在RSM和ANN驱动的参数优化下使用基于石墨烯的异质光催化剂从CO2优化甲醇合成,以实现更好的适用性
Ramesh Kumar1, Jayato Nayak2, Somnath Chowdhury3
1Department of Earth Resources & Environmental Engineering, Hanyang University 222-Wangsimni-ro, Seongdong-gu Seoul 04763 Republic of Korea bhjeon@hanyang.ac.kr.
RSC advances
|April 18, 2024
概括
这项研究优化了使用TiO2 / Cu催化剂与石墨烯从CO2中生产甲醇. 人工神经网络 (ANN) 在预测最佳产量方面超过了响应表面方法 (RSM).
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 将二氧化碳转化为甲醇对于可持续能源至关重要.
- 基于石墨烯的催化剂提高了催化效率.
- 优化催化过程需要强大的预测模型.
研究的目的:
- 评估线性 (RSM) 和非线性 (ANN) 回归模型,以估计现场催化CO2转换.
- 为了优化甲醇产量,使用TiO2/Cu与脱皮石墨烯 (HEG) 相结合.
- 为了比较RSM和ANN模型的预测性能.
主要方法:
- 使用响应表面方法 (RSM) 和人工神经网络 (ANN) 实验和优化甲醇产量.
- 研究HEG负载 (10-40重量%) 对TiO2/Cu催化剂性能的影响.
- 分析影响参数的分析,例如HEG剂量和pH 3的CO2流入速度.
主要成果:
- 在TiO2 / Cu催化剂上装载30%重量的HEG,产生了最高的甲醇转化效率.
- 据RSM和ANN预测的最佳甲醇产量分别为36.3 mg/g和37.3 mg/g.
- 与RSM (R2~0.97) 相比,基于非线性回归的ANN模型显示出更高的确定系数 (R2>0.985).
结论:
- 具有9个输入神经元和1个隐藏层的ANN模型提供了更接近实验结果的预测.
- 无论是RSM和ANN模型都表现良好,但ANN在预测最佳甲醇产量方面表现更准确.
- 该研究强调了HEG-TiO2 / Cu催化剂的有效性以及ANN在CO2转换过程中优化过程的优势.
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