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通过基于自适应神经网络的滑动模式控制器,在废水处理过程中实现稳定高效的除

Yiqi Liu^1,2, Jing Zhang^1,2, Zhuyi Qiu^1,2

¹Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, School of Automation Science & Engineering, South China University of Technology, Guangzhou, 510640, China.

Water research X

|August 29, 2024

概括

一个新的基于自适应神经网络的滑动模式控制策略提高了废水处理的稳定性. 这种方法改善了氨的控制,并减少了脱过程中的能源消耗.

科学领域:

环境工程环境工程
控制系统工程控制系统工程
环境管理中的人工智能

背景情况:

废水处理过程 (WWTP) 面临着非线性和干扰的挑战,阻碍了传统的控制策略.
稳定控制氨的度对于有效的废水处理至关重要.
在脱过程中,高能耗和低效率需要先进的解决方案.

研究的目的:

制定一个强大的控制策略,以稳定地管理WWTP中的氨的度.
提高脱化效率,减少废水处理中的能源消耗.
通过基准模拟模型第2号 (BSM2) 验证拟议的控制策略.

主要方法:

基于直接自适应神经网络的滑动模式控制 (ANNSMC) 策略的设计和实施.
使用一个辐射基函数神经网络 (RBFNN) 进行精确的控制规律近似.
使用利亚普诺夫定理分析系统稳定性,以保证强度和适应性.
在BSM2平台上提出并验证双循环脱控制策略.

主要成果:

该ANNSMC战略确保了系统的稳定性和适应性,以控制氨.
双环脱化策略有效地协作了氨和酸的度.
实验结果显示,拟议的控制策略具有显著的稳定性和稳定性.
与标准和先进的控制方法相比,大大减少了能源消耗.

结论:

该ANNSMC战略提供了一种创新的解决方案,用于稳定控制WWTP中的氨.
双循环脱化策略提高了处理效率,降低了运营成本.
拟议的综合控制方法在废水处理应用中显示出卓越的性能.

关键词:

适应性神经网络是一种自适应性神经网络.BSM2 BSM2 BSM2 BSM2 BSM2 BSM2 BSM2 BSM2 BSM2 BSM2 滑动模式控制器滑动模式控制器废水处理过程废水处理过程

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