摘 要
随着生物技术的快速发展，生物过程参数的精确预测与控制成为提升生产效率和产品质量的关键环节。然而，传统建模方法在处理复杂非线性、动态变化及多变量交互关系时存在局限性，人工智能技术为此提供了新的解决方案。本研究旨在探索人工智能算法在生物过程参数预测与控制中的应用潜力，通过结合机器学习模型与实际生物工艺数据，构建高效、准确的预测与控制系统。研究选取典型生物反应过程为案例，采用深度学习、支持向量机及强化学习等方法，对关键参数如温度、pH值、溶解氧浓度及代谢产物浓度进行建模与优化。结果表明，基于人工智能的模型能够显著提高预测精度，并通过实时反馈控制实现参数的稳定调节。与传统方法相比，该方法在复杂工况下的适应性和鲁棒性更强，且能有效降低能耗与成本。本研究的主要创新点在于提出了一种融合多源异构数据的混合建模策略，同时开发了适用于动态环境的自适应控制算法，为智能化生物制造奠定了理论和技术基础。研究表明，人工智能技术在生物过程领域的应用具有广阔前景，可为相关行业的转型升级提供重要支撑。
关键词：生物过程参数；人工智能算法；机器学习模型；预测与控制；混合建模策略
Abstract
With the rapid development of biotechnology, the precise prediction and control of bioprocess parameters have become critical to enhancing production efficiency and product quality. However, traditional modeling methods face limitations in handling complex nonlinearities, dynamic changes, and multivariable interactions. Artificial intelligence (AI) technologies offer new solutions to these challenges. This study aims to explore the application potential of AI algorithms in the prediction and control of bioprocess parameters by integrating machine learning models with real-world bioprocess data to construct efficient and accurate prediction and control systems. A typical bioreaction process was selected as a case study, and methods such as deep learning, support vector machines, and reinforcement learning were employed to model and optimize key parameters including temperature, pH value, dissolved oxygen concentration, and me tabolite concentration. The results demonstrate that AI-based models significantly improve prediction accuracy and enable stable parameter regulation through real-time feedback control. Compared with traditional methods, this approach exhibits stronger adaptability and robustness under complex operating conditions while effectively reducing energy consumption and costs. The primary innovation of this study lies in proposing a hybrid modeling strategy that integrates multisource heterogeneous data and developing adaptive control algorithms suitable for dynamic environments, thereby laying a theoretical and technical foundation for intelligent biomanufacturing. The findings indicate that AI technologies hold broad prospects in the field of bioprocesses and can provide significant support for the transformation and upgrading of related industries.
Keywords: Biological Process Parameters; Artificial Intelligence Algorithms; Machine Learning Models; Prediction And Control; Hybrid Modeling Strategies
目 录
1绪论 1
1.1人工智能与生物过程参数预测的背景 1
1.2研究意义 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2人工智能技术在生物过程中的适用性分析 2
2.1生物过程参数的特点与挑战 2
2.2常见人工智能算法的适用性评估 3
2.3数据驱动模型在生物过程中的优势 3
2.4深度学习与传统方法的对比分析 4
2.5人工智能技术选择的关键因素 4
3人工智能在生物过程参数预测中的实现路径 5
3.1数据采集与预处理技术 5
3.2预测模型的设计与构建 5
3.3参数优化算法的应用实践 6
3.4实验验证与结果分析 6
3.5预测精度提升的关键策略 7
4人工智能在生物过程控制中的应用研究 7
4.1控制系统的基本需求与设计原则 7
4.2强化学习在动态控制中的应用 8
4.3自适应控制算法的开发与测试 8
4.4实时监测与反馈机制的建立 9
4.5控制效果评估与改进方向 9
结论 11
参考文献 12
致 谢 13
