Energy efficiency of automated gas treatment plants: analysis of modern solutions

Abstract

The article examines the energy efficiency of automated integrated gas treatment units (UKPG) and aims to quantitatively assess the contribution of contemporary technical and algorithmic solutions to reducing specific energy consumption and the carbon footprint at the level of the entire process complex; the empirical base comprises SCADA archives of four UKPGs in different climatic zones for 2019-2023 (over 2,5 million records), and the methodology applies statistical and correlation–regression analysis, stepwise construction of multivariate least-squares models, as well as thermodynamic and simulation modeling in MATLAB/Simulink to evaluate the potential for waste-heat recovery. It is shown that retrofitting gas compressor units with variable-speed drives reduces specific energy consumption for compression by 20,36% while increasing the load factor from 74,3% to 91,2% and decreasing operating variability; switching absorption dehydration from diethylene glycol to the composite TEG-M reduces the specific heat duty for regeneration by 14,5%, while delivering a lower dew point and cutting absorbent losses by 36,1%. Engineering integration of a recuperative heat exchanger to preheat the rich absorbent using exhaust heat raises the inlet temperature to the desorber by 40.3 °C, yielding 27,5% savings in fuel gas and a substantial reduction in CO2 emissions; a regression model with R2=0.723 identifies key drivers of energy use (inlet pressure – strong negative; ambient temperature – strong positive; throughput – moderate positive), laying the groundwork for a predictive optimization loop in the automated process control system (APCS). The discussion emphasizes that isolated measures deliver limited benefits, whereas the synergy of VSDs, advanced absorbents, and heat recovery provides a combined reduction in specific energy consumption of 35-40% under comparable operating conditions; practical implications include deploying advisor models, shifting to proactive setpoint management, and preparing UKPG digital twins for scenario optimization under climatic and feed variability.