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Tissue Machine Fingerprint

Tissue Machine benchmarking establishes current machine performance and provides a basis for evaluating and identifying improvement opportunities. The resulting diagnostic report provides
improvement recommendations and associated estimated return on investment (ROI).

Typical savings potential: $150,000 – $350,000

The ABB Tissue Machine Fingerprint is ABB’s process performance diagnostic service for tissue machine optimization. The Fingerprint generates both a performance benchmark and an improvement plan, consisting of a set of improvement opportunities which are prioritized based on estimated economic benefits. It is a platform-independent, non-invasive service that can be applied to any tissue machine.


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Overview
Machine testing
Improvement plan
Delivery Schedule

Benefits

− Facilitates management decisions by focusing on high impact opportunities for improvement
− Provides clear path to quickly close performance gaps by using the proposed improvement plan
− Provides a solid foundation for continuous improvement based on data

Features
 
− Access to ABB optimization experts
− Process performance benchmarking
− Detailed ROI-based improvement plan
− Clear communications during data collection and diagnosis activities

Process performance indicators
The Fingerprint involves comprehensive testing and analysis designed to measure four key  performance indicators. These performance Indicators are used to assess machine performance and improvement area potential.
− Product Variability
− Machine Response
− Stock Approach Stability
− Profile Capability

Historical data is used to validate test results.

Machine testing
Each performance indicator includes a series of indexes derived from specific machine tests. Each test is performed inside product specifications, utilizing ABB’s diagnostic tools and methodologies. The resulting index is used to evaluate the performance level of different areas of the tissue process including:
− Mechanical vibration and rotational frequencies
− Machine and cross direction controls
− Lab testing procedures
− Sheet break recovery
− Process control system performance
− Coordinated speed control
− Grade change control

Identifying machine area under-performance is the first step in the improvement process. Understanding the problem and having the expertise to provide solutions is assured through ABB’s extensive experience in tissue machine control.

Implementation modules
In order to provide practical solutions for problems often identified in the tissue making process, ABB has developed defined, logical optimization steps for each machine area. The numerical result or index of each test points to the machine area problem and also points to the bottleneck location in the optimization sequence.

Once the entry point has been established for each machine area, the solution to the performance bottleneck and the remaining steps to optimize the process become clear.
The Machine Response indicator is used to determine a performance index for the machine-direction controls. The test entry point is defined by the specific machine tests and data analysis associated with this performance indicator.

The Machine Response indicator includes multi-level testing and analysis applied to Dry Stock, Tissue Weight, Moisture (hood temperature and Yankee steam), and Rush/Drag. Similar testing methodologies are involved with each performance indicator.

Complete Machine Area testing sequences require three to five working days to collect the data required for the diagnosis and to compare improvement recommendations.

Reporting
An Executive Report and a Technical Report are provided to disclose the findings and  recommendations of the process performance diagnosis.
− Technical Report provides supporting data collected during the machine diagnosis.
− Executive Report provides benchmark results, summary of findings, financial impact of  recommendations, and an actionable improvement plan, based on the machine diagnosis.

Improvement plan
The improvement plan defines how to resolve the performance bottleneck and improve performance. In addition, the associated financial impact for each recommendation is provided.

Based upon the findings, recommendations may include: valve replacement; isolating high-frequency machine problems related to rolls, pumps, screens, machine clothing; cleaning up signal conditioning problems; optimizing or adding control logic; updating standard operating procedures; or re-tuning controls for optimal performance.

The Tissue Machine Fingerprint is the first step in achieving and sustaining higher performance levels. Annual Fingerprint, Implementation, and Sustaining services are recommended as part of your service agreement to achieve and continue the improvement process. These can be scheduled within a single or multi-year service agreement.

ABB is a world leader in pulp and paper control applications. In-depth knowledge and experience in this area allows comprehensive evaluation, diagnosis, remedial recommendations and implementation, and the ability to manage and sustain process performance improvement.

Delivery Schedule

Day 1
Project introduction meeting
Set up data collection software and hardware
Begin collecting Quality Control System data for the Product Variability and Stock Approach Stability tests

Day 2
Complete the single-point collections for the Product Variability test
Perform step testing on Weight and Moisture (Yankee steam and hood temperature) for the Machine Response test

Day 3
Complete Machine Response testing with Headbox Step tests

Day 4
Collect steady-state profiles
Perform cross-direction bumps to complete Profile Capability testing

Day 4-5
Perform initial data analysis for Summary of Findings exit meeting
Conduct Exit Meeting

Day 6-8 (off site)
Complete final data analysis and generate executive and technical reports
Communication with the mill precedes scheduled activities to ensure coordination with ongoing mill activities. A daily activity list includes items completed during the day, a summary of findings, and a plan for the following day.

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