long time, thereby improving the control stability and ensuring the drying quality.

[0052] The present invention not only improves the system's feedforward regulation capability, but also enables the system to achieve more reasonable coordination between control targets, thereby effectively alleviating the mutual interference between inputs. In the control results of this study, MPC can effectively achieve the decoupling of y2, significantly reducing the maximum overshoot of y2 when the set value of y1 changes stepwise, and shortening the response time to reach steady state. Taking the early stage of drying as an example, RH out The maximum overshoot is reduced from 1.78% to 0.48%, a decrease of 73.03%, and the maximum response time is reduced from 122.9s to 66.5s, a decrease of 45.89%. d and RH out When the set value of T changes stepwise, the maximum overshoot under MPC control is significantly lower than that under PI control, and the response time is also reduced, which shows that its control performance is significantly better than PI control and can achieve T more accurately and quickly. d and RH out tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the device structure of the present invention;

[0054] Figure 2 It is a schematic diagram of the one-dimensional dynamic modeling idea of ​​the drying chamber;

[0055] Figure 3 It is a Simulink-Dymola co-simulation model of the device of the present invention;

[0056] Figure 4 It is the trend curve of the compressor speed and the air volume of the dehumidification fan after removing the time characteristics;

[0057] Figure 5 It is an identification effect diagram;

[0058] Figure 6 This is a design diagram of the control system of the present invention (dashed line represents energy flow; solid line represents material flow);

[0059] Figure 7 is the response curve of drying temperature and drying chamber outlet humidity;

[0060] Among them, 1 is the condenser, 2 is the expansion valve, 3 is the evaporator, 4 is the compressor, 5 is the circulation fan, 6 is the regenerator, 7 is the drying chamber, 8 is the dehumidification fan, and 9 is the evaporator fan. DETAILED DESCRIPTION

[0061] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0062] like Figure 1As shown, the present invention is a closed heat pump drying device based on waste heat recovery that is in variable working conditions for a long time, including a drying chamber 7 and auxiliary equipment; the auxiliary equipment includes a condenser 1, an expansion valve 2, an evaporator 3, a compressor 4, a circulating fan 5, a regenerator 6, a dehumidification fan 8 and an evaporator fan 9. Among them, the condenser 1, the expansion valve 2, the evaporator 3 and the compressor 4 form a heat pump cycle, and the dehumidification fan 8 draws a small part of the wet air from the outlet of the direct drying chamber 7 into the regenerator 6 for preheating the supplementary fresh air, and then discharges it. The preheated fresh air is then mixed with the remaining wet air from the outlet of the direct drying chamber 7. Driven by the circulating fan 5, the mixed air enters the condenser 1, absorbs heat and is heated to the target drying temperature. Then, the heated wet air re-enters the direct dryer 7 to continue drying the wet material.

[0063] The structure of the drying chamber 7 is a fixed bed, and heat and moisture are transferred between the grain and the hot dry air through convection heat transfer. In this embodiment, the drying chamber adopts the method of dry microelement iteration in the dynamic modeling process, and its method is as follows Figure 2 As shown, the length and width of each drying chamber element are 0.38m, the thickness is 0.02m, and the number of elements is 100.

[0064] The specific steps of the dynamic modeling method of a closed heat pump drying system based on waste heat recovery in a long-term variable operating condition of the present invention are as follows:

[0065] S1: A one-dimensional dynamic model of a drying chamber with a fixed bed structure is constructed based on the heat and moisture transfer laws and heat transfer mechanisms. The internal state variables of the drying chamber dynamic model are constructed using dynamic differential equations, considering the multi-time scale characteristics of the drying chamber variable response: gas flow (seconds) and heat transfer (minutes).

[0066] S2: Based on the dynamic model of the drying chamber constructed in S1, a dynamic model of the auxiliary components of a closed heat pump drying system based on waste heat recovery was constructed, and the heat pump cycle and drying process were simulated. The drying process design adopted a closed cycle and waste heat recovery method to reduce the heat exchange temperature difference while ensuring the drying temperature requirement, thereby improving the drying energy efficiency of the system.

[0067] The closed-loop and waste heat recovery method involves dehumidification fan 8 drawing a portion of the moist air from the drying chamber 7 outlet into regenerator 6, where it is used to preheat the fresh air. The preheated fresh air then mixes with the majority of the moist air from the drying chamber 7 outlet before entering condenser 1, where it absorbs heat and is heated to the target drying temperature.

[0068] S3: Identify the dual-input and dual-output transfer function model of the closed heat pump drying system based on waste heat recovery;

[0069] S4: Model predictive control based on the identification model; the model predictive control method is based on the multi-time scale analysis of the closed heat pump drying system based on waste heat recovery. It takes into account the rapid flow response characteristics and the combined impact of refrigerant and air flow on the drying temperature. Through control design, the drying temperature and drying chamber outlet humidity are dynamically adjusted to reduce overshoot and ensure drying quality.

[0070] The construction of the one-dimensional dynamic model of the drying chamber in S1 specifically includes the following steps:

[0071] S11: Based on the heat and moisture transfer law, a dynamic response model of heat and moisture transfer in the drying chamber is constructed;

[0072] S12: Based on the drying chamber structure, a one-dimensional heat transfer dynamic response model of the drying chamber is constructed.

[0073] In step S11, the following steps are specifically included to calculate the dehumidification capacity of the drying chamber:

[0074] S111: Calculate the drying rate of grain:

[0075] The drying rate is related to the moisture content of the grain and can be calculated using the empirical equation:

[0076]

[0077] Where M is the moisture content of grains and K is the drying rate;

[0078] S112: Calculate the moisture content of the wet air at the inlet and outlet of the drying chamber:

[0079] The moisture content of the wet air at the inlet and outlet of the drying chamber is expressed as:

[0080]

[0081] In the formula, d is the humidity content of moist air, x is the mass fraction of water vapor, q ma is the air flow rate, A is the contact area, ρ is the grain density, ΔL is the micro-thickness, and the subscripts in, out, a, and g represent the inlet and outlet of the drying chamber, air, and grain, respectively.

[0082] S113: Calculate dehumidification capacity;

[0083] The dehumidification amount between grain and hot dry air can be calculated from the drying rate equation and the moisture content and flow rate of the wet air at the inlet and outlet of the drying chamber, that is:

[0084]

[0085] In S12, considering the convective heat transfer between grains and hot dry air and the evaporation of water into water vapor, the dynamic response model of heat transfer in the drying chamber is expressed as:

[0086]

[0087] Where T is temperature, c is constant pressure heat capacity, L a is the latent heat of vaporization, V is the volume of the substance, h is the convective heat transfer coefficient, and the subscripts w and l represent water vapor and water, respectively.

[0088] In S2, based on the constructed dynamic model of the drying chamber, the dynamic models of the auxiliary components of the closed heat pump drying system based on waste heat recovery are further constructed and the heat pump cycle and drying process are simulated, which specifically includes the following steps:

[0089] S21: Build dynamic models of the four core components of a heat pump

[0090] Based on the first law of thermodynamics, the mass conservation equation, and the heat exchange mechanism, dynamic models of the compressor, condenser, expansion valve, and evaporator are established respectively. The compressor part considers the impact of changes in the working fluid enthalpy on the system energy input. The condenser and evaporator use the ε-NTU method or partition modeling method to simulate the two-phase flow heat exchange process and phase change characteristics; the expansion valve modeling reflects the adiabatic pressure reduction behavior during the throttling process. In addition, the temperature / pressure correlation of the physical parameters is corrected based on the actual refrigerants used (such as R134a, R410A, etc.) to ensure that the model responds well to changes in operating conditions. Through these component models, a closed-loop heat pump cycle thermal system can be formed, providing a dynamic basis for subsequent system-level coupled modeling.

[0091] S22: Build a dynamic model of the circulation fan and auxiliary components

[0092] Auxiliary equipment such as circulating fans, condensate drainers, and heat exchanger circulating pumps serve as supporting units for the operation of the system. Their dynamic behavior has a direct impact on the airflow organization and humidity control of the drying process. The fan model usually adopts the form of a first-order or second-order transfer function to describe its response speed, steady-state gain, and hysteresis characteristics to the control signal. For the condensate discharge system, a simple switching logic or a liquid reservoir dynamic model can be used to reflect its periodic drainage characteristics. If there is a heat recovery heat exchanger, a heat capacity model can be introduced to consider the heat recovery delay and heat transfer efficiency. Although this part of the modeling is relatively simplified, it cannot be ignored in the overall dynamic coupling of the system, especially for buffering or strengthening behaviors such as system start-stop fluctuations and changes in heat transfer efficiency.

[0093] S23: Module integration and thermal and moisture interface coupling:

[0094] After completing the dynamic modeling of the drying chamber (Step 1), the heat pump, and its auxiliary components, a modular modeling approach was adopted, achieving thermal and mass coupling between modules through a unified hot and humid air interface. Interface variables primarily include dry-bulb temperature, wet-bulb temperature, absolute humidity, air volume, and heat flux. By setting mass flow conservation equations and energy balance equations, the dynamic responses of temperature and humidity exchange between different subsystems are synchronized, achieving a unified solution for the entire system. Furthermore, intermediate logic judgment and boundary condition processing modules were introduced during the module integration process to enhance the model's adaptability to operating conditions and operational stability.

[0095] S24: Perform system-level dynamic simulation and verification:

[0096] The drying chamber model (step 1), the four major components model of the heat pump, and the auxiliary equipment model are integrated based on a unified hot and humid air fluid interface to form a complete closed heat pump drying system, and then a dynamic simulation test is carried out. The test content includes setting typical operating conditions such as drying load conditions (such as initial moisture content, target moisture content) and environmental disturbances (outdoor temperature and humidity changes). The simulation output includes key performance indicators such as changes in drying room temperature and humidity over time, changes in compressor load, and changes in system energy efficiency (COP). By comparing the simulation results with typical engineering data (such as prior experience and field data), the dynamic accuracy and responsiveness of the model can be verified. This model will serve as an important basis for subsequent system identification and predictive control strategy design.

[0097] Given that the dynamic modeling methods for the core components and auxiliary equipment of heat pump systems are relatively mature and standardized in this field, in order to highlight the innovativeness of the drying chamber modeling in this invention and the key steps of system integration, this article only briefly describes this part and does not elaborate on the specific modeling formulas.

[0098] The closed heat pump drying system based on waste heat recovery is composed of the mass equation and energy equation of each device and has complex dynamic characteristics. The model established using differential equations has a large computational complexity. Therefore, the system is identified to obtain a state space model of the input and output data to simplify the calculations in the control design.

[0099] During the S3 model identification process, the system exhibits nonlinear time-varying characteristics throughout the drying process, which can seriously affect the system's identification results. Therefore, to improve data quality and identification feasibility, a data preprocessing method to remove time characteristics is proposed to improve identification accuracy and reduce the impact of time characteristics on the system's input and output characteristics. The removed time characteristics have the following trends:

[0100]

[0101] In the identification design of step S3, two input variables are considered: compressor speed and dehumidification fan air volume. Three discrete state-space models of order (6th, 6th, and 5th) are used to simulate the input-output characteristics of the system in the early, middle, and late stages of drying, respectively. The discrete state-space models for the three periods are shown below:

[0102]

[0103]

[0104] Where: u1 is the compressor speed, u2 is the air volume of the dehumidification fan, y1 is the drying temperature (T d ), y2 is the drying chamber outlet humidity (RH out ).

[0105] With the compressor speed and dehumidification fan air volume as the control input, and the drying temperature and drying chamber outlet humidity as the controlled variables, a closed-loop step response test is conducted to obtain the identification model of the controlled object. In the control design, for each working condition, constraint parameters are set, including: sampling period τ, modeling time domain N, prediction time domain P, control time domain M, and the step response sequence of the output to the control variable and disturbance variable in the modeling time domain; the constraint parameters set include: control increment constraint Δu max , Δu min , control quantity constraint u max 、u min , output constraint y max 、y min ; where Δu max is the maximum value of the control increment, Δu min is the minimum value of the control increment, u max is the maximum value of the control quantity, u min is the minimum value of the control quantity, y max is the maximum output value, y min The minimum value of the output.

[0106] Figure 4 Figure 3 shows a comparison of the original data and preprocessed data after removing the time characteristic. Since the closed heat pump drying system based on waste heat recovery exhibits open-loop instability, closed-loop identification is employed to improve identification accuracy. Considering the nonlinear time-varying characteristics of the entire closed heat pump drying system based on waste heat recovery, which can affect identification, the control inputs u1 and u2 are preprocessed to remove the time-dependent function f(t) to eliminate the influence of time characteristics on the system's dynamic characteristics.

[0107] Figure 5As a comparison chart of the identification effects, after removing the time characteristics of u1 and u2, the identification accuracies of y1 and y2 in the early, middle and late stages of drying are 85.46% and 84.53%, 94.38% and 85.31%, 96.44% and 86.79% respectively. The average identification accuracies throughout the entire drying process are 92.00% and 85.47% respectively. This shows that the identification results are reliable and can be used as the state space model for MPC design.

[0108] Figure 6 A dual-input, dual-output MPC controller model was constructed, and a controller was designed for a closed heat pump drying system with waste heat recovery. The controller uses the compressor speed and dehumidification fan air volume as manipulated variables, and the drying temperature and the humidity at the drying chamber outlet as controlled variables. The prediction and control ranges are set to 25 seconds and 10 seconds, respectively. This range optimizes the calculation time while ensuring a small prediction error and convergence of the controlled object. The purpose of the controller design is to ensure the stability and fast tracking of the drying temperature and the humidity at the drying chamber outlet. The controlled variables of the control design are measured by temperature sensors and humidity sensors, which are used to record the drying temperature and the humidity at the drying chamber outlet.

[0109] Figure 7 The figure shows the control performance comparison of MPC and PI (proportional-integral) in different drying periods. It can be found that when PI is used, since it can only be adjusted based on the current error and lacks the ability to predict the future dynamics of the system, there are obvious defects in the control of y2, which has a strong coupling: in the early, middle and late stages of drying, when the set value of u1 changes in a step, y2 under PI control shows a large overshoot in the response process, with the maximum value reaching 1.78%, accompanied by obvious oscillation. The system needs to experience two to three fluctuations before it can stabilize to the set value. There is a problem of long response time, which affects the stability and energy efficiency of the drying process. Since the system itself is an open-loop unstable system, real-time intervention of PI control is required to track the rated y1 and y2. In order to ensure real-time tracking effect, a smaller time constant is required to improve the response speed. However, when the set values ​​of y1 and y2 change in a step, a smaller time constant will also lead to significant overshoot and a longer response time. T under PI control d and RH outThe maximum overshoots all occur in the early stage of drying, which are 1.87℃ and 2.39% respectively. The longest response times also occur in the early stage of drying, which are 82.2s and 122.9s respectively. In contrast, the MPC controller can predict the system behavior for multiple time steps in the future based on the system state space model, and optimize the control strategy based on the coupling relationship and constraints between the control inputs. This method not only improves the feedforward regulation capability of the system, but also enables the system to achieve more reasonable coordination between control objectives, thereby effectively alleviating the mutual interference between inputs. In the control results of this study, MPC can effectively realize the decoupling of y2, significantly reduce the maximum overshoot of y2 when the set value of y1 changes in step, and shorten the response time to reach steady state. Taking the early stage of drying as an example, RH out The maximum overshoot is reduced from 1.78% to 0.48%, a decrease of 73.03%, and the maximum response time is reduced from 122.9s to 66.5s, a decrease of 45.89%. d and RH out When the set value of T changes stepwise, the maximum overshoot under MPC control is significantly lower than that under PI control, and the response time is also reduced, which shows that its control performance is significantly better than PI control and can achieve T more accurately and quickly. d and RH out tracking.

[0110] The present invention adopts dynamic modeling of a closed heat pump drying system based on waste heat recovery, as well as system identification and MPC control design. When faced with fluctuating environments and drying chamber outlet temperature and humidity inputs, the present invention can quantitatively supply the drying temperature and drying chamber outlet humidity required by the drying process to the drying system by adjusting the compressor speed and dehumidification fan air volume, thereby achieving precise control of the drying temperature and drying chamber outlet humidity, which is beneficial to the stability of the drying process and improves the drying quality.

[0111] The technical means disclosed in the solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic identification and control method for a closed heat pump drying system based on waste heat recovery, characterized in that: The steps include: Step 1: Construct a dynamic model of the drying chamber based on heat and moisture transfer and heat transfer mechanism; Step 2: Based on the dynamic model of the drying chamber, a dynamic model of the auxiliary components of the closed heat pump drying system based on waste heat recovery is constructed, and the heat pump cycle and drying process are simulated; Step 3: Identify the dual-input and dual-output transfer function model of the closed heat pump drying system mechanism model based on waste heat recovery; Step 4: Perform model predictive control based on the identified transfer function model.

2. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 1 is characterized in that: Step 1 specifically includes the following steps: Step 1.1: Based on the heat and moisture transfer law, a dynamic response model of heat and moisture transfer in the drying chamber is constructed; Step 1.2: Based on the drying chamber structure, construct a one-dimensional heat transfer dynamic response model of the drying chamber.

3. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 2 is characterized in that: Step 1.1 is as follows: Calculate the drying rate of grain: The drying rate is related to the moisture content of the grain and is calculated using the empirical equation: Where M is the moisture content of grain, M e It indicates the minimum moisture content of wet materials that can be achieved by the hot dry air at the inlet of the drying room, and K is the drying rate; Calculate the humidity content of the wet air at the inlet and outlet of the drying chamber: The humidity content of the wet air at the inlet and outlet of the drying chamber is expressed as: In the formula, d is the humidity content of moist air, x is the mass fraction of water vapor, q ma is the air flow rate, A is the contact area, ρ is the grain density, ΔL is the micro-thickness, subscripts in, out, a, g represent the inlet and outlet of the drying chamber, air and grain respectively, and t represents the drying time; Calculate dehumidification capacity; The dehumidification between grain and hot dry air is obtained from the drying rate equation and the moisture content and flow rate of the wet air at the inlet and outlet of the drying chamber, namely: Among them, M0 represents the initial moisture content of the grain before drying.

4. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 3 is characterized in that: Step 1.2 is as follows: Considering the convective heat transfer between grains and hot dry air and the evaporation of water into water vapor, the dynamic response model of heat transfer in the drying chamber is expressed as: Where T is temperature, c is constant pressure heat capacity, L a is the latent heat of vaporization, V is the volume of the substance, h is the convective heat transfer coefficient, and the subscripts cv, w, and l represent convective heat transfer, water vapor, and water, respectively.

5. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 1 is characterized in that: Step 2 specifically includes the following steps: Step 2.1: Build dynamic models of the four core components of the heat pump: Based on the first law of thermodynamics, mass conservation, and heat exchange mechanisms, dynamic models of the compressor, condenser, expansion valve, and evaporator are constructed. These models consider refrigerant flow, phase change heat transfer, and energy transfer processes, and incorporate simulation modeling based on changes in working fluid physical parameters to form a physical model of the closed-loop heat pump cycle. Step 2.2: Build a dynamic model of the circulation fan and auxiliary components: For auxiliary equipment such as fans and condensate drains, a mathematical model is established to describe their dynamic response characteristics. The model is based on the characteristics of system inertia and response delay and is described using first-order or second-order linear differential equations. Step 2.3: Module integration and thermal and moisture interface coupling: Based on the first law of thermodynamics, mass conservation, and heat exchange mechanisms, dynamic models of the compressor, condenser, expansion valve, and evaporator are constructed. These models consider refrigerant flow, phase change heat transfer, and energy transfer processes, and incorporate simulation modeling based on changes in working fluid physical parameters to form a physical model of the closed-loop heat pump cycle. Step 2.4: Perform system-level dynamic simulation and verification: The dynamic model of the drying chamber in step 1, the four major components of the heat pump model, and the auxiliary equipment model are integrated based on a unified hot and humid air fluid interface to construct a complete dynamic model of the closed heat pump drying system. The model adopts a modular modeling method to support the dynamic interaction of heat, moisture, and fluid among various components.

6. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 1 is characterized in that: In step 2, the auxiliary components of the closed heat pump drying system include: a drying chamber and auxiliary equipment; the auxiliary equipment includes a condenser, an expansion valve, an evaporator, a compressor, a circulating fan, a regenerator, a dehumidification fan and an evaporator fan; wherein, the condenser, the expansion valve, the evaporator and the compressor constitute a heat pump cycle, and the dehumidification fan draws part of the wet air from the outlet of the direct drying chamber into the regenerator for preheating the supplementary fresh air, and then discharges it; the preheated fresh air is then mixed with the remaining wet air from the outlet of the direct drying chamber; driven by the circulating fan, the mixed air enters the condenser, absorbs heat and is heated to the target drying temperature; then, the heated wet air re-enters the direct dryer to continue drying the wet material.

7. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 1 is characterized in that: Step 3 is as follows: Use a data preprocessing method to remove time characteristics to reduce the impact of time characteristics on the input and output characteristics of the system. The removed time characteristic trends are as follows: In the identification design, two input variables are considered: compressor speed and dehumidification fan air volume. Three discrete state-space models of order, 6th, 6th, and 5th, are used to simulate the input-output characteristics of the system in the early, middle, and late stages of drying, respectively. The discrete state-space models for the three periods are shown below: Where: u1 is the compressor speed, u2 is the air volume of the dehumidification fan, y1 is the drying temperature T d , y2 is the drying chamber outlet humidity RH out .

8. The dynamic identification and control method of a closed heat pump drying system based on waste heat recovery according to claim 1 is characterized in that: Step 4 specifically involves performing model predictive control based on the identification model. This model predictive control method, based on a multi-time-scale analysis of a closed heat pump drying system based on waste heat recovery, considers the rapid flow response and the combined effects of refrigerant and air flow on drying temperature. Through control design, the drying temperature and drying chamber outlet humidity are dynamically adjusted to reduce overshoot. In the control design, for each working condition, set the constraint parameters, including: sampling period τ, modeling time domain N, prediction time domain P, control time domain M, the step response sequence of the output to the control quantity and disturbance quantity in the modeling time domain; set the constraint parameters including: control increment constraint Δu max , Δu min , control quantity constraint u max 、u min , output constraint y max 、y min ; where Δu max is the maximum value of the control increment, Δu min is the minimum value of the control increment, u max is the maximum value of the control quantity, u min is the minimum value of the control quantity, y max is the maximum output value, y min The minimum value of the output.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to claim 1.

10. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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