Data-Driven Management of Operational Efficiency at Gas Processing Plants: Integrating Process Simulation, Statistical Control, and Economic Optimization

( Vol-12,Issue-4,July - August 2026 )

Author(s): Aleksandr Dovbik

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Page No: 109-117
DOI: 10.22161/ijaems.124.12

Keywords:

gas processing plant, operational efficiency, process simulation, statistical process control, economic optimization of the operating regime, natural gas liquids, process variability, data-driven management.

Abstract:

Operational efficiency at a gas processing plant is shaped by the daily maintenance of the process regime, and the plant's margin depends on how closely that regime runs to its technological limits. Three families of instruments are available at almost every modern facility: rigorous process simulation, statistical analysis of operating data, and economic optimization of the process regime in real time. Each family carries its own software and its own owner within the organization, and each has a limited effect when operating alone. This paper develops an integrated efficiency management loop that binds the 3 families into a single decision cycle. Simulation defines the feasible boundary of the regime, statistical control compresses the dispersion of parameters around the setpoint, and economic optimization determines how far the setpoint may be moved toward the boundary. The conceptual core of the model treats the operating margin as a priced quantity that contracts as variability falls. The methods applied are systems analysis, comparative analysis of English-language publications published between 2021 and 2025, and synthesis with the author's professional experience at gas processing plants in Western Siberia. Generalized indicators from that experience illustrate the loop, covering a decline in process losses from 0.64% to 0.44% of plant throughput, growth in valuable fraction recovery, and a 10% reduction in greenhouse gas emissions. The results are addressed to process engineers, process control specialists, and production managers.

Received: 28 Jul 2026; Received in revised form: 18 Aug 2026; Accepted: 21 Aug 2026; Available online: 25 Aug 2026

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