Yankee Dryer Chemistry Control via Predictive Quality Feedback
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Solution Overview
Problem
Conventional methods for adjusting adhesive coating feed rates on Yankee dryers in creped product manufacturing are labor-intensive and not real-time, leading to inefficiencies in predicting and correcting crepe structure and sheet quality variations, resulting in wasted product and suboptimal production.
Innovation Solution
Implementing a system with online sensors and predictive algorithms to monitor and adjust adhesive coating application on Yankee dryers in real-time, using measured variables such as natural coating potential, blade vibration, and pH to predict creped product quality and automatically regulate chemistry feed rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional manual methods are used to adjust adhesive coating feed rate, then labor intensity is reduced, but real-time monitoring and correction capability is lost
Solution Approach 1:
The system enables self-service automation where the control system automatically monitors natural coating potential, blade vibration, and pH levels, then self-adjusts the adhesive coating feed rate without human intervention. The system serves itself by using sensors to detect process variables and automatically regulating chemistry feed skids to maintain optimal crepe quality.
Solution Approach 2:
The invention implements continuous feedback loops where sensors monitor process variables (natural coating potential, blade vibration, pH) and feed this information back to the control system. The control system processes this feedback and automatically adjusts the adhesive coating feed rate to maintain optimal conditions, creating a closed-loop control system that responds in real-time to quality variations.
2Productivity
If real-time automated monitoring is implemented, then productivity and waste reduction are improved, but device complexity increases
Solution Approach 1:
The control system performs multiple functions through a single integrated platform: it monitors natural coating potential, blade vibration, and pH levels; predicts crepe quality outcomes; regulates adhesive coating feed rate; and provides alerts. This multi-functional system consolidates what would otherwise require separate devices for each monitoring and control task, managing complexity through integration.
Solution Approach 2:
The invention introduces a control system as an intermediary between the physical process (Yankee dryer, adhesive application) and the decision-making process. This intermediary layer processes sensor data, applies predictive algorithms, and automatically adjusts process parameters, serving as a mediator that translates raw sensor information into actionable control decisions without requiring direct human involvement in the control loop.
3Measurement precision
If manual sampling and testing is used to determine natural coating potential, then measurement accuracy is maintained, but time loss and productivity are reduced
Solution Approach 1:
The invention replaces manual mechanical sampling and laboratory testing with electronic sensors and automated analytical systems. Online sensors continuously monitor natural coating potential, blade vibration, and pH levels in real-time, substituting the mechanical sampling process with electronic detection methods that provide immediate results without requiring physical sample collection, transport, or laboratory analysis.
Solution Approach 2:
The system transitions from discontinuous manual sampling to continuous automated monitoring. Sensors continuously measure natural coating potential, blade vibration, and pH levels without interruption, providing an unbroken stream of quality data. This continuous measurement approach eliminates the gaps between manual samples and ensures quality parameters are monitored at every moment of the production process.
Data Source
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AI summary
A system and method are provided for proactive process intervention in manufacturing creped products via a chemical feed stage (108) and a Yankee dryer stage. The method includes generating signals from a plurality of online sensors, corresponding to directly measured variables for respective process components such as, e.g., pH, conductivity, and Yankee blade vibration. Models are developed including retrievable information relating combinations of certain directly measured variables to respective quality characteristics of the creped product. The method further includes indirectly determining quality characteristics (e.g., softness, bulk) for the creped product, substantially in real time, based on, e.g., signals corresponding to directly measured variables, and optionally a predicted natural coating potential. An output feedback signal is automatically generated corresponding to a detected intervention event based on the indirectly determined one or more quality characteristics and respective predetermined targets. The feedback signal may automatically regulate chemistry feed characteristics, substantially in real time.