针对Pichia pastoris发酵过程的增强软传感器的建模和优化
Bo Wang1, Ameng Yu1, Haibo Wang1
1Key Laboratory of Agricultural Measurement and Control Technology and Equipment for Mechanical Industrial Facilities, School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究引入了用于Pichia pastoris发酵的新软传感器模型 (MIC-TCA-INGO-LSSVM),在不断变化的条件下提高了预测准确性. 这种新的方法提高了工业生物工艺的可靠性和性能.
科学领域:
- 生物技术是生物技术.
- 工艺工程是过程工程.
- 机器学习 机器学习
背景情况:
- 由于工作条件的变化,Pichia pastoris发酵中的软传感器模型恶化.
- 精确监测细胞和产品度对于过程优化至关重要.
研究的目的:
- 开发一个强大的软传感器模型,用于Pichia pastoris发酵,适应不同的工作条件.
- 提高软传感器模型的预测准确度和概括能力.
主要方法:
- 转移组件分析 (TCA) 调整跨条件的数据分布.
- 优化最小平方支持向量机 (LSSVM) 使用改进的北方戈斯霍克优化 (INGO).
- 基于最大信息系数 (MIC) 的权重组合的知识转移子模型.
主要成果:
- 与NGO-LSSVM相比,INGO-LSSVM模型将RMSE降低了47.3% (细胞) 和42.1% (产品).
- TCA提高了模型适应不断变化的工作条件的适应性.
- 与单一来源模型相比,MIC加权组合模型 (TCA-MIC-INGO-LSSVM) 进一步将RMSE降低了41.6% (细胞) 和31.3% (产品).
结论:
- 拟议的MIC-TCA-INGO-LSSVM软传感器显示出高可靠性和预测性能.
- 该方法有效地解决了在动态发酵条件下软传感器模型的性能下降.
- 这种方法为生物过程的实时监测提供了显著的改进.
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