DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jiwoong | ko |
dc.contributor.author | Hua, Chuanbo | ko |
dc.contributor.author | Kim, Kyoungpil | ko |
dc.contributor.author | Lin, Subin | ko |
dc.contributor.author | Oh, Gunhak | ko |
dc.contributor.author | Park, Mi-Hyun | ko |
dc.contributor.author | Kang, Seoktae | ko |
dc.date.accessioned | 2024-01-26T03:00:14Z | - |
dc.date.available | 2024-01-26T03:00:14Z | - |
dc.date.created | 2023-12-26 | - |
dc.date.created | 2023-12-26 | - |
dc.date.issued | 2024-02 | - |
dc.identifier.citation | Chemosphere, v.350 | - |
dc.identifier.issn | 0045-6535 | - |
dc.identifier.uri | http://hdl.handle.net/10203/317938 | - |
dc.description.abstract | Water treatment plants are facing challenges that necessitate transition to automated processes using advanced technologies. This study introduces a novel approach to optimize coagulant dosage in water treatment processes by employing a deep learning model. The study utilized minute-by-minute data monitored in real time over a span of five years, marking the first attempt in drinking water process modeling to leverage such a comprehensive dataset. The deep learning model integrates a one-dimensional convolutional neural network (Conv1D) and gated recurrent unit (GRU) to effectively extract features and model complex time-series data. Initially, the model predicted coagulant dosage and sedimentation basin turbidity, validated against a physicochemical model. Subsequently, the model optimized coagulant dosage in two ways: 1) maintaining sedimentation basin turbidity below the 1.0 NTU guideline, and 2) analyzing changes in sedimentation basin turbidity resulting from reduced coagulant dosage (5–20%). The findings of the study highlight the effectiveness of the deep learning model in optimizing coagulant dosage with substantial reductions in coagulant dosage (approximately 22% reduction and 21 million KRW/year). The results demonstrate the potential of deep learning models in enhancing the efficiency and cost-effectiveness of water treatment processes, ultimately facilitating process automation. | - |
dc.language | English | - |
dc.publisher | Elsevier Ltd | - |
dc.title | Optimizing coagulant dosage using deep learning models with large-scale data | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85182277518 | - |
dc.type.rims | ART | - |
dc.citation.volume | 350 | - |
dc.citation.publicationname | Chemosphere | - |
dc.identifier.doi | 10.1016/j.chemosphere.2023.140989 | - |
dc.contributor.localauthor | Kang, Seoktae | - |
dc.contributor.nonIdAuthor | Kim, Kyoungpil | - |
dc.contributor.nonIdAuthor | Lin, Subin | - |
dc.contributor.nonIdAuthor | Oh, Gunhak | - |
dc.contributor.nonIdAuthor | Park, Mi-Hyun | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
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