Optimization of membrane dispersion ethanol precipitation process with a set of temperature control improved equipment

Pan, Jue; Tai, Yanni; Qu, Haibin; Gong, Xingchu

doi:10.21203/rs.3.rs-42371/v1

Cited by 1 publication

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the authors combined industrial practical experience, the density of the concentrated extract (CD), the temperature of the concentrated extract (CT), ethanol concentration (EC), the volume ratio of ethanol to the concentrated extract (ECR), stirring speed (SS), time of ethanol addition (EAt), stirring time after ethanol addition (St), RT, and refrigeration time (Rt) to study the process parameters. DSD was used to analyse the mathematical relationship between process parameters and evaluation indicators, and its advantage is the ability to study the in uence of multiple parameters with a small number of experiments [10,29]. The coding level and factor level of the DSD are shown in Table 1.…”

Section: Experimental Designmentioning

confidence: 99%

“…In recent years, many researchers have studied the ethanol precipitation of TCM, mainly focusing on the optimization of process parameters [2][3][4][5], process monitoring technology [6][7][8], ethanol precipitation equipment [9,10], sediment morphology [11,12], etc. There are also many reports on the optimization of ethanol precipitation parameters based on the quality by design (QbD) concept [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design space calculation and continuous improvement considering a noise parameter: A case study of ethanol precipitation process optimization for the Carthami Flos extract

Tai

Gong

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Background The optimization of process parameters in the pharmaceutical industry is often carried out according to the Quality by Design (QbD) concept. QbD also emphasizes that continuous improvement should be performed in life cycle management. Process parameters that are difficult to control in actual production could be regarded as noise parameters. In this study, a noise parameter was considered, an example of continuous improvement in the design space was provided. Methods The ethanol precipitation process of Carthami Flos (Honghua) extract was optimized based on the QbD concept. The critical process parameters (CPPs) were identified using a definitive screening design. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, the refrigeration temperature was treated as a noise parameter. The design space was then calculated using an exhaustive search-Monte Carlo method. The mathematical models were reestablished when more data were obtained and then the calculated probabilities of reaching the process standards were updated. Results The calculation procedure of design space based on an exhaustive search-Monte Carlo method was proposed. The density of the concentrated extract, ethanol concentration, the volume ratio of ethanol to concentrated extract, stirring time after ethanol addition, and refrigeration temperature were selected as CPPs. The mathematical models of CPPs and evaluation indicators were established, and the coefficient of determination of each model was greater than 0.81. The predictive performance of the models was good. After continuous improvement, the recalculated probability values were more reliable, the design space became larger. Conclusions The calculation of design space and the continuous improvement strategy considering a noise parameter was developed. In industrial production, it is also recommended to adopt this similar idea, that is, continuing to collect industrial data and regularly updating the mathematical models, which can further update the design space and make it more stable and reliable.

show abstract

Section: Experimental Designmentioning

confidence: 99%