Optimizing the Disinfection Inactivation Efficiency in Wastewater Treatment: A Computational Fluid Dynamics Investigation of a Full‐Scale Ozonation Contactor

Elaissaoui Elmeliani, Mohamed El Amine; Aguedal, Hakim; Iddou, Abdelkader; Alaoui, Chakib; Benaissa, Brahim; Belhadj, Mohamed El Amine; Nguyen, The-Anh; Sun, Meng; Terashima, Mitsuharu

doi:10.1002/ceat.202300232

Cited by 4 publications

(1 citation statement)

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“…[2][3][4] 수치해석 모델(예시: 전산유체역학 프로그램)은 이러한 다양한 단위 공정들로부터 효율적인 폐수처리 공정을 설계하 고 처리 목표에 적합한 최적의 운전 조건을 도출하기 위해서 활용될 수 있으며, 다만 유입 부하의 변동과 더불어 폐수처리 공정의 복잡성 및 비선형성의 특성으로 인하여 모든 공정 메 커니즘의 모의 및 해석에는 다소 한계가 있는 것으로 평가되 고 있다. [5][6][7][8] 한편, 최근 인공지능 기반의 기계학습 및 심층학습 알고리 즘은 입력 및 출력 데이터의 복잡하고 비선형적인 관계의 학 습을 통해 예측 성능이 우수한 모델 개발이 가능한 것으로 알려져 있다. 9),10) 예를 들면, Wang 등…”

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Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process

Kim,

Lee,

Jang

et al. 2023

J Korean Soc Environ Eng

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This study assessed the performance of single and modified algorithms based on machine learning and deep learning for wastewater treatment process. More specifically, this study adopted support vector machine (SVM), random forest (RF), and artificial neural network (ANN) for machine learning as well as long short-term memory (LSTM) for deep learning. The performance of these (single) algorithms were compared with that of modified ones processed through hyperparameter tuning, ensemble learning (only for machine learning), and multi-layer stacking (i.e., two layers of LSTM units). The daily effluent of wastewater treatment process observed between 2017 and 2022 in the Cheong-Ju National Industrial Complex was used as input to all tested algorithms, which was evaluated with respect to mean squared error. For the model performance evaluation, discharge and biochemical oxygen demand are selected as dependent variables out of nine measured parameters. Results showed that the performance of any machine learning algorithms was superior to their competitor LSTM. This is mainly attributed to a small amount of input data provided to the LSTM algorithm and unstable effluent wastewater characteristics. Meanwhile, hyperparameter tuning improved the performance of all tested algorithms. However, ensemble learning for machine learning and two-layer stacking for LSTM generally resulted in performance degradation as compared to that of single algorithms, regardless of dependent variables. Therefore, this calls for a careful design and evaluation of modified algorithms, specifically for model architecture and performance improvement processes.

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