A low-energy fluidized bed dryer granulator control system

Abstract

This invention provides a low-energy-consumption fluidized bed dryer granulation machine control system. The system includes a fluidized bed parameter control unit, a multi-dimensional monitoring unit, a moisture content closed-loop control unit, and a waste heat recovery unit. By establishing a quantitative correlation model between inlet air parameters, bed state, and moisture content changes, it dynamically matches hot air parameters with the material's fluidized bed drying requirements. Combined with exhaust waste heat recovery and preheating of the inlet air, it achieves precise energy utilization. Multiple batches of experiments have verified that this invention significantly reduces energy consumption compared to traditional equipment, greatly improves hot air utilization, and achieves high precision in final moisture content control. It is suitable for large-scale production scenarios such as pharmaceutical granules, food additives, and chemical powders, demonstrating significant energy-saving and consumption-reducing effects.

Description

Technical Field

[0001] This invention relates to the field of chemical drying technology, specifically to a low-energy-consumption fluidized bed dryer granulation machine control system. Background Technology

[0002] Fluidized bed granulators, due to their high efficiency in integrating mixing, granulation, and drying, have become core equipment in the pharmaceutical, chemical, and food industries for producing granules, tablets, and powders. In the application of this type of equipment, accurately controlling the moisture content of the final product and reducing production energy consumption are crucial to determining product quality and production costs.

[0003] Currently, the fluidized bed drying granulation equipment commonly used in the industry typically relies on operator experience for control, setting fixed parameters such as inlet air temperature and volume manually or through simple automatic methods. During the drying process, due to dynamic fluctuations in initial material moisture content, ambient temperature and humidity, and bed fluidization state, this fixed parameter supply mode is highly susceptible to "over-drying" or "under-drying." For example, if high temperature and high air volume are maintained after the material moisture content decreases, it not only wastes heat energy but may also damage heat-sensitive materials; conversely, if the initial air temperature is set too low, drying efficiency is low. To address this, some equipment has begun to attempt to recover exhaust waste heat, but the start and stop of the recovery system are often independent of the drying process. Forcibly recovering heat when the exhaust temperature is low can actually increase fan energy consumption, resulting in more harm than good.

[0004] To address the aforementioned issues, some technologies have been developed to improve the situation. For example, Chinese patent CN219756777U discloses an energy-saving and consumption-reducing fluidized bed dryer, which achieves thorough drying by turning the material over and then discharges the hot air used for drying through a mesh screen on the protective chamber for secondary utilization, thereby improving energy recovery. However, this solution only optimizes the local structure and does not establish a quantitative correlation model, thus failing to achieve dynamic matching between hot air parameters and material drying requirements.

[0005] For example, Chinese patent CN120777870A discloses a low-temperature belt drying control system and method for traditional Chinese medicine. This system uses multiple sampling points along the conveyor belt to monitor moisture in real time, and combines time-series differential analysis to dynamically perceive the drying rate and trend, achieving multi-channel coordinated regulation of temperature, belt speed, and vacuum. However, this method lacks a waste heat recovery coordination mechanism, and the model does not have self-learning capabilities.

[0006] Therefore, the core deficiency of existing technologies lies in the lack of a precise quantitative matching mechanism between the inlet air temperature and volume during the drying process and the actual drying requirements of the material (reflected in changes in moisture content). This qualitative disconnect between parameters and moisture content prevents the equipment from dynamically adjusting its operating parameters according to real-time changes in the material's state, resulting in significant ineffective heat energy loss and severely limiting the energy-saving potential of the equipment and the stability of product quality. Therefore, how to achieve precise dynamic matching between hot air parameters and material drying requirements, and on this basis, realize intelligent coordination between energy recovery and the drying process, is a technical problem urgently needing to be solved in this field. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a low-energy-consumption fluidized bed dryer granulator control system to solve the technical problem that "the hot air parameters and material drying requirements cannot be dynamically and accurately matched in the prior art, resulting in high energy consumption and poor moisture content control accuracy".

[0008] To achieve the above objectives, the present invention is implemented using the following technical solution: In a first aspect, the present invention provides a low-energy-consumption fluidized bed dryer granulator control system, comprising: a fluidized bed parameter control unit for adjusting the inlet air temperature, air velocity, and atomization pressure entering the fluidized bed body; a multi-dimensional monitoring unit for real-time acquisition of inlet air parameters, bed state parameters, exhaust parameters, and material moisture content; a moisture content closed-loop control unit connected to the fluidized bed parameter control unit and the multi-dimensional monitoring unit respectively, having a built-in quantitative correlation model for calculating the predicted value of moisture content change based on real-time acquired data, and dynamically adjusting the operating parameters of the fluidized bed parameter control unit based on the deviation between the predicted value and the target value; a waste heat recovery unit connected to the multi-dimensional monitoring unit and the fluidized bed parameter control unit for selectively preheating the air entering the fluidized bed parameter control unit based on the exhaust temperature; the fluidized bed body for carrying materials and performing drying and granulation, having a vibrating airflow distribution plate driven by an electromagnetic vibrator at its bottom, with an adjustable vibration frequency of 5-50Hz, and a spiral feeder at the feed end, with a material thickness deviation ≤ ±0.5mm.

[0009] Specifically, the quantitative correlation model is expressed as: △W=k1×(T) in -T bed ) + k2 × (P bed -P0)-k3×(RH) out -RH in ) + k4×v, where △W is the change in material moisture content, T in T represents the intake air temperature. bed For bed temperature, P bed RH represents the bed pressure, P0 represents the bed reference pressure, and RH represents the bed pressure. out For exhaust humidity, RH inν represents the inlet air humidity, v represents the hot air velocity, and k1, k2, k3, and k4 are material characteristic coefficients calibrated through experiments.

[0010] Specifically, the material characteristic coefficients are as follows: for cephalosporin granules: k1 is 0.85-0.95, k2 is 0.55-0.65, k3 is 0.38-0.42, and k4 is 0.48-0.52; for food additives: k1 is 0.65-0.75, k2 is 0.45-0.55, k3 is 0.28-0.32, and k4 is 0.38-0.42; for chemical powders: k1 is 1-1.2, k2 is 0.65-0.75, k3 is 0.48-0.52, and k4 is 0.58-0.62; where k The calibration methods for k2, k3, and k4 include: selecting target materials and dividing them into 3-5 groups according to particle size, with a sample size of ≥5kg for each group; setting at least 5 different working conditions within the range of inlet air temperature 60-90℃, hot air velocity 1.2-2.0m / s, and bed pressure 12-18kPa; collecting actual parameter values ​​at no less than 10 time points for each working condition; using the least squares method to perform multiple linear regression fitting to ensure that the model prediction error is ≤2%; verifying the fitted coefficients by selecting 2 new working conditions for testing, and supplementing experimental data and refitting if the error exceeds the standard.

[0011] Specifically, the bed reference pressure P0 is the average pressure value of the bed cross section when the material reaches the critical fluidization state in the fluidized bed body. It is obtained by: when the system is started, the air inlet velocity is controlled to slowly increase from zero, and the bed pressure is monitored in real time by a multi-dimensional monitoring unit. When the bed pressure reaches the first inflection point with the increase of air velocity and then tends to stabilize, the pressure value corresponding to the inflection point is determined as the critical fluidization pressure, and P0 is set to 1.2-1.5 times the critical fluidization pressure.

[0012] Specifically, in the quantitative correlation model, the inlet air temperature Based on the heat resistance of the material: Pharmaceutical T in =60-75℃, heat-sensitive materials T in =60-65℃, Chemical Industry T in =75-90℃, ensuring that the retention rate of active ingredients in the material is ≥90%.

[0013] Specifically, the hot air velocity is 1.2-2.0 m / s. When the hot air velocity is lower than 1.2 m / s, the hot air penetration is insufficient and the bed temperature distribution is uneven. When the hot air velocity is higher than 2.0 m / s, the contact time between the hot air and the material is too short, and the heat is discharged before sufficient exchange, resulting in a decrease in thermal efficiency.

[0014] Specifically, the multi-dimensional monitoring unit sets monitoring points 0.8m downstream of the heater outlet, 0.3m upstream of the inlet of the counter-flow plate heat exchanger, and inside the fluidized bed. Each monitoring point is equipped with a temperature sensor with an accuracy of ±0.1℃, a humidity sensor with an accuracy of ±1%RH, a pressure sensor with an accuracy of ±0.01kPa, and a wind speed sensor with an accuracy of ±0.01m / s; the sampling period is ≤0.2s, and the data transmission delay is ≤50ms.

[0015] Specifically, the waste heat recovery unit includes a counter-flow plate heat exchanger and a temperature feedback regulating valve. The temperature feedback regulating valve is linked to the temperature sensor of the multi-dimensional monitoring unit. When the exhaust temperature T... out Automatic heat exchange starts when the temperature is ≥45℃, and the exhaust temperature T out Automatic bypass at temperatures below 40℃; the heat exchange area of ​​the counter-flow plate heat exchanger is 0.8-1.5m². 2 Heat exchange efficiency ≥80%.

[0016] Specifically, the control logic of the moisture content closed-loop control unit is as follows: when the instantaneous drying rate prediction value is lower than the target value, the inlet air temperature is increased first, by 2-3℃ each time; if the inlet air temperature has reached the upper limit of the material's heat resistance, the hot air flow rate is increased, by 0.05-0.2m / s each time; when the instantaneous drying rate prediction value is higher than the target value, the heater power is reduced first, by 3-5% each time, or the atomization pressure is reduced in conjunction, by 0.05MPa each time, to ensure that the final moisture content deviation is ≤±0.2%.

[0017] Secondly, the present invention provides a control method based on the above system, comprising the following steps: (1) Parameter initialization: Set the target final moisture content according to the material type, calculate the change in target moisture content, substitute it into the quantitative correlation model for inverse calculation, determine the initial parameters of air inlet (including air inlet temperature, air inlet humidity, hot air velocity) and bed pressure benchmark, and provide an optimized start point for the system.

[0018] (2) System startup and preheating: The exhaust fan, heater and atomizer are started by the boiling parameter control unit to stabilize the air inlet parameters to the initial value, and the waste heat recovery unit is on standby; the material is evenly spread into the fluidized bed by the feeder, and the initial material moisture content W is collected by the initial moisture content detector. initial .

[0019] (3) Dynamic control: The multi-dimensional monitoring unit collects the inlet air, bed layer, exhaust parameters and the final material moisture content W in real time. final Calculate the actual value of △W, △W = W initial -W finalIf the deviation of △W from the target value is > ±0.3%, adjust the exhaust fan (4) speed, heater (15) power, atomizer (21) pressure and guide grille angle based on the correlation model; when T out When the temperature is ≥45℃, the waste heat recovery unit starts to preheat the incoming air.

[0020] (4) Iterative optimization: After each batch of drying and granulation is completed, the system automatically stores the operating parameters of all time series of this batch, and uses the least squares method to refit and update the material characteristic coefficients k1, k2, k3 and k4 to improve the control accuracy of the next batch of the same material.

[0021] Compared with the prior art, the beneficial effects achieved by the present invention are: (1) This invention establishes a quantitative correlation model of inlet air parameters, bed state, and moisture content changes to achieve dynamic and precise matching between hot air parameters and material drying requirements, avoiding energy supply redundancy. Simultaneously, the waste heat recovery unit intelligently starts and stops based on exhaust temperature. When the exhaust temperature is ≥45℃, it automatically recovers waste heat to preheat the inlet air, avoiding ineffective energy consumption caused by low-temperature exhaust recovery. More importantly, the start / stop threshold of the waste heat recovery unit is coordinated with the drying process determined by the quantitative correlation model: the model's accurate prediction of the drying rate ensures that the waste heat recovery unit bypasses in time when drying enters an inefficient stage, avoiding increased ineffective energy consumption due to forced recovery of low-temperature exhaust. This model-driven coordinated control results in a comprehensive energy-saving effect superior to the simple superposition of model closed-loop control and conventional waste heat recovery. Under the same production capacity, this invention reduces comprehensive energy consumption by 28-32% compared to traditional equipment operating with fixed parameters and without waste heat recovery, and increases the effective utilization rate of hot air from 45% in traditional equipment to 60-65%.

[0022] (2) This invention uses a multi-dimensional monitoring unit to synchronously collect air intake, bed and exhaust parameters at a 0.2s cycle, and combines a quantitative correlation model to calculate the instantaneous drying rate in real time, forming a closed-loop feedback control. This effectively overcomes the interference of material initial state fluctuations and bed inhomogeneity. Continuous production batch verification shows that the standard deviation of the final product moisture content can be stably controlled at 0.15-0.18%, and the range is ≤0.4%, which meets the requirements of pharmaceutical industry GMP for the control of key process parameters.

[0023] (3) This invention is suitable for various material types such as cephalosporin granules, food additives, and chemical powders. For heat-sensitive materials, the retention rate of active ingredients is improved by precisely controlling the bed temperature to not exceed the degradation temperature. The system has self-learning ability and automatically updates the material characteristic coefficients using the least squares method after each batch, achieving iterative optimization that becomes more accurate with each use. The frequency of manual intervention is reduced from several times per hour in traditional equipment to only needing to set the target value for each batch. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the control system of the low-energy boiling dryer granulator described in an embodiment of the present invention.

[0025] Attached reference numerals: 1. Chimney; 2. Exhaust filter; 3. Exhaust duct; 4. Exhaust fan; 5. Counter-flow plate heat exchanger; 6. Temperature feedback regulating valve; 7. Collection bucket; 8. Collection box; 9. Frame; 10. Material bucket; 11. Base; 12. Material trolley; 13. Central control box; 14. Waste heat return preheating pipe; 15. Heater; 16. Heating box; 17. Filter box cover; 18. Filter; 19. Air inlet duct; 20. Observation window; 21. Atomizer; 22. Filter bag; 23. Top tank; 24. Body tank. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] Example 1; This embodiment takes the drying and granulation process of cephalosporin granules as an example to provide a detailed description of the low-energy boiling dryer granulator control system and its control method described in this invention.

[0028] I. System Configuration The low-energy-consumption fluidized bed dryer granulator control system used in this embodiment, such as... Figure 1 As shown, the specific configuration is as follows: (1) Fluidized bed body: The fluidized bed body is located inside the machine body tank 24. The machine body tank 24 also has a top tank 23 inside, and a movable material bucket 10 is connected to the bottom. The material bucket 10 is placed on a material trolley 12 with a base 11 and casters. The diameter of the fluidized bed body is 1.5m, and the effective drying area is 1.77m². 2 The bottom of the bed is equipped with a vibrating airflow distribution plate driven by an electromagnetic vibrator, with a vibration frequency of 25Hz; the feed end is equipped with a spiral feeder, and the thickness deviation of the material is controlled within ±0.5mm.

[0029] (2) Boiling parameter control unit: including exhaust fan 4, heater 15 and atomizer 21. The exhaust fan 4 is specifically four 9-19 type variable frequency centrifugal fans, each with a rated air volume of 1200m³. 3 / h, each fan is independently frequency-controlled; each fan is equipped with a heater 15, the total power of the heater 15 is 72kW, and it is independently controlled in 6 sections; an atomizer 21 with a pressure continuously adjustable from 0.2 to 0.8MPa is configured; a guide grille with an angle adjustable from 0 to 60° is installed in the air inlet duct 19.

[0030] (3) Multi-dimensional monitoring unit: An air inlet monitoring point is set up 0.8m downstream of the outlet of heater 15 along the air inlet duct 19, equipped with a temperature sensor with an accuracy of ±0.1℃ and a humidity sensor with an accuracy of ±1%RH; three sets of bed monitoring points are evenly arranged along the longitudinal direction of the fluidized bed, each set containing two sensors, one above the other, equipped with a temperature sensor with an accuracy of ±0.1℃ and a pressure sensor with an accuracy of ±0.01kPa; an exhaust monitoring point is set up 0.3m upstream of the inlet of counter-current plate heat exchanger 5 along the exhaust duct 3, equipped with a temperature sensor with an accuracy of ±0.1℃ and a humidity sensor with an accuracy of ±1%RH; a wind speed sensor with an accuracy of ±0.01m / s is also configured. The sampling period of all sensors is 0.2s, and the data transmission delay is ≤50ms.

[0031] (4) Moisture content closed-loop control unit: integrated in the central control box 13, with a built-in quantitative correlation model, and connected to the sensors of the multi-dimensional monitoring unit and the actuators of the boiling parameter control unit, as well as the exhaust fan 4, counter-flow plate heat exchanger 5, and atomizer 21, to realize closed-loop control of feed moisture content, bed state, air intake and exhaust parameters and final moisture content.

[0032] The quantitative correlation model is as follows: △W=k1×(T in -T bed ) + k2 × (P bed -P0)-k3×(RH) out -RH in )+k4×v The material characteristic coefficients used are the pre-calibrated values ​​for cephalosporin granules: k1=0.9, k2=0.6, k3=0.4, k4=0.5.

[0033] (5) Waste heat recovery unit: including counter-flow plate heat exchanger 5, temperature feedback regulating valve 6 and waste heat return preheating pipe 14, wherein the core component counter-flow plate heat exchanger 5 has a heat exchange area of ​​1.0m², and the material is selected from 316L stainless steel to meet the hygiene and corrosion resistance requirements of cephalosporin granule production; the heat exchanger is equipped with temperature feedback regulating valve 6, which is linked with the exhaust temperature sensor of the multi-dimensional monitoring unit, and can dynamically adjust the heat exchange efficiency according to the real-time exhaust temperature; the temperature feedback regulating valve 6 is linked with the temperature sensor of the multi-dimensional monitoring unit to start preheating of fresh air intake and switch bypass, so as to realize efficient recovery and reuse of waste heat of dry exhaust gas, and further reduce system energy consumption.

[0034] In this embodiment, the above-described system is used to dry and granulate cephalosporin granules, with a processing capacity of 150 kg / batch. The initial moisture content, measured by a capacitive initial moisture content detector at the feed end, is 85.0 ± 0.5%. The target final moisture content set in the process specification is 12.0 ± 0.5%. Therefore, the target moisture content change value ΔW total The drying rate was 73.0%. Based on the drying characteristic curve of the material and the results of previous small-scale experiments, the expected drying time was 30-35 minutes, corresponding to a target average drying rate of 2.1-2.4% / min. In this embodiment, a target average drying rate of 2.2% / min was used as the control benchmark.

[0035] This invention provides a control method for a low-energy-consumption fluidized bed dryer granulator control system, the specific steps of which are as follows: S1. Parameter Initialization The central controller of the moisture content closed-loop control unit is based on the total target moisture content change value ΔW. total And the pre-calibrated material characteristic coefficients k1, k2, k3, and k4, setting the initial values ​​for each parameter: inlet air temperature T in Set to 70℃, intake air humidity RH in The system was set to 20% RH, hot air velocity v to 1.5 m / s, and bed reference pressure P0 to 12 kPa. Meanwhile, according to the material process specifications, the atomization pressure was set to 0.5 MPa, and the conveyor belt speed to 0.3 m / min.

[0036] S2. System Startup and Warm-up The boiling parameter control unit activates the exhaust fan 4, heater 15, and atomizer 21 to stabilize the inlet air parameters to the initial set value. Simultaneously, the waste heat recovery unit is in standby mode, and its temperature feedback regulating valve defaults to bypassing the heat exchanger. The spiral feeder and conveyor belt are activated to spread the material into the fluidized bed body at a uniform thickness, with the thickness deviation controlled within ±0.5mm. The capacitive initial moisture content detector at the feed end collects and records the initial moisture content W in real time. initial .

[0037] S3. Dynamic Control During the drying process, the multi-dimensional monitoring unit collects the inlet air temperature T in real time at a period of 0.2 seconds. in RH of incoming air in Bed temperature T bed Bed pressure P bed Hot air velocity v, exhaust temperature T out Exhaust humidity (RH) out The data is then transmitted to the central controller.

[0038] It should be noted that the change in material moisture content represented by ΔW in this model is the instantaneous drying rate, not the total change. The ΔW calculated in real time... pred Used for comparison with the target average drying rate to determine whether the drying process deviates from expectations. Taking the parameters at a certain point in the drying process as an example, the sensor measures: bed temperature T. bed =51℃, bed pressure P bed =15kPa, exhaust humidity RH out =64%RH, intake air humidity RH in =20%RH (maintain setpoint), hot air velocity v=1.5m / s (maintain setpoint), inlet air temperature T in =70℃ (maintain the set value), substitute real-time data into the model to calculate the instantaneous drying rate: △W pred =0.9×(70-51)+0.6×(15-12)-0.4×(64-20)+0.5×1.5=2.05% / min. At this point, the calculated instantaneous drying rate of 2.05% / min is compared with the target average drying rate of 2.28% / min, and the deviation is -0.23% / min. In this embodiment, during the drying process, T... in The temperature was maintained stably at 70±0.5℃, and the bed temperature T was... bed The temperature was stabilized at 51±1℃, and the bed pressure P bed Fluctuations within the range of 14-16 kPa indicate good fluidization.

[0039] If the deviation exceeds the allowable range of ±0.3% / min, it is judged as "insufficient drying". At this time, the central controller sends an adjustment command to the boiling parameter control unit according to the preset decision logic: when drying is insufficient, the inlet air temperature T is increased first. in Each time the temperature is increased by 2℃, if T in The material's heat resistance limit has been reached. The heat resistance limit for cephalosporin granules is 75℃. Therefore, the hot air flow rate v is increased by 0.1 m / s each time.

[0040] If the calculated instantaneous drying rate is higher than the target rate, it is judged as "over-drying". The heater power is reduced first, by 5% each time. If a rapid response is required, the atomization pressure is reduced in conjunction, by 0.05MPa each time.

[0041] In this embodiment, when the drying process reaches the 15th minute, the exhaust temperature T collected by the multi-dimensional monitoring unit is... out When the temperature rises to 46°C and reaches the preset threshold of 45°C, the temperature feedback regulating valve of the waste heat recovery unit automatically switches and introduces the exhaust gas into the counter-flow plate heat exchanger 5 to preheat the air entering the boiling parameter control unit. After preheating, the air inlet temperature is increased from the ambient temperature of 25°C to about 42°C.

[0042] S4. Iterative Optimization: After the drying and granulation of this batch is completed, the moisture content closed-loop control unit automatically stores all operating parameters of this batch (including T at each time point). in v, P bed RH in RH out And the final actual ΔW), and the least squares method is used to refit and update the material characteristic coefficients k1, k2, k3, and k4 to improve the control accuracy of the next batch.

[0043] Example 2; This embodiment takes the drying and granulation process of food additive microcrystalline cellulose as an example to further illustrate the control system and control method of the low-energy boiling dryer granulator described in this invention.

[0044] This embodiment uses the same low-energy boiling dryer granulator control system as Embodiment 1. Its equipment configuration, sensor arrangement, data acquisition and transmission method, waste heat recovery unit structure and control logic are all the same as those in Embodiment 1. The difference is that this embodiment has made adaptive adjustments to some equipment specifications and control parameters for different material types, as detailed below.

[0045] I. System Configuration Adjustment The low-energy fluidized bed dryer granulator control system used in this embodiment has a fluidized bed body diameter of 1.2m and an effective drying area of ​​1.13m². The fluidized bed parameter control unit includes three exhaust fans 4 with a total power of 45kW. The waste heat recovery unit has a heat exchange area of ​​0.8m². The configuration of the remaining units is the same as in Embodiment 1.

[0046] II. Control Methods This embodiment uses the above-described system to dry and granulate microcrystalline cellulose (MCC, PH-101), with a processing capacity of 100 kg / batch. The initial moisture content, measured by a capacitive initial moisture meter at the feed end, was 82.0 ± 0.5%. The target final moisture content set in the process specification is 11.0 ± 0.5%, therefore the target moisture content change value ΔW total The result was 71.0%. Based on the drying characteristic curve of the material and the results of previous small-scale experiments, the expected drying time was 25-30 minutes, corresponding to a target average drying rate of 2.4-2.8% / min. In this embodiment, a target average drying rate of 2.6% / min was used as the control benchmark.

[0047] The control steps are the same as in Example 1, except for the following parameters: S1. Parameter Initialization Inlet air temperature T in Set to 65℃, intake air humidity RH inThe system was set to 22%RH, hot air velocity v was set to 1.4m / s, bed reference pressure P0 was set to 13kPa, atomization pressure was set to 0.4MPa, and conveyor belt speed was set to 0.35m / min. The material characteristic coefficients were pre-calibrated using food additives: k1=0.7, k2=0.5, k3=0.3, k4=0.4.

[0048] S2. System startup and preheating, this step is the same as in Example 1.

[0049] S3. Dynamic Control During the drying process, the monitoring method is the same as in Example 1. In this example, the exhaust humidity RH is... out The initial RH level was 88% during the drying process, gradually decreasing as drying progressed, reaching a peak of 65% RH after 12 minutes of drying. The system stabilized the RH level within the 62-68% range by fine-tuning the airflow. Bed pressure P bed The pressure remained stable within the range of 15-17 kPa, which is 2-4 kPa higher than the bed reference pressure P0 of 13 kPa, indicating that the microcrystalline cellulose particles were in good fluidization state. The dehumidification module controlled the inlet air humidity near the set value of 22%RH, and the central controller stabilized the hot air velocity v within the range of 1.4±0.05 m / s. Specifically in this embodiment, during the drying process T in The temperature was maintained stably at 65±0.5℃, and the bed temperature T was... bed The temperature was stabilized at 46±1℃, and the bed pressure P bed It fluctuates within the range of 15-17 kPa; when drying reaches the 12th minute, the exhaust temperature T out The temperature rose to 44°C, but had not yet reached the 45°C activation threshold; drying continued until the 18th minute, T... out When the temperature rises to 46°C, the temperature feedback regulating valve of the waste heat recovery unit automatically switches, directing the exhaust gas into the counter-flow plate heat exchanger 5. After preheating, the inlet air temperature increases from the ambient temperature of 25°C to approximately 40°C.

[0050] S4. Iterative Optimization: After the drying and granulation of this batch is completed, the moisture content closed-loop control unit automatically stores all operating parameters of this batch and uses the least squares method to refit and update the material characteristic coefficients. Calculations show that k1 is updated to 0.68, k2 to 0.52, k3 to 0.31, and k4 to 0.41, which are used to improve the control accuracy for the next batch. Comparative Example 1: Under the same capacity and quality requirements as Example 1, Comparative Example 1 was compared using an FL-120 fluidized bed dryer granulator, operated according to conventional parameters, with a fixed inlet air temperature of 75°C, an air velocity of 1.8 m / s, and no waste heat recovery.

[0051] Comparative Example 2: Under the same capacity and quality requirements as Example 2, Comparative Example 2 was compared using an FL-120 fluidized bed dryer granulator, operated according to conventional parameters, with a fixed inlet air temperature of 75°C, an air velocity of 1.8 m / s, and no waste heat recovery.

[0052] Results and Analysis Test methods: The drying and granulation results of each embodiment and comparative example were tested. The final product moisture content was monitored in real time using a near-infrared final moisture content detector at the discharge end. The samples were sampled and verified using an offline rapid moisture analyzer (MettlerToledo HC103). The arithmetic mean of the sampled samples was taken as the batch final moisture content measurement result. The deviation between the final moisture content of each batch and the target value set in the process specification was calculated, and the standard deviation of consecutive batches was statistically analyzed. The unit product energy consumption was calculated as the ratio of the total batch energy consumption to the batch processing volume. The effective utilization rate of hot air was calculated as the proportion of the effective heat used for moisture evaporation to the total heat supplied by the incoming air. The effective heat was determined based on the material moisture evaporation and the latent heat of water vaporization. The angle of repose was determined using the fixed funnel method: the funnel was fixed at a certain height above the horizontal platform, and the particles to be tested were allowed to fall freely onto the platform to form a cone. The angle between the inclined plane of the cone and the horizontal plane was measured. The measurement was repeated three times, and the arithmetic mean was taken as the angle of repose measurement result, which was used to evaluate the particle flowability. The specific results are shown in Table 1.

[0053] Table 1.

[0054]

[0055] It should be noted that in Example 1 and Comparative Example 1, which involve cephalosporin granules, the angle of repose was not used as an evaluation index in this test, and is therefore indicated by "—". In Example 2 and Comparative Example 2, which involve microcrystalline cellulose, the angle of repose was measured as a flowability evaluation index.

[0056] As can be seen from Table 1, the control system described in this invention has achieved significant results in the drying and granulation of cephalosporin granules (Example 1) and microcrystalline cellulose (Example 2).

[0057] Regarding drying efficiency, the drying time of Example 1 was shortened by 15.8% compared to Comparative Example 1, and the drying time of Example 2 was shortened by 20.0% compared to Comparative Example 2, indicating that the present invention effectively improves drying efficiency through dynamic parameter matching.

[0058] Regarding the accuracy of moisture content control, the standard deviation of the final moisture content in Example 1 was 0.17%, significantly lower than that of Comparative Example 1 (0.36%); the standard deviation of the final moisture content in Example 2 was 0.15%, significantly lower than that of Comparative Example 2 (0.31%). This indicates that the closed-loop control system described in this invention can effectively overcome the interference of material initial state fluctuations and bed inhomogeneity, and significantly improve the consistency of the final product moisture content.

[0059] In terms of energy consumption, the energy consumption per unit of product in Example 1 was reduced by 30.5% compared to Comparative Example 1, and the effective utilization rate of hot air increased from 45% to 62%; the energy consumption per unit of product in Example 2 was reduced by 27.5% compared to Comparative Example 2, and the effective utilization rate of hot air increased from 46% to 61%. This shows that the present invention achieves significant energy-saving effects through dynamic parameter matching and waste heat recovery synergistic control.

[0060] Regarding product quality, the angle of repose in Example 2 was 32°, which was better than that in Comparative Example 2 (36°), indicating that the present invention, through uniform drying and precise control, makes the particle size distribution more concentrated and improves the flowability.

[0061] In summary, the low-energy fluidized bed dryer granulator control system of the present invention is superior to traditional equipment in terms of drying efficiency, moisture content control accuracy, energy consumption reduction, and product quality, thus verifying the technical superiority of the present invention.

[0062] Stability verification at different production scales To verify the stability and energy-saving effect of the equipment under different production scales, drying tests of cephalosporin granules were conducted using equipment with different bed diameters. A traditional fluidized bed dryer without waste heat recovery was used as a comparison benchmark. The energy consumption reduction rate of the system described in this invention was calculated, and the results are shown in Table 2.

[0063] Table 2

[0064] As can be seen from Table 2, the system described in this invention can maintain excellent energy-saving performance under different production scales, with the energy consumption reduction rate remaining stable between 29.5% and 31.2%; the standard deviation of the final moisture content is controlled within the range of 0.16% to 0.18%, indicating that the system has good scale adaptability and operational stability, and is suitable for large-scale production scenarios with different capacity requirements.

[0065] Long-term operational stability verification: To further verify the long-term reliability of the system, 30 consecutive batches of production verification were conducted, and the results are shown in Table 3.

[0066] Table 3

[0067] The above verification results show that the system described in this invention controls energy consumption fluctuations within ±2.5% during continuous production, achieves final moisture content control accuracy of ±0.2%, and has a zero equipment failure rate during 30 consecutive batches of operation, fully demonstrating that the system has good long-term operational stability and reliability.

[0068] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No markings in the claims should be construed as limiting the scope of the claims.

Claims

1. A low-energy-consumption fluidized bed dryer granulator control system, characterized in that, include: The boiling parameter control unit is used to adjust the inlet air temperature, air velocity and atomization pressure entering the fluidized bed body; A multi-dimensional monitoring unit is used to collect inlet air parameters, bed state parameters, exhaust parameters, and material moisture content in real time; A closed-loop control unit for moisture content is connected to the boiling parameter control unit and the multi-dimensional monitoring unit, respectively. It is used to calculate the predicted value of moisture content change based on the real-time data collected by the multi-dimensional monitoring unit and a quantitative correlation model, and to dynamically adjust the operating parameters of the boiling parameter control unit based on the deviation between the predicted value and the target value. The moisture content closed-loop control unit has a built-in quantitative correlation model, which is expressed as follows: △W=k1×(T in -T bed )+k2×(P bed -P0)-k3×(RH out -RH in )+k4×v Where △W is the change in moisture content of the material, and T in T represents the intake air temperature. bed For bed temperature, P bed P0 is the bed pressure, and RH is the bed reference pressure. out For exhaust humidity, RH in Where is the inlet air humidity, v is the hot air velocity, and k1, k2, k3, and k4 are material characteristic coefficients calibrated through experiments; The waste heat recovery unit is connected to the multi-dimensional monitoring unit and the boiling parameter control unit. It is used to selectively preheat the air entering the boiling parameter control unit according to the exhaust temperature collected by the multi-dimensional monitoring unit, so as to realize the efficient recovery and reuse of waste heat from the drying exhaust gas.

2. The control system according to claim 1, characterized in that, The moisture content closed-loop control unit sets the inlet air temperature T in the quantitative correlation model based on the material's heat resistance. in Pharmaceutical category T in For heat-sensitive materials, the temperature range is 60-75℃. in The temperature is 60-65℃, for chemical products (T). in The temperature is 75-90℃.

3. The control system according to claim 1, characterized in that, The parameter ranges in the quantitative correlation model are as follows: inlet air humidity RH in The ambient humidity is 15-25%RH; the rotary dehumidifier should be started when the ambient humidity is >25%RH; the hot air velocity v is 1.2-2.0 m / s; the bed pressure P bed 12-18 kPa; Exhaust parameter constraints: Exhaust temperature T out ≥40℃; exhaust humidity RH out ≤70%RH; heat transfer temperature difference T in -T bed Controlled at 15-25℃, pressure difference P bed -P0 is controlled at 2-8 kPa.

4. The control system according to claim 1, characterized in that, The material property coefficients are as follows: for cephalosporin granules: k1 is 0.85-0.95, k2 is 0.55-0.65, k3 is 0.38-0.42, and k4 is 0.48-0.52; for food additives: k1 is 0.65-0.75, k2 is 0.45-0.55, k3 is 0.28-0.32, and k4 is 0.38-0.42; for chemical powders: k1 is 1-1.2, k2 is 0.65-0.75, k3 is 0.48-0.52, and k4 is 0.58-0.

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5. The control system according to claim 1, characterized in that, The control logic of the moisture content closed-loop control unit is as follows: when ΔW < target value, T is increased first. in Each temperature increase should be 2-3℃, not exceeding the pre-set upper limit of the material's heat resistance; if T in If the upper limit is reached, increase the wind speed v by 0.05-0.2 m / s each time; when ΔW > target value, reduce the heater power by 3-5% each time, and reduce the atomization pressure by 0.05 MPa each time, to ensure that the final moisture content deviation is ≤ ±0.2%.

6. The control system according to claim 1, characterized in that, The multi-dimensional monitoring unit sets monitoring points at the heater (15) outlet, the counter-flow plate heat exchanger (5) inlet, and the fluidized bed. Each monitoring point is equipped with a temperature sensor with an accuracy of ±0.1℃, a humidity sensor with an accuracy of ±1%RH, a pressure sensor with an accuracy of ±0.01kPa, and a wind speed sensor with an accuracy of ±0.01m / s. The sampling period is ≤0.2s and the data transmission delay is ≤50ms.

7. The control system according to claim 1, characterized in that, The waste heat recovery unit includes a counter-flow plate heat exchanger (5) and a temperature feedback regulating valve (6). The temperature feedback regulating valve (6) is linked to the temperature sensor of the multi-dimensional monitoring unit. When the exhaust temperature T out Automatic heat exchange starts when the temperature is ≥45℃, and the exhaust temperature T out Automatic bypass when the temperature is below 40℃.

8. The control system according to claim 1, characterized in that, The bottom of the fluidized bed body is equipped with a vibrating airflow distribution plate driven by an electromagnetic vibrator, with an adjustable vibration frequency of 5-50Hz; the feed end is equipped with a spiral feeder, with a material thickness deviation of ≤±0.5mm.

9. A control method for the control system as described in any one of claims 1-8, characterized in that, Includes the following steps: (1) Parameter initialization: Set the target final moisture content according to the material type, calculate the target value of ΔW, and substitute it into the correlation model to determine the initial parameters of the air intake (T). in RH in (v) and bed pressure reference P0; (2) System startup and preheating: The exhaust fan (4), heater (15) and atomizer (21) are started by the boiling parameter control unit to stabilize the air inlet parameters to the initial value, and the waste heat recovery unit is on standby; the material is evenly spread into the fluidized bed by the feeder, and the initial moisture content W of the material is collected by the initial moisture content detector. initial ; (3) Dynamic control: The multi-dimensional monitoring unit collects the inlet air, bed layer, exhaust parameters and the final material moisture content W in real time. final Calculate the actual value of △W, △W = W initial -W final If the deviation of △W from the target value is > ±0.3%, adjust the exhaust fan (4) speed, heater (15) power, atomizer (21) pressure and guide grille angle based on the correlation model; when T out When the temperature is ≥45℃, the waste heat recovery unit is activated to preheat the incoming air; (4) Iterative optimization: After each batch of drying and granulation is completed, all operating parameters are stored, and the material characteristic coefficients k1, k2, k3 and k4 are updated using the least squares method to improve the control accuracy of the next batch.

10. The control method according to claim 9, characterized in that, The calibration method for the material characteristic coefficients k1, k2, k3, and k4 includes: selecting target materials and dividing them into 3-5 groups according to particle size; setting at least 5 working conditions within the range of inlet air temperature 60-90℃, hot air velocity 1.2-2.0m / s, and bed pressure 12-18kPa; collecting parameters at no less than 10 time points for each working condition; and using the least squares method for multiple linear regression fitting, with a model prediction error ≤2%.

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