ABSTRACT
ObjectiveA feedforward control model for dry granulation of polysaccharide components was established to guide the adjustment and optimization of critical process parameters （CPPs） in the design space， so as to reduce the impact of fluctuations in raw materials properties on the quality of medicines. MethodTaking Astragali Radix extract powder as the model drug， the design space of dry granulation CPPs was determined by Box-Behnken design. Astragali Radix mixed powder with different powder properties were prepared by mixture design， the variance inflation factor （VIF） was used to diagnose the multicollinearity of the powder properties， and principal component analysis （PCA） was used to extract the characteristic data of the model. Radial basis function neural network （RBFNN） was used to establish a feedforward control model for reflecting the relationship between the powder properties of polysaccharide components， dry granulation CPPs and one-time molding rate. ResultThe design space for dry granulation CPPs of polysaccharide components was 16-35 Hz for feeding speed， 10-23 Hz for roller speed， and 10-46 kg·cm-2 for roller pressure. The established RBFNN feedforward control model had a good predictive effect on the one-time molding rate of dry granulation of polysaccharide components， which could be used to guide the adjustment and optimization of CPPs in the design space， the relative error was 0.38%-6.73%， and the average relative error was 3.42%. ConclusionThe established feedforward control model can well reflect the relationship between the powder properties of the polysaccharide components， the dry granulation CPPs and the one-time molding rate of the granules， which can be used to guide the adjustment and optimization of CPPs in the design space， reduce the impact of material property fluctuation on product quality， and provide ideas for promoting the quality of traditional Chinese medicine from passive control to active control.
ABSTRACT
This paper aims to construct a Bayesian(BN) fault diagnosis model of traditional Chinese medicine dry granulation based on the failure model and effect analysis(FMEA), effectively control risk factors and ensure the quality of granules.Firstly, the risk ana-lysis of dry granulation process was carried out with FMEA, and the selected medium and high risk factors were taken as node variables to establish corresponding BN network with causality.According to the mathematical reasoning method of probability theory, the model was accurately inferred and verified by Netica, and the granule nonconformance was used as the evidence for reversed reasoning to determine the most likely cause of the failure that affected the granule quality.The BN fault diagnosis model of traditional Chinese medicine dry gra-nulation was established based on the medium and high risk factors of process, prescription and equipment screened out by FMEA, such as roller pressure, raw material viscosity, clearance between rollers in the paper.The fault diagnosis of traditional Chinese medicine dry granulation process was then carried out according to the model, and the posterior probability of each node under the premise of nonconforming granule quality was obtained.This method could provide strong support for operators to quickly eliminate faults and make decisions, so as to improve the efficiency and accuracy for fault diagnosis and prediction, with innovation in its application.