化学品生物制造过程机器学习的研究进展

doi:10.11949/0438-1157.20250043

摘要/Abstract

摘要：

化学品生物制造过程具有绿色低碳、环境友好和可持续性优势，在化学工业中的作用日益重要。然而，化学品生物制造过程受代谢调控和外源环境等诸多因素影响，其生物合成的监测、控制、优化及产物分离，都具有实时动态复杂性。机器学习在无须明确机理条件下即可捕捉动态过程数据中的复杂非线性关系，有助于掌握化学品生物制造过程的复杂规律，进行过程优化和预测。本文对机器学习范式、主流算法及其在生物合成过程优化、监测与控制、生物分离过程开发和生物燃料化学品生产中的研究进展进行了综述分析，探讨了未来机器学习用于化学品生物制造过程中的问题。

关键词: 机器学习, 生物制造, 生物过程工程, 生物分离, 算法

Abstract:

Chemical biomanufacturing process has the advantages of green, low-carbon, environmentally friendly and sustainable, and its role in the chemical industry is becoming increasingly important. However, the biosynthesis monitoring, control and optimization as well as the separation of products during the biomanufacturing processes always have real-time dynamic complexity due to the influences of metabolicregulation and external environmental factors. The complex nonlinear relationships in data from these dynamic processes can be obtained by machine learning methodology without the need for explicit understanding of process mechanisms, which could then be helpful to reveal the bioprocess laws, and thus improve the optimization and prediction of biomanufacturing processes. In this paper, recent advances regarding the machine learning methodology, key algorithms and typical applications in the optimization, monitoring and control of biosynthesis processes, the development of bioseparation processes, and the production of biofuel chemicals, were summarized and analyzed. The challenges to be addressed in further applications of machine learning in the manufacturing process of chemicals were also discussed.

Key words: machine learning, biomanufacturing, bioprocess engineering, bioseparation, algorithm

中图分类号:

TQ 033

陈治宏, 吴佳伟, 楼小玲, 贠军贤. 化学品生物制造过程机器学习的研究进展[J]. 化工学报, 2025, 76(8): 3789-3804.

Zhihong CHEN, Jiawei WU, Xiaoling LOU, Junxian YUN. Recent advances in machine learning for biomanufacturing of chemicals[J]. CIESC Journal, 2025, 76(8): 3789-3804.

图/表 10

图1 机器学习工作流程

Fig.1 Workflow of machine learning

图2 机器学习分类

Fig.2 Classification of machine learning

图3 SVM原理图

Fig.3 Schematic diagram of support vector machine

图4 ANN及深度学习原理图

Fig.4 Schematic diagram of artificial neural network and deep learning

图5 集成学习原理图（基模型为集成学习中的弱学习器，是构成集成学习模型的基本单元，通过对不同样本的分别训练获得；权重更新为当前模型根据上一个基模型的表现更新样本权重，使后续基模型能更好关注难分类样本；元模型为集成学习中的最终决策模型，负责综合基模型的输出；集成/输出为对多个基模型的结果根据投票或加权平均方式进行输出结合，形成最终预测结果；不同颜色和形状的图形代表具有差异的数据输入点）

Fig.5 Schematic diagrams of ensemble learning(The base model is the weak learner in ensemble learning, which is the basic unit obtained by training different samples. Weight updating means that the model updates the weight of samples according to the performance of the previous base model, so that the subsequent base model can pay more attention to the difficult-to-classify samples. The meta-model is the final decision model for synthesizing the output of the base model. Integration/output is the output combination of the results of multiple base models according to voting or weighted average to form the final prediction. Symbols with different colors and shapes represent different data input points)

表1 机器学习在生物合成过程优化中的应用

Table 1 Applications of machine learning in the optimization of biosynthesis processes

应用	算法	结果	文献
大肠杆菌发酵生产柠檬烯和红没药烯	PCA	产量提高40%	[72]
淡水蓝藻培养生产藻胆蛋白	GSMM、CNN	产量提高61.76%	[73]
黄曲霉发酵生产纤维素酶	GPR、RBFNN等	产量提高3倍	[76]
醋酸菌发酵生产乙酸	FNN	产量达1.5～2.0 g/100 ml	[77]
嗜热地芽孢杆菌发酵生产脂肪酶	BPNN	酶活提高4.7倍	[78]
马克斯克鲁维酵母发酵生产生物乙醇	ANN	产量提高至25.4 g/L	[79]
微藻培养生产红没药烯	CNN	产率提高22%	[82]

表2 机器学习在生物合成过程监测及控制中的应用

Table 2 Applications of machine learning in the monitoring and control of biosynthesis processes

应用	算法	结果	文献
生物反应器泡沫传感器	CNN	识别准确率达98%	[85]
毕赤酵母发酵生产乙肝表面抗原	FNN	R²: 0.969；RMSE: 2.717	[87]
食物垃圾厌氧消化	CNN-BdLSTM	R²: 0.978；RMSE: 0.031	[89]
发酵生产链激酶和青霉素	DNN	RMSE: 0.0274～0.0565	[37]
乳酸菌发酵生产奶油奶酪	LSTM	R²: 0.99	[22]
微藻培养生产叶黄素	ANN	平均误差为2.6%～11.7%	[91]

图6 复合晶胶的微观形貌及其基础性能预测建模[119]

Fig.6 Micromorphology of composite cryogels and prediction modeling of basic properties [119]

表3 机器学习在生物分离过程开发中的应用

Table 3 Applications of machine learning in the development of bioseparation processes

应用	算法	结果	文献
微滤分离酵母悬液	ANN	平均误差小于10%	[98]
超滤分离牛血清白蛋白	ANN	平均误差小于2.7%	[99]
超滤分离苹果皮多酚	SVM、BPNN	R²: 0.9997；RMSE: 0.03894	[101]
离子对色谱分离寡核苷酸	SVM	RMSE < 0.2	[104]
反相液相色谱分离肽	SVM	平均误差为2.0%～4.2%	[105]
混合模式树脂分离蛋白	RF、GBDT	R²: 0.79～0.82	[109]
蛋白质A层析分离单克隆抗体	PCA-IF、LSTM等	R²: 0.96；RMSE: 0.014	[120]

表4 机器学习在生物燃料化学品生产中的应用

Table 4 Applications of machine learning in the production of biofuel chemicals

应用	算法	结果	文献
厌氧消化生产甲烷	KNN	R²: 0.75	[123]
厌氧消化生产甲烷	SVM	R²: 0.92；RMSE: 0.167	[125]
发酵生产生物乙醇	FNN	R²: 0.91	[126]
酯交换生产生物柴油	KNN、RF	RMSE: 5.178	[61]
酯交换生产生物柴油	Huber-Adaboost、DT-Adaboost	R²: 0.996；RMSE: 1.82	[127]

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