Article(id=1190335353585238256, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1190335347767743264, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2024-0977, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1725897600000, receivedDateStr=2024-09-10, revisedDate=1734105600000, revisedDateStr=2024-12-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1761727663655, onlineDateStr=2025-10-29, pubDate=1744387200000, pubDateStr=2025-04-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1761727663655, onlineIssueDateStr=2025-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1761727663655, creator=13701087609, updateTime=1761727663655, updator=13701087609, issue=Issue{id=1190335347767743264, tenantId=1146029695717560320, journalId=1189982191388893191, year='2025', volume='60', issue='4', pageStart='843', pageEnd='1182', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1761727662269, creator=13701087609, updateTime=1761729313427, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1190342273276678997, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1190335347767743264, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1190342273276678998, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1190335347767743264, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1137, endPage=1146, ext={EN=ArticleExt(id=1190335353753010417, articleId=1190335353585238256, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=Research on the calibration method of discrete element physical property parameters in the pressing process of binary mixed particles, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

The study of micromechanical behavior during tablet pressing is often limited to phenomenological studies, accurate calibration of the discrete element model parameters of binary particles is a prerequisite for conducting modeling research on tablet compression. In this study, we take pregelatinized starch and microcrystalline cellulose as the research objects, and apply the Edinburgh elasto-plastic adhesion (EEPA) contact model to establish a discrete elemental simulation model for binary material pressing. Taking tablet force-hardness and force-volume reduction as response values, the optimal values of the discrete meta-parameters that significantly affect tablet pressing are obtained by applying Plackett-Burman design, Latin hypercubic sampling, Kriging model, and Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) calibration. The results obtained the optimum combination of discrete element method (DEM) parameters for pregelatinized starch, Poisson's ratio 0.257, shear modulus 1×109 Pa, granule-particle static friction coefficient 0.165, unit normal stiffness 2.419 2×109 N·m-3, unit tangential stiffness 7.954 6×109 N·m-3, and strength of adhesive force -0.009 155 8 N. For microcrystalline cellulose, Poisson's ratio 0.381, shear modulus 1.04×109 Pa, particle-particle static friction coefficient 0.719, unit normal stiffness 3.171 5×109 N·m-3, unit tangential stiffness 6.746 2×109 N·m-3, and strength of adhesion -0.038 7 N. For optimum combinations of the DEM parameters for the binary blend of excipients, particle-particle collision recovery coefficient 0.1, unit normal stiffness 9.947 1×109 N·m-3, unit tangential stiffness 1.994 5×109 N·m-3, and adhesion force strength -0.060 35. The simulation results under the optimal parameter combination are similar to the experimental results, indicating that the calibrated parameters can be used for discrete element simulation research and provide theoretical basis and data support for the subsequent intelligent and continuous production of tablets.

, correspAuthors=Hui-min SUN, Chuan-yun DAI, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2025 Acta Pharmaceutica Sinica. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=An-qi LUO, Meng-tao ZHAO, Chi MA, Qin XIAO, Zheng LU, Tian-bing GUAN, Shuang-kou CHEN, Hui-min SUN, Hai-jun HUANG, Ai-ping WANG, Chuan-yun DAI), CN=ArticleExt(id=1190335922492244522, articleId=1190335353585238256, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=二元混合颗粒压制过程离散元物性参数标定方法研究, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

针对现阶段研究片剂压制过程中微观力学行为常局限于唯象研究的问题, 准确标定二元颗粒的离散元模型参数, 是开展片剂压制建模研究的先决条件。本研究以预胶化淀粉和微晶纤维素为研究对象, 应用Edinburgh elasto-plastic adhesion (EEPA) 接触模型建立二元物料压制离散元仿真模型。以片剂压力-硬度、压力-体积减少量为响应值, 应用Plackett-Burman设计、拉丁超立方抽样、Kriging模型, 以及非支配排序遗传算法(NSGA-Ⅱ) 标定得到显著影响片剂压制的离散元(discrete element method, DEM) 参数最优值。结果表明, 预胶化淀粉的DEM参数的最佳组合, 即泊松比0.257、剪切模量1×109 Pa、颗粒-颗粒间静摩擦系数0.165、单位法向刚度2.419 2×109 N·m-3、单位切向刚度7.954 6×109 N·m-3、黏附力强度-0.009 155 8 N; 微晶纤维素的DEM参数的最佳组合, 即泊松比0.381、剪切模量1.04×109 Pa、颗粒-颗粒间静摩擦系数0.719、单位法向刚度3.171 5×109 N·m-3、单位切向刚度6.746 2×109 N·m-3、黏附力强度-0.038 7 N; 二元混合辅料间的DEM参数的最佳组合, 即颗粒-颗粒间碰撞恢复系数0.1, 单位法向刚度9.947 1×109 N·m-3, 单位切向刚度1.994 5×109 N·m-3, 黏附力强度-0.060 35 N。最佳参数组合下的模拟与物理实验结果相似, 表明标定得到的参数可用于离散元仿真研究, 为后续片剂的智能化与连续化生产提供理论基础和数据支撑。

, correspAuthors=孙会敏, 戴传云, authorNote=null, correspAuthorsNote=
孙会敏, Tel / Fax: 86-10-67095721, E-mail:
戴传云, Tel / Fax: 86-23-65022212, E-mail:
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Evaluation of empirical Bayesian kriging[J]. Spat Stat, 2019, 32: 100368., articleTitle=null, refAbstract=null), Reference(id=1190355003614269785, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=21, rfOrder=20, authorNames=null, journalName=null, refType=null, unstructuredReference=Wang PQ, Ju RS, Zhang M, et al. Data collection strategy of HLA simulation system based on non-dominated genetic algorithm [J]. 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Powder Technol, 2008, 188: 153-165., articleTitle=null, refAbstract=null)], funds=[Fund(id=1190355001248682307, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, awardId=2023NSCQ-LZX0187, language=CN, fundingSource=重庆市自然科学基金创新发展联合基金项目(2023NSCQ-LZX0187), fundOrder=null, country=null), Fund(id=1190355001311596868, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, awardId=CSTB2023NSCQ-MSX0712, language=CN, fundingSource=重庆市自然基金面上项目(CSTB2023NSCQ-MSX0712), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1190354995124998373, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, xref=1, ext=[AuthorCompanyExt(id=1190354995133386982, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995124998373, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China), AuthorCompanyExt(id=1190354995141775591, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995124998373, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 重庆科技大学化学化工学院, 重庆 401331)]), AuthorCompany(id=1190354995204690152, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, xref=2, ext=[AuthorCompanyExt(id=1190354995213078761, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995204690152, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2China National Institute for Food and Drug Control, Beijing 100050, China), AuthorCompanyExt(id=1190354995221467370, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995204690152, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 中国食品药品检定研究院, 北京 100050)]), AuthorCompany(id=1190354995288576235, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, xref=3, ext=[AuthorCompanyExt(id=1190354995296964844, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995288576235, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes, College of Traditional Chinese Medicine, Chongqing University of Chinese Medicine, Chongqing 402760, China), AuthorCompanyExt(id=1190354995301159149, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, companyId=1190354995288576235, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760)])], figs=[ArticleFig(id=1190354999516434733, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=ZrZsyE9RY/aZXMpUOsgujw==, figureFileBig=mVhB609bN2ywk1FqdF+nXA==, tableContent=null), ArticleFig(id=1190354999671623982, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Figure 1, caption=

Normal contact force-displacement curve for the Edinburgh elasto-plastic adhesion (EEPA) contact model. K1: The original loading stiffness; K2: Reloading stiffness; f0: Constant drag force; Kadh: Attenuation stiffness

, figureFileSmall=ZrZsyE9RY/aZXMpUOsgujw==, figureFileBig=mVhB609bN2ywk1FqdF+nXA==, tableContent=null), ArticleFig(id=1190354999768092975, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=PA6YpgQsRQkUWH+ciCNHZg==, figureFileBig=2kLF54biFkd6Bfigdz3Alw==, tableContent=null), ArticleFig(id=1190354999822618928, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Figure 2, caption=

Schematic diagram of particle cluster

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Experiment and DEM simulation relationship. A: PGS; B: MCC; C: PGS-MCC; D: PGS; E: MCC. A-C: hardness versus compressive force; D, E: volume reduction versus compressive force

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Kriging model accuracy validation results. A: PGS; B: MCC; C: PGS-MCC; D: PGS; E: MCC. A-C: hardness versus compressive force; D, E: volume reduction versus compressive force

, figureFileSmall=HqfsCy0wCWYg68VHvSlJSg==, figureFileBig=aHHpdfFOaegfPgBtWFGvoQ==, tableContent=null), ArticleFig(id=1190355000132997429, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=KXyVxwX0gIFb5GCugbwhxA==, figureFileBig=M4qP3yIU0FuokObY2rhbLA==, tableContent=null), ArticleFig(id=1190355000191717686, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Figure 5, caption=

Validation results for DEM parameters. A: PGS; B: MCC; C: PGS-MCC; D: PGS; E: MCC. A-C: hardness versus compressive force; D, E: volume reduction versus compressive force

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Partial interaction response diagram. A-D: PGS; E-H: MCC; I-J: PGS-MCC

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Parameter type Parameter Numerical range
Intrinsic parameters Poisson's ratio 0.2-0.4
Shear modulus / Pa 5.0×107-1.0×108
Contact parameters Particle-particle recovery coefficient 0.3-0.6
Particle-particle static friction coefficient 0.2-0.4
Particle-particle rolling friction coefficient 0.2-0.4
Particle-stainless steel recovery coefficient 0.3-0.6
Particle-stainless steel static friction coefficient 0.2-0.4
Particle-stainless steel rolling friction coefficient 0.2-0.4
EEPA model parameters Normal stiffness per unit area / N·m-3 5.0×108-1.0×109
Shear stiffness per unit area / N·m-3 5.0×108-1.0×109
Critical normal stress / MPa 5-10
Critical shear stress / MPa 5-10
Constant pull-off force / N -1.0×10-4 - -5.0×10-5
Surface energy / J·m-2 0.02-0.04
Contact plasticity ratio 2-4
Tangential stiff multiplier 0.3-0.6
), ArticleFig(id=1190355000514679098, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Table 1, caption=

Discrete element method (DEM) parameters for compaction and diametrical compression

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Parameter type Parameter Numerical range
Intrinsic parameters Poisson's ratio 0.2-0.4
Shear modulus / Pa 5.0×107-1.0×108
Contact parameters Particle-particle recovery coefficient 0.3-0.6
Particle-particle static friction coefficient 0.2-0.4
Particle-particle rolling friction coefficient 0.2-0.4
Particle-stainless steel recovery coefficient 0.3-0.6
Particle-stainless steel static friction coefficient 0.2-0.4
Particle-stainless steel rolling friction coefficient 0.2-0.4
EEPA model parameters Normal stiffness per unit area / N·m-3 5.0×108-1.0×109
Shear stiffness per unit area / N·m-3 5.0×108-1.0×109
Critical normal stress / MPa 5-10
Critical shear stress / MPa 5-10
Constant pull-off force / N -1.0×10-4 - -5.0×10-5
Surface energy / J·m-2 0.02-0.04
Contact plasticity ratio 2-4
Tangential stiff multiplier 0.3-0.6
), ArticleFig(id=1190355000573399355, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No. Poisson's ratio Shear modulus/Pa Recovery coefficient Static friction coefficient Rolling friction coefficient Recovery coefficient Static friction coefficient Rolling friction coefficient Normal stiffness per unit area/N·m-3 Shear stiffness per unit area/N·m-3 Critical normal stress/MPa Critical shear stress/MPa Constant pull-off force/N Surface energy/J·m-2 Contact plasticity ratio Tangential stiff multiplier Hardness/N Compressive force/N
1 0.4 5.0×107 0.6 0.2 0.2 0.3 0.2 0.4 1.0×109 5.0×108 1 1 -1.0×104 0.02 4 0.6 33.68 2.3×103
2 0.4 5.0×107 0.6 0.4 0.2 0.3 0.4 0.4 1.0×109 1.0×109 0.5 1 -1.0×104 0.04 2 0.3 48.84 2.4×103
3 0.4 1.0×108 0.3 0.4 0.2 0.6 0.2 0.2 5.0×108 5.0×108 1 1 -1.0×104 0.04 4 0.3 30.83 4.6×103
4 0.2 5.0×107 0.3 0.4 0.4 0.3 0.4 0.4 5.0×108 5.0×108 1 1 -5.0×105 0.04 2 0.6 24.52 1.6×103
5 0.2 1.0×108 0.6 0.2 0.4 0.6 0.2 0.2 1.0×109 1.0×109 1 1 -1.0×104 0.04 2 0.6 41.29 3.3×103
6 0.4 1.0×108 0.3 0.4 0.4 0.3 0.2 0.4 1.0×109 1.0×109 1 0.5 -5.0×105 0.02 4 0.3 45.77 4.4×103
7 0.2 5.0×107 0.3 0.2 0.2 0.3 0.2 0.2 5.0×108 5.0×108 0.5 0.5 -1.0×104 0.02 2 0.3 22.99 1.6×103
8 0.2 1.0×108 0.3 0.4 0.2 0.3 0.2 0.2 1.0×109 1.0×109 0.5 1 -5.0×105 0.02 2 0.6 49.47 3.1×103
9 0.4 5.0×107 0.3 0.4 0.4 0.6 0.4 0.2 1.0×109 5.0×108 1 0.5 -1.0×104 0.02 2 0.6 33.45 2.2×103
10 0.2 5.0×107 0.3 0.2 0.4 0.6 0.2 0.4 1.0×109 5.0×108 0.5 1 -5.0×105 0.04 4 0.3 30.76 1.6×103
11 0.4 1.0×108 0.6 0.4 0.2 0.6 0.2 0.4 5.0×108 5.0×108 0.5 0.5 -5.0×105 0.04 2 0.6 28.92 4.8×103
12 0.4 1.0×108 0.6 0.2 0.4 0.3 0.4 0.2 5.0×108 5.0×108 0.5 1 -5.0×105 0.02 4 0.6 34.27 4.4×103
13 0.2 1.0×108 0.3 0.2 0.2 0.3 0.4 0.4 5.0×108 1.0×109 1 0.5 -1.0×104 0.04 4 0.6 34.55 3.2×103
14 0.3 7.5×108 0.45 0.3 0.3 0.45 0.3 0.3 7.5×108 7.5×108 0.75 0.75 -7.5×105 0.03 3 0.45 34.84 2.9×103
15 0.4 1.0×108 0.3 0.2 0.4 0.6 0.4 0.4 5.0×108 1.0×109 0.5 1 -1.0×104 0.02 2 0.3 28.64 4.3×103
16 0.4 5.0×107 0.3 0.2 0.2 0.6 0.4 0.2 1.0×109 1.0×109 0.5 0.5 -5.0×105 0.04 4 0.6 39.00 2.1×103
17 0.2 1.0×108 0.6 0.2 0.2 0.6 0.4 0.4 1.0×109 5.0×108 1 0.5 -5.0×105 0.02 2 0.3 45.80 3.5×103
18 0.2 5.0×107 0.6 0.4 0.4 0.6 0.2 0.4 5.0×108 1.0×109 0.5 0.5 -1.0×104 0.02 4 0.6 33.28 1.8×103
19 0.2 1.0×108 0.6 0.4 0.4 0.3 0.4 0.2 1.0×109 5.0×108 0.5 0.5 -1.0×104 0.04 4 0.3 51.16 3.6×103
20 0.4 5.0×107 0.6 0.2 0.4 0.3 0.2 0.2 5.0×108 1.0×109 1 0.5 -5.0×105 0.04 2 0.3 25.12 2.2×103
21 0.2 5.0×107 0.6 0.4 0.2 0.6 0.4 0.2 5.0×108 1.0×109 10 10 -5.0×105 0.02 4 0.3 35.81 1.9×103
), ArticleFig(id=1190355000648896828, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Table 2, caption=

The design and results of Plackett-Burman experimental

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No. Poisson's ratio Shear modulus/Pa Recovery coefficient Static friction coefficient Rolling friction coefficient Recovery coefficient Static friction coefficient Rolling friction coefficient Normal stiffness per unit area/N·m-3 Shear stiffness per unit area/N·m-3 Critical normal stress/MPa Critical shear stress/MPa Constant pull-off force/N Surface energy/J·m-2 Contact plasticity ratio Tangential stiff multiplier Hardness/N Compressive force/N
1 0.4 5.0×107 0.6 0.2 0.2 0.3 0.2 0.4 1.0×109 5.0×108 1 1 -1.0×104 0.02 4 0.6 33.68 2.3×103
2 0.4 5.0×107 0.6 0.4 0.2 0.3 0.4 0.4 1.0×109 1.0×109 0.5 1 -1.0×104 0.04 2 0.3 48.84 2.4×103
3 0.4 1.0×108 0.3 0.4 0.2 0.6 0.2 0.2 5.0×108 5.0×108 1 1 -1.0×104 0.04 4 0.3 30.83 4.6×103
4 0.2 5.0×107 0.3 0.4 0.4 0.3 0.4 0.4 5.0×108 5.0×108 1 1 -5.0×105 0.04 2 0.6 24.52 1.6×103
5 0.2 1.0×108 0.6 0.2 0.4 0.6 0.2 0.2 1.0×109 1.0×109 1 1 -1.0×104 0.04 2 0.6 41.29 3.3×103
6 0.4 1.0×108 0.3 0.4 0.4 0.3 0.2 0.4 1.0×109 1.0×109 1 0.5 -5.0×105 0.02 4 0.3 45.77 4.4×103
7 0.2 5.0×107 0.3 0.2 0.2 0.3 0.2 0.2 5.0×108 5.0×108 0.5 0.5 -1.0×104 0.02 2 0.3 22.99 1.6×103
8 0.2 1.0×108 0.3 0.4 0.2 0.3 0.2 0.2 1.0×109 1.0×109 0.5 1 -5.0×105 0.02 2 0.6 49.47 3.1×103
9 0.4 5.0×107 0.3 0.4 0.4 0.6 0.4 0.2 1.0×109 5.0×108 1 0.5 -1.0×104 0.02 2 0.6 33.45 2.2×103
10 0.2 5.0×107 0.3 0.2 0.4 0.6 0.2 0.4 1.0×109 5.0×108 0.5 1 -5.0×105 0.04 4 0.3 30.76 1.6×103
11 0.4 1.0×108 0.6 0.4 0.2 0.6 0.2 0.4 5.0×108 5.0×108 0.5 0.5 -5.0×105 0.04 2 0.6 28.92 4.8×103
12 0.4 1.0×108 0.6 0.2 0.4 0.3 0.4 0.2 5.0×108 5.0×108 0.5 1 -5.0×105 0.02 4 0.6 34.27 4.4×103
13 0.2 1.0×108 0.3 0.2 0.2 0.3 0.4 0.4 5.0×108 1.0×109 1 0.5 -1.0×104 0.04 4 0.6 34.55 3.2×103
14 0.3 7.5×108 0.45 0.3 0.3 0.45 0.3 0.3 7.5×108 7.5×108 0.75 0.75 -7.5×105 0.03 3 0.45 34.84 2.9×103
15 0.4 1.0×108 0.3 0.2 0.4 0.6 0.4 0.4 5.0×108 1.0×109 0.5 1 -1.0×104 0.02 2 0.3 28.64 4.3×103
16 0.4 5.0×107 0.3 0.2 0.2 0.6 0.4 0.2 1.0×109 1.0×109 0.5 0.5 -5.0×105 0.04 4 0.6 39.00 2.1×103
17 0.2 1.0×108 0.6 0.2 0.2 0.6 0.4 0.4 1.0×109 5.0×108 1 0.5 -5.0×105 0.02 2 0.3 45.80 3.5×103
18 0.2 5.0×107 0.6 0.4 0.4 0.6 0.2 0.4 5.0×108 1.0×109 0.5 0.5 -1.0×104 0.02 4 0.6 33.28 1.8×103
19 0.2 1.0×108 0.6 0.4 0.4 0.3 0.4 0.2 1.0×109 5.0×108 0.5 0.5 -1.0×104 0.04 4 0.3 51.16 3.6×103
20 0.4 5.0×107 0.6 0.2 0.4 0.3 0.2 0.2 5.0×108 1.0×109 1 0.5 -5.0×105 0.04 2 0.3 25.12 2.2×103
21 0.2 5.0×107 0.6 0.4 0.2 0.6 0.4 0.2 5.0×108 1.0×109 10 10 -5.0×105 0.02 4 0.3 35.81 1.9×103
), ArticleFig(id=1190355000716005693, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
Parameter Effect Sum of squares F-value P-value S/N
Hardness
Poisson's ratio -2.11 22.25 2.89 0.19 9
Shear modulus / Pa 6.32 199.89 25.92 0.015 2
Particle-particle recovery coefficient 3.82 72.90 9.45 0.054 5
Particle-particle static friction coefficient 4.59 105.54 13.69 0.034 3
Particle-particle rolling friction coefficient -2.16 23.40 3.03 0.18 8
Particle-stainless steel recovery coefficient -2.26 25.47 3.30 0.17 7
Particle-stainless steel static friction coefficient 3.39 57.49 7.46 0.072 6
Particle-stainless steel rolling friction coefficient -0.86 3.71 0.48 0.54 13
Normal stiffness per unit area / N·m-3 12.03 723.46 93.82 2.3×10-3 1
Shear stiffness per unit area / N·m-3 4.54 102.93 13.35 0.035 4
Critical normal stress / MPa -1.65 13.64 1.77 0.28 11
Critical shear stress / MPa -0.19 0.19 0.024 0.89 15
Constant pull-off force / N 0.073 0.026 3.4×10-3 0.96 16
Surface energy / J·m-2 -0.82 3.33 0.43 0.56 14
Contact plasticity ratio 2.01 20.12 2.61 0.20 10
Tangential stiff multiplier -1.33 8.83 1.15 0.36 12
Compressive force
Poisson's ratio 852.96 3.64×106 122.45 1.6×10-3 2
Shear modulus / Pa 1 948.46 1.90×107 638.95 1.0×10-4 1
Particle-particle recovery coefficient 151.82 1.15×105 3.88 0.14 6
Particle-particle static friction coefficient 190.72 1.82×105 6.12 0.089 4
Particle-particle rolling friction coefficient -24.76 3.07×103 0.10 0.77 15
Particle-stainless steel recovery coefficient 101.60 5.16×104 1.74 0.28 8
Particle-stainless steel static friction coefficient -47.76 1.14×104 0.38 0.58 12
Particle-stainless steel rolling friction coefficient 71.72 2.57×104 0.86 0.42 11
Normal stiffness per unit area / N·m-3 -199.66 1.99×105 6.71 0.08 3
Shear stiffness per unit area / N·m-3 -155.86 1.22×105 4.09 0.14 5
Critical normal stress / MPa -79.16 3.13×105 1.05 0.38 10
Critical shear stress / MPa -2.80 39.20 1.3×10-3 0.97 16
Constant pull-off force / N 33.80 5.71×103 0.19 0.69 13
Surface energy / J·m-2 28.26 3.99×103 0.13 0.74 14
Contact plasticity ratio 81.68 3.34×104 1.12 0.37 9
Tangential stiff multiplier -125.30 7.85×104 2.64 0.20 7
), ArticleFig(id=1190355000795697470, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Table 3, caption=

Significance analysis of Plackett-Burman test parameters

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Parameter Effect Sum of squares F-value P-value S/N
Hardness
Poisson's ratio -2.11 22.25 2.89 0.19 9
Shear modulus / Pa 6.32 199.89 25.92 0.015 2
Particle-particle recovery coefficient 3.82 72.90 9.45 0.054 5
Particle-particle static friction coefficient 4.59 105.54 13.69 0.034 3
Particle-particle rolling friction coefficient -2.16 23.40 3.03 0.18 8
Particle-stainless steel recovery coefficient -2.26 25.47 3.30 0.17 7
Particle-stainless steel static friction coefficient 3.39 57.49 7.46 0.072 6
Particle-stainless steel rolling friction coefficient -0.86 3.71 0.48 0.54 13
Normal stiffness per unit area / N·m-3 12.03 723.46 93.82 2.3×10-3 1
Shear stiffness per unit area / N·m-3 4.54 102.93 13.35 0.035 4
Critical normal stress / MPa -1.65 13.64 1.77 0.28 11
Critical shear stress / MPa -0.19 0.19 0.024 0.89 15
Constant pull-off force / N 0.073 0.026 3.4×10-3 0.96 16
Surface energy / J·m-2 -0.82 3.33 0.43 0.56 14
Contact plasticity ratio 2.01 20.12 2.61 0.20 10
Tangential stiff multiplier -1.33 8.83 1.15 0.36 12
Compressive force
Poisson's ratio 852.96 3.64×106 122.45 1.6×10-3 2
Shear modulus / Pa 1 948.46 1.90×107 638.95 1.0×10-4 1
Particle-particle recovery coefficient 151.82 1.15×105 3.88 0.14 6
Particle-particle static friction coefficient 190.72 1.82×105 6.12 0.089 4
Particle-particle rolling friction coefficient -24.76 3.07×103 0.10 0.77 15
Particle-stainless steel recovery coefficient 101.60 5.16×104 1.74 0.28 8
Particle-stainless steel static friction coefficient -47.76 1.14×104 0.38 0.58 12
Particle-stainless steel rolling friction coefficient 71.72 2.57×104 0.86 0.42 11
Normal stiffness per unit area / N·m-3 -199.66 1.99×105 6.71 0.08 3
Shear stiffness per unit area / N·m-3 -155.86 1.22×105 4.09 0.14 5
Critical normal stress / MPa -79.16 3.13×105 1.05 0.38 10
Critical shear stress / MPa -2.80 39.20 1.3×10-3 0.97 16
Constant pull-off force / N 33.80 5.71×103 0.19 0.69 13
Surface energy / J·m-2 28.26 3.99×103 0.13 0.74 14
Contact plasticity ratio 81.68 3.34×104 1.12 0.37 9
Tangential stiff multiplier -125.30 7.85×104 2.64 0.20 7
), ArticleFig(id=1190355000871194943, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No. A B C D E F Force-hardness Force-volume reduction
PGS
1 0.30 4.45 0.24 59.03 1.0 -7.6×10-3 774.10 1.1×103
2 0.36 2.66 0.27 41.97 96.59 -5.9×10-3 924.53 1.0×103
3 0.23 2.1 0.35 96.59 14.66 -8.6×10-3 508.50 851.71
4 0.21 3.34 0.59 24.90 93.17 -6.6×10-3 862.70 1.2×103
5 0.20 1.55 0.84 55.62 41.97 -7.9×10-3 421.22 832.54
6 0.29 4.86 0.32 28.31 52.21 -5.2×10-3 996.72 1.2×103
7 0.37 3.21 0.21 65.86 55.62 -9.8×10-3 504.62 1.0×103
8 0.34 4.31 0.79 69.28 21.48 -9.5×10-3 558.45 1.4×103
9 0.25 1.14 0.29 45.38 79.52 -8.4×10-3 314.99 302.76
10 0.39 1.83 0.73 72.69 65.86 -8.1×10-3 528.48 1.1×103
11 0.23 2.24 0.38 52.21 24.90 -5.0×10-3 952.54 951.14
12 0.22 4.59 0.68 79.52 38.55 -6.9×10-3 814.88 1.3×103
13 0.26 3.48 0.76 11.24 11.24 -7.0×10-3 805.25 1.3×103
14 0.34 3.90 0.90 48.79 62.45 -5.3×10-3 1.0×103 1.5×103
15 0.28 4.17 0.18 14.66 89.76 -8.9×10-3 699.92 1.0×103
16 0.21 3.62 0.46 35.14 31.72 -0.01 449.22 1.1×103
17 0.24 3.76 0.10 82.93 69.28 -7.2×10-3 890.87 958.86
18 0.26 2.93 0.65 93.17 82.93 -9.7×10-3 412.50 1.2×103
19 0.32 2.38 0.82 86.34 4.41 -6.4×10-3 814.23 1.2×103
20 0.27 1.97 0.62 89.76 86.34 -5.7×10-3 850.07 1.0×103
21 0.39 1.69 0.40 4.41 72.69 -8.8×10-3 464.13 895.16
22 0.30 3.07 0.87 21.48 76.10 -9.3×10-3 420.52 1.3×103
23 0.32 5.00 0.57 62.45 100.0 -7.8×10-3 681.88 1.4×103
24 0.38 4.72 0.51 7.83 45.38 -8.3×10-3 670.05 1.3×103
25 0.33 1.41 0.54 38.55 7.83 -9.1×10-3 308.18 779.56
26 0.37 4.03 0.43 100.0 48.79 -6.2×10-3 880.86 1.2×103
27 0.4 2.79 0.49 31.72 18.07 -5.5×10-3 927.53 1.2×103
28 0.31 1.00 0.71 18.07 59.03 -6.0×10-3 600.13 602.56
MCC
1 0.34 79.52 0.71 1.93 9.07 -0.033 321.86 272.43
2 0.31 45.38 0.10 6.59 7.83 -8.1×10-3 684.60 99.94
3 0.36 14.66 0.82 2.24 3.48 -0.031 104.74 41.66
4 0.32 41.97 0.13 9.69 2.55 -0.034 286.41 96.16
5 0.24 69.28 0.24 1.00 3.79 -0.019 527.61 173.95
6 0.20 65.86 0.40 4.10 10.00 -0.028 373.69 192.24
7 0.37 18.07 0.27 3.48 8.76 -0.038 0.58 -0.46
8 0.38 20.00 0.49 8.14 5.34 -0.021 112.30 74.38
9 0.28 40.00 0.60 4.41 7.21 -5.0×10-3 579.06 179.05
10 0.29 28.31 0.46 5.34 1.62 -6.6×10-3 668.30 113.04
11 0.26 35.14 0.54 1.31 5.97 -0.05 85.15 140.60
12 0.23 24.90 0.62 9.38 7.52 -0.016 449.62 99.74
13 0.28 11.24 0.57 2.55 8.14 -0.011 472.10 -52.05
14 0.33 93.17 0.73 3.17 1.93 -0.027 430.88 280.31
15 0.32 89.76 0.16 4.72 6.28 -0.041 327.03 187.83
16 0.40 55.62 0.38 1.62 5.66 -0.013 607.19 210.33
17 0.34 76.10 0.51 9.07 9.69 -0.025 417.58 238.31
18 0.30 62.45 0.79 10.00 2.24 -0.024 462.37 247.85
19 0.21 48.79 0.85 3.79 4.10 -0.022 419.50 215.79
20 0.39 59.03 0.65 6.90 4.72 -0.047 132.62 239.77
21 0.23 72.69 0.43 5.66 1.00 -0.045 140.07 210.02
22 0.26 38.55 0.32 8.76 8.45 -0.048 37.01 128.27
23 0.35 31.72 0.21 2.86 1.31 -0.036 187.01 90.31
24 0.37 52.21 0.90 5.97 6.59 -9.7×10-3 652.08 257.59
25 0.21 82.93 0.29 8.45 4.41 -0.017 557.58 200.60
26 0.39 86.34 0.35 7.83 3.17 -0.014 604.68 221.46
27 0.25 96.59 0.76 7.21 6.90 -0.042 196.30 269.72
28 0.30 21.48 0.87 6.28 9.38 -0.039 36.01 114.58
), ArticleFig(id=1190355000942498112, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Table 4, caption=

Latin hypercube test sample and simulation results. PGS: Pregelatinized starch; MCC: Microcrystalline cellulose; A: Poisson's ratio; B: Shear modulus; C: Particle-particle static friction coefficient; D: Normal stiffness per unit area; E: Shear stiffness per unit area; F: Constant pull-off force

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No. A B C D E F Force-hardness Force-volume reduction
PGS
1 0.30 4.45 0.24 59.03 1.0 -7.6×10-3 774.10 1.1×103
2 0.36 2.66 0.27 41.97 96.59 -5.9×10-3 924.53 1.0×103
3 0.23 2.1 0.35 96.59 14.66 -8.6×10-3 508.50 851.71
4 0.21 3.34 0.59 24.90 93.17 -6.6×10-3 862.70 1.2×103
5 0.20 1.55 0.84 55.62 41.97 -7.9×10-3 421.22 832.54
6 0.29 4.86 0.32 28.31 52.21 -5.2×10-3 996.72 1.2×103
7 0.37 3.21 0.21 65.86 55.62 -9.8×10-3 504.62 1.0×103
8 0.34 4.31 0.79 69.28 21.48 -9.5×10-3 558.45 1.4×103
9 0.25 1.14 0.29 45.38 79.52 -8.4×10-3 314.99 302.76
10 0.39 1.83 0.73 72.69 65.86 -8.1×10-3 528.48 1.1×103
11 0.23 2.24 0.38 52.21 24.90 -5.0×10-3 952.54 951.14
12 0.22 4.59 0.68 79.52 38.55 -6.9×10-3 814.88 1.3×103
13 0.26 3.48 0.76 11.24 11.24 -7.0×10-3 805.25 1.3×103
14 0.34 3.90 0.90 48.79 62.45 -5.3×10-3 1.0×103 1.5×103
15 0.28 4.17 0.18 14.66 89.76 -8.9×10-3 699.92 1.0×103
16 0.21 3.62 0.46 35.14 31.72 -0.01 449.22 1.1×103
17 0.24 3.76 0.10 82.93 69.28 -7.2×10-3 890.87 958.86
18 0.26 2.93 0.65 93.17 82.93 -9.7×10-3 412.50 1.2×103
19 0.32 2.38 0.82 86.34 4.41 -6.4×10-3 814.23 1.2×103
20 0.27 1.97 0.62 89.76 86.34 -5.7×10-3 850.07 1.0×103
21 0.39 1.69 0.40 4.41 72.69 -8.8×10-3 464.13 895.16
22 0.30 3.07 0.87 21.48 76.10 -9.3×10-3 420.52 1.3×103
23 0.32 5.00 0.57 62.45 100.0 -7.8×10-3 681.88 1.4×103
24 0.38 4.72 0.51 7.83 45.38 -8.3×10-3 670.05 1.3×103
25 0.33 1.41 0.54 38.55 7.83 -9.1×10-3 308.18 779.56
26 0.37 4.03 0.43 100.0 48.79 -6.2×10-3 880.86 1.2×103
27 0.4 2.79 0.49 31.72 18.07 -5.5×10-3 927.53 1.2×103
28 0.31 1.00 0.71 18.07 59.03 -6.0×10-3 600.13 602.56
MCC
1 0.34 79.52 0.71 1.93 9.07 -0.033 321.86 272.43
2 0.31 45.38 0.10 6.59 7.83 -8.1×10-3 684.60 99.94
3 0.36 14.66 0.82 2.24 3.48 -0.031 104.74 41.66
4 0.32 41.97 0.13 9.69 2.55 -0.034 286.41 96.16
5 0.24 69.28 0.24 1.00 3.79 -0.019 527.61 173.95
6 0.20 65.86 0.40 4.10 10.00 -0.028 373.69 192.24
7 0.37 18.07 0.27 3.48 8.76 -0.038 0.58 -0.46
8 0.38 20.00 0.49 8.14 5.34 -0.021 112.30 74.38
9 0.28 40.00 0.60 4.41 7.21 -5.0×10-3 579.06 179.05
10 0.29 28.31 0.46 5.34 1.62 -6.6×10-3 668.30 113.04
11 0.26 35.14 0.54 1.31 5.97 -0.05 85.15 140.60
12 0.23 24.90 0.62 9.38 7.52 -0.016 449.62 99.74
13 0.28 11.24 0.57 2.55 8.14 -0.011 472.10 -52.05
14 0.33 93.17 0.73 3.17 1.93 -0.027 430.88 280.31
15 0.32 89.76 0.16 4.72 6.28 -0.041 327.03 187.83
16 0.40 55.62 0.38 1.62 5.66 -0.013 607.19 210.33
17 0.34 76.10 0.51 9.07 9.69 -0.025 417.58 238.31
18 0.30 62.45 0.79 10.00 2.24 -0.024 462.37 247.85
19 0.21 48.79 0.85 3.79 4.10 -0.022 419.50 215.79
20 0.39 59.03 0.65 6.90 4.72 -0.047 132.62 239.77
21 0.23 72.69 0.43 5.66 1.00 -0.045 140.07 210.02
22 0.26 38.55 0.32 8.76 8.45 -0.048 37.01 128.27
23 0.35 31.72 0.21 2.86 1.31 -0.036 187.01 90.31
24 0.37 52.21 0.90 5.97 6.59 -9.7×10-3 652.08 257.59
25 0.21 82.93 0.29 8.45 4.41 -0.017 557.58 200.60
26 0.39 86.34 0.35 7.83 3.17 -0.014 604.68 221.46
27 0.25 96.59 0.76 7.21 6.90 -0.042 196.30 269.72
28 0.30 21.48 0.87 6.28 9.38 -0.039 36.01 114.58
), ArticleFig(id=1190355001026384193, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
No. A B C D Force-hardness
1 0.27 9.05 9.05 -0.053 77.66
2 0.10 6.21 6.21 -0.036 297.01
3 0.86 3.37 8.11 -0.043 295.90
4 0.77 9.53 7.16 -0.051 186.13
5 0.65 5.74 10.00 -0.062 125.60
6 0.23 1.47 3.84 -0.049 156.99
7 0.44 3.84 2.42 -0.068 97.74
8 0.69 8.11 1.47 -0.055 172.10
9 0.39 10.00 4.32 -0.040 367.22
10 0.56 7.16 8.58 -0.032 334.05
11 0.73 1.95 1.95 -0.047 311.44
12 0.90 4.79 4.79 -0.064 186.53
13 0.31 2.89 9.53 -0.045 256.02
14 0.60 1.00 6.68 -0.059 127.62
15 0.48 8.58 5.74 -0.070 143.76
), ArticleFig(id=1190355001131241794, tenantId=1146029695717560320, journalId=1189982191388893191, articleId=1190335353585238256, language=CN, label=Table 5, caption=

Latin hypercube test sample and simulation results (PGS-MCC). A: Particle-particle static friction coefficient; B: Normal stiffness per unit area; C: Shear stiffness per unit area; D: Constant pull-off force

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No. A B C D Force-hardness
1 0.27 9.05 9.05 -0.053 77.66
2 0.10 6.21 6.21 -0.036 297.01
3 0.86 3.37 8.11 -0.043 295.90
4 0.77 9.53 7.16 -0.051 186.13
5 0.65 5.74 10.00 -0.062 125.60
6 0.23 1.47 3.84 -0.049 156.99
7 0.44 3.84 2.42 -0.068 97.74
8 0.69 8.11 1.47 -0.055 172.10
9 0.39 10.00 4.32 -0.040 367.22
10 0.56 7.16 8.58 -0.032 334.05
11 0.73 1.95 1.95 -0.047 311.44
12 0.90 4.79 4.79 -0.064 186.53
13 0.31 2.89 9.53 -0.045 256.02
14 0.60 1.00 6.68 -0.059 127.62
15 0.48 8.58 5.74 -0.070 143.76
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二元混合颗粒压制过程离散元物性参数标定方法研究
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罗安琪 1 , 赵孟涛 1 , 马驰 1 , 肖琴 1 , 路政 1 , 管天冰 1 , 陈双扣 1 , 孙会敏 2, * , 黄海军 3 , 王爱平 3 , 戴传云 3, *
药学学报 | 研究论文 2025,60(4): 1137-1146
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药学学报 | 研究论文 2025, 60(4): 1137-1146
二元混合颗粒压制过程离散元物性参数标定方法研究
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罗安琪1, 赵孟涛1, 马驰1, 肖琴1, 路政1, 管天冰1, 陈双扣1, 孙会敏2, * , 黄海军3, 王爱平3, 戴传云3, *
作者信息
  • 1 重庆科技大学化学化工学院, 重庆 401331
  • 2 中国食品药品检定研究院, 北京 100050
  • 3 重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

通讯作者:

孙会敏, Tel / Fax: 86-10-67095721, E-mail:
戴传云, Tel / Fax: 86-23-65022212, E-mail:
Research on the calibration method of discrete element physical property parameters in the pressing process of binary mixed particles
An-qi LUO1, Meng-tao ZHAO1, Chi MA1, Qin XIAO1, Zheng LU1, Tian-bing GUAN1, Shuang-kou CHEN1, Hui-min SUN2, * , Hai-jun HUANG3, Ai-ping WANG3, Chuan-yun DAI3, *
Affiliations
  • 1College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China
  • 2China National Institute for Food and Drug Control, Beijing 100050, China
  • 3Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes, College of Traditional Chinese Medicine, Chongqing University of Chinese Medicine, Chongqing 402760, China
出版时间: 2025-04-12 doi: 10.16438/j.0513-4870.2024-0977
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针对现阶段研究片剂压制过程中微观力学行为常局限于唯象研究的问题, 准确标定二元颗粒的离散元模型参数, 是开展片剂压制建模研究的先决条件。本研究以预胶化淀粉和微晶纤维素为研究对象, 应用Edinburgh elasto-plastic adhesion (EEPA) 接触模型建立二元物料压制离散元仿真模型。以片剂压力-硬度、压力-体积减少量为响应值, 应用Plackett-Burman设计、拉丁超立方抽样、Kriging模型, 以及非支配排序遗传算法(NSGA-Ⅱ) 标定得到显著影响片剂压制的离散元(discrete element method, DEM) 参数最优值。结果表明, 预胶化淀粉的DEM参数的最佳组合, 即泊松比0.257、剪切模量1×109 Pa、颗粒-颗粒间静摩擦系数0.165、单位法向刚度2.419 2×109 N·m-3、单位切向刚度7.954 6×109 N·m-3、黏附力强度-0.009 155 8 N; 微晶纤维素的DEM参数的最佳组合, 即泊松比0.381、剪切模量1.04×109 Pa、颗粒-颗粒间静摩擦系数0.719、单位法向刚度3.171 5×109 N·m-3、单位切向刚度6.746 2×109 N·m-3、黏附力强度-0.038 7 N; 二元混合辅料间的DEM参数的最佳组合, 即颗粒-颗粒间碰撞恢复系数0.1, 单位法向刚度9.947 1×109 N·m-3, 单位切向刚度1.994 5×109 N·m-3, 黏附力强度-0.060 35 N。最佳参数组合下的模拟与物理实验结果相似, 表明标定得到的参数可用于离散元仿真研究, 为后续片剂的智能化与连续化生产提供理论基础和数据支撑。

二元混合颗粒  /  离散元法  /  参数标定  /  压缩

The study of micromechanical behavior during tablet pressing is often limited to phenomenological studies, accurate calibration of the discrete element model parameters of binary particles is a prerequisite for conducting modeling research on tablet compression. In this study, we take pregelatinized starch and microcrystalline cellulose as the research objects, and apply the Edinburgh elasto-plastic adhesion (EEPA) contact model to establish a discrete elemental simulation model for binary material pressing. Taking tablet force-hardness and force-volume reduction as response values, the optimal values of the discrete meta-parameters that significantly affect tablet pressing are obtained by applying Plackett-Burman design, Latin hypercubic sampling, Kriging model, and Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) calibration. The results obtained the optimum combination of discrete element method (DEM) parameters for pregelatinized starch, Poisson's ratio 0.257, shear modulus 1×109 Pa, granule-particle static friction coefficient 0.165, unit normal stiffness 2.419 2×109 N·m-3, unit tangential stiffness 7.954 6×109 N·m-3, and strength of adhesive force -0.009 155 8 N. For microcrystalline cellulose, Poisson's ratio 0.381, shear modulus 1.04×109 Pa, particle-particle static friction coefficient 0.719, unit normal stiffness 3.171 5×109 N·m-3, unit tangential stiffness 6.746 2×109 N·m-3, and strength of adhesion -0.038 7 N. For optimum combinations of the DEM parameters for the binary blend of excipients, particle-particle collision recovery coefficient 0.1, unit normal stiffness 9.947 1×109 N·m-3, unit tangential stiffness 1.994 5×109 N·m-3, and adhesion force strength -0.060 35. The simulation results under the optimal parameter combination are similar to the experimental results, indicating that the calibrated parameters can be used for discrete element simulation research and provide theoretical basis and data support for the subsequent intelligent and continuous production of tablets.

bimodal mixed particle  /  discrete element method  /  parameter calibration  /  compression
罗安琪, 赵孟涛, 马驰, 肖琴, 路政, 管天冰, 陈双扣, 孙会敏, 黄海军, 王爱平, 戴传云. 二元混合颗粒压制过程离散元物性参数标定方法研究. 药学学报, 2025 , 60 (4) : 1137 -1146 . DOI: 10.16438/j.0513-4870.2024-0977
An-qi LUO, Meng-tao ZHAO, Chi MA, Qin XIAO, Zheng LU, Tian-bing GUAN, Shuang-kou CHEN, Hui-min SUN, Hai-jun HUANG, Ai-ping WANG, Chuan-yun DAI. Research on the calibration method of discrete element physical property parameters in the pressing process of binary mixed particles[J]. Acta Pharmaceutica Sinica, 2025 , 60 (4) : 1137 -1146 . DOI: 10.16438/j.0513-4870.2024-0977
口服固体制剂(oral solid dosage forms, OSD) 因其便于携带、运输方便及质量稳定等优点, 为目前使用广泛的药物剂型之一[1, 2]。OSD生产常涉及多种不同原辅料, 进行多个单元操作而成。因此, 如何提取其中蕴含的变量间影响规律, 建立可靠的关键性能参数的预估模型, 是制剂工艺研究的重要环节之一[3]。颗粒粉末在OSD生产过程中的压缩规律与产品质量紧密相关, 现阶段对片剂压制过程中微观力学行为分析常局限于唯象研究[4], 实验中的微观信息仍难以获得, 进行仿真模拟则能有效观测到其微观信息。因此, 模拟标定辅料颗粒压缩过程中DEM参数, 基于参数影响的压缩规律对工艺和产品质量有至关重要的意义。
离散元法(discrete element method, DEM) 是众多数学模型中应用最广泛的一种数值计算方法[5-7]。该方法由Cundall等[8]提出, 最初主要用于岩石颗粒建模仿真, 经过国内外学者的持续改进, 其应用领域又扩展到能准确进行物质颗粒流动的不连续性质情况及有效进行离散颗粒的研究[9-11]。因此, 在药物制剂中, DEM还被用于粉体混合、填充和压片等过程, 研究其在宏观角度难以检测到的微观信息[12, 13]。应用DEM进行模拟仿真时必须选取相应模型以表达颗粒运动时的力学特性, 正确标定DEM参数是能精确进行仿真的关键。
预胶化淀粉(pregelatinized starch, PGS) 和微晶纤维素(microcrystalline cellulose, MCC) 是OSD常用药用辅料, 对其药用辅料建立其仿真模型能为制剂的发展打下理论基础。本实验室前期已成功标定出PGS和MCC等物料DEM参数[14-16], 但尚且只能得出一种辅料的参数, 如果辅料种类增加, 辅料属性的不同, 颗粒与颗粒间的参数势必会有所改变。因此, 本研究在单一物料的基础上对其二元混合物料的参数标定方法进行研究, DEM参数准确标定依旧是仿真模拟准确的关键因素。
本研究以PGS和MCC为研究对象, 首先, 根据压制过程中片剂压力-硬度、压力-体积减少量为响应值, 应用Plackett-Burman设计、最优拉丁超立方抽样、Kriging模型及非支配遗传算法(NSGA-Ⅱ) 标定得到片剂压制DEM参数的最优参数组合; 然后, 对其不同参数进行了交互影响分析; 最后, 根据标定得到的最佳DEM参数组合进行压制过程模拟, 对比仿真结果与物理实验结果, 从而证明DEM参数标定的正确性。在本研究中, 压制过程参数标定方法的建立为药用辅料DEM参数的标定提供了新的思路, 成功建立起MCC、PGS及其二元混合辅料压制和硬度仿真模型, 确定不同物理属性的辅料模型参数, 为后续片剂的智能化与连续化生产提供数字化模型[17]
药品与仪器   MCC (型号SH101B, 批号170502)、PGS (型号SH-YJ-DP, 批号180604) (安徽山河药用辅料股份有限公司); 旋转式压片机(型号ZP10A, 北京新龙立科技有限公司); 智能片剂四用仪(型号ZPJ-4, 天津市鑫洲科技有限公司)。
片剂压制实验   使用旋转式压片机直接将各辅料进行压片, 实验选择直径8 mm的浅凹模具分别对MCC、PGS及其二元混合(1∶1混合) 辅料压制获得不同强度的片剂。首先将药用辅料倒入加料仓, 在重力作用下辅料进入模具, 设置上冲压力的极限值, 当压力达到后, 上冲进行卸载, 片剂被下冲从冲模中推出得到片剂。通过测定压缩前模具内物料体积与压缩后得到的片剂体积之差得其体积减少量。
采用智能片剂四用测定仪测量不同压力下的同一辅料及混合辅料片剂的硬度。将药片置于测试台, 左侧加载板以0.008 5 m·s-1的速度对片剂进行挤压, 药片破碎时所受压力即为片剂的硬度。
仿真模型   由于颗粒材料压缩过程的特殊性, 许多颗粒材料在压缩过程中表现出弹塑性变形, 并伴随着内聚力的增加, 再随着力的卸载, 颗粒材料不能恢复到原始状态所对应的滞回力。为了捕捉相关的滞回、内聚力和范德华力, 本文选择了爱丁堡弹塑性胶黏(EEPA) 模型[18]作为直压模拟过程中的颗粒与颗粒间的接触模型, 模型的颗粒接触和法向力重叠(fn-δ) 曲线的示意如图 1所示。纵坐标fn用于表示颗粒间的滞回弹簧力, 其正值代表颗粒间的排斥力, 负值则代表颗粒间的黏结力; 横坐标δ表示颗粒间的重叠量。伴随外力的施加(K1δn)、撤销所施加的外力[K2(δ$ {}_{p}^{n} $-δn)] 以及随着外力的消失(-Kadhδn), 相应的路径会发生变化, 从而捕捉到不同条件下的受力情况。
为了能真实模拟辅料的形态, 本实验构建一种主要由许多小颗粒构成的颗粒单元(称为团簇)。由于颗粒尺寸和形状会影响计算速度导致模拟成本增加, 因此在构建颗粒时, 本实验设定MCC和PGS子颗粒的尺寸半径为50 μm, 团簇的半径为0.4 mm (图 2)。后续建立与片剂压制实验相同的仿真模型进行片剂压制及硬度测试的仿真模拟。
Plackett-Burman试验   Plackett-Burman试验设计是一种因子数较多而不明确其响应变量的显著影响所采用的试验设计方法, 其目的在于凭借最少的试验次数, 尽可能在总考察因素中筛选出最为关键的几个因素。本研究应用Design Expert 12软件进行Plackett-Burman试验, 筛选了显著性影响压力及硬度的DEM参数并比较各参数影响响应值的显著性。各参数的高低水平见表 1
拉丁超立方抽样   拉丁超立方抽样是一种利用分层的思想在较低抽样样本量的情况下, 大幅提升了抽样效率, 并且能确保抽样的随机性与均匀性的一种抽样方法。为减少输入变量之间的相关性, 通过拉丁超立方抽样使每个分量的取值范围均分为相同区间, 并对其DEM参数进行均匀采样[19], 利用拉丁超立方抽样获取DEM参数, 用DEM模拟不同DEM参数组合下辅料压缩时压力、成片后片剂硬度及压缩前后物料的体积差。
Kriging模型   Kriging模型是一种随机插值函数模型。可以有效地估计空间数据的趋势, 并且具有较好的全局收敛性和较高的拟合精度。考虑计算时长影响, 本文采用Kriging模型[20]对DEM参数进行优化。Kriging模型选用高斯函数对10个数据集进行误差评估, 并根据其相关系数R2来判断准确性且主要通过测量函数值的分布, 预测未知值来衡量不确定性。
参数优化与验证   非支配排序遗传算法(NSGA-Ⅱ) 是一种多目标进化算法, 具有运行效率高, 产生局部收敛性几率较小, 多目标优化时效果较好等特点。通过NSGA-Ⅱ, 颗粒DEM参数校准的多目标优化问题得到了最理想的参数集。种群规模为100, 进化代数为100, 杂交概率为0.9, 交叉分布指数为10, 方差分布指数为20[21]。量化模拟过程压力-硬度、压力-体积减少量曲线与物理实验曲线的拟合度。通过Loss函数量化模型预测值与真实值之间的差异, 以量化值为优化目标, 拟合度量化值由公式(1) 计算。最终优化得到最佳的参数组合, 利用DEM软件对最优DEM参数组合进行模拟, 评估模拟结果与物理结果曲线的差异, 以评估参数的准确性。
$ \mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s}\left({f}_{1},{f}_{2}\right)=\underset{\mathit{a}}{\overset{\mathit{b}}{\int }}{\left[{f}_{1}\left(x\right)-{f}_{2}\left(x\right)\right]}^{2}\mathrm{d}x $
其中, f1(x) 为预测值, f2(x) 为真实值。
Plackett-Burman实验结果见表 2。对该压力及硬度结果分别进行方差分析, 表 3说明了显著性分析结果, 不同参数对片剂压力及硬度的影响不同。其中对压力影响显著(P < 0.05) 的参数包括: 泊松比和剪切模量, 其余参数影响不显著; 对硬度影响显著(P < 0.05) 的参数包括: 剪切模量、颗粒-颗粒静摩擦系数、单位法向刚度及单位切向刚度。实验表明, 黏附力强度会显著影响模拟过程中片剂的硬度, 在Plackett-Burman实验中其未表现出对片剂硬度的显著性影响, 可能是因为其数值选择过小的原因。综上所述, 单物料压片过程选取泊松比(A)、剪切模量(B)、颗粒-颗粒静摩擦系数(C)、单位法向刚度(D)、单位切向刚度(E) 及黏附力强度(F) 这6个对于片剂的介观力学行为具有显著影响的参数进行后续标定。二元混合物料压片过程主要侧重两种物料间的参数, 对单个物料的参数影响不大, 因此这里主要对颗粒-颗粒静摩擦系数(A)、单位法向刚度(B)、单位切向刚度(C) 及黏附力强度(D) 这4种参数进行参数标定。
PGS和MCC两种单一物料通过对有显著性影响的泊松比(A)、剪切模量(B)、颗粒-颗粒静摩擦系数(C)、单位法向刚度(D)、单位切向刚度(E) 及黏附力强度(F) 6个参数进行拉丁超立方抽样抽取28组DEM参数组合, 以压力-硬度、压力-体积减少量曲线与物理实验的拟合度为优化目标。对于二元混合物料来说, 主要针对颗粒之间的参数即颗粒-颗粒静摩擦系数(A)、单位法向刚度(B)、单位切向刚度(C) 及黏附力强度(D) 4个参数进行拉丁超立方抽样抽取15组参数组合, 以压力-硬度曲线与物理实验的拟合度为优化目标。
PGS、MCC及二元混合物料的拟合度量化结果分别如表 45所示, 结果表明, PGS、MCC及二元混合物料曲线在低压阶段, 片剂的硬度及体积减少量增长较快, 与PGS相比, MCC在较低压力条件下的硬度较大, 也证明MCC的良好可压缩性和可压实性[22, 23]。随着压力增大, 片剂的硬度及体积减少量增加速度减缓, 最终趋向于平衡。此外, 不同的参数组合得出的曲线不同, 这里选取PGS、MCC及二元混合物料的硬度最优模拟参数组合25、7、1以及PGS、MCC的体积减少量最优参数组合9、13拟合度量化值对物理曲线进行拟合, 结果如图 3所示, 为了其参数精度更加准确后续又针对Kriging模型和非支配排序遗传算法(NSGA-Ⅱ) 进行参数的进一步优化。
本研究选取拉丁超立方抽样中的10组样本点验证Kriging模型的精度, PGS、MCC及二元混合物料的Kriging模型验证结果如图 4所示。PGS压力-硬度及压力-体积减少量的Kriging模型精度分别为0.920 64和0.928 13; MCC压力硬度及压力-体积减少量的Kriging模型精度分别为0.954 71和0.961 25; 二元混合物料压力-硬度Kriging模型精度分别为0.943 66。R2是确定对任意抽样集的线性回归分析是否足够[24]。结果表明, MCC及PGS的Kriging模型均大于要求的拟合精度0.9, 满足分析要求, 说明Kriging模型精度拟合情况较好。
基于Kriging模型和非支配排序遗传算法(NSGA-Ⅱ), PGS的DEM参数的最佳组合, 即A = 0.257, B = 1e+09 Pa, C = 0.165, D = 2.419 2e+09 N·m-3, E = 7.954 6e+09 N·m-3, F = -0.009 155 8 N, 验证结果如图 5AD; MCC的DEM参数的最佳组合, 即A = 0.381, B = 1.04e+09 Pa, C = 0.719, D = 3.171 5e+09 N·m-3, E = 6.746 2e+09 N·m-3, F = -0.038 7 N, 验证结果如图 5BE; 二元混合辅料的DEM参数的最佳组合, 即A = 0.1, B = 9.947 1e+09 N·m-3, C = 1.994 5e+09 N·m-3, D = -0.060 35 N, 验证结果如图 5C所示, 其中PGS和MCC的结果存在差异主要是由于弹塑性定义不够准确以及颗粒形状建模的简化导致[25, 26]
分别对PGS、MCC及二元混合物料压力-硬度曲线和压力-体积减少量曲线的交互作用响应面分析。图 6AB表明, AB、BD对PGS压力-硬度曲线存在交互影响, 且B在AB和BD的交互项中表现出对压力-硬度曲线更明显的影响; 图 6CD表明, AB、BC对PGS压力-体积减少量曲线存在交互作用, B在AB、BC交互项中表现出对压力-体积减少量曲线的影响更为明显; 图 6EF表明, AB、BE对MCC压力-硬度曲线存在交互影响, B对其交互影响作用更大; 图 6GH表明, CD和DF对MCC压力-体积减少量曲线存在交互作用, D占主要作用; 图 6IJ表明, AC、BC对二元混合物料压力-硬度曲线存在交互作用。对于二元混合物料, A在AC交互项中表现出对压力-硬度曲线更为明显影响; B在BC交互项中表现出对压力-硬度曲线更明显的影响。上述结果表明, 剪切模量和单位法向刚度在交互项中是片剂介观力学行为影响的主要原因。
采用EEPA接触模型, 以物理实验及仿真模拟压力-硬度、压力-体积减少量曲线的拟合度为优化目标, 采用Kriging模型结合非支配排序遗传算法(NSGA-Ⅱ), 标定得到PGS的DEM参数的最佳组合, 即A = 0.257, B = 1e+09 Pa, C = 0.165, D = 2.419 2e+09 N·m-3, E = 7.954 6e+09 N·m-3, F = -0.009 155 8 N; MCC的DEM参数的最佳组合, 即A = 0.381, B = 1.04e+09 Pa, C = 0.719, D = 3.171 5e+09 N·m-3, E = 6.746 2e+09 N·m-3, F = -0.038 7 N; 二元混合辅料的DEM参数的最佳组合, 即A = 0.1, B = 9.947 1e+09 N·m-3, C = 1.994 5e+09 N·m-3, D = -0.060 35 N。最佳参数组合下的模拟与物理实验结果相似, 表明标定得到的参数真实有效。
建立片剂压制及硬度测试仿真模型, 以物理实验及仿真模拟压力-硬度、压力-体积减少量曲线的拟合度为优化目标, 采用Kriging模型结合非支配排序遗传算法(NSGA-Ⅱ), 标定得到MCC、PGS及二元混合物料的DEM参数。一元到二元参数标定的变化主要由于物料属性的不同, 导致颗粒间的DEM参数变量增多, 因此, 标定DEM参数所用的试验设计模型也需有所改变, 才能对参数进行更加准确的标定。实验室前期对一元物料标定研究, 主要通过Plackett-Burman、最陡爬坡及Box-Behnken等试验设计模型来获得相应DEM参数, 而本研究则通过上述试验设计模型标定出二元物料的DEM参数。后续的研究可对此进行延伸, 由最初的一元物料参数研究不断优化标定方法, 从而获得多元混合物料的参数, 该参数标定方法的建立为其他药用辅料DEM的标定提供了新的思路。
  • 重庆市自然科学基金创新发展联合基金项目(2023NSCQ-LZX0187)
  • 重庆市自然基金面上项目(CSTB2023NSCQ-MSX0712)
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doi: 10.16438/j.0513-4870.2024-0977
  • 接收时间:2024-09-10
  • 首发时间:2025-10-29
  • 出版时间:2025-04-12
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  • 收稿日期:2024-09-10
  • 修回日期:2024-12-14
基金
重庆市自然科学基金创新发展联合基金项目(2023NSCQ-LZX0187)
重庆市自然基金面上项目(CSTB2023NSCQ-MSX0712)
作者信息
    1 重庆科技大学化学化工学院, 重庆 401331
    2 中国食品药品检定研究院, 北京 100050
    3 重庆中医药学院中药学院, 制药过程数字化重庆市重点实验室, 重庆 402760

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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