Process integration for food colorant production from Hibiscus sabdariffa calyx: A case of multi-gene genetic programming (MGGP) model and techno-economics

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@Article{ADEYI:2022:AEJ,

• author = "Oladayo Adeyi and Abiola J. Adeyi and Emmanuel O. Oke and Bernard I. Okolo and Abayomi O. Olalere and John A. Otolorin and Samuel Okhale and Abiola E. Taiwo and Sunday O. Oladunni and Kelechi N. Akatobi",
• title = "Process integration for food colorant production from Hibiscus sabdariffa calyx: A case of multi-gene genetic programming ({MGGP)} model and techno-economics",
• journal = "Alexandria Engineering Journal",
• volume = "61",
• number = "7",
• pages = "5235--5252",
• year = "2022",
• ISSN = "1110-0168",
• DOI = "doi:10.1016/j.aej.2021.10.049",
• URL = "https://www.sciencedirect.com/science/article/pii/S1110016821006931",
• keywords = "genetic algorithms, genetic programming, Heat assisted technology based process, Multi-gene genetic programming, Annual production rate, Unit production cost, Techno-economics, calyx",
• abstract = "This work presents an integrated heat-assisted extraction process for the production of crude anthocyanins powder (CAnysP) from Hibiscus sabdariffa calyx using SuperPro Designer. The influence of process scale-up (0.04 -1000L) and variables (temperature, time and ethanol proportion in solvent) were investigated by adopting a circumscribed central composite design on techno-economic parameters such as annual production rate (APR) and unit production cost (UPC) CAnysP. The individual runs in the CCCD were taken as different process scenario and simulated independently. Multi-gene genetic programming (MGGP) was further used to develop robust predictive models. The robustness of the model and sensitivity analysis were ascertained using the Monte Carlo simulation. The process scenario at 30 min, 30 degreeC, 50percent and 1000 L possessed the highest CAnysP APR and lowest UPC. MGGP- models predicted R2 = 0.9984 for CAnysP APR and R2 = 0.9643 for UPC and certainty (99.98percent for CAnysP APR and 98.47percent for UPC)",

}

Genetic Programming entries for Oladayo Adeyi Abiola John Adeyi Emmanuel Olusola Oke Bernard I Okolo Olusegun Abayomi Olalere John A Otolorin Samuel Okhale Abiola E Taiwo Sunday O Oladunni Kelechi N Akatobi

Citations