功能链路混合人工神经网络用于预测动态膜生物反应器中持续的生物生产
Ashutosh Kumar Pandey1, Sarat Chandra Nayak2, Sang-Hyoun Kim1
1Department of Civil and Environmental Engineering, Yonsei University, Seoul 03722, Republic of Korea.
Bioresource technology
|February 26, 2024
概括
这项研究引入了一种混合机器学习模型 (PSO-FLN) 来预测生物的生产. 该模型准确地预测了的生产速度和产量,超过了传统方法.
科学领域:
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
- 机器学习 机器学习
背景情况:
- 传统的机器学习方法与连续生物生产的非线性动态作斗争.
- 精确预测生产率 (HPR) 和产量 (HY) 对于优化生物反应器性能至关重要.
研究的目的:
- 开发和评估一种混合机器学习模型,用于预测生物生产中的动态膜反应堆性能.
- 预测关键绩效指标:生产率 (HPR) 和产量 (HY).
主要方法:
- 通过将粒子群优化 (PSO) 与功能链接人工神经网络 (FLN) 集成来开发混合算法.
- 利用基于实验室的每日操作数据与12个输入变量用于模型培训和验证.
- 采用沙普利添加式解释 (SHAP) 来确定影响关键参数.
主要成果:
- 该PSO-FLN混合模型在预测HPR (R2=0.97) 和HY (R2=0.80) 方面表现出卓越的性能.
- 实现了较低的预测误差:HPR为0.014%,HY为0.023%.
- 确定了有机载荷率 (OLR) 和黄油酸度作为HPR的关键积极影响因素.
结论:
- 该PSO-FLN模型有效地处理高精度的生物生产中的复杂,非线性数据集.
- 这种方法提供了一种计算效率高的方法,用于实时预测生物反应器性能.
- 这些发现为通过先进的机器学习优化生物生产过程提供了宝贵的见解.
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