Performance Optimization for control loops and Performance Optimization for QCS use a secure, remote-enabled service delivery platform deployed at customer sites that allows users to view, scan and track Key Performance Indicators (KPIs) that help identify potential equipment and process problems. This platform automatically gathers large amounts of data, and performs initial analysis on this data to expeditiously detect trends that may indicate impending problems, so that ABB service professionals can more quickly help customers find and resolve issues that impede availability, quality and productivity.
The KPI analysis provided by these services identify, classify and prioritize issues based on severity, process area and criticality. If issues are found, customer or ABB service professionals can be alerted proactively via email or text message so that mitigating actions can be taken.
At the paper mill in India, the mill’s automatic control loops must perform as flawlessly as possible to achieve high productivity and quality. To accomplish this, mill engineers wanted to augment existing QCS and DCS services delivered by ABB with automatic data collection and analysis that would help them proactively identify emerging problems on a more consistent basis.
Performance Optimization for control loops identifies and corrects automatic control loop issues to improve control performance and ensure optimum results from a control system investment. The service analyzes and identifies control loops that should be optimized so that process control can be improved and sustained.
One of the mill‘s paper machines has 220 control loops controlling the machine’s stock preparation, wet end and dry end sections. The mill‘s managers are always looking for new technologies that can help them be more competitive. So they were interested to see how the monitoring capabilities of ABB’s Performance Optimization for control loops might detect and correct troublesome control loops.
Mill managers implemented ABB’s Performance Optimization for control loops, and the mill uses the service to diagnose critical issues, including control output saturation, over control, slow control, signal noise and process disturbances.
After implementation, ABB demonstrated the benefits of what the Performance Optimization for control loops identified by tuning five of the paper machine’s problematic control loops. The tuning process was tracked in detail, and mill engineers could clearly view all related data through a real-time graphical user interface. After the control loops were tuned, the reduction in process variability of the loops tuned varied from 18% to 67%.
The mill’s instrumentation team has found the Performance Optimization for control loops to be very useful in proactively diagnosing issues related to control, process and field instrumentation. Mill managers have been so impressed with the service’s benefits that they asked ABB to expand Performance Optimization for control loops to the pulp mill area so they can identify control loop performance issues in the chemical recovery area.
Performance Optimization for QCS is designed to diagnose, implement and sustain papermaking performance characteristics.
Product quality is a priority for the company, and mill managers are highly motivated to seek the most effective tools available for mitigating quality issues. They were very interested in employing the Performance Optimization for QCS after ABB demonstrated the service’s in-depth analysis of reel data. After managers assessed how effectively the service could help them improve paper quality, they decided to implement the service on the paper machine.
Following implementation, the Performance Optimization for QCS reel analysis indicated that moisture and conditioned weight on the paper machine had more variability than comparable industry best practices and standards. To reduce variability, ABB recommended actions that included tuning the cross-direction basis weight controller and other control loops in the wet end and stock preparation areas.
The service also showed that some sensor parameters, including the zero gap of the caliper sensor and the measurements of the basis weight sensor, were not in the expected range. Additionally, basis weight sensor noise had long-term drift. Mill personnel were able to use this information to improve sensor stability and process variability, which helped them establish the proactive system maintenance needed to avoid unplanned downtime and keep quality production high.