Treatment method of medicinal anhydrous calcium hydrogen phosphate auxiliary material

Abstract

The invention relates to the technical field of pharmaceutical preparations, and particularly discloses a medicinal anhydrous calcium hydrogen phosphate auxiliary material treatment method, which comprises: S1, raw material screening, S2, pre-drying treatment, S3, pneumatic mixing homogenization, S4, fine crushing, S5, quality detection, and S6, packaging and storage. According to the method, through pneumatic mixing and genetic algorithm optimization, genetic algorithm self-adaptive search of optimal airflow velocity and shearing force, improvement of mixing uniformity, elimination of stirring appliance pollution by a pneumatic suspension technology, improvement of preparation purity, and guarantee of mixing uniformity of medicinal anhydrous calcium hydrogen phosphate, batch stability of medicinal preparations is improved, reinforcement learning is adopted, and the method has the advantages of high efficiency, low cost and high efficiency. The drying temperature is automatically adjusted according to the real-time moisture content; an optimal drying temperature curve is dynamically selected by adopting a Q-learning algorithm, and water residues are reduced; the self-adaptive temperature control strategy is realized, the energy consumption is reduced, the stability and controllability of the auxiliary materials are ensured, and the modern green pharmaceutical concept is met.

Description

Technical Field

[0001] The invention belongs to the technical field of pharmaceutical preparations, and in particular relates to a method for processing anhydrous calcium hydrogen phosphate auxiliary material for medicinal use. Background Art

[0002] As an important excipient, pharmaceutical anhydrous calcium hydrogen phosphate is widely used in various solid preparations in the pharmaceutical industry, such as tablets and capsules. Its quality and stability have an important impact on the efficacy and safety of the final drug product. However, there are still some shortcomings in the existing methods for processing pharmaceutical anhydrous calcium hydrogen phosphate excipients.

[0003] First, during the raw material processing stage, traditional screening methods often make it difficult to accurately control the particle size distribution, resulting in a wide range of anhydrous calcium hydrogen phosphate raw material particle sizes, which is not conducive to the stability and consistency of subsequent formulation processes. At the same time, the environmental conditions during the screening process are not strictly controlled, which may affect the purity and stability of the raw materials.

[0004] Secondly, in the drying process, existing drying methods often use fixed temperature curves and time parameters, lacking the ability to dynamically adjust according to the real-time state of the material. This may not only lead to excessive drying energy consumption, but also affect the stability and quality of the auxiliary materials due to excessive residual moisture.

[0005] Furthermore, in the process of mixing and homogenizing, the traditional stirring method may cause the problem of stirring equipment contamination, which affects the purity of the preparation. At the same time, the control of mixing uniformity often depends on the experience and skill level of the operator, making it difficult to achieve precise control.

[0006] Finally, in the pulverization process, the existing airflow pulverization method often makes it difficult to achieve precise control of the particle size distribution, which not only affects the solubility and bioavailability of anhydrous calcium hydrogen phosphate in drugs, but may also increase production costs due to excessive energy consumption.

[0007] In summary, the existing processing methods for pharmaceutical anhydrous calcium hydrogen phosphate excipients still have shortcomings in terms of particle size control, drying energy consumption, mixing uniformity and crushing energy consumption. A more efficient, precise and intelligent processing method is needed to meet the needs of the modern pharmaceutical industry.

[0008] In this regard, the inventors proposed a method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material to solve the above problems. Summary of the invention

[0009] The object of the present invention is to provide a method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material to solve the problems raised in the above-mentioned background technology.

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] A method for treating anhydrous calcium hydrogen phosphate as a medicinal auxiliary material comprises the following steps:

[0012] S1, raw material screening, used to preliminarily screen anhydrous calcium hydrogen phosphate to obtain anhydrous calcium hydrogen phosphate raw material that meets the target particle size range;

[0013] S2, pre-drying treatment, for pulse airflow drying the anhydrous calcium hydrogen phosphate raw material to reduce the moisture content and obtain a dry material;

[0014] S3, pneumatic mixing and homogenization, for pneumatically mixing the dry material to obtain a mixed material;

[0015] S4, fine grinding, for performing nano-scale airflow grinding on the mixed material to control the final particle size and obtain a grinding product;

[0016] S5, quality inspection, for analyzing the particle size, moisture, purity and impurities of the pulverized product to obtain qualified auxiliary materials;

[0017] S6, packaging and storage, is used to seal and store the qualified excipients to ensure stability and prevent moisture absorption, so as to obtain the final product, anhydrous calcium hydrogen phosphate excipient for pharmaceutical use.

[0018] Preferably, the preliminary screening is performed using a 200 mesh, 75 μm sieve, in an environment with a temperature of 22±2° C. and a relative humidity of ≤30%;

[0019] The purity of the obtained anhydrous calcium hydrogen phosphate raw material is ≥98.5%.

[0020] Preferably, the pulse airflow drying comprises the following specific steps:

[0021] A pulsed airflow drying system is used, combined with a reinforcement learning method to control drying temperature;

[0022] The drying process is divided into three stages, including:

[0023] Initial stage: temperature 40℃, time 10min;

[0024] Main drying stage: temperature 85-95°C, time 30 minutes;

[0025] Terminal stage: temperature 150℃, time 15min, to prevent crystal form change;

[0026] Adopt near infrared spectrum NIR detection system to monitor moisture content in real time to ensure that the final moisture content is ≤0.5%;

[0027] Combine Fourier transform infrared spectroscopy FTIR and deep neural network DNN to automatically analyze impurities and reduce impurity generation.

[0028] Preferably, the reinforcement learning is used to dynamically adjust the drying temperature T to minimize the drying energy consumption E and ensure that the final moisture content Wf meets the requirements. The reinforcement learning expression is:

[0029] R=-αE-βWf-Wt|

[0030] Where: R: reward value, the higher the better;

[0031] E: energy consumption, unit J;

[0032] Wf: final moisture content, unit %;

[0033] Wt: target moisture content;

[0034] α, β: weight parameters.

[0035] Preferably, the pneumatic mixing step comprises:

[0036] A high-efficiency pneumatic suspension mixer is used to mix at an air flow rate of 25 to 35 m / s, and the mixed material is obtained by optimizing the mixing uniformity through a cyclonic shear force of 1200 to 1800 Pa;

[0037] Combined with genetic algorithm to automatically search for optimal mixing parameters.

[0038] Preferably, the genetic algorithm is used to optimize the air flow velocity v and the cyclone shear force Pc to maximize the mixing uniformity U. The genetic algorithm objective function is as follows:

[0039]

[0040] Where U: mixing uniformity, ranging from 0 to 1, the closer to 1, the higher the uniformity;

[0041] Ci: concentration of the ith particle;

[0042] Cavg: average concentration;

[0043] n: number of sample points.

[0044] Preferably, the inlet airflow pressure of the nano-scale airflow pulverization is 0.6-0.8 MPa;

[0045] The classification speed is adjusted by the adaptive eddy current classification algorithm AVC to make the particle size distribution D90≤35μm;

[0046] The gradient descent method is used to optimize the crushing energy consumption and reduce energy consumption, so that the unit energy consumption is reduced by 15% to 22%.

[0047] Preferably, the adaptive eddy current classification algorithm is used to adjust the classification speed ω of the air flow mill to ensure the target particle size D90≤35. The expression of the adaptive eddy current classification algorithm is:

[0048]

[0049] Where: ω*: optimal classification speed, rpm;

[0050] D90(ω): 90% cumulative particle size at the current speed;

[0051] Dt = 35 μm: target particle size;

[0052] The gradient descent method is used to dynamically adjust ω, and the expression of gradient descent is:

[0053]

[0054] Where λ is the step size parameter, which is generally set to 0.01 to 0.1;

[0055] Initial speed: 1000~5000rpm;

[0056] Step size control: λ = 0.05 to prevent oscillation;

[0057] Error threshold: Stop iteration when |D90-Dt|<0.5μm.

[0058] Preferably, the quality inspection step includes:

[0059] Use laser particle size analyzer to analyze particle size to ensure D90≤35μm;

[0060] The moisture content was determined by Karl Fischer method to ensure ≤0.1%;

[0061] The crystal purity is analyzed by X-ray diffraction (XRD), and the impurity content is ≤0.05% by high performance liquid chromatography (HPLC).

[0062] Preferably, the method for automatic impurity analysis is:

[0063] Use FTIR to obtain the absorption spectrum X of anhydrous calcium hydrogen phosphate, and use DNN to predict the impurity content y:

[0064] y=f(W·X+b)

[0065] Where: X is the spectrum data matrix collected by FT IR;

[0066] W is the weight matrix of the neural network;

[0067] b is the bias term;

[0068] f is a nonlinear activation function, ReLU or Sigmoid;

[0069] Input layer: FT IR absorption spectrum, 4000~400cm -1 , 1024 dimensions;

[0070] Hidden layer: 3 layers, 256, 128, 64 neurons per layer, activation function ReLU;

[0071] Output layer: 1 neuron, indicating the impurity content, unit %;

[0072] Optimization algorithm: Adam, learning rate 0.001.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] (1) The present invention uses pneumatic mixing and genetic algorithm optimization. The genetic algorithm adaptively searches for the optimal airflow velocity and shear force to improve mixing uniformity. The pneumatic suspension technology eliminates contamination from stirring equipment, improves the purity of the preparation, and ensures the mixing uniformity of the medicinal anhydrous calcium hydrogen phosphate, thereby improving the batch stability of the drug preparation.

[0075] (2) The present invention adopts reinforcement learning to automatically adjust the drying temperature according to the real-time moisture content; adopts Ql earning algorithm to dynamically select the optimal drying temperature curve to reduce residual moisture; implements adaptive temperature control strategy, reduces energy consumption, ensures the stability and controllability of excipients, and conforms to the modern green pharmaceutical concept.

[0076] (3) The present invention realizes precise particle size control and reduces ineffective energy consumption by intelligently controlling air flow pressure. The present invention integrates pulverization dynamics and intelligent control theory to construct a precise particle size control model, thereby improving the solubility and bioavailability of anhydrous calcium hydrogen phosphate in drugs and ensuring the consistency of the preparation. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The present invention is a flow chart of a method for treating anhydrous calcium hydrogen phosphate as an auxiliary material for medicinal use. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] Embodiment 1:

[0080] See also Figure 1As shown, a method for treating anhydrous calcium hydrogen phosphate excipient for pharmaceutical use comprises:

[0081] S1, raw material screening, used to preliminarily screen anhydrous calcium hydrogen phosphate to obtain anhydrous calcium hydrogen phosphate raw material that meets the target particle size range;

[0082] S2, pre-drying treatment, for pulse airflow drying the anhydrous calcium hydrogen phosphate raw material to reduce the moisture content and obtain a dry material;

[0083] S3, pneumatic mixing and homogenization, for pneumatically mixing the dry materials to improve particle uniformity and obtain a mixed material;

[0084] S4, fine grinding, for performing nano-scale airflow grinding on the mixed material to control the final particle size and obtain a grinding product;

[0085] S5, quality inspection, used to analyze the particle size, moisture, purity and impurities of the pulverized product to ensure that it meets the pharmaceutical standards and obtains qualified excipients;

[0086] S6, packaging and storage, is used to seal and store the qualified excipients to ensure stability and prevent moisture absorption, so as to obtain the final product, anhydrous calcium hydrogen phosphate excipient for pharmaceutical use.

[0087] Specifically, the preliminary screening is performed using a 200-mesh, 75 μm sieve in an environment with a temperature of 22±2° C. and a relative humidity of ≤30%;

[0088] The purity of the obtained anhydrous calcium hydrogen phosphate raw material is ≥98.5%.

[0089] Specifically, the pulse airflow drying comprises the following steps:

[0090] A pulsed airflow drying system is used, combined with a reinforcement learning method to control drying temperature;

[0091] The drying process is divided into three stages, including:

[0092] Initial stage: temperature 40℃, time 10min;

[0093] Main drying stage: temperature 85-95°C, time 30 minutes;

[0094] Terminal stage: temperature 150℃, time 15min, to prevent crystal form change;

[0095] Adopt near infrared spectrum NIR detection system to monitor moisture content in real time to ensure that the final moisture content is ≤0.5%;

[0096] Combine Fourier transform infrared spectroscopy FT IR and deep neural network DNN to automatically analyze impurities and reduce impurity generation.

[0097] Specifically, the reinforcement learning is used to dynamically adjust the drying temperature T to minimize the drying energy consumption E and ensure that the final moisture content Wf meets the requirements (≤0.5%). The reinforcement learning expression is:

[0098] R=-αE-β|Wf-Wt|

[0099] in:

[0100] R: reward value, the higher the better;

[0101] E: energy consumption, unit J;

[0102] Wf: final moisture content, unit %;

[0103] Wt: target moisture content (0.5%);

[0104] α, β: weight parameters (set according to actual needs);

[0105] Parameter Description

[0106] State space: S = {T, W}, that is, the current drying temperature T and moisture W.

[0107] Action space: A = {+5℃, -5℃, 0℃} (temperature adjustment strategy).

[0108] Strategy update: Use the Ql earning method to update the temperature adjustment strategy:

[0109]

[0110] Where Q(s,a) is the Q value of taking action a in state s;

[0111] η is the learning rate (0.1 to 0.5);

[0112] γ is the discount factor (0.9);

[0113] Dynamically adjust drying temperature to ensure efficient water removal while reducing energy consumption;

[0114] Improve the automation of the drying process and avoid repeated manual temperature adjustments;

[0115] Maintain the stable crystal form of anhydrous calcium hydrogen phosphate to ensure product quality.

[0116] Specifically, the pneumatic mixing step includes:

[0117] A high-efficiency pneumatic suspension mixer is used to mix at an air flow rate of 25 to 35 m / s, and the mixed material is obtained by optimizing the mixing uniformity through a cyclonic shear force of 1200 to 1800 Pa;

[0118] Combined with genetic algorithm, the optimal mixing parameters are automatically searched to improve the mixing uniformity.

[0119] Specifically, the genetic algorithm is used to optimize the air flow velocity v and the cyclone shear force Pc to maximize the mixing uniformity U. The genetic algorithm objective function is as follows:

[0120]

[0121] Where U: mixing uniformity, ranging from 0 to 1, the closer to 1, the higher the uniformity;

[0122] Ci: concentration of the ith particle;

[0123] Cavg: average concentration;

[0124] n: number of sample points;

[0125] Population initialization: Set the initial airflow velocity range v∈[25,35]m / s and the cyclone shear force range Pc∈[1200,1800].

[0126] Fitness function: Use U as the fitness to screen the optimal hybrid solution.

[0127] Selection, crossover, and mutation: Roulette wheel selection, single-point crossover, and 0.05 probability mutation were used.

[0128] Optimize mixing uniformity, reduce particle agglomeration, and improve mixing stability.

[0129] Reduce the time of adjusting experimental parameters and intelligently search for the optimal airflow parameters.

[0130] Ensure batch consistency of anhydrous calcium hydrogen phosphate for pharmaceutical use and improve production efficiency.

[0131] Specifically, the inlet airflow pressure of the nano-scale airflow pulverization is 0.6-0.8 MPa;

[0132] The classification speed is adjusted by the adaptive eddy current classification algorithm AVC to make the particle size distribution D90≤35μm;

[0133] The gradient descent method is used to optimize the crushing energy consumption and reduce energy consumption, so that the unit energy consumption is reduced by 15% to 22%.

[0134] Specifically, the adaptive eddy current classification algorithm is used to adjust the classification speed ω of the air flow mill to ensure the target particle size D90≤35. The expression of the adaptive eddy current classification algorithm is:

[0135]

[0136] Where: ω*: optimal classification speed, rpm;

[0137] D90(ω): 90% cumulative particle size at the current speed;

[0138] Dt = 35 μm: target particle size;

[0139] The gradient descent method is used to dynamically adjust ω, and the expression of gradient descent is:

[0140]

[0141] Where λ is the step size parameter, which is generally set to 0.01 to 0.1;

[0142] Initial speed: 1000~5000rpm;

[0143] Step size control: λ = 0.05 to prevent oscillation;

[0144] Error threshold: When |D90-Dt|<0.5μm, the iteration is stopped;

[0145] Precisely control the crushing particle size to ensure particle size uniformity and avoid over-crushing;

[0146] Optimize energy consumption and reduce excess energy loss during the crushing process;

[0147] Improve the dispersibility of medicinal anhydrous calcium hydrogen phosphate and enhance its efficacy.

[0148] Specifically, the steps of quality inspection include:

[0149] Use laser particle size analyzer to analyze particle size to ensure D90≤35μm;

[0150] The moisture content was determined by Karl Fischer method to ensure ≤0.5%;

[0151] The crystal purity is analyzed by X-ray diffraction (XRD), and the impurity content is ≤0.05% by high performance liquid chromatography (HPLC).

[0152] Specifically, the method for automatic impurity analysis is:

[0153] Use FTIR to obtain the absorption spectrum X of anhydrous calcium hydrogen phosphate, and use DNN to predict the impurity content y:

[0154] y=f(W·X+b)

[0155] Where: X is the spectrum data matrix collected by FT IR;

[0156] W is the weight matrix of the neural network;

[0157] b is the bias term;

[0158] f is a nonlinear activation function, ReLU or Sigmoid;

[0159] Input layer: FT IR absorption spectrum, 4000~400cm -1 , 1024 dimensions;

[0160] Hidden layer: 3 layers, 256, 128, 64 neurons per layer, activation function ReLU;

[0161] Output layer: 1 neuron, indicating the impurity content, unit %;

[0162] Optimization algorithm: Adam, learning rate 0.001;

[0163] Real-time detection of impurity content to reduce human errors.

[0164] Improve the detection speed, 5 to 10 times faster than the traditional HPLC method.

[0165] Intelligently identify impurity types and automatically learn the spectral characteristics of different impurities through training data.

[0166] As can be seen from the above, the present invention uses pneumatic mixing and genetic algorithm optimization, the genetic algorithm adaptively searches for the optimal airflow velocity and shear force, improves the mixing uniformity, and the pneumatic suspension technology eliminates the pollution of the stirring device, improves the purity of the preparation, ensures the mixing uniformity of the medicinal anhydrous calcium hydrogen phosphate, thereby improving the batch stability of the drug preparation;

[0167] Reinforcement learning is used to automatically adjust the drying temperature according to the real-time moisture content; the Qleaning algorithm is used to dynamically select the optimal drying temperature curve to reduce residual moisture; an adaptive temperature control strategy is implemented to reduce energy consumption, ensure the stability and controllability of excipients, and comply with the modern green pharmaceutical concept;

[0168] By intelligently controlling the air flow pressure, precise particle size control is achieved and ineffective energy consumption is reduced. The present invention integrates pulverization dynamics and intelligent control theory to construct a precise particle size control model, improve the solubility and bioavailability of anhydrous calcium hydrogen phosphate in drugs, and ensure the consistency of the preparation.

[0169] Embodiment 2:

[0170] Efficient and homogeneous pneumatic mixing optimization

[0171] Genetic algorithm (GA) was used to optimize pneumatic mixing parameters to improve the mixing uniformity of anhydrous calcium hydrogen phosphate, reduce particle agglomeration, and ensure the consistency of pharmaceutical excipients.

[0172] Implementation steps and parameters

[0173] (1) Experimental conditions

[0174] Raw material: anhydrous calcium hydrogen phosphate (DCPA), initial particle size D90 = 37.5 μm, batch size 50 kg.

[0175] Equipment: High-efficiency pneumatic suspension mixer (rated air flow velocity 20-40m / s).

[0176] Experimental environment: temperature 22°C, relative humidity 30%.

[0177] (2) Optimization algorithm

[0178] Objective function (maximizing uniformity U):

[0179]

[0180] Parameter search range:

[0181] Air velocity: v∈[25,35]m / s

[0182] Cyclone shear force: Pc∈[1200,1800]Pa

[0183] Mixing time: t∈[10,20]min

[0184] (3) Experimental results

[0185] After 50 generations of GA evolution, the optimal parameters are:

[0186] Air flow speed: 30.8m / s

[0187] Cyclone shear force: 1550Pa

[0188] Mixing time: 13min

[0189] Comparison before and after optimization:

[0190] The mixing uniformity U increased by 18.6% (before optimization: U=0.85; after optimization: U=0.99).

[0191] The particle agglomeration rate was reduced by 42.3% (before optimization: 13.5%; after optimization: 7.8%).

[0192] The moisture content of the final mixture is stable ≤0.5%, meeting the pharmacopoeia standard.

[0193] From the above, it can be seen that the optimized mixing uniformity is significantly improved, particle agglomeration is avoided, and the stability of tablet and capsule preparations is improved.

[0194] Mixing time is reduced by 35%, which improves production efficiency and reduces energy consumption.

[0195] Automatically adjust mixing parameters through intelligent optimization, reducing human intervention and improving batch consistency.

[0196] Embodiment three:

[0197] Intelligent drying optimization and crushing

[0198] Target

[0199] The drying temperature was dynamically adjusted by optimizing reinforcement learning (RL) to ensure that the moisture content of anhydrous calcium hydrogen phosphate was reduced to ≤0.5%, and the crushing particle size was optimized by using an adaptive eddy current classification algorithm (AVC) to ensure that D90 was ≤35μm, thereby improving the dispersibility of the drug.

[0200] Implementation steps and parameters

[0201] (1) Experimental conditions

[0202] Raw materials: sieved anhydrous calcium hydrogen phosphate (DCPA), initial moisture 1.5%, batch 100kg.

[0203] equipment:

[0204] Pulse airflow drying system (variable temperature control, 40~95℃).

[0205] Nano-scale air flow mill (inlet air flow pressure 0.6~0.8MPa).

[0206] Experimental environment: temperature 24°C, relative humidity 28%.

[0207] (2) Intelligent drying optimization

[0208] Objective function (minimize drying energy consumption E and moisture Wf ≤ 0.5%):

[0209] R=-αE-β|Wf-Wt|

[0210] Reinforcement learning parameters:

[0211] State space: S = {T, W} (drying temperature T, moisture W).

[0212] Action space: A = {+5℃, -5℃, 0℃} (temperature adjustment strategy).

[0213] Learning rate: η=0.3.

[0214] Discount factor: γ = 0.9.

[0215] (3) Optimize drying parameters

[0216] After 10,000 rounds of training, the optimal temperature change curve is obtained:

[0217] Initial temperature: 45℃(10min)

[0218] Heating stage: 80℃(25min)

[0219] High temperature dehydration: 95℃(30min)

[0220] Cooling stability: 50℃(15min)

[0221] (4) Optimize crushing parameters

[0222] Objective function (minimizing particle size deviation):

[0223]

[0224] Gradient descent iterations:

[0225] Initial speed: 3500rpm

[0226] Final optimized speed: 4120rpm

[0227] The final D90=34.8 μm, meeting the pharmaceutical standards.

[0228] (5) Experimental results

[0229] Comparison before and after optimization:

[0230] The drying energy consumption was reduced by 22.5% (before optimization: 6.8 kWh / kg, after optimization: 5.3 kWh / kg).

[0231] The final moisture content is stable at ≤0.5%, and the qualified rate is increased by 28%.

[0232] The particle size distribution is more uniform, and D90 is controlled at 34.8 μm (before optimization: D90 = 37.5 μm).

[0233] The crushing energy consumption was reduced by 15.7%, and the unit energy consumption was reduced from 2.6kWh / kg to 2.2kWh / kg.

[0234] From the above, it can be seen that the intelligent drying process reduces energy consumption, improves efficiency and ensures the stability of anhydrous calcium hydrogen phosphate.

[0235] Intelligent pulverization optimization improves the uniformity of particle distribution, reduces the proportion of ultrafine powder, and improves the solubility of preparations.

[0236] Intelligent monitoring throughout the process and automatic adjustment of parameters can improve the intelligence level of the process and reduce human intervention.

[0237] Through intelligent optimization algorithms, the production efficiency and quality stability of anhydrous calcium hydrogen phosphate for pharmaceutical use can be effectively improved, the process flow can be made more intelligent, manual intervention can be reduced, and production consistency can be improved. At the same time, energy consumption can be reduced and output can be increased, which has significant economic benefits and market competitiveness in industrial applications.

[0238] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0239] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0240] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for treating anhydrous calcium hydrogen phosphate as an auxiliary material for medicinal use, characterized in that: The following steps are involved: S1, raw material screening, used to preliminarily screen anhydrous calcium hydrogen phosphate to obtain anhydrous calcium hydrogen phosphate raw material that meets the target particle size range; S2, pre-drying treatment, for pulse airflow drying the anhydrous calcium hydrogen phosphate raw material to reduce the moisture content and obtain a dry material; S3, pneumatic mixing and homogenization, for pneumatically mixing the dry material to obtain a mixed material; S4, fine grinding, for performing nano-scale airflow grinding on the mixed material to control the final particle size and obtain a grinding product; S5, quality inspection, for analyzing the particle size, moisture, purity and impurities of the pulverized product to obtain qualified auxiliary materials; S6, packaging and storage, is used to seal and store the qualified excipients to ensure stability and prevent moisture absorption, so as to obtain the final product, anhydrous calcium hydrogen phosphate excipient for pharmaceutical use.

2. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 1, characterized in that: The preliminary screening is performed using a 200 mesh, 75 μm sieve, in an environment with a temperature of 22±2°C and a relative humidity of ≤30%; The purity of the obtained anhydrous calcium hydrogen phosphate raw material is ≥98.5%.

3. A method for treating anhydrous calcium hydrogen phosphate as an auxiliary material for pharmaceutical use according to claim 1, characterized in that: The specific steps of pulse airflow drying are: A pulsed airflow drying system is used, combined with a reinforcement learning method to control drying temperature; The drying process is divided into three stages, including: Initial stage: temperature 40℃, time 10min; Main drying stage: temperature 85-95°C, time 30 minutes; Terminal stage: temperature 150℃, time 15min, to prevent crystal form change; Adopt near infrared spectrum NIR detection system to monitor moisture content in real time to ensure that the final moisture content is ≤0.5%; Combine Fourier transform infrared spectroscopy FTIR and deep neural network DNN to automatically analyze impurities and reduce impurity generation.

4. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 3, characterized in that: The reinforcement learning is used to dynamically adjust the drying temperature T to minimize the drying energy consumption E and ensure that the final moisture content Wf meets the requirements. The reinforcement learning expression is: R=-αE-βWf-Wt Where: R: reward value, the higher the better; E: energy consumption, unit J; Wf: final moisture content, unit %; Wt: target moisture content; α, β: weight parameters.

5. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 1, characterized in that: The pneumatic mixing step comprises: A high-efficiency pneumatic suspension mixer is used to mix at an air flow rate of 25 to 35 m / s, and the mixed material is obtained by optimizing the mixing uniformity through a cyclonic shear force of 1200 to 1800 Pa; Combined with genetic algorithm to automatically search for optimal mixing parameters.

6. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 5, characterized in that: The genetic algorithm is used to optimize the air flow velocity v and the cyclone shear force Pc to maximize the mixing uniformity U. The genetic algorithm objective function is as follows: Where U: mixing uniformity, ranging from 0 to 1, the closer to 1, the higher the uniformity; Ci: concentration of the ith particle; Cavg: average concentration; n: number of sample points.

7. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 1, characterized in that: The inlet air flow pressure of the nano-scale air flow milling is 0.6-0.8 MPa; The classification speed is adjusted by the adaptive eddy current classification algorithm AVC to make the particle size distribution D90≤35μm; The gradient descent method is used to optimize the crushing energy consumption and reduce energy consumption, so that the unit energy consumption is reduced by 15% to 22%.

8. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 7, characterized in that: The adaptive eddy current classification algorithm is used to adjust the classification speed ω of the air flow mill to ensure the target particle size D90≤35. The expression of the adaptive eddy current classification algorithm is: Where: ω*: optimal classification speed, rpm; D90(ω): 90% cumulative particle size at the current speed; Dt = 35 μm: target particle size; The gradient descent method is used to dynamically adjust ω, and the expression of gradient descent is: Where λ is the step size parameter, which is generally set to 0.01 to 0.1; Initial speed: 1000~5000rpm; Step size control: λ = 0.05 to prevent oscillation; Error threshold: Stop iteration when |D90-Dt|<0.5μm.

9. A method for treating anhydrous calcium hydrogen phosphate as a pharmaceutical auxiliary material according to claim 1, characterized in that: The steps of quality inspection include: Use laser particle size analyzer to analyze particle size to ensure D90≤35μm; The moisture content was determined by Karl Fischer method to ensure ≤0.5%; The crystal purity is analyzed by X-ray diffraction (XRD), and the impurity content is ≤0.05% by high performance liquid chromatography (HPLC).

10. A method for treating anhydrous calcium hydrogen phosphate as an auxiliary material for pharmaceutical use according to claim 9, characterized in that: The method for automatic impurity analysis is: Use FTIR to obtain the absorption spectrum X of anhydrous calcium hydrogen phosphate, and use DNN to predict the impurity content y: y=f(W·X+b) Where: X is the spectrum data matrix collected by FTIR; W is the weight matrix of the neural network; b is the bias term; f is a nonlinear activation function, ReLU or Sigmoid; Input layer: FTIR absorption spectrum, 4000~400cm -1 , 1024 dimensions; Hidden layer: 3 layers, 256, 128, 64 neurons per layer, activation function ReLU; Output layer: 1 neuron, indicating the impurity content, unit %; Optimization algorithm: Adam, learning rate 0.001.

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