Role of Correlation among Physical Factors in Probabilistic Simulation of Emissions of Volatile Organic Compounds from Floating Storage and Offloading Vent Stack 10.32526/ennrj/22/20230339

Main Article Content

Chalee Seekramon
Chalor Jarusutthirak
Pawee Klongvessa

Abstract

This research investigated the roles of correlations among physical factors in the probabilistic simulation of volatile organic compounds (VOCs) emitted from a marine vessel (known as floating storage and offloading, FSO), located in the Gulf of Thailand. The physical factors in this study were wave height, ambient temperature, storage temperature, storage quantity, Reid vapor pressure, and the daily incoming rate. These physical factors were transformed into normally distributed data and a second-order multiple linear regression (MLR) with interaction effects, that were then used to determine the relationship between the transformed physical factors and the VOC venting volume from the FSO. The dataset of relevant predictors (transformed physical factors and interactions) that provided the maximum adjusted coefficient of determination was chosen for inclusion in the MLR. After that, two datasets of 1,000 venting volumes (one with and one without correlations among physical factors) were simulated. In the simulation, 1,000 datasets of six physical factors were generated according to observed averages and standard deviations. Cholesky randomization was used to generate the correlated physical factors for the simulation with correlation among physical factors. The averages of VOC venting volumes calculated from the generated physical factors when correlations among physical factors were and were not applied were 211,610 and 210,906 ft3, respectively (observed average was 210,984 ft3), with standard deviations of 38,828 and 40,787 ft3, respectively (observed standard deviation was 67,961 ft3), and skewness values of 0.74 and 0.51, respectively (observed skewness was 0.71). Therefore, correlation among the physical factors improved the skewness and provided better simulation results for VOC emission.

Article Details

How to Cite
Seekramon, C., Jarusutthirak, C., & Klongvessa, P. (2024). Role of Correlation among Physical Factors in Probabilistic Simulation of Emissions of Volatile Organic Compounds from Floating Storage and Offloading Vent Stack: 10.32526/ennrj/22/20230339. Environment and Natural Resources Journal, 22(4), 335–345. Retrieved from https://ph02.tci-thaijo.org/index.php/ennrj/article/view/251981
Section
Original Research Articles

References

Alam MS, Nikolova I, Singh A, MacKenzie AR, Harrison RM. Experimental vapour pressures of eight n-alkanes (C17, C18, C20, C22, C24, C26, C28 and C31) measured at ambient temperatures. Atmospheric Environment 2019;213:739-45.

Alshanbari HM, Odhah OH, Ahmad Z, Khan F, El-Bagoury AA-AH. A new probability distribution: Model, theory and analyzing the recovery time data. Axioms 2023;12(5):477-94.

Batterman S, Su FC, Li S, Mukherjee B, Jia C. Personal exposure to mixtures of volatile organic compounds: Modeling and further analysis of the RIOPA data. Research Reports: Health Effects Institute 2014;181:3-63.

Cao W, Li J, Joksimovic D, Yuan A, Banting D. Probabilistic spill occurrence simulation for chemical spills management. Journal of Hazardous Materials 2013;256:517-26.

Cho JH, Lee JH. Multiple linear regression models for predicting non point-source pollutant discharge from a highland agricultural region. Water 2018;10(9):Article No. 1156.

Deligiannis P, Zouridis P, Galis A. VOC emissions assessment from the cargo area of tanker vessels. Proceedings of Smart and Green Technology for the Future of Marine Industries International Conference; 2016 Apr 25-26; Glasgow: UK; 2016.

Drysdale R. OCIMF annual report 2019 [Internet]. 2019 [cited 2023 Sep 20]. Available from: https://www.ocimf.org/ document-libary/29-ocimf-annual-report-2019/file.

Gjesteland I, Hollund BE, Kirkeleit J, Daling PS, Sørheim KR, Bråtveit M. Determinants of airborne benzene evaporating from fresh crude oils released into seawater. Marine Pollution Bulletin 2019;140:395-402.

Golub GH, Loan CFV. Matrix Computations. Baltimore: The Johns Hopkins University; 1983.

Headrick TC, Kowalchuk RK. The power method transformation: Its probability density function, distribution function, and its further use for fitting data. Journal of Statistical Computation and Simulation 2007;77(3):229-49.

Hu G, Butler J, Littlejohns J, Wang Q, Guoneng L. Simulation of cargo VOC emissions from petroleum tankers in transit in Canadian waters. Engineering Applications of Computational Fluid Mechanics 2020;14(1):522-33.

Jia Z, Wang J, Zhou X, Zhou Y, Li Y, Li B, et al. Identification of the sources and influencing factors of potentially toxic elements accumulation in the soil from a typical karst region in Guangxi, Southwest China. Environmental Pollution 2020; 256:Article No. 113505.

Lang J, Zhou Y, Chen D, Xing X, Wei L, Wang X, et al. Investigating the contribution of shipping emissions to atmospheric PM2.5 using a combined source apportionment approach. Environmental Pollution 2017;229:557-66.

Lin C-C, Yu K-P, Zhao P, Lee GW-M. Evaluation of impact factors on VOC emissions and concentrations from wooden flooring based on chamber tests. Building and Environment 2009;44(3):525-33.

Lin W-T, Tsai R-Y, Chen H-L, Tsay Y-S, Lee C-C. Probabilistic prediction models and influence factors of indoor formaldehyde and VOC levels in newly renovated houses. Atmosphere 2022;13(5):Article No. 675.

Lynd LD, O’brien BJ. Advances in risk-benefit evaluation using probabilistic simulation methods: An application to the prophylaxis of deep vein thrombosis. Journal of Clinical Epidemiology 2004;57(8):795-803.

Mo Z, Lu S, Shao M. Volatile organic compound (VOC) emissions and health risk assessment in paint and coatings industry in the Yangtze River Delta, China. Environmental Pollution 2021;269:Article No. 115740.

Pham H. A new criterion for model selection. Mathematics 2019;7(12):Article No. 1215.

Rudd HJ, Hill NA. Measures to reduce emissions of VOCs during loading and unloading of ships in the EU [Internet]. 2001 [cited 2023 Sep 18]. Available from: http://ec.europa.eu /environment/archives/air/pdf/vocloading.pdf.

Seekramon C. Emission of Volatile Organic Compounds from Natural Gas Liquid Tank Vessel [dissertation]. Thailand, Mahidol University; 2015.

Seekramon C. Application of Simulation Models for Estimation of Benzene Emission on Floating Storage and Offloading (FSO) and Health Risk Assessment of Benzene Exposure [dissertation]. Thailand, Kasetsart University; 2023.

Shao P, Xu X, Zhang X, Xu J, Wang Y, Ma Z. Impact of volatile organic compounds and photochemical activities on particulate matters during a high ozone episode at urban, suburb and regional background stations in Beijing. Atmospheric Environment 2020;236:Article No. 117629.

Stricklin E. Evaporation loss measurement from storage tanks [Internet]. 2014 [cited 2023 Oct 10]. Available from: https://technokontrol.com/pdf/evaporation/evaporation-loss-measurement.pdf.

Templeton GF, Burney LL. Using a two-step transformation to address non-normality from a business value of information technology perspective. Journal of Information Systems 2017;31(2):149-64.

Trefethen LN, Bau D. Numerical Linear Algebra. Philadelphia, United States: Society for Industrial and Applied Mathematics; 1997.

Vos D, Duddy M, Bronneburg J. The evolution of inert-gas systems on SBM FPSOs: The problem of venting and a straightforward solution. SPE Projects Facilities and Construction 2007;2(2):1-11.

Yang T, Zhang P, Xu B, Xiong J. Predicting VOC emissions from materials in vehicle cabins: Determination of the key parameters and the influence of environmental factors. International Journal of Heat and Mass Transfer 2017;110: 671-9.