LAPSE:2020.0125
Published Article
LAPSE:2020.0125
Modeling and Economic Optimization of the Membrane Module for Ultrafiltration of Protein Solution Using a Genetic Algorithm
Tuan-Anh Nguyen, Shiro Yoshikawa
February 2, 2020
The performance of cross-flow ultrafiltration is greatly influenced by permeate flux behavior, which depends on many factors, including solution properties, membrane characteristics, and operating conditions. Currently, most research focuses on improving membrane performance, both in terms of permeability and selectivity. Only a few studies have paid attention to how the membrane module is configured and operated. In this study, the geometric design and operating conditions of a membrane module are considered as multivariable optimization variables. The objective function is the annual cost. The cost consists of a capital investment depending on the plant scale and an operating expense associated with energy consumption. In the optimization problem, the channel dimensions (width × length × height), and operating conditions (the inlet pressure and recirculation flow rate) were considered as decision variables. The operating configuration of the membrane plant is assumed to be feed and bleed mode, and a model including the pressure drop is introduced. The model is used to simulate the membrane plant and calculate the membrane area and energy usage, which are directly related to the total cost. The genetic algorithm is used for the optimization. The effect of individual parameters on the total cost is discussed.
Keywords
cross-flow, membrane module, Modelling, Optimization, protein solution, ultrafiltration
Suggested Citation
Nguyen TA, Yoshikawa S. Modeling and Economic Optimization of the Membrane Module for Ultrafiltration of Protein Solution Using a Genetic Algorithm. (2020). LAPSE:2020.0125
Author Affiliations
Nguyen TA: Faculty of Chemical Engineering, Ho Chi Minh City University of Technology, VNU-HCM, 268 Ly Thuong Kiet, Ho Chi Minh City 70000, Vietnam [ORCID]
Yoshikawa S: Department of Chemical Science and Engineering, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Journal Name
Processes
Volume
8
Issue
1
Article Number
E4
Year
2019
Publication Date
2019-12-18
Published Version
ISSN
2227-9717
Version Comments
Original Submission
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PII: pr8010004, Publication Type: Journal Article
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LAPSE:2020.0125
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doi:10.3390/pr8010004
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Feb 2, 2020
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CC BY 4.0
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Feb 2, 2020
 
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Original Submitter
Calvin Tsay
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