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棕榈油水混合物(棕榈油厂废水模型)中剩余油回收的渐进冷冻优化

Optimization of Progressive Freezing for Residual Oil Recovery from a Palm Oil-Water Mixture (POME Model).

作者信息

Anuar Muhammad Athir Mohamed, Amran Nurul Aini, Ruslan Muhammad Syafiq Hazwan

机构信息

Chemical Engineering Department, University Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.

HICOE-Center for Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia.

出版信息

ACS Omega. 2021 Jan 20;6(4):2707-2716. doi: 10.1021/acsomega.0c04897. eCollection 2021 Feb 2.

Abstract

Oil and grease remain the dominant contaminants in the palm oil mill effluent (POME) despite the conventional treatment of POME. The removal of residual oil from palm oil-water mixture (POME model) using the progressive freezing process was investigated. An optimization technique called response surface methodology (RSM) with the design of rotatable central composite design was applied to figure out the optimum experimental variables generated by Design-Expert software (version 6.0.4. Stat-Ease, trial version). Besides, RSM also helps to investigate the interactive effects among the independent variables compared to one factor at a time. The variables involved are coolant temperature, (4-12 °C), freezing time, (20-60 min), and circulation flow, (200-600 rpm). The statistical analysis showed that a two-factor interaction model was developed using the obtained experimental data with a coefficient of determination ( ) value of 0.9582. From the RSM-generated model, the optimum conditions for extraction of oil from the POME model were a coolant temperature of 6 °C in 50 min freezing time with a circulation flowrate of 500 rpm. The validation of the model showed that the predicted oil yield and experimental oil yield were 92.56 and 93.20%, respectively.

摘要

尽管对棕榈油厂废水(POME)进行了常规处理,但油脂仍然是其中的主要污染物。研究了采用逐步冷冻法从棕榈油水混合物(POME模型)中去除残留油的方法。应用了一种称为响应面法(RSM)的优化技术,并采用了旋转中心复合设计,以确定由Design-Expert软件(版本6.0.4,Stat-Ease,试用版)生成的最佳实验变量。此外,与一次只考虑一个因素相比,RSM还有助于研究自变量之间的交互作用。涉及的变量有冷却剂温度(4-12℃)、冷冻时间(20-60分钟)和循环流量(200-600转/分钟)。统计分析表明,利用获得的实验数据建立了一个双因素交互模型,决定系数( )值为0.9582。根据RSM生成的模型,从POME模型中提取油的最佳条件是冷却剂温度为6℃,冷冻时间为50分钟,循环流量为500转/分钟。模型验证表明,预测的出油率和实验出油率分别为92.56%和93.20%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3955/7860092/b1c1649476cd/ao0c04897_0002.jpg

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