Cake Moisture Estimation Based on Image Analysis and Regression Model for Controlling the Compression Time of Filter Press in Sludge Dewatering

Gorde:
Xehetasun bibliografikoak
Argitaratua izan da:Processes vol. 13, no. 6 (2025), p. 1919-1938
Egile nagusia: Rumahorbo Poltak Sandro
Beste egile batzuk: Yazawa Nobuhiro, Ito Hiroki, Sugimoto, Jun, Kondo Satoshi, Okada Yoshifumi, Sato Kazuhiko, Warut, Timprae, Watanabe, Shinya
Argitaratua:
MDPI AG
Gaiak:
Sarrera elektronikoa:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiketak: Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

MARC

LEADER 00000nab a2200000uu 4500
001 3223938932
003 UK-CbPIL
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