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Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization

Author

Listed:
  • Mohammed Achite

    (Hassiba Benbouali University of Chlef)

  • Saeed Samadianfard

    (University of Tabriz)

  • Nehal Elshaboury

    (Construction and Project Management Research Institute, Housing and Building National Research Centre)

  • Milad Sharafi

    (Urmia University)

Abstract

In water treatment plants (WTPs), the most common processes are coagulation and flocculation. Determination of the coagulant dosage is one of the most difficult procedures in the water treatment process, and it is commonly determined using the jar test technique. Given that this approach is time-consuming, expensive, prone to human error, and greatly influenced by raw water quality changes, this research presents a prediction model to make proactive decisions about coagulant doses based on changes in raw water characteristics. The model eliminates the need to employ expensive chemicals for jar testing regularly and allows for rapid response to abrupt changes in water quality. The forecasting model is developed using standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) optimization, namely GA-RF, to simulate coagulant dose in the Sidi Yacoub WTP in Algeria. Different input scenarios are used in the development of the conventional and hybrid models to determine the best input combination. This study establishes the most efficient model for assessing the coagulation process using four evaluation metrics; correlation coefficient (CC), scattered index (SI), Willmott’s index of agreement (WI), and mean absolute percentage error (MAPE). According to the findings, the GA-RF model (CC = 0.975, SI = 0.150, WI = 0.986, and MAPE = 7.9), which accounts for raw water production, turbidity water, conductivity, and suspended materials input parameters, outperforms the other models. The proposed model will help operators to not only reduce costs and time spent performing experimental jar testing but also to anticipate optimum coagulant dose and project water quality under real-world conditions.

Suggested Citation

  • Mohammed Achite & Saeed Samadianfard & Nehal Elshaboury & Milad Sharafi, 2023. "Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 11189-11207, October.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:10:d:10.1007_s10668-022-02523-z
    DOI: 10.1007/s10668-022-02523-z
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    References listed on IDEAS

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    1. Song, Chenyu & Zhang, Haiping, 2020. "Study on turbidity prediction method of reservoirs based on long short term memory neural network," Ecological Modelling, Elsevier, vol. 432(C).
    2. Chamanthi Denisha Jayaweera & Norashid Aziz, 2022. "An efficient neural network model for aiding the coagulation process of water treatment plants," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1069-1085, January.
    3. Stephen Stajkowski & Deepak Kumar & Pijush Samui & Hossein Bonakdari & Bahram Gharabaghi, 2020. "Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction," Sustainability, MDPI, vol. 12(13), pages 1-18, July.
    4. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 845-863, February.
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