Digital twin heat pump heat recovery control method and system for tea beverage process

Abstract

The invention discloses a digital twin heat pump heat recovery control method and system for a tea beverage process. The method comprises the following steps: establishing a physical model and an energy transfer model based on each key unit parameter of a heat recovery system in a tea beverage process, and constructing a corresponding digital twin platform; sensors are installed on key units of a heat recovery system in the tea beverage technology, and operation state data are collected and uploaded to a digital twin platform; performing online simulation and prediction on real-time data by using a digital twin model; based on a model predictive control algorithm and self-adaptive adjustment, optimally controlling operation parameters of a heat pump unit, a medium flow rate, a heat exchanger state and starting and stopping of an auxiliary heating / cooling unit, and achieving control targets of maximum recovery of waste heat and precise satisfaction of a process temperature; and the optimized control instruction is fed back to the field control unit to realize closed-loop regulation and control. Refined and intelligent control over the heat recovery process of the heat pump in the tea beverage technology is achieved, the waste heat utilization rate and the system energy efficiency are greatly improved, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of production systems for tea beverages, and in particular to a digital twin heat pump heat recovery control method and system for tea beverage processes. Background Art

[0002] The industrial production process of tea beverages usually involves multiple heat - using and cooling links. Typical processes include steps such as extraction, sugar melting, and blending. Among them, in the extraction and sugar - melting processes, water usually needs to be heated to 80°C - 100°C to meet the temperature requirements for the extraction of active ingredients and sugar dissolution, while in the blending link, the water temperature is required to be reduced to 15°C - 20°C to meet the taste and preservation conditions of the final beverage. Therefore, such a production process has both a large amount of high - temperature heating requirements and significant low - temperature cooling requirements.

[0003] In traditional tea beverage production, steam boilers are usually relied on for heating and cooling towers or ice water machines for refrigeration, resulting in high overall energy consumption and low energy utilization efficiency. At the same time, although the hot water after processes such as extraction still has a relatively high temperature, the current common practice is to directly discharge it into the cooling system, which not only causes a large amount of waste heat, but also requires additional cold source resources for cooling, further increasing the operating cost.

[0004] In recent years, with the continuous improvement of the requirements for energy conservation, emission reduction, and green manufacturing, some enterprises have begun to use heat pump systems to recover the waste heat generated in tea beverage production. The current heat pump systems are mostly physical control systems, lacking the ability to model and predict the overall thermodynamic process. When the production load fluctuates, the temperature difference changes, or the equipment performance deteriorates, the system is difficult to achieve adaptive adjustment and optimal energy scheduling. In addition, the existing control strategies are generally based on fixed logic or simple PID regulation, and cannot weigh the dynamic relationship between the heating and cooling loads in real - time in a complex multi - variable coupling system, making it difficult to achieve the global optimal control of the heat recovery system. Summary of the Invention

[0005] In order to overcome the above - mentioned disadvantages of the prior art, the purpose of the present invention is to provide a digital twin heat pump heat recovery control method and system for tea beverage processes.

[0006] The technical solution adopted by the present invention to solve its technical problems is: A digital twin heat pump heat recovery control method for tea beverage processes, including the following steps:

[0007] Establish a physical model and an energy transfer model based on the key unit parameters of the heat recovery system in the tea beverage process, and construct a corresponding digital twin platform;

[0008] Install sensors on each key unit of the heat recovery system in the tea beverage process, collect data on the operating state, and upload it to the digital twin platform;

[0009] Use a digital twin model to conduct online simulation and prediction on real-time data to obtain the heat transfer and temperature change conditions;

[0010] Based on the model predictive control algorithm and adaptive regulation, optimize and control the operating parameters of the heat pump unit, the medium flow rate, the state of the heat exchanger, and the start and stop of the auxiliary heating / cooling unit to achieve the control objectives of maximum waste heat recovery and precise satisfaction of the process temperature;

[0011] Feed the optimized control instructions back to the on-site control unit to achieve closed-loop regulation.

[0012] As a further improvement of the present invention: The physical model includes a heat balance model, an energy transfer model, and a fluid dynamics model, and adaptively corrects the model parameters using historical data and real-time data.

[0013] As a further improvement of the present invention: The adaptive correction includes the following steps:

[0014] Establish an initial physical model according to the heat pump system design parameters, heat exchanger structure, pipeline layout, and medium characteristics;

[0015] Heat balance model: Based on the principle of energy conservation, establish the relationship between the energy input, output, and loss at each node;

[0016] Energy transfer model: Based on the heat transfer mechanism of the heat exchanger, establish a mathematical model between the fluid heat capacity, temperature difference, heat transfer area, and heat transfer efficiency;

[0017] Fluid dynamics model: Used to describe the flow velocity, pressure drop, and flow distribution characteristics of the water flow and refrigerant flow in the system.

[0018] As a further improvement of the present invention: By comparing the difference between the real-time collected data and the output of the current digital twin model, calculate the key performance indicators; According to the preset tolerance range and deviation threshold, determine whether the prediction accuracy of the current model meets the control requirements. If the error exceeds the set threshold, trigger the adaptive correction process.

[0019] As a further improvement of the present invention: The adaptive correction further includes the following steps:

[0020] Calculation of the error feedback term: Quantify the model prediction error to form a feedback signal;

[0021] Parameter gradient update: Calculate the update gradient of each key parameter based on the feedback signal to determine the parameter adjustment direction and amplitude;

[0022] Parameter smoothing and filtering: Smooth the updated parameters.

[0023] As a further improvement of the present invention: The calculation of the error feedback term includes the following steps:

[0024] Compare and calculate the error value from the real-time collected data;

[0025] Normalize each error term and multiply it by a preset weight factor;

[0026] For the i-th measurement quantity, normalize the error using its rated range or standard deviation σ(i):

[0027] e norm (i) = e(i) / σ(i);

[0028] Set the weight factor W(i), which is determined according to process requirements and system control sensitivity;

[0029] Multiply the normalized error term by the weight to obtain the weighted error:

[0030] e weighted( i) = W(i)·e norm (i);

[0031] Synthesize the error terms after normalization and weighting for all key measurement points into a total error feedback signal.

[0032] As a further improvement of the present invention: The digital twin platform includes an equipment entity layer, an entity data layer, a data prediction and correction layer, and a data transmission layer.

[0033] As a further improvement of the present invention: The equipment entity layer includes a hot water tank, a first heating unit, a hot side for extraction, a first cooling unit, a water side for blending, a heat recovery pipeline, and a high-temperature heat pump unit. For the heat recovery pipeline, one end thereof draws hot water before cooling from the first cooling unit. After the hot water passes through the first heat exchanger, it returns to the first cooling unit, mixes with the water in the first cooling unit and then flows to the water side for blending; The high-temperature heat pump unit has a high-temperature heat absorption side, a high-temperature heat release side, and a high-temperature magnetic levitation centrifugal compressor. The high-temperature heat absorption side absorbs heat in the first heat exchanger, and after doing work through the high-temperature magnetic levitation centrifugal compressor, releases heat in the second heat exchanger through the high-temperature heat release side; The hot water tank is provided with a water outlet pipeline, and the water outlet pipeline is connected between the first heating unit and the hot side for extraction after passing through the second heat exchanger.

[0034] As a further improvement of the present invention: The high-temperature heat absorption side has a first circulation pipeline, and the refrigerant in the first circulation pipeline exchanges heat with the flowing hot water when passing through the first heat exchanger;

[0035] The high-temperature heat release side has a second circulation pipeline, and the second circulation pipeline flows through the second heat exchanger;

[0036] The water flow in the outlet pipeline exchanges heat with the refrigerant in the second circulation pipeline when passing through the second heat exchanger, and after heat exchange, it is mixed with the hot water heated by the first heating unit and supplied to the hot side for extraction.

[0037] As a further improvement of the present invention: it further includes a second heating unit, a heat side for melting sugar, a second cooling unit, and a water side for blending, which are sequentially connected by a hot water tank;

[0038] After passing through the second heat exchanger, the outlet pipeline is divided into two paths. One path is connected to the water outlet end of the first heating unit and is mixed with the hot water heated by the first heating unit to supply hot water to the hot side for extraction, and the other path is connected to the water outlet end of the second heating unit and is mixed with the hot water heated by the second heating unit to supply hot water to the heat side for melting sugar.

[0039] As a further improvement of the present invention: it further includes a low-temperature heat pump unit;

[0040] The hot water in the heat recovery pipeline enters the third heat exchanger after passing through the first heat exchanger, and exchanges heat again in the third heat exchanger and returns to the first cooling unit;

[0041] The low-temperature heat pump unit has a low-temperature heat absorption side, a low-temperature heat release side, and a low-temperature magnetic levitation centrifugal compressor. The low-temperature heat absorption side absorbs heat in the third heat exchanger, and after doing work through the low-temperature magnetic levitation centrifugal compressor, it releases heat in the fourth heat exchanger through the low-temperature heat release side;

[0042] The hot water tank is provided with an inlet pipeline, and the inlet pipeline absorbs heat when flowing through the fourth heat exchanger.

[0043] The present invention further includes a digital twin heat pump heat recovery control system for the tea beverage process, including:

[0044] Sensor acquisition unit: used to collect the operating state parameters of temperature, flow rate, pressure, and energy consumption of each key unit of the heat recovery system, such as the hot water tank, the first heating unit, the first heat exchanger, the second heat exchanger, the first cooling unit, and the high-temperature heat pump unit, in real time;

[0045] Data transmission unit: used to upload the real-time data collected by the sensors to the digital twin platform;

[0046] Digital twin platform: including: entity data layer, data prediction and correction layer, control decision layer;

[0047] Execution unit: used to receive the optimized control instructions generated by the control decision layer and control the start-stop and operating parameters of the heat pump unit, valves, pumps, and auxiliary heating or cooling equipment.

[0048] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0049] (1) Through the accurate simulation and prediction of the heat absorption and release processes of the high-temperature heat pump unit in the first and second heat exchangers by the digital twin platform, combined with the model predictive control and adaptive regulation algorithms, the waste heat recovery and utilization of the hot water after extraction are maximized, the usage of steam heating and cooling tower / ice water cooling is reduced, and the thermal energy circulation utilization rate is significantly improved;

[0050] (2) Real-time collect the temperature and flow data at multiple points such as the hot water tank, heating unit, heat exchanger, and blending water side. Through the online correction and closed-loop control of the digital twin model, the water temperature on the heating side is stably maintained within the process requirement range of 80°C - 100°C, and the water temperature on the blending side is stably maintained within the range of 15°C - 20°C;

[0051] (3) Use historical and real-time data to adaptively correct the parameters of the heat balance, energy transfer, and fluid dynamics models, which can automatically compensate for model errors caused by factors such as equipment performance degradation and environmental changes, and ensure the long-term high-precision operation of the digital twin model;

[0052] (4) The optimization control strategy with MPC as the core effectively reduces the dependence on auxiliary energy, and at the same time reduces the operating load of the cooling tower and ice water system, so as to achieve the lowest energy consumption and the lowest operating cost on the premise of ensuring process requirements;

[0053] (5) By comparing the deviation between the digital twin prediction and the on-site measured data, the system can detect the fault risks such as sensor anomalies, reduced heat exchange efficiency, or compressor performance degradation in real time, and issue early warnings in a timely manner, providing accurate diagnostic basis for operation and maintenance personnel, and improving the reliability and availability of the equipment. Description of the Drawings

[0054] In order to more clearly illustrate the technical solution, the drawings required for the implementation will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0055] Figure 1 It is a schematic structural diagram of the equipment entity layer of the present invention.

[0056] Reference Signs:

[0057] 10: First heat exchanger, 20: Second heat exchanger, 30: Third heat exchanger, 40: First heat exchanger, 50: High-temperature heat pump unit, 51: High-temperature heat absorption side, 52: High-temperature heat release side, 60: Low-temperature heat pump unit, 61: Low-temperature heat absorption side, 62: Low-temperature heat release side; 100: Hot water tank, 110: Outlet pipeline, 200: First temperature increase unit, 300: Heat side for extraction, 400: First temperature decrease unit, 500: Water side for blending, 600: Second temperature increase unit, 700: Heat side for melting sugar, 800: Second temperature decrease unit, 900: Water side for blending. Detailed implementation manners

[0058] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0060] Most of the waste heat water at 80°C - 85°C generated after the extraction or sugar melting process directly enters the cooling tower or is mixed with ice water for cooling, failing to be effectively recycled and utilized, resulting in a large amount of waste and emission of high-temperature waste heat. Since heating and cooling are two independent energy systems, there is a lack of linkage and energy feedback mechanism between them, and the waste heat in one process cannot be directly transferred to meet the heating demand in another process. Existing heat pump systems mostly rely on fixed logic control or simple PID regulation, which are designed for a single operating point and cannot meet the simultaneous requirements of multiple processes and temperature zones such as extraction, sugar melting, and blending. When the production load fluctuates (such as changes in tea product formulas, differences in raw material temperatures), environmental temperature differences, or equipment performance deteriorates, the system is difficult to adjust key parameters such as compressor power and heat exchanger flow rate in a timely manner, resulting in large fluctuations in recovery efficiency and increased energy consumption. Currently, most heat recovery systems do not conduct mathematical modeling on the entire heat-flow field and lack overall simulation analysis of heat balance, fluid dynamics, and heat transfer processes. It is impossible to predict changes in heat demand and cold demand based on production plans or real-time operating conditions in the future, and thus it is impossible to achieve precise energy scheduling and optimization of waste heat reuse.

[0061] To solve the problems in the existing technology, the present invention provides a digital twin heat pump heat recovery control method and system for tea beverage processes.

[0062] The digital twin heat pump heat recovery control method for tea beverage processes includes the following steps:

[0063] Based on the key unit parameters of the heat recovery system in the tea beverage process, establish a physical model and an energy transfer model, and construct a corresponding digital twin platform;

[0064] Install sensors on each key unit of the heat recovery system in the tea beverage process, collect data on the operating state, and upload it to the digital twin platform;

[0065] Use the digital twin model to conduct online simulation and prediction on real-time data to obtain the heat energy transfer and temperature change conditions;

[0066] Based on the model predictive control algorithm and adaptive regulation, optimize and control the operating parameters of the heat pump unit, the flow rate of the medium, the state of the heat exchanger, and the start and stop of the auxiliary heating / cooling unit to achieve the control objectives of maximum waste heat recovery and precise satisfaction of the process temperature;

[0067] Feed back the optimized control instructions to the on-site control unit to achieve closed-loop regulation.

[0068] The digital twin heat pump heat recovery control system for tea beverage processes includes:

[0069] Sensor acquisition unit: It is used to collect the operating state parameters of temperature, flow rate, pressure and energy consumption of each key unit of the heat recovery system, such as hot water tank, first heating unit, first heat exchanger, second heat exchanger, first cooling unit, high-temperature heat pump unit, etc. in real time;

[0070] Data transmission unit: It is used to upload the real-time data collected by the sensor to the digital twin platform;

[0071] Digital twin platform: It includes: physical data layer, data prediction and correction layer, control decision layer;

[0072] Execution unit: It is used to receive the optimized control instructions generated by the control decision layer, and control the start-stop and operating parameters of the heat pump unit, valves, pumps and auxiliary heating or cooling equipment.

[0073] It realizes the refined and intelligent control of the heat pump heat recovery process in the tea beverage process, not only greatly improves the waste heat utilization rate and system energy efficiency, but also reduces the operating cost and enhances the stability and reliability of the system.

[0074] As an embodiment of the present invention, the physical model includes a heat balance model, an energy transfer model and a fluid dynamics model, and the model parameters are adaptively corrected using historical data and real-time data.

[0075] Among them, the adaptive correction includes the following steps:

[0076] Establish an initial physical model according to the heat pump system design parameters, heat exchanger structure, pipeline layout and medium characteristics;

[0077] Heat balance model: Based on the principle of energy conservation, establish the relationship between energy input, output and loss at each node (such as hot water tank, heating unit, heat exchanger);

[0078] Energy transfer model: Based on the heat transfer mechanism of the heat exchanger, establish a mathematical model between the fluid heat capacity, temperature difference, heat transfer area and heat transfer efficiency;

[0079] Fluid dynamics model: It is used to describe the flow velocity, pressure drop and flow distribution characteristics of water flow and refrigerant flow in the system.

[0080] Through the initial heat balance, energy transfer, and fluid dynamics models established based on the design parameters of the heat pump system, the heat exchanger structure, the pipeline layout, and the medium characteristics, combined with the adaptive correction of historical data and real-time data, the model parameters can be continuously corrected, eliminating the errors caused by equipment performance degradation, sensor drift, and operating condition fluctuations, making the digital twin model always highly consistent with the actual on-site state; the high-precision heat balance model and energy transfer model can accurately reflect the energy input, output, and loss at each node; the fluid dynamics model can truly depict the fluid velocity, pressure drop, and distribution inside the pipeline; accurate model calculations and dynamic parameter corrections can maximize the utilization of waste heat and reduce the auxiliary steam and cooling energy consumption.

[0081] Furthermore, by comparing the difference between the real-time collected data and the output of the current digital twin model, the key performance indicators are calculated; according to the preset tolerance range and deviation threshold, it is determined whether the prediction accuracy of the current model meets the control requirements. If the error exceeds the set threshold, the adaptive correction process is triggered.

[0082] Furthermore, the adaptive correction further includes the following steps:

[0083] Calculation of the error feedback term: Quantify the model prediction error to form a feedback signal;

[0084] Parameter gradient update: Calculate the update gradient of each key parameter based on the feedback signal to determine the parameter adjustment direction and amplitude;

[0085] Parameter smoothing and filtering: Smooth the updated parameters.

[0086] As a further improvement of the present invention: The calculation of the error feedback term includes the following steps:

[0087] Obtain the error value by comparing the real-time collected data;

[0088] Normalize each error term and multiply it by a preset weight factor;

[0089] For the i-th measured quantity, normalize the error using its rated range or standard deviation σ(i):

[0090] e norm (i) = e(i) / σ(i);

[0091] Set the weight factor W(i), which is determined according to the process requirements and the sensitivity to system control;

[0092] Multiply the normalized error term by the weight to obtain the weighted error:

[0093] e weighted( i) = W(i)·e norm (i);

[0094] The error terms after normalizing and weighting all key measurement points are combined into a total error feedback signal.

[0095] By comparing the real-time collected data with the output of the current digital twin model, the error of the key performance indicators can be accurately calculated. When the error exceeds the preset tolerance range and deviation threshold, the system can trigger the adaptive correction process in a timely manner; through the calculation of the error feedback term and the update of the parameter gradient, the model parameters are continuously optimized and adjusted during the production process. According to the parameter update direction and amplitude calculated from the feedback signal, the deviation of the model can be accurately corrected, ensuring that the heat pump system can still maintain efficient operation under complex and changeable working conditions. Based on the error feedback and parameter update, parameter smoothing and filtering processing are carried out, which can effectively eliminate the mutation effect caused by system fluctuations, environmental changes or measurement noise, making the parameter adjustment process smoother, avoiding over-regulation or oscillation, and improving the stability and response speed of the system. By normalizing the error terms between the real-time data and the model output and combining with the preset weight factors, the contribution of each error term to the total error feedback signal can be adjusted according to the importance of different measurement quantities and the sensitivity to system control. This refined weighting process enables the system to dynamically adjust the control strategy according to different process requirements and operating conditions, comprehensively using error feedback, weighted error calculation, parameter update and smoothing filtering, so that the model can adapt to influencing factors such as production load, environmental temperature changes and equipment degradation in real time.

[0096] As an embodiment of the present invention, the digital twin platform includes an equipment entity layer, an entity data layer, a data prediction and correction layer, and a data transmission layer.

[0097] Further, the equipment entity layer includes a hot water tank 100, a first heating unit 200, a heat extraction hot side 300, a first cooling unit 400, a blending water side 500, and further includes:

[0098] A heat recovery pipeline, one end of which draws hot water before cooling from the first cooling unit 400, and the hot water flows back to the first cooling unit 400 after passing through the first heat exchanger 10, and is mixed with the water in the first cooling unit 400 and then flows to the blending water side 500;

[0099] A high-temperature heat pump unit 50, having a high-temperature heat absorption side 51, a high-temperature heat release side 52, and a high-temperature magnetic levitation centrifugal compressor, wherein the high-temperature heat absorption side 51 absorbs heat in the first heat exchanger 10, and after doing work by the high-temperature magnetic levitation centrifugal compressor, heat is released by the high-temperature heat release side 52 in the second heat exchanger 20;

[0100] The hot water tank 100 is provided with a water outlet pipeline 110, and the water outlet pipeline 110 is connected between the first heating unit 200 and the heat side 300 for extraction after passing through the second heat exchanger 20.

[0101] Further, the high-temperature heat absorption side 51 of the high-temperature heat pump unit 50 exchanges heat and recovers the waste heat of the hot water in the heat recovery pipeline through the first heat exchanger 10. Taking the example of the hot water tank 100 supplying 600 tons of water per day to the heat side 300 for extraction, the water (45 °C) coming out of the hot water tank 100 is heated to 95 °C hot water by the first heating unit 200, and then after being used for heat in the heat side 300 for extraction, it is cooled to 80 °C and enters the first cooling unit 400. At this time, most of the 80 °C hot water is led into the heat recovery pipeline and is absorbed by the high-temperature heat absorption side 51 in the first heat exchanger 10. The hot water in the heat recovery pipeline is cooled to 53 °C after being absorbed by the first heat exchanger 10, and the refrigerant temperature of the high-temperature heat absorption side 51 can be heated from 43 °C to 53 °C (flow rate is 84 m 3 / h), and the heat absorption amount is 975 kW. Combining with the work done by the high-temperature magnetic levitation centrifugal compressor (heating COP = 4.9), heat is released through the high-temperature heat release side 52 in the second heat exchanger 20. The refrigerant of the high-temperature heat release side 52 releases heat and is cooled from 83 °C to 73 °C (flow rate is 105 m 3 / h), and the heat release amount of the high-temperature heat release side 52 is 1225 kW. At the same time, the water outlet pipeline 110 of the hot water tank 100 passes through the second heat exchanger 20 and absorbs heat, and the water temperature can be heated from 45 °C to 80 °C. The water outlet pipeline 110 mixes the 80 °C high-temperature hot water with the high-temperature hot water coming out of the first heating unit 200 and then supplies hot water to the heat side 300 for extraction.

[0102] Further, the high-temperature heat absorption side 51 has a first circulation pipeline, and the refrigerant in the first circulation pipeline exchanges heat with the flowing hot water when passing through the first heat exchanger 10; the high-temperature heat release side 52 has a second circulation pipeline, and the second circulation pipeline flows through the second heat exchanger 20; when the water flow in the water outlet pipeline 110 passes through the second heat exchanger 20, it exchanges heat with the refrigerant in the second circulation pipeline, and after heat exchange, it mixes with the hot water heated by the first heating unit 200 and supplies hot water to the heat side 300 for extraction.

[0103] Main functions of the present invention: By constructing a three-dimensional model of thermal equilibrium, energy transfer, and fluid dynamics, and combining historical and real-time data for adaptive correction, the present invention achieves high-precision simulation and prediction of the heat pump heat recovery system for tea beverages; adopts model predictive control and adaptive regulation algorithms to globally optimize and control heat pump units, heat exchangers, valves, and auxiliary heating / cooling units, ensuring precise stability of the extraction and formulation process temperatures, maximizing waste heat recovery, significantly reducing steam and power consumption, and thus reducing operating costs; at the same time, through real-time error monitoring, weighted feedback, and gradient update to trigger rapid correction, it can promptly identify changes in equipment performance or sensor drift and issue warnings, greatly improving the system response speed and operation and maintenance management efficiency.

[0104] In summary, after reading the present invention document, various other corresponding transformation schemes made by ordinary technicians in the art without creative mental labor according to the technical solutions and technical concepts of the present invention all fall within the scope protected by the present invention.

Claims

1. A digital twin heat pump heat recovery control method for tea beverage process, characterized in that: The following steps are involved: Based on the key unit parameters of the heat recovery system in the tea beverage process, a physical model and energy transfer model are established, and a corresponding digital twin platform is constructed; Install sensors on key units of the heat recovery system in the tea beverage process to collect data on the operating status and upload it to the digital twin platform; Use digital twin models to simulate and predict real-time data online to obtain heat transfer and temperature changes; Based on the model predictive control algorithm and adaptive regulation, the operating parameters of the heat pump unit, medium flow rate, heat exchanger status, and the start and stop of the auxiliary heating / cooling unit are optimized to achieve the control goals of maximum waste heat recovery and accurate process temperature satisfaction; The optimized control instructions are fed back to the on-site control unit to achieve closed-loop control.

2. The digital twin heat pump heat recovery control method for tea beverage process according to claim 1 is characterized in that: The physical model includes a heat balance model, an energy transfer model and a fluid dynamics model, and the model parameters are adaptively corrected using historical data and real-time data.

3. The digital twin heat pump heat recovery control method for tea beverage process according to claim 2 is characterized in that: The adaptive correction comprises the following steps: Establish an initial physical model based on the heat pump system design parameters, heat exchanger structure, pipeline layout and medium characteristics; Thermal balance model: Based on the principle of energy conservation, the relationship between energy input, output and loss of each node is established; Energy transfer model: Based on the heat transfer mechanism of the heat exchanger, a mathematical model between fluid heat capacity, temperature difference, heat transfer area and heat transfer efficiency is established; Fluid dynamics model: used to characterize the flow velocity, pressure drop and flow distribution characteristics of water flow and refrigerant flow in the system.

4. The digital twin heat pump heat recovery control method for tea beverage process according to claim 1 is characterized in that: By comparing the difference between the real-time collected data and the current digital twin model output, the key performance indicators are calculated; according to the preset tolerance range and deviation threshold, it is determined whether the prediction accuracy of the current model meets the control requirements. If the error exceeds the set threshold, the adaptive correction process is triggered.

5. The digital twin heat pump heat recovery control method for tea beverage process according to claim 4 is characterized in that: The adaptive correction further comprises the following steps: Calculation of error feedback term: quantify the model prediction error to form a feedback signal; Parameter gradient update: Calculate the update gradient of each key parameter based on the feedback signal to determine the direction and amplitude of parameter adjustment; Parameter smoothing and filtering: Smoothing the updated parameters.

6. The digital twin heat pump heat recovery control method for tea beverage process according to claim 5, characterized in that: The calculation of the error feedback term includes the following steps: Compare the real-time collected data to obtain the error value; Normalize each error term and multiply it by a preset weight factor; For the i-th measurement, the error is normalized using its rated range or standard deviation: a weight factor is set, which is determined based on the process requirements and the sensitivity to system control; The normalized error term is multiplied by the weight to obtain the weighted error: The normalized and weighted error terms of all key measurement points are synthesized into a total error feedback signal.

7. The digital twin heat pump heat recovery control method for tea beverage process according to claim 1, characterized in that: The digital twin platform includes a device entity layer, a entity data layer, a data prediction and correction layer, and a data transmission layer.

8. The digital twin heat pump heat recovery control method for tea beverage process according to claim 7, characterized in that: The physical layer of the equipment includes a hot water tank, a first heating unit, a hot side for extraction, a first cooling unit, a water side for allocation, a heat recovery pipeline and a high-temperature heat pump unit. One end of the heat recovery pipeline is connected to the hot water before cooling from the first cooling unit. The hot water flows back to the first cooling unit after passing through the first heat exchanger, and flows to the water side for allocation after mixing with the water in the first cooling unit and cooling. The high-temperature heat pump unit has a high-temperature heat absorption side, a high-temperature heat release side and a high-temperature magnetic levitation centrifugal compressor. The high-temperature heat absorption side absorbs heat in the first heat exchanger, and releases heat in the second heat exchanger through the high-temperature magnetic levitation centrifugal compressor after doing work. The hot water tank is provided with a water outlet pipeline, and the water outlet pipeline is connected between the first heating unit and the hot side for extraction after passing through the second heat exchanger.

9. The digital twin heat pump heat recovery control method for tea beverage process according to claim 8, characterized in that: The high-temperature heat absorption side has a first circulation pipeline, and the refrigerant in the first circulation pipeline exchanges heat with the hot water flowing through when passing through the first heat exchanger; The high-temperature heat release side has a second circulation pipeline, and the second circulation pipeline flows through the second heat exchanger; The water in the water outlet pipeline exchanges heat with the refrigerant in the second circulation pipeline when flowing through the second heat exchanger, and after the heat exchange, is mixed with the hot water heated by the first heating unit to supply hot water to the extraction hot side.

10. A digital twin heat pump heat recovery control system for tea beverage technology, characterized in that: include: Sensor acquisition unit: used to collect the operating status parameters of the temperature, flow, pressure and energy consumption of each key unit of the heat recovery system such as the hot water tank, the first heating unit, the first heat exchanger, the second heat exchanger, the first cooling unit, and the high-temperature heat pump unit in real time; Data transmission unit: used to upload the real-time data collected by the sensor to the digital twin platform; Digital twin platform: including: physical data layer, data prediction and correction layer, control decision layer; Execution unit: used to receive the optimization control instructions generated by the control decision layer, and control the start and stop and operation parameters of the heat pump unit, valves, pumps and auxiliary heating or cooling equipment.

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