イメージベースのフローク機能と操作パラメータを使用して,Cr (VI) 除去とフローク収縮のためのマルチモダル機械学習
Yaqi Zhu1, Anlei Wei2, Jirui Zou1
1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.
Journal of environmental management
|February 20, 2026
まとめ
この研究は,電気凝固を用いた廃水からのクロム除去を改善するための新しいAIフレームワークを導入しています. このモデルは性能を正確に予測し,電気凝固を変化する条件に適応させ,よりよい排水処理を実現します.
科学分野:
- 環境工学環境工学とは
- 水処理技術について
- 環境科学における人工知能
背景:
- 電気凝固は,六価クロム (Cr(VI)) の除去に有効ですが,変動する排水条件に苦しんでいます.
- pH,電解質濃度,および速度のような要因は,花束の形成と沈着に影響を与え,プロセスの効率を制限します.
研究 の 目的:
- 電気凝固中のCr (VI) 除去とフラックの沈着を予測するためのマルチモダルの機械学習フレームワークを開発する.
- 異なる運用条件下での電気凝固プロセスの適応性と精度を向上させる.
主な方法:
- 画像ベースのフラック機能を抽出するために,ディープラーニング (ResNet50) を利用しました.
- これらの機能と動作パラメータを分類回帰および直接回帰モデルに統合しました.
- サポートベクトルマシン,バッグ分類器,エクストラツリーなど,機械学習アルゴリズムを採用した.
主要な成果:
- 画像機能と操作パラメータを統合したマルチモダルのアプローチにより,予測の精度が大幅に改善されました.
- 直接回帰モデルは高いR2値を達成しました:Cr (VI) 除去では0.971で,フラック沈着では0.986でした.
- このフレームワークは,電気凝固の効率を予測する伝統的な方法と比較して優れたパフォーマンスを示しました.
結論:
- 開発されたマルチモダルの機械学習フレームワークは,電気凝固の最適化のための堅牢なソリューションを提供します.
- このアプローチは,予測の精度とプロセスの適応性を向上させ,様々な条件で効果的な排水処理を実現します.
- 操作パラメータを備えたディープラーニングベースの画像分析の利用を先駆けて,排水処理の最適化を進めています.
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