微藻生物处理中的人工智能和/或机器学习算法
1Department of Bioengineering, Faculty of Engineering, Ege University, Izmir 35100, Turkey.
Bioengineering (Basel, Switzerland)
|November 27, 2024
概括
人工智能 (AI) 和机器学习 (ML) 提高了微藻生产效率和控制. 虽然存在挑战,但AI/ML在微藻过程中的可扩展性,成本降低和环境影响方面提供了显著的好处.
科学领域:
- 生物技术是生物技术.
- 工艺工程是过程工程.
- 计算科学 计算科学
背景情况:
- 微藻种植对于生物燃料,食品和药品至关重要.
- 传统方法在效率,控制和可扩展性方面存在局限性.
- 新兴的AI和ML技术为这些挑战提供了潜在的解决方案.
研究的目的:
- 审查AI/ML在微藻过程中的应用.
- 分析AI/ML实施的好处和挑战.
- 确定AI/ML在该领域的未来研究方向.
主要方法:
- 在微藻种植中对AI/ML应用的文献综述.
- 对常用的ML算法 (SVM,GA,DT,RF,ANN,DL) 的分析.
- 检查AI/ML整合的挑战和建议解决方案.
主要成果:
- 人工智能/ML显著改善了实时监测,物种识别,生长优化,收获和净化.
- 像SVM,GA,ANN和DL这样的算法显示出有前途,但面临着计算成本和透明度等问题.
- 在系统性能,可扩展性,资源效率,成本降低和环境影响方面取得了明显的改进.
结论:
- 在微藻过程中,AI/ML的整合提供了实质性的优势.
- 克服数据可用性,模型复杂性和监管障碍是更广泛采用的关键.
- 未来的工作应该专注于基于模拟的数据,模块化设计和适应性学习,用于强大的AI/ML系统.
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