Development of prediction model of ozone dosage and residual ozone concentration using machine learning methods in ozone process of drinking water treatment process

Abstract

[EN] The ozone process, which is the latter process of the water purification process, injects ozone to remove taste odor substances from tap water. Still, it is difficult to work the ozone process due to recent changes in water quality, such as taste and odor substances due to climate change. Therefore, this study developed an ozone injection rate determination model and a residual ozone concentration prediction model to properly remove flavor odor substances from raw water and proposed an operational diagnosis and optimal decision-making method for the ozone process in water purification. An ozone injection rate determination model and a residual ozone concentration prediction model were developed using data on water quality, flow rate, and operating conditions measured at Seoul's Y water purification plant. Two models were developed: the random forest and the MLP models. The performance difference between the two was verified by comparing the correlation coefficient and error index. Bayesian optimization, a global search method within a given composition space, was used to determine hyperparameters for each model. RMSE was selected as an objective function to determine the optimal hyperparameter through cross-validation. If the above model is applied to the ozone process, it is expected that an immediate response to changes in raw water quality and human error prevention will be possible.