人工神经网络和遗传算法结合发酵动力学来调节L-氨酸发酵
Hui Li1, Jiajun Chen1, Xingyan Li1
1College of Biotechnology and Pharmaceutical Engineering, State Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing, 211816, China.
Bioresource technology
|December 4, 2023
概括
这项研究介绍了一种人工神经网络 (ANN) 和遗传算法 (GA),用于优化氨酸发酵控制. 这种新的方法可以将氨酸的产量提高到213.0g·L-1,从而改善生物过程的调节.
科学领域:
- 生物技术是生物技术.
- 工业微生物学 工业微生物学
- 工艺工程是过程工程.
背景情况:
- 发酵对于生物制品至关重要,但缺乏有效的过程调节.
- 优化发酵控制对于工业生物工艺至关重要.
研究的目的:
- 利用人工神经网络 (ANN) 和遗传算法 (GA) 开发一种创新的素发酵控制策略.
- 预测和优化关键的发酵动力学:特定的素形成率 (qp),特定的基质消耗率 (qs) 和特定的细胞生长率 (μ).
主要方法:
- 将GA与ANN结合起来,用于开发综合控制战略.
- 使用一个三层的前向向后传播ANN模型 (4:10:1).
- 通过GA优化发酵参数.
主要成果:
- 该ANN-GA模型成功预测并优化了发酵动力学.
- 确定了关键因素的最佳控制参数,包括碳比,残糖,氨和溶解氧.
- 在优化条件下,氨酸度达到213.0±5.10 g·L−1的峰值.
结论:
- 开发的ANN-GA控制策略显著提高了氨酸发酵效率.
- 这种新的方法表明了优化各种其他生物制品发酵的潜力.
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