基于的浮式螺旋电极冷等离子体模型和优化
G Divya Deepak1, Gajanan Anne1, Subraya Krishna Bhat2
1Department of Mechanical and Industrial Engineering , Manipal Institute of Technology Manipal Academy of Higher Education , Karnataka, 576104, Manipal, India.
Scientific reports
|September 29, 2025
概括
这项研究使用机器学习来模拟和优化冷大气压等离子体 (CAP) 设备. 开发的人工神经网络 (ANN) 准确地预测了CAP的性能,确保了潜在的生物医学应用的安全,非热操作.
科学领域:
- 生物医学工程 生物医学工程
- 等离子体物理学的物理学
- 机器学习 机器学习
背景情况:
- 冷大气压等离子体 (CAP) 提供了对生物医学应用至关重要的非热处理.
- 现有的CAP设备需要精确的控制来保持非热状态.
研究的目的:
- 开发一种基于机器学习的模型,用于对新型浮螺旋电极CAP设备的流程优化.
- 为了确保设备在生物医学用途的安全冷血模式下运行.
主要方法:
- 开发了一种人工神经网络 (ANN) 模型,将过程参数 (供应电压,频率) 与性能指标 (功耗,喷射长度) 相关联起来.
- 使用复合可取性方法进行多响应优化.
- 利用后勤回归机器学习模型 (ANN分类器,K-NN,SVM) 来分类排放类型.
主要成果:
- 该ANN模型表现出普遍性和稳定性,通过实验数据和预测得到验证.
- 机器学习分类器成功地确定了冷等离子体操作范围内的放电类型.
- 该研究为优化生物医学应用中CAP设备建立了一个框架.
结论:
- 机器学习和统计方法的结合有效地建模和优化CAP设备.
- 经过验证的模型确保在非热等离子体状态下运行,这对于生物医学应用中的安全至关重要.
- 需要进一步进行生物测试,以确认这种CAP系统在生物医学用途中的潜力.
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