Best Practices Awards: Reliability — Decision-support system lets rules dictate maintenance

Refinery uses protection systems in conjunction with condition-monitoring and decision-support software.

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The Plant Services Best Practices Awards recognize management techniques, work processes, and product and service implementations that exemplify the definition of a best practice, which the Society of Maintenance and Reliability Professionals (SMRP) defines as: “a process, technique or innovative use of resources that has a proven record of success in providing significant improvement in cost, schedule, quality, performance, safety, environment or other measurable factors that impact the health of an organization.”

Entries must demonstrate how to implement a best practice, show the potential payoffs in both qualitative and quantitative terms, and provide inspiration for those who must overcome cultural inertia and make effective changes. Entries may be submitted by plant personnel, vendors, engineering firms, consultants or anyone who is familiar with the application and has permission to make it public knowledge. Our 2010 categories also include Equipment, Management and Energy Efficiency, but this round’s focus is on Reliability.

Every contender offered an impressive reliability practice that can increase productivity, improve efficiency or reduce costs. Judging criteria included percentage reductions or cost savings, return on investment and broadness of applicability, with recognition given for innovation and creativity.

Plant Services 2010 Best Practices Awards

The winning practice was submitted by Jayesh Patel, reliability manager, Valero Refinery in Paulsboro, New Jersey. By managing its equipment below the alert level, the refinery is able to be proactive in its machinery management, allowing Valero to mitigate reactive work and the associated process interuptions. The results of this shift to proactive maintenance are improved product quality, improved machinery availability and increased profits.

Condition monitoring is combined with decision-support capabilities that utilize prewritten rules, as well as additional customized rules set by Valero.

The combination allowed the refinery to schedule maintenance without the additional pressure of emergency conditions, and Valero’s successful implementation won the votes of our judges to become this round’s Best Practice in Reliability.

Winner: Decision-support system lets rules dictate maintenance
Refinery uses protection systems in conjunction with condition-monitoring and decision-support software

Valero’s Paulsboro Refinery has a capacity of 195,000 barrels per day and employs nearly 550 individuals. Condition-based maintenance is used extensively and employs a mix of permanent and portable technologies, depending on asset criticality. Low-criticality assets are addressed by a portable data collection system. High- and mid-criticality assets are addressed by online systems. For its most critical assets, Paulsboro uses Bently Nevada continuous machinery protection systems in conjunction with System 1 software. These assets include gas turbines, steam-driven and motor-driven centrifugal compressors, hydrogen reciprocating compressors, utility air compressors and liquid ring compressors for flare gas recovery. Mid-criticality assets in the refinery’s coker unit are addressed by the Bently Nevada Trendmaster system, a permanently wired “sensor bus” architecture that monitors conditions several times per hour. Both the continuous monitoring systems and the Trendmaster architecture are tied into System 1 software for a unified online condition-monitoring environment.

One of the keys to Paulsboro’s success with condition-based maintenance is its practice of managing machinery “below the alert level.” Alarms set to notify machinery specialists of impending problems allow uninterrupted operation while appropriate actions, such as scheduling maintenance, planning an outage or recommending changes to operating or process conditions, are taken. This proactive maintenance drives the Paulsboro refinery to the far left limits of the P-F curve, resulting in higher product quality, improved asset availability and increased operating profits.

The P-F curve shows qualitative time relationship between potential failure (P) and functional failure (F). The further to the left (closer to P0) one can operate for any given asset, the easier it is to plan maintenance and lower the likelihood of surprise functional failures.
The P-F curve shows qualitative time relationship between potential failure (P) and functional failure (F). The further to the left (closer to P0) one can operate for any given asset, the easier it is to plan maintenance and lower the likelihood of surprise functional failures.

Managing too many alarm levels can become onerous, and a balance must be found in the quest to move farther to the left on the P-F curve. One way to achieve this is by relying not only on level-type alarms, but also on technologies that automate the data analysis and anomaly detection processes that human experts would use if manually reviewing data. Paulsboro has used the System 1 software’s decision-support capabilities to embed subject-matter expertise for a particular asset or class of assets and detect asset problems automatically. While many users employ the decision-support module to detect anomalies with the rotating machinery monitored by System 1 software, what has set the Paulsboro facility apart is its use of the system on non-rotating assets, as well. By bringing process data from the plant’s distributed control system (DCS), turbine control systems and process historian into the System 1 database, Paulsboro is able to apply the decision-support engine in analyzing and detecting anomalies on assets for which only process measurements are available, addressing applications outside of conventional condition monitoring and detecting problems in non-rotating portions of turbomachinery.

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