Data-driven intelligent modeling, optimization, and global sensitivity analysis of a xanthan gum biosynthesis process

被引:3
作者
Amenaghawon, Andrew Nosakhare [1 ]
Igemhokhai, Shedrach [1 ,2 ]
Eshiemogie, Stanley Aimhanesi [1 ]
Ugbodu, Favour [1 ]
Evbarunegbe, Nelson Iyore [3 ]
机构
[1] Univ Benin, Dept Chem Engn, Bioresources Valorizat Lab, Benin, Edo State, Nigeria
[2] Univ Benin, Dept Petr Engn, Benin, Edo State, Nigeria
[3] Univ Massachusetts, Dept Chem Engn, Amherst, MA 01003 USA
基金
英国科研创新办公室;
关键词
Xanthan gum; Stimulant; Pineapple waste; Machine learning; Cross; -validation; Optimization; EXTREME LEARNING-MACHINE; RESPONSE-SURFACE METHODOLOGY; FERMENTATION; ULTRASOUND; BIODIESEL; STRAINS; WASTE; OIL;
D O I
10.1016/j.heliyon.2024.e25432
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
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.
引用
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页数:18
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