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使用絮凝剂辅助过滤一步法从稀悬浮液中动态脱水微藻。

Single-step dynamic dewatering of microalgae from dilute suspensions using flocculant assisted filtration.

机构信息

Biofuel Engine Research Facility (BERF), School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology (QUT), 2 George Street, Brisbane, QLD, 4000, Australia.

Advanced Water Management Centre (AWMC), University of Queensland (UQ), St Lucia, Brisbane, QLD, 4072, Australia.

出版信息

Microb Cell Fact. 2020 Dec 4;19(1):222. doi: 10.1186/s12934-020-01472-4.

DOI:10.1186/s12934-020-01472-4
PMID:33276792
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7716443/
Abstract

BACKGROUND

Dewatering constitutes a major challenge to the production of microalgae, accounting for 20-30% of the product cost. This presents a setback for the applicability of microalgae in the development of several sustainable products. This study presents an investigation into the dynamic dewatering of microalgae in a combined flocculation-filtration process. The effect of process conditions on the performance of 12 flocculants and their mixtures was assessed.

RESULTS

The mechanism of flocculation via the electrostatic path was dominated by charge neutralization and subsequently followed bridging in a 'sweep flocculation' process. Cationic polyacrylamide (CPAM) based flocculants recorded the highest biomass retention with PAM1 and PAM2 attaining 99 and 98% retention with flocculant dosages of 10 and 15 mg/L respectively. Polyvinylamine (PVAM) was also found to improve system stability across the pH range 4-10. Alum was observed to be only effective in charge neutralization, bringing the system close to its isoelectric point (IEP). Chemometric analysis using the multi-criteria decision methods, PROMETHEE and GAIA, was applied to provide a sequential performance ranking based on the net outranking flow (ф) from 207 observations. A graphical exploration of the flocculant performance pattern, grouping the observations into clusters in relation to the decision axis ([Formula: see text]), which indicated the weighted resultant of most favorable performance for all criteria was explored.

CONCLUSION

CPAM based flocculants and their mixtures demonstrated superior performance due to their viscoelastic behaviour under turbulence. The use of PVAM or alum in mixtures with CPAM reduced the required doses of both flocculants, which will provide beneficial financial impact for largescale microalgae dewatering in a flocculant assisted dynamic filtration process. Chemometric analysis based on the physico-chemical properties of the system provides a time saving assessment of performance across several criteria. The study findings provide an important foundation for flocculant assisted dynamic filtration processes.

摘要

背景

脱水是微藻生产的主要挑战,占产品成本的 20-30%。这对微藻在开发几种可持续产品中的适用性造成了阻碍。本研究探讨了在絮凝-过滤组合工艺中微藻的动态脱水。评估了工艺条件对 12 种絮凝剂及其混合物性能的影响。

结果

通过静电途径的絮凝机制主要由电荷中和主导,随后在“扫絮凝”过程中遵循桥接。基于阳离子聚丙烯酰胺(CPAM)的絮凝剂记录了最高的生物质保留率,PAM1 和 PAM2 在 10 和 15mg/L 的絮凝剂剂量下分别达到 99%和 98%的保留率。聚乙烯亚胺(PVAM)也被发现可以提高整个 pH 值范围 4-10 内的系统稳定性。明矾仅在电荷中和方面有效,使系统接近等电点(IEP)。使用多准则决策方法 PROMETHEE 和 GAIA 对化学计量分析,根据 207 次观测的净超优流(ф)提供了顺序性能排名。通过图形探索絮凝剂性能模式,将观测值按照决策轴([公式:见文本])分组到聚类中,这表明对所有标准的加权结果是最有利的性能,探索了加权结果。

结论

基于 CPAM 的絮凝剂及其混合物表现出优异的性能,因为它们在湍流下具有粘弹性。在与 CPAM 混合使用 PVAM 或明矾时,两种絮凝剂的所需剂量都会减少,这将为絮凝剂辅助动态过滤过程中的大规模微藻脱水提供有益的经济影响。基于系统物理化学性质的化学计量分析提供了对跨多个标准的性能的节省时间的评估。该研究结果为絮凝剂辅助动态过滤过程提供了重要的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/5a9d99b95434/12934_2020_1472_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/0dff6dd68098/12934_2020_1472_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/81353d1a30a1/12934_2020_1472_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/15ffe192d1b5/12934_2020_1472_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/246ad746b927/12934_2020_1472_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/cfae8d8f9535/12934_2020_1472_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/59757560c368/12934_2020_1472_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/00ab61520d65/12934_2020_1472_Fig7a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/5a9d99b95434/12934_2020_1472_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/0dff6dd68098/12934_2020_1472_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/81353d1a30a1/12934_2020_1472_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/15ffe192d1b5/12934_2020_1472_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/246ad746b927/12934_2020_1472_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/cfae8d8f9535/12934_2020_1472_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/59757560c368/12934_2020_1472_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/00ab61520d65/12934_2020_1472_Fig7a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cad8/7716443/5a9d99b95434/12934_2020_1472_Fig8_HTML.jpg

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