Optimization of industrial-scale centrifugal separation of biological products: comparing the performance of tubular and disc stack centrifuges

离心机 堆栈(抽象数据类型) 转速 计算流体力学 体积流量 流量(数学) 机械 离心力 色谱法 材料科学 化学 机械工程 计算机科学 工程类 物理 核物理学 程序设计语言
作者
Parvaneh Esmaeilnejad-Ahranjani,Monireh Hajimoradi
出处
期刊:Biochemical Engineering Journal [Elsevier]
卷期号:178: 108281-108281 被引量:9
标识
DOI:10.1016/j.bej.2021.108281
摘要

It is reported for the first time, the selection of an industrial centrifuge with the optimized geometry and operational conditions to efficiently separate the diphtheria bacterial cell debris from the culture medium and particularly to harvest the purified bacterial toxin by using the computational fluid dynamics (CFD) simulation and experimental approaches. The industrial-scale tubular and disc stack centrifuges each with two different sizes were first simulated to quantify their complex hydrodynamics. An optimal balance among the rotational speed, feed flow rate, and possible cell damages was required to efficiently separate the bacterial cells. It was also revealed that both large-sized tubular and disc stack centrifuges perform remarkably better than the smaller ones. Ultimately, according to the CFD simulation results, among four centrifuges, the large-sized disc stack centrifuge with the rotational speeds upper than 5500 rpm and the feed flow rates lower than 100 L h−1 was a potential candidate to utilize in the real process. Through performing experiments by using an industrial disc stack centrifuge, a good agreement was found between the CFD and experimental data in terms of the optimized rotational speed and feed flow rate required for the separation of cells. The Ramon flocculation assay confirmed the preserved quality and quantity of the main product of this bioseparation process, ‘the bacterial toxin purified from the bacterial cell debris’.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
鲸鱼完成签到,获得积分10
1秒前
huangqinxue完成签到,获得积分10
1秒前
2秒前
2秒前
Tina完成签到,获得积分10
2秒前
电催化皮皮完成签到,获得积分10
2秒前
大模型应助阿蒙采纳,获得10
3秒前
duguqiubai4完成签到,获得积分10
3秒前
4秒前
meta完成签到,获得积分10
4秒前
大饼完成签到,获得积分10
5秒前
爆米花应助WJM采纳,获得10
5秒前
xiexuqin完成签到,获得积分10
5秒前
5秒前
silentJeremy发布了新的文献求助200
6秒前
JonyiCheng完成签到,获得积分10
6秒前
科研通AI5应助典雅又夏采纳,获得10
7秒前
风趣的无剑完成签到,获得积分10
7秒前
7秒前
anpucle发布了新的文献求助10
7秒前
跳不起来的大神完成签到 ,获得积分10
7秒前
科研乐色完成签到,获得积分10
7秒前
Drew完成签到,获得积分10
9秒前
挤爆沙丁鱼完成签到 ,获得积分10
9秒前
彭于晏应助fff采纳,获得10
9秒前
9秒前
Agernon应助yaya采纳,获得10
9秒前
四夕完成签到 ,获得积分10
10秒前
汉堡包应助执着的小蘑菇采纳,获得10
10秒前
西哈哈发布了新的文献求助10
10秒前
搜集达人应助酷炫大树采纳,获得10
11秒前
11秒前
11秒前
外向的沅完成签到,获得积分20
11秒前
bkagyin应助zy采纳,获得10
12秒前
香蕉觅云应助好了采纳,获得10
12秒前
南逸然发布了新的文献求助10
13秒前
13秒前
xiaohe完成签到,获得积分10
13秒前
13秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527469
求助须知:如何正确求助?哪些是违规求助? 3107497
关于积分的说明 9285892
捐赠科研通 2805298
什么是DOI,文献DOI怎么找? 1539865
邀请新用户注册赠送积分活动 716714
科研通“疑难数据库(出版商)”最低求助积分说明 709678