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Journal articles
A study of wetting tension solutions, TAPPI JOURNAL, August 1994, Vol. 77(8)
A study of wetting tension solutions, TAPPI JOURNAL, August 1994, Vol. 77(8)
Journal articles
Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025
ABSTRACT: Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on a few laboratory measurements and a limited number of online specific chemistry-related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena, which can cause production instability, including deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert-driven interpretation. This study is aimed at utilizing a hybrid solution comprising chemistry and physics models and machine learning models for stabilizing chemistry-related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modeling. A cornerstone in the approach is to formulate explanations of the findings from the models; that is, to explain in plain text what the findings mean and how operational changes can mitigate the identified risks. The approach has been demonstrated for several different applications, including deposit control in the wet end, both raw water treatment and usage, and wastewater treatment. This approach provides mill personnel with knowledge of identified phenomena and recommendations on how to stabilize chemistry-related processes. Instead of using close to black box machine learning models, a hybrid solution including chemistry/physics models can enhance the performance of artificial intelligence (AI) deployed systems. A successful way of gaining the trust from mill personnel is by creating a plain text explanation of the findings from the hybrid models. The correlation between the likelihood of a phenomena and disturbance and the explanations are derived and validated by application and chemistry and physics experts.
Journal articles
Metals buildup in TCF bleach plant closure: laboratory simulation, SOLUTIONS! & TAPPI JOURNAL, May 2002 (208KB)
Metals buildup in TCF bleach plant closure: laboratory simulation, SOLUTIONS! & TAPPI JOURNAL, May 2002 (208KB)
Journal articles
Alkali impregnation of hardwood chips, TAPPI JOURNAL & Solutions! February 2005, Vol. 4(2) (215KB)
Alkali impregnation of hardwood chips, TAPPI JOURNAL & Solutions! February 2005, Vol. 4(2) (215KB)
Journal articles
Sulfuric acid emissions from combination boilers, Solutions! & TAPPI JOURNAL, May 2004, Vol. 3(5) (128KB)
Sulfuric acid emissions from combination boilers, Solutions! & TAPPI JOURNAL, May 2004, Vol. 3(5) (128KB)
Journal articles
Evaluation of a molybdenum sulfide reference electrode in hot alkaline solutions, TAPPI JOURNAL July 2010
Evaluation of a molybdenum sulfide reference electrode in hot alkaline solutions, TAPPI JOURNAL July 2010
Journal articles
Economic benefits achieved from an odor reduction project, Solutions! & TAPPI JOURNAL, January 2003 (531KB)
Economic benefits achieved from an odor reduction project, Solutions! & TAPPI JOURNAL, January 2003 (531KB)
Journal articles
Experiences with lower furnace tube cracking, TAPPI JOURNAL & Solutions! January 2005
Experiences with lower furnace tube cracking, TAPPI JOURNAL & Solutions! January 2005
Journal articles
Predictive advisory solutions for chemistry management, control, and optimization, TAPPI Journal March 2025
ABSTRACT: Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on a few laboratory measurements and a limited number of online specific chemistry-related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena, which can cause production instability, including deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert-driven interpretation. This study is aimed at utilizing a hybrid solution comprising chemistry and physics models and machine learning models for stabilizing chemistry-related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modeling. A cornerstone in the approach is to formulate explanations of the findings from the models; that is, to explain in plain text what the findings mean and how operational changes can mitigate the identified risks. The approach has been demonstrated for several different applications, including deposit control in the wet end, both raw water treatment and usage, and wastewater treatment. This approach provides mill personnel with knowledge of identified phenomena and recommendations on how to stabilize chemistry-related processes. Instead of using close to black box machine learning models, a hybrid solution including chemistry/physics models can enhance the performance of artificial intelligence (AI) deployed systems. A successful way of gaining the trust from mill personnel is by creating a plain text explanation of the findings from the hybrid models. The correlation between the likelihood of a phenomena and disturbance and the explanations are derived and validated by application and chemistry and physics experts.
Journal articles
Characterization of facial tissue softness, Solutions! & TAPPI JOURNAL, April 2004, Vol. 3(4) (185KB)
Characterization of facial tissue softness, Solutions! & TAPPI JOURNAL, April 2004, Vol. 3(4) (185KB)