黄原胶
灵敏度(控制系统)
过程(计算)
生物合成
生化工程
化学
计算机科学
工艺工程
生物化学
工程类
材料科学
酶
电子工程
流变学
复合材料
操作系统
作者
Andrew N. Amenaghawon,Shedrach Igemhokhai,Stanley Aimhanesi Eshiemogie,Favour Ugbodu,Nelson Iyore Evbarunegbe
出处
期刊:Heliyon
[Elsevier]
日期:2024-01-01
卷期号:: e25432-e25432
标识
DOI:10.1016/j.heliyon.2024.e25432
摘要
In this study, the focus was to produce xanthan gum from pineapple waste using Xanthomonas campestris. Six machine learning models were employed to optimize fermentation time and key metabolic stimulants (KH2PO4 and NH4NO3). The production of xanthan gum was optimized using two evolutionary optimization algorithms, particle swarm optimization, and genetic algorithm while the importance of input features was ranked using global sensitivity analysis. KH2PO4 was the most important input and was found to be beneficial for xanthan gum production, while a limited amount of nitrogen was needed. The extreme learning machine model was the most adequate for modeling xanthan gum production, predicting a maximum xanthan yield of 10.34 g/l (an 11.9 % increase over the control) at a fermentation time of 3 days, KH2PO4 of 15 g/l, and NH4NO3 of 2 g/l. This study has provided important insights into the intelligent modeling of a biostimulated process for valorizing pineapple waste.
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