使用纳米过膜从合成废水中清除咖啡因和偏醇:人工神经网络和响应表面方法的比较研究
Nkechi Ezeogu1, Petr Mikulášek1, Chijioke Elijah Onu2,3
1Institute of Environmental and Chemical Engineering, Faculty of Chemical Technology, University of Pardubice, Studentská 573, 532 10 Pardubice, Czech Republic.
Membranes
|August 27, 2025
概括
用于预测纳米过中药物排斥的人工神经网络 (ANN) 和响应表面方法 (RSM) 模型进行了比较. 对于从废水中去除咖啡因和类药物,ANN模型的预测准确度更高.
科学领域:
- 环境科学
- 化学工程
- 计算智能
背景情况:
- 制药废水带来了环境挑战.
- 纳米过 (NF) 是去除制药污染物的关键技术.
- 优化NF流程需要准确的预测模型.
研究的目的:
- 将人工神经网络 (ANN) 和响应表面方法 (RSM) 模型的预测性能进行比较.
- 使用 AFC 40 和 AFC 80 NF 膜预测咖啡因和的排斥效率.
- 确定最有效的计算智能技术,以优化NF过程中的药物清除.
主要方法:
- 在变化的跨膜压力,料度和流量下对NF性能进行实验研究.
- 开发和应用ANN和RSM模型来预测污染物排放.
- 使用统计指标 (R2,RMSE,MPSED,HYBRID,AAD) 评估模型的预测能力.
主要成果:
- 对药物排斥的预测准确性很高.
- 与RSM相比,ANN模型始终实现了更高的R2值和更低的错误指标.
- 在AFC 40和AFC 80膜中预测咖啡因和甲醇排斥的性能优异.
结论:
- 人工神经网络 (ANN) 为优化制药废水处理中的NF膜过程提供了强大而卓越的建模方法.
- 这些发现支持使用ANN来提高药物清除系统的效率和降低运营成本.
- 准确的预测建模对于推进可持续的废水处理技术至关重要.
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