Cake Moisture Estimation Based on Image Analysis and Regression Model for Controlling the Compression Time of Filter Press in Sludge Dewatering
Gorde:
| Argitaratua izan da: | Processes vol. 13, no. 6 (2025), p. 1919-1938 |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , , , , , |
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MDPI AG
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| Sarrera elektronikoa: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13061919 |2 doi | |
| 035 | |a 3223938932 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Rumahorbo Poltak Sandro |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 245 | 1 | |a Cake Moisture Estimation Based on Image Analysis and Regression Model for Controlling the Compression Time of Filter Press in Sludge Dewatering | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study proposes practical methods for estimating the moisture content of sludge, represented by the cake moisture, in the filter press dewatering process. Because the cake moisture and filtrate volume are difficult to measure directly, the proposed approaches utilize indirectly measurable data, including drain outlet images and the differential pressure during the compression phase. By analyzing the correlations between these parameters and the cake moisture, estimation models were developed using mathematical approximations. In the image-based approach, image processing techniques were applied to isolate the dewatered region, and the relationship between the pixel count and actual filtrate volume was analyzed to estimate the cake moisture based on the calculated filtrate volume per minute. In the pressure-based approach, two models were proposed: one that directly estimates the cake moisture from the differential pressure, and another that models the relationship among the differential pressure, filtrate volume, and cake moisture. Unlike complex machine learning techniques, the proposed methods employ simple and interpretable mathematical functions, offering both practicality and reliability. Validation using real-world operational data confirmed the accuracy and effectiveness of the proposed approaches. | |
| 653 | |a Machine learning | ||
| 653 | |a Cameras | ||
| 653 | |a Image analysis | ||
| 653 | |a Image compression | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Sludge | ||
| 653 | |a Functions (mathematics) | ||
| 653 | |a Regression models | ||
| 653 | |a Mathematical functions | ||
| 653 | |a Dewatering | ||
| 653 | |a Image processing | ||
| 653 | |a Moisture content | ||
| 653 | |a Compression | ||
| 653 | |a Mathematical models | ||
| 653 | |a Filter presses | ||
| 653 | |a Differential pressure | ||
| 653 | |a Estimation | ||
| 653 | |a Water content | ||
| 700 | 1 | |a Yazawa Nobuhiro |u Engineering Planning Center, DX Promotion Section, Tsukishima JFE Aqua Solution Co., Ltd., Kawasaki 212-0013, Japan; n_yazawa@tjas.co.jp (N.Y.); h_ito@tjas.co.jp (H.I.); jun_sugimoto@tjas.co.jp (J.S.) | |
| 700 | 1 | |a Ito Hiroki |u Engineering Planning Center, DX Promotion Section, Tsukishima JFE Aqua Solution Co., Ltd., Kawasaki 212-0013, Japan; n_yazawa@tjas.co.jp (N.Y.); h_ito@tjas.co.jp (H.I.); jun_sugimoto@tjas.co.jp (J.S.) | |
| 700 | 1 | |a Sugimoto, Jun |u Engineering Planning Center, DX Promotion Section, Tsukishima JFE Aqua Solution Co., Ltd., Kawasaki 212-0013, Japan; n_yazawa@tjas.co.jp (N.Y.); h_ito@tjas.co.jp (H.I.); jun_sugimoto@tjas.co.jp (J.S.) | |
| 700 | 1 | |a Kondo Satoshi |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 700 | 1 | |a Okada Yoshifumi |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 700 | 1 | |a Sato Kazuhiko |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 700 | 1 | |a Warut, Timprae |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 700 | 1 | |a Watanabe, Shinya |u Graduate School of Engineering, Muroran Institute of Technology, Muroran 050-8585, Japan; 22096509@muroran-it.ac.jp (P.S.R.); okada@muroran-it.ac.jp (Y.O.); kazu@muroran-it.ac.jp (K.S.); 23096501@muroran-it.ac.jp (W.T.) | |
| 773 | 0 | |t Processes |g vol. 13, no. 6 (2025), p. 1919-1938 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223938932/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223938932/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223938932/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |