The natural gas industry faces mounting pressure to reconcile growing energy demands with stringent climate commitments. Natural gas dehydration via Temperature Swing Adsorption (TSA) using molecular sieves constitutes a critical yet energy-intensive process, consuming 70% of total refinery energy primarily during regeneration operations. Traditional dehydration units operate on static, 30-year-old control strategies with fixed cycles, resulting in substantial energy waste, premature tower switching, and fragmented optimization approaches where improvements in energy efficiency often compromise product quality or environmental performance.
This research presents a novel methodology for real-time, multi-objective optimization of dehydration processes through an integrated three-layer intelligent soft sensor framework. The research addresses a fundamental measurement gap: approximately 30 critical performance indicators—including process characteristic (digital twin), all 15 IMPACT 2002+ environmental midpoint categories, comprehensive energy and exergy metrics, and economic costs—cannot be measured directly with commercial instrumentation. To overcome this limitation, a hierarchical soft sensor architecture was developed using systematic simulation-based data generation, Response Surface Methodology, and validated mathematical correlations linking readily measurable process parameters (temperature, pressure, flow rate) to unmeasurable objective functions.
The methodology integrates 5 specialized software platforms: Aspen Adsim for detailed packed-bed adsorption process; Aspen HYSYS for rigorous energy and exergy analysis; SimaPro employing IMPACT 2002+ methodology for comprehensive life cycle assessment; and Design-Expert to DOE and RSM analyses and Matlab for desireblity optimization and formula modification. This integration enables, for the first time, minute-by-minute environmental impact tracking synchronized with dynamic process conditions, replacing traditiona
