The impact of advanced process control on LPG production

This article summarizes the findings from the APC Benefit project, which evaluates the impact of a proprietary advanced process control (APC) systema implemented at the Salalah liquefied petroleum gas (LPG) plant in Oman in 2024. Key performance enhancements were observed in propane and butane product quality, condensate stabilization and overall plant efficiency. Significant improvements in ethane (C2), propane (C3), pentane (C5) concentrations, and Reid Vapor Pressure (RVP) levels resulted in annual commercial benefits ranging between $886,888 and $1,318,260.

The APC system was introduced at the Salalah LPG plant to optimize production processes and maximize product quality while adhering to stringent specification limits. Using the APC softwarea integrated with the plant’s distributed control system (DCS), the project aimed to address key operational challenges, including feed variability and product quality constraints.

Background on APC. APC systems employ sophisticated control algorithms to optimize process variables in real time. Unlike traditional control mechanisms, APC leverages predictive modeling, real-time analytics and multivariable process adjustments to achieve precise control over plant operations. By integrating APC into its DCS, the Salalah LPG plant aimed to minimize variations in feedstock properties, stabilize operations and ensure consistent product quality while maintaining regulatory compliance.

Prediction and inferential modeling in APC systems. APC employs predictive control and inferential modeling to anticipate and adjust process parameters before deviations occur. These models use historical and real-time data to forecast the impact of variable changes on plant performance. Key aspects include:

• Multivariable predictive control (MPC): APC systems predict future plant behavior based on current data and adjust parameters accordingly.
• Inferential sensors: Where direct measurements are unavailable, inferential models estimate values based on correlated process variables.
• Dynamic response analysis: APC continuously learns from process trends to improve the accuracy of its predictions. For example, if feed gas composition changes, the APC system forecasts how this will affect propane recovery and adjusts column temperatures and pressures accordingly to maintain optimal output.1,2,3

How prediction works in APC. Prediction in APC is primarily driven by inferential modeling and multivariable regression techniques. The system continuously monitors key process parameters and applies predictive analytics to determine future states of the system. These predictive capabilities help in:

• Identifying trends: APC analyzes historical and real-time data trends to anticipate potential process upsets.
• Scenario analysis: The system can simulate different operating scenarios to evaluate the best corrective action.
• Automated decision-making: Based on predictions, APC makes real-time adjustments to maintain stability and efficiency.

For example, the APC system at the Salalah LPG plant utilizes real-time inferential models to predict propane purity based on upstream column conditions. If a deviation is detected, corrective actions are taken before the product moves to the next processing stage.4

Project scope and implementation. The APC implementation covered three major processing areas:

1. LPG extraction: De-ethanizer and related equipment
2. LPG fractionation: De-propanizer and de-butanizer columns
3. Condensate stabilization: Stabilizer column operations.

The feed gas for the plant originates from three sources with varying characteristics, requiring rapid response to fluctuations. The APC system's ability to adapt dynamically has significantly improved operational reliability. This variability posed significant challenges in maintaining operational stability. The APC system dynamically adjusted control setpoints based on real-time process data, effectively managing these variations.5,6

Pre- and post-APC performance. A comparison of plant performance before and after APC implementation highlights the following key changes:

• C2 in propane
  • Before APC (2022–2023): The C2 concentration was 0.21% (well below the specification limit of 2%).
  • After APC (2024): Increased to 1.6%, a 661.9% improvement, optimizing the propane product and contributing an annual benefit of $436,915.
• C3 in butane
  • Before APC: The C3 concentration in butane was 0.27% (below the specification limit of 2%).
  • After APC: This parameter was maintained effectively without significant deterioration, ensuring high propane yield while adhering to butane quality standards.
• C5 in butane
  • Before APC: The C5 concentration in butane was 0.25% (below the specification limit of 1%).
  • After APC: Increased to 0.71%, a 184% improvement, contributing an annual benefit of $182,162. This enhancement ensures the maximum recovery of higher-value hydrocarbons, reducing losses to low-value streams.
• Condensate RVP
  • Before APC: The RVP was 83.7 KPa (lower than the specification limit of 86 KPa).
  • After APC: Increased to 84.12 KPa, maximizing condensate recovery and contributing an annual benefit of $259,639.

Figures from the report (FIGS. 1–3) clearly illustrate the control achieved by the APC system and the improved product stability.

FIG. 1. I-MR chart C2 in C3 after implementing the APC system^a.

FIG. 2. I-MR chart C5 in C4 after implementing the APC system^a.

FIG. 3. I-MR chart of condensate RVP after implementing the APC system^a.

Key observations include:

• Propane optimization: The APC system effectively maximized the ethane (C2) content in propane products by manipulating key variables in the de-ethanizer and de-propanizer columns. Trends (FIG. 1) show that despite upstream disturbances, the system maintained C2 levels within specification limits, significantly improving propane yield.
• Butane quality control: The APC systema ensured optimal butane product quality by precisely controlling C3 and C5 concentrations. The inferential property estimation models reduced the dynamic lag in column responses, allowing for enhanced product stability.
• Condensate stabilization: The APC systema optimized condensate recovery by consistently maintaining RVP levels near the upper specification limit. FIG. 3 demonstrates the APC system’s ability to ensure tighter control over RVP, thereby improving condensate yield.
• Economic impact: The cumulative benefits of the APC systema, calculated at $886,888 annually under base-case conditions, represent a substantial return on investment. Under current market conditions, this figure rises to $1,318,260 annually, emphasizing the APC system’s adaptability to varying operational contexts.7,8

Reducing operator workload. APC systems also reduce operator workload by automating routine adjustments, thereby improving safety and efficiency. Key benefits include:

• Automation of repetitive tasks: Reduces manual intervention, allowing operators to focus on critical decision-making.
• Enhanced process monitoring: Real-time data analytics enable proactive problem-solving.
• Improved safety: By stabilizing process variables, the APC system minimizes process upsets, reducing the likelihood of emergency shutdowns.

Sustainability and environmental compliance. With increasing emphasis on sustainability, APC systems contribute to reducing environmental impact by:

• Optimizing energy consumption: Minimizing excess fuel usage and improving energy efficiency.
• Reducing flaring and emissions: Improved process control reduces waste gases, leading to lower greenhouse gas emissions.
• Enhancing resource utilization: Maximizing hydrocarbon recovery minimizes raw material wastage and supports sustainability goals.

The future of APC in LPG production. With the successful implementation of the APC systema at the Salalah LPG Plant, future enhancements could include:

• Machine-learning integration: Leveraging AI-driven analytics to improve predictive control models.
• Remote monitoring and optimization: Implementing cloud-based monitoring solutions for real-time performance tracking.

Takeaways. The implementation of APC at the Salalah LPG plant has demonstrated significant improvements in production efficiency, hydrocarbon recovery and economic performance. By leveraging advanced control strategies, the plant has optimized operations while reducing operator workload and enhancing safety. The success of this project sets a benchmark for similar facilities seeking to integrate APC solutions to maximize operational efficiency and profitability.

NOTE

^a Schneider Electric’s EcoStruxure APC software

LITERATURE CITED

1 Poe, W.A. and S. Mokhatab, S., Modeling, control, and optimization of natural gas processing plants, 1st Ed., Gulf Professional Publishing, 2016.

2 Ragothaman, A. and W. A. Anderson, W.A., “Air quality impacts of petroleum refining and petrochemical industries,” Environments, 2017.

3 Shivajee, V., R. K. Singh and S. Rastogi, “Manufacturing conversion cost reduction using quality control tools and digitization of real-time data,” Journal of Cleaner Production, Vol. 237, November 2019.

4 Warguła, Ł., M. Kukla, P. Lijewski, M. Dobrzyński and F. Markiewicz, “Influence of the use of liquefied petroleum gas (LPG) systems in woodchippers powered by small engines on exhaust emissions and operating costs,” Energies, 2020.

5 Liu, Y.A. and N. Sharma, “Introduction to integrated process modeling, advanced control, and data analytics in optimizing polyolefin manufacturing,” Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Virginia, ResearchGate, 2023, online: https://engrxiv.org/preprint/view/4406/7681

6 Mizuno, S., Management for quality improvement: The 7 new QC tools, Productivity Press, New York, August 2020.

7 Mac Kinnon, M. A., J. Brouwer and S. Samuelsen, “The role of natural gas and its infrastructure in mitigating greenhouse gas emissions, improving regional air quality, and renewable resource integration,” Progress in Energy and Combustion Science, Vol. 64, January 2018.

8 Folkson, R., Alternative fuels and advanced vehicle technologies for improved environmental performance, Woodhead Publishing, 2014.

ABOUT THE AUTHOR

A. F. AL SHANFARI is an experienced process control manager at OQBI in Salalah, Oman, known for a proven track record in leading multidisciplinary teams and successfully executing complex projects in plant optimization and advanced process control. Al Shanfari has built a strong reputation for delivering measurable improvements in operational efficiency, energy savings and process reliability across the oil and gas sector.

Driven by a passion for innovation, Al Shanfari consistently integrates cutting-edge technologies and data-driven solutions to transform operational challenges into strategic advantages. His leadership style is rooted in collaboration, continuous improvement and the empowerment of local talent, aligning with national objectives like Oman’s Vision 2040.

With a deep understanding of control systems, real-time analytics and industrial automation, he plays a pivotal role in bridging traditional plant operations with modern digital transformation. Al Shanfari is also recognized for fostering a culture of performance excellence, mentoring young engineers and promoting sustainable practices within industrial environments.

Related Articles