Abstract

Determining the right chemical dosage is crucial for maintaining water quality, speeding the water treatment process, and minimize potential operational costs. Since it is well known that water quality parameters are non-linear, an overdose of chemical substances can damage water quality by causing a drop in pH, while an inadequate amount results in water that does not meet standards. Jar test is a technique for determining out the optimal dosage. But it requires a lot of time, samples, and is subject to human error. IoT technology is embedded with Artificial Intelligence (AI), allowing it to work as such to the human brain, and many studies are currently using it to build prediction models. As a result, the objective of this research is to investigate AI techniques that can be integrated into IoT technology based on existing research. The systematic analysis of existing research was done in this study using the systematic literature review (SRL) methodology. From the past 10 years, we discovered a total of 35 that used Artificial Intelligence (AI) techniques to create prediction models.

Original languageEnglish
Title of host publication2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350334012
DOIs
Publication statusPublished - 2023
Event8th IEEE International Conference for Convergence in Technology, I2CT 2023 - Pune, India
Duration: 7 Apr 20239 Apr 2023

Publication series

Name2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023

Conference

Conference8th IEEE International Conference for Convergence in Technology, I2CT 2023
Country/TerritoryIndia
CityPune
Period7/04/239/04/23

Keywords

  • Artificial Intelligence
  • Coagulant Dosage
  • Coagulant Dosage Determination
  • Machine Learning
  • Variable Selection

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