scholarly journals Design Space Calculation and Continuous Improvement Considering a Noise Parameter: A Case Study of Ethanol Precipitation Process Optimization for Carthami Flos Extract

Separations ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. 74
Author(s):  
Yanni Tai ◽  
Haibin Qu ◽  
Xingchu Gong
Keyword(s):  
Mathematical Models ◽  
Continuous Improvement ◽  
Process Parameters ◽  
Design Space ◽  
Volume Ratio ◽  
Life Cycle Management ◽  
Precipitation Process ◽  
Noise Parameter ◽  
Ethanol Precipitation ◽  
Screening Design

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 can be regarded as noise parameters. In this study, based on the QbD concept, the ethanol precipitation process of Carthami Flos extract was optimized, considering a noise parameter. 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 critical process parameters (CPPs), using a definitive screening design. The mathematical models among CPPs and evaluation indicators were established. Considering that the refrigeration temperature of industrial ethanol precipitation is often difficult to control with seasonal changes, refrigeration temperature was treated as a noise parameter. A calculation method for the design space in the presence of the noise parameter was proposed. The design space was calculated according to the probability of reaching the standards of evaluation indicators. Controlling parameters within the design space was expected to reduce the influence of noise parameter fluctuations on the quality of the ethanol precipitation supernatant. With more data obtained, the design space was updated. In industry, it is also recommended to adopt a similar idea: that is, continuing to collect industrial data and regularly updating mathematical models, which can further update the design space and make it more stable and reliable.

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

2021 ◽  
Author(s):  
Yanni Tai ◽  
Haibin Qu ◽  
Xingchu Gong
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Mathematical Models ◽  
Continuous Improvement ◽  
Process Parameters ◽  
Design Space ◽  
Exhaustive Search ◽  
Precipitation Process ◽  
Noise Parameter ◽  
Ethanol Precipitation

Abstract 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.

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

2020 ◽  
Author(s):  
Jingjing Pan ◽  
Yanni Tai ◽  
Haibin Qu ◽  
Xingchu Gong
Keyword(s):  
Temperature Control ◽  
Control Strategy ◽  
Design Space ◽  
Control Method ◽  
Precipitation Process ◽  
Separation And Purification ◽  
Critical Material ◽  
Astragali Radix ◽  
Ethanol Precipitation ◽  
Screening Design

Abstract Background Ethanol precipitation is an important separation and purification process in the traditional Chinese medicine (TCM) industry. However, ethanol precipitation process suffers from loss of active ingredients, low batch-to-batch consistency of supernatant composition, and long standing time. These problems are mainly caused by low efficient equipment and imperfect control strategy. Methods In the present study, a membrane dispersion ethanol precipitation device which can achieve rapid cooling was developed for Astragali radix ethanol precipitation. Ethanol precipitation process was optimized according to Quality by design concept. To identify critical material attributes (CMAs), ten batches of Astragali radix was used to prepare concentrates. Definitive screening design was used to investigate the relationships among critical process parameters, CMAs, and process critical quality attributes (CQAs). Results Calycosin-7-O-β-D-glucoside content, the sucrose content, and the electrical conductivity were found to be CMAs after the correlation analysis and stepwise regression modelling. Quadratic models were developed and design space was calculated according to the probability of attaining process CQA standards. A material quality control strategy was proposed. High quality and low quality Astragali radix concentrates can be discriminated by the inequalities. Low quality Astragali radix concentrates should not be released for ethanol precipitation process directly. Verification experiment results indicated accurate models and reliable design space. Conclusions The temperature control method and control strategy in this study are effective and promising for ethanol precipitation process of other TCMs or foods.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jingjing Pan ◽  
Yanni Tai ◽  
Haibin Qu ◽  
Xingchu Gong
Keyword(s):  
Temperature Control ◽  
Control Strategy ◽  
Design Space ◽  
Control Method ◽  
Precipitation Process ◽  
Separation And Purification ◽  
Critical Material ◽  
Astragali Radix ◽  
Ethanol Precipitation ◽  
Experimental Apparatus

Abstract Ethanol precipitation is an important separation and purification process in the traditional Chinese medicines (TCMs) industry. In the present study, a membrane dispersion micromixer was applied to achieve good mixing for the ethanol precipitation process of Astragali radix concentrate. New experimental apparatus was set up to rapidly lower the temperature of ethanol solution before mixing with the concentrate. Ethanol precipitation process was optimized according to Quality by design concept. To identify critical material attributes (CMAs), ten batches of Astragali radix were used to prepare concentrates. Calycosin-7-O-β-D-glucoside content, the sucrose content, and the electrical conductivity were found to be CMAs after the correlation analysis and stepwise regression modelling. Definitive screening design was used to investigate the relationships among critical process parameters, CMAs, and process critical quality attributes (CQAs). Quadratic models were developed and design space was calculated according to the probability of attaining process CQA standards. A material quality control strategy was proposed. High quality and low quality Astragali radix concentrates can be discriminated by the inequalities. Low quality Astragali radix concentrates should not be released for ethanol precipitation process directly. Verification experiment results indicated accurate models and reliable design space. The temperature control method and control strategy are promising for ethanol precipitation process of other TCMs or foods.

Optimization of Process Parameters for Production of Pectinase using Bacillus Subtilis MF447840.1

2019 ◽  
Vol 13 (1) ◽  
pp. 69-73 ◽  
Author(s):  
Ram Balak Mahto ◽  
Mukesh Yadav ◽  
Soumya Sasmal ◽  
Biswnath Bhunia
Keyword(s):  
Bacillus Subtilis ◽  
Process Parameters ◽  
Volume Ratio ◽  
Optimum Process ◽  
Agitation Rate ◽  
Fermentation Condition ◽  
Optimization Of Process Parameters ◽  
Food And Beverage ◽  
Pectinase Production ◽  
Sterile Product

Background: Pectinase enzyme has immense industrial prospects in the food and beverage industries. </P><P> Objective: In our investigation, we find out the optimum process parameters suitable for better pectinase generation by Bacillus subtilis MF447840.1 using submerged fermentation. </P><P> Method: 2% (OD600 nm = 0.2) of pure Bacillus subtilis MF447840.1 bacterial culture was inoculated in sterile product production media. The production media components used for this study were 1 g/l of pectin, 2 g/l of (NH4)2SO4, 1 g/l of NaCl, 0.25 g/l of K2HPO4, 0.25 g/l of KH2PO4 and 1 g/l of MgSO4 for pectinase generation. We reviewed all recent patents on pectinase production and utilization. The various process parameters were observed by changing one variable time method. </P><P> Results: The optimum fermentation condition of different parameters was noticed to be 5% inoculums, 25% volume ratio, temperature (37°C), pH (7.4) and agitation rate (120 rpm) following 4 days incubation. </P><P> Conclusion: Maximum pectinase generation was noticed as 345 ± 12.35 U following 4 days incubation.

Numerical Analysis on the Extruded Volume and Length Ratios of Backward Tube to Forward Rod in Combined Extrusion Processes

2006 ◽  
Vol 519-521 ◽  
pp. 919-924 ◽  
Author(s):  
B.S. Ham ◽  
J.H. Ok ◽  
Jung Min Seo ◽  
Beong Bok Hwang ◽  
K.H. Min ◽  
...  
Keyword(s):  
Process Parameters ◽  
Material Flow ◽  
Volume Ratio ◽  
Forward Direction ◽  
Extrusion Process ◽  
Forming Load ◽  
Tube Extrusion ◽  
Corner Radius ◽  
Combined Operation ◽  
Simulation Results

This paper is concerned with forward rod extrusion combined simultaneously with backward tube extrusion process in both steady and transient states. The analysis has been conducted in numerical manner by employing a rigid-plastic finite element method. AA 2024 aluminum alloy was selected as a model material for analysis. Among many process parameters, major design factors chosen for analysis include frictional condition, thickness of tube in backward direction, punch corner radius, and die corner radius. The main goal of this study is to investigate the material flow characteristics in combined extrusion process, i.e. forward rod extrusion combined simultaneously with backward tube extrusion process. Simulation results have been summarized in term of relationships between process parameters and extruded length and volume ratios, and between process parameters and force requirements, respectively. The extruded length ratio is defined as the ratio of tube length extruded in backward direction to rod length extruded in forward direction, and the volume ratio as that of extruded volume in backward direction to that in forward direction, respectively. It has been revealed from the simulation results that material flow into both backward and forward directions are mostly influenced by the backward tube thickness, and other process parameters such as die corner radius etc. have little influence on the volume ratio particularly in steady state of combined extrusion process. The pressure distributions along the tool-workpiece interface have been also analyzed such that the pressure exerted on die is not so significant in this particular process such as combined operation process. Comparisons between multi-stage forming process in sequence operation and one stage combined operation have been also made in terms of forming load and pressure exerted on die. The simulation results shows that the combined extrusion process has the greatest advantage of lower forming load comparing to that in sequence operation.

scholarly journals Design Space and QbD Approach for Production of Drug Nanocrystals by Wet Media Milling Techniques

Pharmaceutics ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 104 ◽  
Author(s):  
Leena Peltonen
Keyword(s):  
Process Parameters ◽  
Design Space ◽  
Milling Process ◽  
Production Chain ◽  
Media Milling ◽  
Process Understanding ◽  
Poorly Soluble ◽  
Critical Process Parameters ◽  
Input Variables ◽  
Definition Of

Drug nanocrystals are nanosized solid drug particles, the most important application of which is the improvement of solubility properties of poorly soluble drug materials. Drug nanocrystals can be produced by many different techniques, but the mostly used are different kinds of media milling techniques; in milling, particle size of bulk sized drug material is decreased, with the aid of milling beads, to nanometer scale. Utilization of Quality by Design, QbD, approach in nanomilling improves the process-understanding of the system, and recently, the number of studies using the QbD approach in nanomilling has increased. In the QbD approach, the quality is built into the products and processes throughout the whole production chain. Definition of Critical Quality Attributes, CQAs, determines the targeted final product properties. CQAs are confirmed by setting Critical Process Parameters, CPPs, which include both process parameters but also input variables, like stabilizer amount or the solid state form of the drug. Finally, Design Space determines the limits in which CPPs should be in order to reach CQAs. This review discusses the milling process and process variables, CPPs, their impact on product properties, CQAs and challenges of the QbD approach in nanomilling studies.

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