Development of a Mathematical Model for Monitoring Recovery Boiler Dissolving Tank Sounds

In the chemical recovery process of kraft pulp mills, molten smelt falls into the dissolving tank where it interacts violently with hot water. These smelt-water interactions allow for fast smelt dissolution, however too many violent interactions can also cause equipment damage. In severe cases, violent smelt-water interactions may result in dissolving tank explosions, costing millions of dollars to pulp mills. One way to monitor smelt-water interactions within the dissolving tank is through the sound they generate. In this work, an acoustic model of smelt-water interaction was developed to examine dissolving tank sound characteristics and operating factors affecting the sound intensity. Field studies were conducted to obtain acoustic data at several mill sites. Laboratory experiments were then conducted to study each part of the smelt-water interaction process. The results of field measurements and laboratory experiments allowed for better understanding of the physical mechanisms involved in smelt-water interactions in the dissolving tank. This model is stochastic in nature and describes the physical processes from the moment molten smelt droplets enter water to the acoustic signals produced by numerous vapour bubble expansions and collapses. Each component of the model was verified through empirical data. The simulation results of the integrated model were then compared against acoustic measurements taken from mill visits. The model predictions were in good agreement with the sounds recorded from pulp mills under various operating conditions. The model could also accurately predict other mill variables such as the temperature of green liquor in the dissolving tank based on acoustic signals. In addition, the model provides predictions of changes within the dissolving tank when parameters such as smelt droplet size distributions and smelt flow rate are varied. The results obtained through these simulations show trends that are in agreement with findings from other studies. The results also suggest that dissolving tank water temperature, smelt flow rate, and smelt droplet size are amongst the most important factors in the intensity of explosion events. The model and algorithmic procedures developed in this thesis work may be used to develop an acoustic monitoring system for recovery boiler dissolving tanks.

Department dc:contributor.department

Chemical Engineering Applied Chemistry

Year dc:date.issued

2022

Author dc:creator

• Wang, Yu Xiang Brian

Advisors dc:contributor.advisor

• Tran, Honghi
• Wong, Willy

• Acoustic Monitoring
• Chemical Recovery
• Industrial Application
• Pulp and paper
• Smelt-water Interaction
• Stochastic Model

Handle dc:identifier.uri

http://hdl.handle.net/1807/124948

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/124948