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2024 Vol.57, Issue 3

Research Article

Study on water quality prediction in water treatment plants using AI techniques

AI 기법을 활용한 정수장 수질예측에 관한 연구

Seungmin Leea
,
Yujin Kangb
,
Jinwoo Songc
,
Juhwan Kimd
,
Hung Soo Kime
,
Soojun Kimf*
이 승민a
,
강 유진b
,
송 진우c
,
김 주환d
,
김 형수e
,
김 수전f*
aMaster’s Course, Program in Smart City Engineering, Inha University, Incheon, Korea
bDoctor’s Course, Program in Smart City Engineering, Inha University, Incheon, Korea
cOfficial, Jemulpo Renaissance Planning Division, Incheon Metropolitan City, Korea
dResearch Professor, Department of Civil Engineering, Inha University, Incheon, Korea
eProfessor, Department of Civil Engineering, Inha University, Incheon, Korea
fProfessor, Department of Civil Engineering, Inha University, Incheon, Korea
a인하대학교 스마트시티공학과 석사과정
b인하대학교 스마트시티공학과 박사과정
c인천광역시 제물포르네상스계획과 주무관
d인하대학교 사회인프라공학과 연구교수
e인하대학교 사회인프라공학과 교수
f인하대학교 사회인프라공학과 교수
*Corresponding Author
31 March 2024. pp. 151-164
PDF XML Citation
Abstract

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH3, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

Keywords
AI
Multivariate Regression
Pre-Residual Chlorine
Residual Chlorine Concentration Prediction Model
Chlorine Control

상수도 공급을 위한 정수장에서 전염소 또는 중염소 공정이 도입된 수처리 공정의 염소농도 관리에 필요한 공정제어를 위하여 AI 기술을 활용한 수질예측 기법이 연구되고 있다. 본 연구에서는 정수장 수처리 공정에서 실시간으로 관측, 생산되고 있는 수량･수질자료를 이용하여 염소소독 공정제어 자동화를 목적으로 침전지 후단의 잔류염소 농도를 예측하기 위한 AI 기반 예측모형을 개발하였다. AI 기반 예측모형은 과거 수질 관측자료를 학습하여 이후 시점의 수질에 대한 예측이 가능한 기법으로, 복잡한 물리･화학･생물학적 수질모형과 달리 간단하고 효율적이다. 다중회귀 모형과 AI 기반 모형인 랜덤포레스트와 LSTM을 이용하여 정수장의 침전지 후단 잔류염소 농도를 예측하여 비교하였다. 최적의 잔류염소 농도 예측을 위한 AI모형의 입출력 구조로는 침전지 전단의 잔류염소 농도, 침전지 탁도, pH, 수온, 전기전도도, 원수의 유입량, 알칼리도, NH3 등을 독립변수로, 예측하고자 하는 침전지 유출수의 잔류염소 농도를 종속변수로 선정하였다. 독립변수는 침전지 후단의 잔류염소에 영향이 있는 정수장에서 확보가 가능한 관측자료중에서 분석을 통해 선별하였으며, 분석 결과 연구대상 정수장인 정수장에서는 중회귀모형, 신경망모형, 모델트리 및 랜덤포레스트 모형을 비교한 결과 랜덤포레스트에 기반한 모형오차가 가장 낮게 도출되는 결과를 얻을 수 있었다. 본 연구에서 제시하는 침전지 후단의 적정 잔류염소 농도 예측값은 이전 처리단계에서 염소주입량의 실시간 제어가 가능토록 할 수 있어 수처리 효율 향상과 약품비 절감에 도움이 될 것으로 기대된다.

키워드
AI
다중회귀
전염소
잔류염소 농도 예측모형
염소제어
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Information
  • Publisher :KOREA WATER RESOURECES ASSOCIATION
  • Publisher(Ko) :한국수자원학회
  • Journal Title :Journal of Korea Water Resources Association
  • Journal Title(Ko) :한국수자원학회 논문집
  • Volume : 57
  • No :3
  • Pages :151-164
  • Received Date : 2023-12-27
  • Revised Date : 2024-02-18
  • Accepted Date : 2024-02-19
  • DOI :https://doi.org/10.3741/JKWRA.2024.57.3.151
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