Advancing water disinfection strategies: assessing disinfection efficiency with a Bayesian Regularized artificial neural network model

ABSTRACT

It is important to find the estimation methodology with the highest accuracy in order to determine the parameters of water disinfection and to provide the most ideal disinfection. In this study, the usability of artificial neural networks in predicting response disinfection efficiency in electrochemical water disinfection processes was investigated. An artificial neural network model was developed using a total of 17 data sets and Response Disinfection Efficiency values were estimated from the model. Current density, treatment time and interelectrode spacing values are defined as input parameters in the network model, which has a multilayer perceptron architecture with 10 neurons in its hidden layer. The coefficient of determination value for the developed model was 0.98682 and the average deviation rate was −0.1%. The study findings showed that neural networks are an ideal tool that can be used to predict response disinfection efficiency in electrochemical water disinfection processes.

KEYWORDS:

• Electrochemical water disinfection
• response disinfection efficiency
• current density
• treatment time
• interelectrode spacing
• artificial neural network

Acknowledgments

The author thanks (Ditta et al. 2023) for their work and contributions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Additional information

Notes on contributors

Andaç Batur Çolak

Dr. Andaç Batur Çolak’s main field of science is mechanical engineering, and he conducts studies and articles in this field on topics such as heat transfer, nanofluids, and artificial intelligence applications. In addition, it also accompanies multidisciplinary scientific research in many different fields, such as physics, electronics, chemical interactions, materials, and medicine. Dr. Çolak, who taught in the Department of Mechanical Engineering, also took part as a researcher in national and international scientific projects. He worked in management positions in private sector companies in the field of energy and served as a consultant in international organizations. Besides conducting scientific studies, he also works as a manager in a public energy company. Dr. Çolak, who has been head of sessions and made scientific presentations at many international scientific congresses, has many scientific publications in high-impact journals in his field. Dr. Çolak, who was included in the “top 2% scientists” list published by Stanford University in 2022 and 2023, is also on the advisory board of İstanbul Ticaret University.