Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach
Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach
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废水中 COD 和 TN 去除电化学降解的多变量优化：逆计算机器学习方法
DOI：
10.1016/j.seppur.2022.121129
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发表时间：
2022-04
影响因子：
8.6
通讯作者：
Ruihao Zheng
中科院分区：
文献类型：
--
作者：
Jiaqian Yang;Jining Jia;Jiade Wang;Qingqing Zhou;Ruihao Zheng
将遗传算法（GA）与前向神经网络相结合，建立了一种逆计算机器学习（ML）框架，用于优化电化学法去除废水中化学需氧量（COD）和总氮（TN）的运行参数。传统的神经网络（NN）通常无法实现从期望的减排到可行的输入条件的逆计算，并且从小规模数据集训练的NN往往不可靠的多变量模拟。为了解决这一问题，我们采用遗传算法调整网络的权值和偏差，以提高神经网络的稳定性和泛化性能，并采用迭代进化和全局搜索的遗传算法策略进行多工况优化。神经遗传模型（GANN）显著性和误差分析表明，GANN具有良好的上级稳定性和泛化能力，其最高R2分别为0.946（COD）、0.874（TN），最低RMSE分别为0.022（COD）、0.028（TN）。遗传算法的引入对于提高小规模数据集神经网络的预测精度具有重要意义。用25个数据点训练的GANN的平均误差低于用54个数据点训练的RSM模型的平均误差。根据验证结果，GANN建议的方案实现了最佳的COD（93.6%）和TN（62.8%）降解效率，分别比原始数据集中的最佳值高6.1%和9.9%。所提出的多变量全局优化策略可以扩展到其他情况，GANN的计算框架也有助于更可靠的ML模型的进展。
An inverse computation machine learning (ML) framework that couples genetic algorithm (GA) to feedforward neural network was developed to optimize the operating parameters for maximizing electrochemical removal of chemical oxygen demand (COD) and total nitrogen (TN) from wastewater. Conventional neural networks (NN) are generally unable to implement inverse computation from a desired abatement to feasible input conditions, and NN trained from small-scale datasets tends to be unreliable for multivariate simulation. To address this issue, we employed GA for tuning the weights and biases of the network to enhance the stability and generalization performance of NN, the optimization of multiple operating conditions was performed by the GA strategy of iterative evolution and global search.In this work, we investigated and analyzed the performance of multivariate optimization approaches such as orthogonal design, response surface methodology (RSM), traditional NN, and the developed neuro-genetic model (GANN). Significance and error analysis demonstrate that GANN exhibits superior stability and generalization ability with the highest R2of 0.946 (COD), 0.874 (TN), and the lowest RMSE of 0.022 (COD), 0.028 (TN). The introduction of GA is significant for improving the prediction accuracy of NN derived from small-scale datasets. The average error of the GANN trained by 25 data points is lower than that of the RSM model derived from 54 data points. According to the validation outcomes, the scheme suggested by GANN achieves the best COD (93.6%) and TN (62.8%) degradation efficiencies, which are 6.1% and 9.9% higher than the optimal values in the original dataset, respectively. The proposed multivariate global optimization strategy can be extended to other cases, and the computational framework of GANN also contributes to the progress of more reliable ML models.
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影响因子：
8.6
作者：
Hu, Jun;Bian, Xinze;Dai, Qizhou
通讯作者：
Dai, Qizhou
DOI：
10.4018/978-1-7998-1192-3.ch008
发表时间：
2020
期刊：
Advances in Systems Analysis, Software Engineering, and High Performance Computing
影响因子：
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作者：
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通讯作者：
Menaga D.;R. S.
DOI：
10.4018/978-1-5225-9096-5.ch007
发表时间：
2021-07
期刊：
Smart Computational Intelligence in Biomedical and Health Informatics
影响因子：
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作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
通讯作者：
A. Sinha;S. Gupta;Anurag Tiwari;Amrita Chaturvedi
DOI：
10.4018/978-1-5225-7862-8.ch007
发表时间：
2019
期刊：
Handbook of Research on Deep Learning Innovations and Trends
影响因子：
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作者：
K. Lakhtaria;Darshankumar Modi
通讯作者：
K. Lakhtaria;Darshankumar Modi
DOI：
10.1007/978-3-319-97436-1_3
发表时间：
2018-08
期刊：
Artificial Intelligence for Business
影响因子：
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作者：
R. Akerkar
通讯作者：
R. Akerkar