A. F. Al-Shanfari, OQBi, Salalah, Oman
The effective operation of a liquefied petroleum gas (LPG) plant relies heavily on the competence and efficiency of panel operators during their shifts. However, traditional methods of operation often lack real-time visibility and comprehensive data analysis, leading to inefficiencies and potential safety hazards. This article proposes the integration of visualization tools into LPG plant operations to enhance the performance of panel operators. By providing intuitive and interactive interfaces, these tools enable operators to monitor plant processes, analyze data trends and make more timely informed decisions.1 Through a combination of case studies and practical insights, this article explores the benefits and challenges of implementing visualization tools in LPG plants. Moreover, it discusses strategies for optimizing operator training and workflow integration to maximize the effectiveness of these tools in improving operational performance, safety and overall plant productivity.
LPG panel operators. LPG plants are complex industrial facilities where safety, efficiency and productivity are paramount. Panel operators play a crucial role in ensuring smooth operations by monitoring various processes, adjusting parameters and responding promptly to alarms. However, conventional methods of operation often rely on static displays and manual data analysis, which can hinder operators' ability to react swiftly to changing conditions and optimize plant performance. In recent years, the emergence of visualization tools has provided an opportunity to revolutionize the way LPG plants are operated, offering real-time insights and actionable information in a user-friendly format.2 By leveraging these tools, operators can gain a deeper understanding of plant dynamics, proactively identify potential issues and streamline decision-making processes.
Visualization tools are designed to streamline complex data into actionable insights, enabling panel operators to:
Monitor real-time process data and key performance indicators (KPIs)3
Identify trends, patterns and variances
Respond promptly to deviations and alarms
Optimize plant performance and efficiency
Enhance situational awareness and decision-making.4
Data visualization tools enhance plant operations. Visualization tools bridge the gaps between operators, supervisors and process teams, fostering a culture of collaboration and transparency. By sharing a unified view of plant operations, teams can:
Align strategies and goals
Improve communication and response times
Enhance knowledge sharing and training
Reduce errors and miscommunication.
A visualization tool was implemented within the distributed control system (DCS) room at an LPG plant. This tool has demonstrably enhanced panel operator performance in optimizing KPIs.4
KPIs related to product composition. The focus areas were:
C2 in the C3 product: This KPI refers to the concentration of ethane (C2) within the LPG fraction designated as C3 (propane). Optimizing this KPI signifies controlling the amount of C2 in the C3 product stream.
C5 in the C4 product: Similarly, this KPI signifies controlling the concentration of pentane (C5) within the LPG fraction designated as C4 (butane).
Performance improvement: Because they could see their performance data, operators were empowered to identify areas for improvement and adjust their strategies, resulting in cost savings of approximately $390,000.
Data-driven culture: Visualization tools foster a data-centric approach to process optimization within the DCS room, likely contributing to cost savings.
The project's success highlights the value of leveraging data visualization to optimize LPG product composition. This approach not only improves operator performance but also translates to tangible cost savings for the plant.
Implementation strategies. Recommendations for implementation include identifying KPIs and critical metrics, selecting a suitable visualization tool for LPG plant operations, developing customized dashboards for real-time monitoring, providing comprehensive training and support, and continuously refining and improving the visualization tool.5
How does the tool work? The tool displays live data showing the percentage of target achievement for C2 in C3—and presumably C5 in C4—alongside the target percentage itself (e.g., 2% for C2 and 1% for C5). This percentage likely represents how close the current concentration is to the target, expressed as a percentage of the target value. For example, if the current C2 concentration is 1.5% and the target is 2%, the tool might display a performance percentage of 80%. This signifies the operators are achieving 80% of the target level for C2 in C3 at that specific moment.
The rest of the explanation regarding performance thresholds, actionable insights, 24/7 monitoring and shift performance comparison remains valid. The core principle is that the tool provides a clear and easy-to-understand visual representation of how close operators are to achieving the desired product composition. This allows them to readily assess their performance and adjust as needed (FIG. 1).
Analyzing the performance before implementing the new tool. Even though historical data showed operators consistently achieved lower concentrations of C2 in C3 and C5 in C4 products compared to the target specifications of 2% for C2 and 1% for C5, a high standard deviation indicated significant variability in these values. This inconsistency suggests operators may not have been consistently achieving the desired product composition, potentially leading to issues with product quality or downstream processes.
The individual moving range (I-MR) chart for C2 in C3 shows the performance before the visualization tool was implemented:
Mean (center line): The center line for the I-MR chart is at approximately 0.77 (FIG. 2). This indicates that the historical average concentration of C2 in C3 was around 0.77%, which is lower than the target specification of 2%.
Variation (control limits): The control limits for the I-MR chart are the upper control limit (UCL) at 1.198 and the lower control limit (LCL) at 0.337. Since all the plotted points are between these control limits, one can infer that the historical variations in C2 concentration were within the expected range. However, a wider range between the UCL and LCL would indicate higher variability, and a narrower range would suggest tighter control.
Individual points: All the plotted points are below the center line, which confirms that C2 concentration in C3 was consistently lower than the target of 2%. Additionally, all the points being within the control limits suggests that the achieved concentrations were relatively consistent, although it cannot be definitively said how tight that consistency was without knowing the historical standard deviation.
Overall, the I-MR chart for C2 in C3 aligns with the previous explanation. Before the visualization tool, operators were achieving consistently lower concentrations of C2 than desired (around 0.77% compared to the 2% target), with the variations being within the expected range.
I-MR chart after implementing the optimization tool. The I-MR chart indicated improvement in the mean (average) and standard deviation of C2 concentration in C3 after implementing the visualization tool (FIG. 3). The center line (X) has shifted closer to the target specification of 2%, now at around 1.347. This suggests that operators are achieving C2 concentrations closer to the desired level. Additionally, the control limits (UCL and LCL) appear to be narrower compared to those before implementation. Narrower control limits imply a reduction in the variation of C2 concentration, signifying greater consistency in reaching the target. Overall, the I-MR chart suggests the visualization tool helped operators achieve a more desirable average C2 content in C3 with better consistency.
Shift performance before and after. The implemented visualization tool demonstrably improved C2 concentration in C3 production. The average level—represented by a shift in the line graph—has moved significantly closer to the target of 1.4. Additionally, it is likely the tool also reduced performance variability. Interestingly, this improvement was not uniform across all shifts. While Shift B previously had the second-lowest performance in optimizing the C5 in C4 KPI, it has now become the best-performing shift. This suggests the visualization tool had a particularly significant impact on Shift B's ability to achieve the desired C5 concentration (FIG. 4).
Takeaway. Visualization tools are a powerful solution for enhancing panel operator performance in LPG plants. By providing real-time data, simplifying complex information and facilitating communication, visualization tools enable operators to optimize plant performance, reduce errors and improve safety. By implementing these tools, LPG plants can achieve significant improvements in efficiency, productivity and overall performance. HP
REFERENCES
Shah, P., “Importance of real-time data visualization in the process industry,” Automation World.
Chen, J., “A historical data analysis and visualization tool for energy management and planning,” Energies, 2018.
Chen, Y., “Real-time monitoring and visualization of LPG plant operations using IoT and big data analytics,” IEEE Transactions and Industrial Informatic, 2021.
Smith, J., A. Johnson, et al., “Enhancing operator performance through visualization tools in process industries,” Journal of Industrial Engineering, 2020.
Lee, K., S. Kim, et al., “Augmented reality applications for enhancing operator situational awareness in LPG plants,” IEEE Transactions on Industrial Infomatics, 2018.