软胶囊滴丸制药生产过程的建模与优化控制研究
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摘要
本论文以软胶囊滴丸制药生产工艺过程为研究对象,以提高软胶囊滴丸产品合格率和质量稳定性为研究目标,基于智能控制、数据驱动建模与智能优化算法对该过程进行建模与优化控制研究。主要工作如下:
首先,对软胶囊滴丸制药生产工艺过程进行了系统分析:软胶囊滴丸过程包含了多个子系统,各子系统本身也各具特点,如明胶溶液生成过程子系统包含多个环节,期间会发生复杂的物理和化学变化,具有明显的非线性,中间控制变量较多,是一个十分复杂的工业过程;明胶温度子系统、石蜡油液位子系统和脉冲压力子系统中则存在不同程度的非线性、参数时变性和纯滞后等问题。在此基础上,针对该过程中存在的问题和各子系统的不同特点,设计相应的控制策略,以期达到较好的控制效果。
接下来,对明胶溶液生成过程子系统进行了研究,提出一种多阶段逆模型建模方法,控制明胶溶液生成过程工艺参数。明胶溶液生成过程包含多个阶段,整体建模方法往往将其简化为一个阶段,不考虑大量的中间变量和参数。本研究采用多阶段建模方法,能够克服整体建模方法的缺点并提高模型精度;同时采用逆模型方法,可以根据胶液质量指标的要求来确定控制变量的设定值,方便操作和控制。
然后,分别对明胶温度子系统、石蜡油液位子系统、脉冲压力子系统进行建模和控制研究,分别建立了各子系统的数学模型;将模糊控制、自适应控制、PID控制以及基于模式识别的智能控制方法相结合,设计出混合型模糊PID(Fuzzy-PID)、模型参考模糊自适应PID(MRFA-PID)、基于模式识别的自适应控制(PR-PID)三种新型智能控制策略,并分别应用于上述子系统,解决该过程中的非线性、参数时变性和纯滞后等问题,在保证控制系统稳态精度的同时,改善系统的动态性能,减少超调量和调节时间,提高控制系统的鲁棒性。
本文研究的最终目标是提高软胶囊滴丸产品合格率和质量稳定性。需要对影响软胶囊滴丸的关键工艺参数,包括各子系统的设定值进行优化设计。因此,在完成对各子系统的建模和优化控制的基础上,对软胶囊滴丸过程产品质量进行建模和优化研究。首先详细分析了软胶囊滴丸生产过程质量控制的性能要求和影响产品生产过程质量的因素,给出软胶囊滴丸产品的二级层次结构质量指标体系和操作变量;软胶囊滴丸生产过程中,面向产品质量的建模由于变量众多,关系复杂,统计建模和机理建模均无法取得理想效果,因此本文以软胶囊滴丸制剂过程工艺参数胶液黏度、胶液温度、石蜡油温度等为输入,以圆整度、拖尾情况、完好率和胶丝情况等产品质量指标为输出,采用最小二乘支持向量回归机等基于数据驱动的智能建模方法建立较高精度的软胶囊滴丸产品质量模型。
在此基础上,本文用层次分析法(AHP)确定各二级指标的权重,然后,基于微粒群(PSO)算法求解以提高软胶囊滴丸成品率为最终目标的多目标优化问题,对模型输入参数空间进行寻优,确定成品率最高时所对应的工艺参数标称值,对产品质量进行优化控制。经生产试制,利用优化后的工艺参数值进行离线指导能使该制剂过程的成品率提高约2.7个百分点,表明利用LSSVM与PSO算法对软胶囊滴丸制剂过程进行建模与优化是合理的,为软胶囊制剂过程的优化提供了一条新的途径。
This article takes soft capsule dropping pills pharmaceutical production process as the research object.In order to improve the yield of soft capsule dropping pills and its stability of product quality,the modeling and optimizing control are studied based on intelligent control,data-driven modeling and intelligent optimization algorithms. The main work in this dissertation is presented as follows.
Firstly,the soft capsule dropping pills pharmaceutical production process is analyzed systematically.The soft capsule dropping pills process contains a number of subsystems,each subsystem with its own characteristics,such as gelatin solution generation subsystem is a very complex industrial processe,which includes a number of links,there are complex physical and chemical changes in the process,with obvious non-linear and many intermediate control variables.The gelatin temperature subsystem,paraffin oil level subsystem and pulse pressure subsystem are also very complexed with the characteristics of non-linear,time-varying parameters and time delay and so on.On this basis,aiming at the problems of the process and the different characteristics of the subsystems,design the appropriate control strategy to achieve better control results.
Then,the gelatin solution production process subsystem was studied.This paper investigates multistage inverse modeling for a class of serially connected industrial large-scale systems.To control the quality of the production,inverse modeling is to obtain the model of the required process conditions and the control variable set points of a process system by backward reasoning with the specified product qualities as a starting point.The existing methods of inverse modeling establish the inverse model for the whole process which is generally difficult and rough.To reduce the difficulty and to improve the accuracy of the model,multistage inverse modeling method is proposed in this paper.The inverse models are established by using least squares support vector machine(LSSVM)and BP neural network (BP-NN)respectively.As an illustration of the effectiveness of the proposed method, we consider the production quality control problem of the gelatin solution production process.The simulation results indicate the model based on the proposed method has smaller error and higher hit rates.
And then,the modeling and control of gelatin temperature control subsystem, paraffin oil level subsystem and pulse pressure subsystem are studied respectively. The mathematical models of the subsystems are established respectively.Aiming at the problems of nonlinear,time-variant and coupling existing in soft capsule dropping pills system,three kinds of novel intelligent control strategies are proposed based on fuzzy logic controller,adaptive control,PID controller and intelligent control based on pattern recognition method.They are namely hybrid fuzzy PID(Fuzzy-PID), model reference fuzzy adaptive PID(MRFA-PID),and adaptive control based on pattern recognition(PR-PID).The proposed methods can improve the dynamic response,regulation precision and robustness of the closed-loop system as well as guarantees the basic requirement on stability and product quality.They are used in the above three subsystems respectively.
The ultimate goal of this study is to improve the soft capsule dropping pills pass rate and stability of product quality.The key process parameters of soft capsule dropping pills,including settings of the subsystems need an an optimization design. Therefore,on the basis of the completion of various sub-systems modeling and optimization control,the process of soft capsule dropping pills product quality modeling and optimization were studied.Soft capsule dropping pills product quality model was established based on data-driven modeling method.Soft capsule dropping pills product quality control system is a multi-input and multi-output complex system. First of all,the process parameters and a two-level hierarchy index system of soft capsule pills product quality were proposed based on the analysis to the production process.In the soft capsule dropping pills production process,there are so many variables in product quality-oriented modeling and the relationship among them are quite complex,which make physical-driven and statistical modeling very difficult, even if not impossible.So,data-driven modeling method is used to establish a higher precision product quality model of the soft capsule dropping pills.The model was established based on least squares support vector machine(LSSVM),whose inputs are the process parameters,namely gelatin solution viscosity,gelatin solution temperature,paraffin oil temperature,and pulse pressure,and outputs are the secondary quality indexes,namely spherical degree,tailed degree,breaking pill rate and collodion silk degree.
On this basis,Analysis hierarchy process(AHP)was used to determine the weights of the secondary quality indexes.And then,particle swarm optimization(PSO) algorithm was used to optimize the process parameters in order to improve the yield of soft capsule pills,which is a multi-objective optimization problem.The nominal values of the process parameters corresponding to the highest yield can be obtained. The yield increases by 2.7 percent when the optimizing parameters are used to the soft capsule dropping pills process via off-line instruction,which indicating that the method of using LSSVM and PSO in product quality modeling and optimization of soft capsule dropping pills is reasonable.It provides a new way for soft capsules process optimization.
首先,对软胶囊滴丸制药生产工艺过程进行了系统分析:软胶囊滴丸过程包含了多个子系统,各子系统本身也各具特点,如明胶溶液生成过程子系统包含多个环节,期间会发生复杂的物理和化学变化,具有明显的非线性,中间控制变量较多,是一个十分复杂的工业过程;明胶温度子系统、石蜡油液位子系统和脉冲压力子系统中则存在不同程度的非线性、参数时变性和纯滞后等问题。在此基础上,针对该过程中存在的问题和各子系统的不同特点,设计相应的控制策略,以期达到较好的控制效果。
接下来,对明胶溶液生成过程子系统进行了研究,提出一种多阶段逆模型建模方法,控制明胶溶液生成过程工艺参数。明胶溶液生成过程包含多个阶段,整体建模方法往往将其简化为一个阶段,不考虑大量的中间变量和参数。本研究采用多阶段建模方法,能够克服整体建模方法的缺点并提高模型精度;同时采用逆模型方法,可以根据胶液质量指标的要求来确定控制变量的设定值,方便操作和控制。
然后,分别对明胶温度子系统、石蜡油液位子系统、脉冲压力子系统进行建模和控制研究,分别建立了各子系统的数学模型;将模糊控制、自适应控制、PID控制以及基于模式识别的智能控制方法相结合,设计出混合型模糊PID(Fuzzy-PID)、模型参考模糊自适应PID(MRFA-PID)、基于模式识别的自适应控制(PR-PID)三种新型智能控制策略,并分别应用于上述子系统,解决该过程中的非线性、参数时变性和纯滞后等问题,在保证控制系统稳态精度的同时,改善系统的动态性能,减少超调量和调节时间,提高控制系统的鲁棒性。
本文研究的最终目标是提高软胶囊滴丸产品合格率和质量稳定性。需要对影响软胶囊滴丸的关键工艺参数,包括各子系统的设定值进行优化设计。因此,在完成对各子系统的建模和优化控制的基础上,对软胶囊滴丸过程产品质量进行建模和优化研究。首先详细分析了软胶囊滴丸生产过程质量控制的性能要求和影响产品生产过程质量的因素,给出软胶囊滴丸产品的二级层次结构质量指标体系和操作变量;软胶囊滴丸生产过程中,面向产品质量的建模由于变量众多,关系复杂,统计建模和机理建模均无法取得理想效果,因此本文以软胶囊滴丸制剂过程工艺参数胶液黏度、胶液温度、石蜡油温度等为输入,以圆整度、拖尾情况、完好率和胶丝情况等产品质量指标为输出,采用最小二乘支持向量回归机等基于数据驱动的智能建模方法建立较高精度的软胶囊滴丸产品质量模型。
在此基础上,本文用层次分析法(AHP)确定各二级指标的权重,然后,基于微粒群(PSO)算法求解以提高软胶囊滴丸成品率为最终目标的多目标优化问题,对模型输入参数空间进行寻优,确定成品率最高时所对应的工艺参数标称值,对产品质量进行优化控制。经生产试制,利用优化后的工艺参数值进行离线指导能使该制剂过程的成品率提高约2.7个百分点,表明利用LSSVM与PSO算法对软胶囊滴丸制剂过程进行建模与优化是合理的,为软胶囊制剂过程的优化提供了一条新的途径。
This article takes soft capsule dropping pills pharmaceutical production process as the research object.In order to improve the yield of soft capsule dropping pills and its stability of product quality,the modeling and optimizing control are studied based on intelligent control,data-driven modeling and intelligent optimization algorithms. The main work in this dissertation is presented as follows.
Firstly,the soft capsule dropping pills pharmaceutical production process is analyzed systematically.The soft capsule dropping pills process contains a number of subsystems,each subsystem with its own characteristics,such as gelatin solution generation subsystem is a very complex industrial processe,which includes a number of links,there are complex physical and chemical changes in the process,with obvious non-linear and many intermediate control variables.The gelatin temperature subsystem,paraffin oil level subsystem and pulse pressure subsystem are also very complexed with the characteristics of non-linear,time-varying parameters and time delay and so on.On this basis,aiming at the problems of the process and the different characteristics of the subsystems,design the appropriate control strategy to achieve better control results.
Then,the gelatin solution production process subsystem was studied.This paper investigates multistage inverse modeling for a class of serially connected industrial large-scale systems.To control the quality of the production,inverse modeling is to obtain the model of the required process conditions and the control variable set points of a process system by backward reasoning with the specified product qualities as a starting point.The existing methods of inverse modeling establish the inverse model for the whole process which is generally difficult and rough.To reduce the difficulty and to improve the accuracy of the model,multistage inverse modeling method is proposed in this paper.The inverse models are established by using least squares support vector machine(LSSVM)and BP neural network (BP-NN)respectively.As an illustration of the effectiveness of the proposed method, we consider the production quality control problem of the gelatin solution production process.The simulation results indicate the model based on the proposed method has smaller error and higher hit rates.
And then,the modeling and control of gelatin temperature control subsystem, paraffin oil level subsystem and pulse pressure subsystem are studied respectively. The mathematical models of the subsystems are established respectively.Aiming at the problems of nonlinear,time-variant and coupling existing in soft capsule dropping pills system,three kinds of novel intelligent control strategies are proposed based on fuzzy logic controller,adaptive control,PID controller and intelligent control based on pattern recognition method.They are namely hybrid fuzzy PID(Fuzzy-PID), model reference fuzzy adaptive PID(MRFA-PID),and adaptive control based on pattern recognition(PR-PID).The proposed methods can improve the dynamic response,regulation precision and robustness of the closed-loop system as well as guarantees the basic requirement on stability and product quality.They are used in the above three subsystems respectively.
The ultimate goal of this study is to improve the soft capsule dropping pills pass rate and stability of product quality.The key process parameters of soft capsule dropping pills,including settings of the subsystems need an an optimization design. Therefore,on the basis of the completion of various sub-systems modeling and optimization control,the process of soft capsule dropping pills product quality modeling and optimization were studied.Soft capsule dropping pills product quality model was established based on data-driven modeling method.Soft capsule dropping pills product quality control system is a multi-input and multi-output complex system. First of all,the process parameters and a two-level hierarchy index system of soft capsule pills product quality were proposed based on the analysis to the production process.In the soft capsule dropping pills production process,there are so many variables in product quality-oriented modeling and the relationship among them are quite complex,which make physical-driven and statistical modeling very difficult, even if not impossible.So,data-driven modeling method is used to establish a higher precision product quality model of the soft capsule dropping pills.The model was established based on least squares support vector machine(LSSVM),whose inputs are the process parameters,namely gelatin solution viscosity,gelatin solution temperature,paraffin oil temperature,and pulse pressure,and outputs are the secondary quality indexes,namely spherical degree,tailed degree,breaking pill rate and collodion silk degree.
On this basis,Analysis hierarchy process(AHP)was used to determine the weights of the secondary quality indexes.And then,particle swarm optimization(PSO) algorithm was used to optimize the process parameters in order to improve the yield of soft capsule pills,which is a multi-objective optimization problem.The nominal values of the process parameters corresponding to the highest yield can be obtained. The yield increases by 2.7 percent when the optimizing parameters are used to the soft capsule dropping pills process via off-line instruction,which indicating that the method of using LSSVM and PSO in product quality modeling and optimization of soft capsule dropping pills is reasonable.It provides a new way for soft capsules process optimization.
引文
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