TO: Texas Air Research Center
FROM: Dr. Qiang Xu
Dan F. Smith Department of Chemical Engineering, Lamar University
Beaumont, TX 77710
SUBJECT: Annual Progress Report
PROJECT NUMBER: 413LUB0137A
PROJECT TITLE: Study on Proactive Air-quality Control by Coupling Emission Source Reduction and Air Quality Modeling
PROJECT PERIOD: September 1, 2013 through July 15, 2015
DATE: October 5, 2015
Project Description
Texas will continue facing its ozone nonattainment issues with more non-attainment counties added when the currently EPA-proposed new primary and secondary National Ambient Air Quality Standards for ground-level ozone take effect. This leads to an urgent need for Texas to continue researches in improving scientific understanding of the mechanisms of ozone formation and transportation, as well as developing effective air-quality control strategies. Southeast Texas is heavily populated by chemical plants; and their emissions have been identified to have significant impacts on the regional air quality. For instance, flares from plant start-ups, shutdowns, and process upsets emit large amounts of VOCs, HRVOCs, and NOx, resulting in potential high-ozone concentration events and tremendous raw material/energy losses. Actually, flare emissions from chemical process systems have been extensively studied in recent years. Hourly dynamic emissions under different process operation scenarios and flare minimization strategies can be predicted. This provides more accurate point source information on zone precursor emissions than that from annual statistic reports. Therefore, dynamic emission data from process system level can be used by air quality modeling to study the impact on ozone formation and transportation in both large temporal and spatial scales, which can eventually benefit air-quality controls related to chemical industry emissions.
Facing this opportunity, this project will develop a proactive air-quality control methodology by coupling emission source reduction and air quality modeling. In this study, the best air-quality control strategies are achieved at two different levels. In the chemical process level, in-plant optimization controls for emission source minimization will be explored to reduce total amount of emission, duration of intensive emission, as well as the number of instances potentially causing significant emissions. These could be accomplished with the help of plant-wide dynamic simulation and optimization. Based on the validated large-scale process models, various optimization activities can be virtually carried out and studied, such as optimal start-up and shutdown operations, smart process upset handling, and process design retrofit for flare gas recovery. These in-plant controls target on emission minimization within the plant; meanwhile, the associated dynamic simulations help to obtain hourly dynamic emission data, which can be used to update TCEQ emission inventories for air quality modeling. Based on the acquired VOCs and NOX emissions from the chemical process level, CAMx based air-quality modeling, simulation, and optimization will be conducted. The modeling activities will not only predict plant emission effects on regional air quality (e.g., ozone concentration increment due to a plant start-up), but also minimize regional air quality impacts of those planned emission events through optimization of available out-plant control factors, such as selections of date and starting time for plant start-up/shutdown operations, air-quality conscious scheduling for multi-plant turnaround operations, as well as new plant site determination. The August 2006 ozone episode will be selected to represent meteorological conditions during the CAMx based simulation and optimization. The required emission inventories from different sources are obtained from TCEQ, i.e., point, mobile, area, and biogenic sources, plus point source update from this study.
This project is to acquire effective air-quality control strategies via multi-scale dynamic simulations and optimizations. It will enrich the knowledge on emission categories which have the greatest impacts on ozone formation; meanwhile, it is projected to provide scientific foundation and technical support to conduct cost-effective air-quality controls.
Objectives
1. Explore various in-plant control strategies for emission source reduction and obtain hourly dynamic emission data.
2. Employ CAMx modeling to precisely study effects on the increment of regional ozone concentrations due to planned emission events under different plant operation scenarios, such as plant start-ups, shutdowns, process upsets, etc..
3. Optimize out-plant control strategies for emission impact minimization via CAMx based simulation and optimization, such as smart selections of date and starting time of start-up/shutdown operations, scheduling multi-plant turnaround operations, as well as new plant site determination.
4. Acquire cost-effective and air-quality conscious control strategies for all stake holders, including TCEQ, chemical industry, and residential communities.
Methodology
The proposed methodology framework is shown in Figure 1. It includes three stages of work. In the first stage, detailed process and control information of an investigated chemical process will be collected; then a plant-wide dynamic model will be developed and validated iteratively to obtain reliable process models to utilize in the next stage. In the second stage, in-plant control strategies for emission reduction will be explored and identified through dynamic optimizations. The optimization solutions will be virtually examined for their operational feasibility and safety by plant-wide dynamic simulations. Various emission minimization strategies will be considered, e.g., optimal start-up and shutdown operations, smart process upset handling, and process design retrofit for flare gas recovery. These in-plant controls target on emission minimization within a plant; meanwhile, the associated dynamic simulations help obtain the hourly dynamic emission data, which can be used to update TCEQ emission inventories for air quality modeling. If both operational feasibility and safety tests are satisfied, it indicates the identified design and operational strategy for in-plant emission reduction are viable; otherwise, the troubleshooting will be conducted until a desirable solution is obtained.
Figure 1. General methodology framework.
Based on the obtained VOCs and NOX emissions from the chemical process level, CAMx based air-quality modeling, simulation, and optimization will be conducted in the third stage. Two objectives should be achieved in this stage: i) air quality modeling is able to predict plant emission effects on regional air quality (e.g., ozone concentration increment due to a plant start-up/shutdown); ii) regional air quality impacts for planned emission events can be minimized further through optimization of available out-plant control factors, such as selections of date and starting time of start-up/shutdown operations, air-quality conscious scheduling for multi-plant turnaround operations, as well as new plant site determination. In this stage, the August 2006 ozone episode will be selected to represent meteorological conditions during the CAMx based simulation and optimization. The required emission inventories from different sources (i.e., point, mobile, area, and biogenic sources) are obtained from TCEQ accompanied by updates from this study. Based on the proposed three-stage multi-scale dynamic simulations and optimizations, scientific insights of in-plant emission minimization and out-plant ozone impact minimization will be simultaneously provided. The PIs envision that the obtained results will help obtain best Texas air-quality control strategies associated with chemical industries.
Accomplishments/Problems
We have developed several plant-wide process dynamic models as test beds. Based on those, the following accomplishments have been made:
· Emission Source Characterization during An Ethylene Plant Shutdown
· Developed A Shutdown Strategy for Flare Minimization at An Olefin Plant
· Developed A Start-up Strategy for Flare Minimization at An Olefin Plant
· Study on Energy Consumption and Emission Generation for An Ethylene Plant under Different Start-up Strategies
· Analysis of Regional Air-quality Impacts Associated with Flare Emissions from An Ethylene Plant Shutdown
· Optimal Scheduling Strategies for Multiple Chemical Plant Start-ups to Minimize Regional Air Quality Impacts
· Methodology for Optimal Site Selection of New Chemical Plants based on Air-quality Concerns
· Methodology for Air-quality Monitoring Network Design
List of Publications and Presentations
a) Peer-reviewed journal publications
1. Cai, T. X., Wang, S. J.*, Xu, Q.*, “Monte Carlo Optimization for Site Selection of New Chemical Plants”, Journal of Environmental Management, 163, 28-38, 2015.
2. Chen, M., Wang, S. J.*, Xu, Q.*, “Multi-objective Optimization for Air-quality Monitoring Network Design”, Industrial & Engineering Chemistry Research, 54(31), 7743-7750, 2015.
3. Dinh H., Zhang, S. J., Xu, Y. L., Xu, Q.*, Eljack F., El-Halwagi M. “A Generic Approach of Using Dynamic Simulation for Emission Reduction under Abnormal Operations: Scenario Study of An Ethylene Plant Startup”, Industrial & Engineering Chemistry Research, 53(39), 15089-15100, 2014.
4. Wang, Z. Y., Xu, Q.*, Ho, T. C., “Emission Source Characterization during An Ethylene Plant Shutdown”, Chemical Engineering & Technology, DOI: 10.1002/ceat.201300849, 37, 1-12, 2014.
5. Zhao, Y.C., Zhang, J., Qiu, T., Zhao, J.S., Xu, Q.*, “Flare Minimization during Start-ups of An Integrated Cryogenic Separation System via Dynamic Simulation”, Industrial & Engineering Chemistry Research, 53(4), 1553-1562, 2014.
6. Wei, T., Hou, X. F., Yu, J. T., Zhang, J., Xu, Q.*, Zhao, J. S., Qiu, T., “Shutdown Strategy for Flare Minimization at An Olefin Plant”, Chemical Engineering & Technology, 37(4), 1-7, 2014.
7. Fu, J., Xu, Q.*, “Simultaneous Study on Energy Consumption and Emission Generation for An Ethylene Plant under Different Start-up Strategies”, Computers & Chemical Engineering, 56, 68-79, 2013.
8. Cai, T. X., Wang, S. J., Xu, Q.*, “Scheduling of Multiple Chemical Plant Start-ups to Minimize Regional Air Quality Impacts”, Computers & Chemical Engineering, 54, 68-78, 2013. (2013 Best paper award of AIChE Environmental Division)
9. Cai, T. X., Wang, S. J. Xu, Q.*, Ho, T.C., “Proactive Abnormal Emission Identification via Air-quality Monitoring Network”, Industrial & Engineering Chemistry Research, 52(26), 9189-9202, 2013.
b) under reivew
1. Wang, Z. Y., Xu, Q.*, Ho, T. C., “Study on Regional Air-quality Impacts Associated with Flare Emissions from An Ethylene Plant Shutdown”, submitted, Atmospheric Environment, 2015.
c) Presentations
1. Dinh, H., Zhang, S. J., Xu, Q., Eljack F., “Pressure-Driven Dynamic Simulation for Flare Minimization and Greenhouse Gas Reduction during an Ethylene Plant Startup”, AIChE Annual Meeting, Salt Lake City, UT, November 8-13, 2015.
2. Wang, Z. Y., Xu, Y. L., Dinh H., Xu, Q., Ho, T., "Air-Quality Evaluation on Flare Minimization Strategies for An Ethylene Plant Shutdown", AIChE Spring Meeting, Austin, TX, April 26-30, 2015.
3. Zhang J., Wang, Z. Y., Ho, T., Xu, Q., "Effect of Ozone Concentrations from Local Emission Sources", AIChE Spring Meeting, Austin, TX, April 26-30, 2015.
4. Kurle, Y., Xu, Q., "Boil-Off Gas Minimization and Recovery Options at LNG Loading Terminals", AIChE Spring Meeting, Austin, TX, April 26-30, 2015.
5. "Flare Minimization during Ethylene Plant Turnaround Operations via Dynamic Simulation and Optimization", 6th AIChE Southwest Process Technology Conference, Galveston, Texas, October 9-10, 2014.
6. “Industrial Flare Minimization via Dynamic Simulation and Optimization”, Yunnan University, Kunming, P. R. China, July 4, 2014.
7. “Industrial Flare Minimization via Dynamic Simulation and Optimization”, Qatar University, Qatar, May, 5, 2014.
8. “Flare Minimization for Chemical Industry”, The Goodyear Tire & Rubber Company, Beaumont, Texas, April 10, 2014.
9. Zhang, J., Wang, Z. Y., Xu, Q., Ho, T. C. “Air Quality Impact of the Startup of a Single Olefin Plant”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
10. Zhang, J., Wang, Z. Y., Xu, Q., Ho, T. C. “Chemical Plant Startup Simulations for Flare Emission Reduction”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
11. Zhang, S. J., Xu, Q. “Robust Optimization for Design and Operation of Chilling Train System in an Olefin Plant”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
12. Xu, Y. L., Zhou W. P., Xu, Q. “Plant-Wide Simulation of Ethanol Oxidation Process for Acetic Acid Production”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
13. Wang, Z. Y., Xu, Q., Ho, T. C. “Dynamic Simulation for Flare Minimization Strategy in an Ethylene Plant Shutdown”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
14. Wang, Z. Y., Zhang J., Xu, Q., Ho, T. C. “Dynamic Simulation for Optimal Operation of Distillation Column Startups in an Ethylene Plan”, AIChE Spring Meeting, New Orleans, LA, March 30 - April 3, 2014.
15. Cai, T.X., Xu, Q. “Regional Air Quality Improvement By Scheduling of Multiple Chemical Plant Start-Ups”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.
16. Cai, T.X., Xu, Q. “Monte Carlo Optimization for Site Selection of New Chemical Plant”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.
17. Cai, T.X., Xu, Q. “Data Integration for Proactive Abnormal Emission Identification Via Air-Quality Monitoring Network”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.
18. Wang, Z. Y., Xu, Q., Ho, T. C., “Emission Source Characterization during An Ethylene Plant Shutdown”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.
19. Wang, Z. Y., Xu, Q., Ho, T. C., “Impact of Flaring Emissions on Regional Air Quality Associated with An Ethylene Plant Shutdown through CAMx Simulations”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.
20. Wang, Z. Y., Zhang, J., Xu, Q., Ho, T. C., “Simultaneous Reduction of Chemical Plant Start-up Flaring and Regional Air Quality Impact via Multi-scale Dynamic Modeling”, AIChE Annual National Meeting, San Francisco, CA, November 3~8, 2013.