Each performance indicator is made up of a series of indices derived from specific process 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 calendering process including:
− Mechanical vibration & rotational frequencies
− Machine and cross direction controls
− Process control system performance
− Machine startup and shutdown operation
Identifying that a calendering process area is under-performing 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 sheet finishing control.
Supercalender Implementation Modules
In order to provide practical solutions for problems often identified in the calendering process, ABB has developed defined, logical optimization steps for each calendering process area. Once the entry point has been established for each process 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 multilevel testing and analysis applied to top and bottom gloss controls. Similar testing methodologies are involved with each performance indicator. Complete process area testing sequences require three to five working days to collect the data required to complete the diagnosis and develop improvement recommendations.
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.