响应方法优化和人工神经网络建模,用于使用臭氧电凝混合工艺去除硫甲
Nguyen Trong Nghia1, Bui Thi Kim Tuyen2, Ngo Thi Quynh2
1Faculty of Chemical and Environmental Technology, Hung Yen University of Technology and Education, Khoai Chau District, Hung Yen 17817, Vietnam.
这项研究优化了臭氧电凝系统 (O-EC) 来从水中去除硫甲醇 (SMX),达到99.65%的效率. 在废水处理中,O-EC系统显示出有效的抗生素去除的前景.
科学领域:
- 环境科学 环境科学
- 水处理技术水处理技术
- 化学工程是化学工程的重要组成部分.
背景情况:
- 抗生素耐药性是一个日益增长的全球威胁,需要有效的水处理方法.
- 传统的废水处理系统很难去除新出现的污染物,如药品.
- 臭氧电凝 (O-EC) 为增强污染物去除提供了一种协同方法.
研究的目的:
- 优化和建模使用臭氧电凝 (O-EC) 系统去除硫甲醇 (SMX).
- 确定影响SMX移除效率的关键操作参数.
- 评估响应表面方法和人工神经网络对O-EC过程的预测能力.
主要方法:
- 中央复合设计被用于实验优化.
- 响应表面方法 (RSM) 用于建模和优化参数:电流密度,反应时间,pH和臭氧剂量.
- 人工神经网络 (ANN) 建模被用来预测SMX删除性能.
主要成果:
- 在最佳条件下实现了99.65%的SMX去除:电流密度为33.2A/m2,时间为37.8分钟,pH为8.4分钟,臭氧剂量为0.7g/h.
- RSM四边形模型准确地预测了移除过程.
- ANN模型与实验数据有很好的一致性;pH值是最有影响力的因素.
结论:
- O-EC 系统在从水溶液中去除硫甲醇方面非常有效.
- 优化的O-EC参数确保了有效的抗生素去除,减轻了耐药性传播.
- 该研究验证了O-EC作为制药废水处理的可行技术.
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