A soft sensor net based on the symplectic decomposition-global attention reconstruction architecture for biopharmaceutical industry

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-07-23 DOI:10.1016/j.asoc.2025.113636

Simengxu Qiao, Yichen Song, Qunshan He, Shifan Chen, He Zhang, Xinggao Liu

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引用次数: 0

Abstract

Non-linearity, time-varying properties, and high noise levels in biopharmaceutical process data have been recognized as critical factors affecting the accuracy of data-driven soft sensors. To address these issues and enhance prediction precision, we introduce BPSN, an innovative soft sensor framework grounded in the symplectic decomposition-global attention reconstruction architecture. Symplectic geometry mode decomposition effectively adapts to data complexity and reduces noise. A reconstruction module combines global attention mechanism and reversible instance normalization to enhance sharp signal features via Manhattan distance while addressing internal drift. Experiments show that the proposed soft sensor model outperforms state-of-the-art models in predicting key indicators: bacterial concentration, viscosity, and reducing sugar content in the erythromycin fermentation process. This illustrates its practical applicability and exceptional performance in biopharmaceutical industry. The source code is available at: https://github.com/Joss0623/BioPharmaSoftNet.git.

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生物制药行业基于辛分解-全局注意力重构体系结构的软传感器网络

生物制药过程数据中的非线性、时变特性和高噪声水平已被认为是影响数据驱动软传感器精度的关键因素。为了解决这些问题并提高预测精度，我们引入了一种基于辛分解-全局注意力重建架构的创新软传感器框架BPSN。辛几何模态分解有效地适应了数据的复杂性，降低了噪声。重建模块结合了全局注意机制和可逆实例归一化，通过曼哈顿距离增强信号特征，同时解决内部漂移。实验表明，所提出的软传感器模型在预测红霉素发酵过程中的细菌浓度、粘度和还原糖含量等关键指标方面优于现有的模型。这说明了它在生物制药行业的实用性和卓越性能。源代码可从https://github.com/Joss0623/BioPharmaSoftNet.git获得。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.