(31c) Flare Emission and Greenhouse Gas Reduction Study during an Ethylene Plant Startup on Recycle through Dynamic Simulation and Process Optimization

Currently, emission characterization and reduction have been gaining more attention in the Chemical Process Industry and playing a more prominent part in industrial sustainability. Flare emissions release volatile organic compounds (VOCs) and pollutants into the atmosphere, which react with nitrous oxide in sunlight to generate ozone and smog.  Therefore, it is essential to limit this amount to improve local and regional air quality, especially around chemical plant locations. Besides, environmental performance also takes into account material usage, energy consumptions, non-product outputs and pollutant releases. From the economic point of view, reducing burned-off gases saves material, helps avoiding penalty and also creates a more effective way to distribute energy, thus lowering utility cost. Ethylene production process is one of the biggest and most complicated processes in petrochemical industry. A typical start-up of an ethylene plant flares approximately 5.0 million lbs of raw materials and generates at least 15.4 million lbs of CO₂, 40.0 Klbs CO, 7.4 Klbs NO_x, 15.1 Klbs hydrocarbons, and 100.0 Klbs HRVOCs. Hence, managing flare emission during startup, shutdown and malfunction of an ethylene plant are very important. Since flaring occurs in both normal and abnormal conditions, flare source reduction approach employs simulation and optimization, emphasizes the upstream section of flare system and the whole plant integration, in which more upstream modification and optimization contribute in limiting unwanted products.

In this study, rigorous steady-state and dynamic models of a front-end De-Ethanizer ethylene plant with sulfur recovery unit are constructed to serve as both the generic foundation to further explore causes roots of process abnormal behaviors and subsequently an optimization tool to provide guidance to both design and operational strategies. Rigorous steady-state and dynamic models are constructed and validated before case studies are carried out. Based on these models, possible flaring sources and incidents are identified, which are demonstrated extensively in this paper throughout three case studies of CGC startup, precooling chilling train, and withdrawal from full-reflux columns during an approach of flareless startup on total recycle.

Because charged gas is continuously produced from furnaces and sent to flare to retain compressors’ inlet pressure, CGC startup is reportedly the most critical step and has largest amount of vent gas. Several scenario cases of charged gas at different flowrates and compositions are examined to find the least flaring one.  Another case study focuses in recycling off-specs from full-reflux C2-/C3-supplier columns to CGC as partial substitute for furnace feed. In the chilling train section, specifications of equipment sizes and heat capacities are considered for simulating pre-cooling process. Optimal procedure is achieved for utilizing methane to cool down cold boxes at a reasonable temperature-dropping rate in order to protect these equipments. In dynamic simulation environment, all of the major steps and options are simulated and evaluated while flaring can also be predicted. These results serve as a basis for emission calculation and greenhouse gas study such as CO₂ control. The process can be considered from smaller scale (sectional model) to more comprehensive scale (whole plant model). The best results in these case studies are incorporated and validated in the integrated model of the whole plant in order to stabilize the system in the shortest time while minimizing material and energy loss.

Following this representative, utilizing dynamic simulation as a driving force for operation during abnormal conditions effectively provides an in-depth understanding and provides more detailed planning and expectations for all related personnel before the real action takes place. This procedure contributes a great deal in evaluating new strategies, predicting process dynamic behaviors, and achieving more precise timing to control commissioning and startup strategy.