December 2020

Columns

Executive Viewpoint: How digital transformation improves safety and reduces emissions

Digital transformation is a strategy for improving performance by applying technologies for measurement, connectivity, data storage, analytics and visualization

Carugo, M., Global Refining and Chemical Programs

Digital transformation is a strategy for improving performance by applying technologies for measurement, connectivity, data storage, analytics and visualization. All five of these activities have been ongoing in the refining and petrochemical industries for decades, but technology advancements are now lowering costs and easing implementation.

Measurement technology continues to improve, primarily with smart wired and wireless digital instruments transmitting not only primary process variable data, but also secondary variables, along with a host of calibration, diagnostic and other information.

Connectivity has evolved from basic 4-20mA hardwiring to digital wired and wireless networks. Data storage costs are declining rapidly, driven by a host of technological advancements, such as cloud computing resources.

Analytics formerly required manual examination of data by process experts, perhaps using highly customized spreadsheets as a basic yet inefficient tool. Today, specialized software applications are available to automatically perform analysis, often designed to evaluate assets, such as a heat exchangers, compressors or pressure relief valves—or higher-level performance, like sitewide energy efficiency.

Finally, visualization has progressed from mimic boards and personal computers in a control room to secure worldwide availability via any device capable of hosting a web browser, such as a laptop, smartphone or tablet.

All of these and other advancements are extending the purview of digital transformation from automation and optimization of core production processes to the operational areas of reliability, safety, energy and emissions. Improvements in these areas using digital transformation can propel companies to top-quartile performance, boosting the bottom line while helping to meet social responsibility and sustainability goals.

The digital transformation journey

Industry executives understand the potential gains, but many harbor uncertainty and anxiety when it comes to creating value from digital transformation. McKinsey and Co. reported that less than 30% of current digital transformation efforts are succeeding, and only 16% are seeing sustained performance improvement.

Analysts at Gartner predict that digital transformation initiatives will take twice as long and cost twice as much as anticipated through 2021. LNS analysts see a lack of focus on improving plant operations as the main failure mode.

How can companies cut through the hype, select the right digital transformation technologies and realize quantifiable results? It is all about the right approach, primarily:

  • Starting with a goal in mind
  • Getting the most out of the right data
  • Empowering your people
  • Multiplying your successes.

This digital transformation journey requires evolution from reactive to prescriptive maintenance.

Reactive maintenance is defined as running equipment to the point of failure, resulting in high repair costs at best, and catastrophic failures at worst, with corresponding unplanned shutdowns and related safety incidents. Common incidents include those related to worker and process safety, loss of containment and cybersecurity.

Most companies have at least moved from reactive to preventive maintenance based on runtime hours or a calendar, but this means most assets are either serviced too soon—driving up costs—or too late, with all the negative effects of a reactive approach.

Predictive maintenance is where digital transformation enters the equation by driving down costs for monitoring asset health and predicting events, allowing companies to perform proactive maintenance. The most advanced step is prescriptive maintenance enabled by machine learning to automatically identify root causes and enable just-in-time maintenance.

The following examples show how this digital transformation journey works in practice to improve operations, focusing primarily on safety and reducing emissions.

Predicting and solving valve problems

Control valves are used extensively in process plants to regulate flows. These critical assets cannot be maintained in a reactive manner as unplanned failures can cause serious safety-related incidents, including those related to occupational safety, loss of containment and process safety.

Remote monitoring of control valves enables prediction of problems before they occur, allowing issues to be addressed proactively. Valve diagnostics begin with data collection, facilitated by digital valve controllers, which provide extensive information for use by asset management, distributed control and other host systems.

Offline diagnostic testing characterizes nominal performance, creating a valve signature based on high-resolution samples of actuator pressure and travel. Once the signature is created, online diagnostics collect information from the valve while in operation, utilizing established thresholds to indicate when valve performance is being compromised.

Subject matter experts (SMEs) review these and other data to identify problems, often utilizing data interpretation tools powered by anomaly detection algorithms. SME knowledge and expertise are applied to determine if a problem has occurred or is predicted, and to direct corrective actions needed to remedy the issue. Algorithms are continually updated as new leading indicators are identified through analysts’ findings, and via artificial intelligence and machine-learning algorithms.

SMEs provide remote assistance services to empower local technicians via mobile device connectivity, allowing them to securely share their field of view with SMEs through augmented reality software. A specific valve installation can be automatically identified, along with its maintenance history and repair instructions. Step-by-step instructions overlaid in the user’s application serve to support installation, calibration or repair actions.

Pressure relief valve (PRV) monitoring

PRVs activate only in an emergency but must perform when needed. Failure to activate can cause pressure to increase to dangerous levels, eventually resulting in ruptures to pipes or vessels. This loss of containment spews process media into the plant, possibly resulting in a fire or explosion. More commonly, PRVs operate as designed to relieve pressure, but often do not seal correctly after conditions return to normal, resulting in continuous emissions to the environment.

Acoustic monitoring devices equipped with wired or WirelessHART transmitters can be mounted directly to pipes adjacent to PRVs to verify correct operation (FIG. 1). These devices sense vibrations in the discharge pipe due to turbulences generated by fluid flowing through the valve and transmitted directly through the pipe wall. Vibrations are analyzed by the device to determine valve position, with this data sent to host systems either locally via wired or wireless networks, or remotely via the internet.

FIG. 1. The acoustic monitoring device shown in the upper right of this diagram detects vibrations and uses these readings to determine valve position.

PRVs are designed to open once a preset pressure value is reached. If the process recovers and system pressure returns to normal, or if operators reduce pressure sufficiently, then the PRV should close again automatically. If everything is working correctly, it will seal, and the mechanical vibration will cease. Data from the acoustic transmitter can verify the action, reporting the time the discharge began and ended, while giving some approximate indication of how serious the discharge was based on the amplitude of the sound.

However, sometimes things go wrong and a small particle of debris from the process can lodge on the valve seat, causing leakage. Like a full overpressure-driven release, small leakages also generate turbulence inside the discharge pipe, causing mechanical vibration detectable by the acoustic transmitter. The importance of detecting leakage as soon as it starts is driven by a compounding effect over time. A 0.1% leakage, if left unaddressed for 1 yr, equals a full release from a PRV for 6 hr.

Corrosion monitoring

A typical 250,000-bpd refinery can save $5 MM/yr–$12 MM/yr by using a digital transformation solution to monitor metal thickness in real-time. This solution requires full implementation costs of approximately $2 MM, resulting in a quick return on investment. Like most digital transformation initiatives, metal thickness monitoring can start small with the highest risk and most critical assets, such as pipes with a history of corrosion-related failure.

Quantifiable savings can be obtained through better feedstock selection to avoid the most corrosive fluids, improved use of inhibitors to reduce corrosion, and proactive maintenance. Other benefits include a safer working environment with fewer emissions as leaks can be predicted, before they occur, by monitoring metal thickness.

Measurements are made using non-intrusive wireless sensors, with collected data sent to a host system running software specifically designed to analyze metal thickness data. The software’s sophisticated visualization and analytical tools reduce the time needed for data analysis and interpretation.

Let the journey begin

Digital transformation is most effectively implemented one step at a time by focusing on operational issues at the plant level. Once improvements are made to resolve one issue, the next item on the list can be addressed, eventually resulting in substantial operational improvements as one success leads to another. HP

The Author

Related Articles

From the Archive

Comments

Comments

{{ error }}
{{ comment.comment.Name }} • {{ comment.timeAgo }}
{{ comment.comment.Text }}