Calculation of Optimal VOCs Emission Reduction Based on Improved SEIRS Model in Cloud Environment

doi:10.16182/j.issn1004731x.joss.21-1177

Abstract

Abstract:

Volatile organic compounds (VOCs) emissions in different regions are correlated and influenced. In order to minimize the impact of VOCs on the atmospheric environment and achieve synergistic governance of VOCs regions, an optimal emission reduction model is established with the maximum VOCs emission reduction as the primary goal. An improved SEIRS infectious disease dynamics optimization algorithm considering environmental pollution(SEIRS-CE) is proposed and the model is solved in cloud environment. Taking Xi'an city as an example, the SEIRS-CE algorithm is used in Ali cloud server to calculate the emission reduction of VOCs associated with 13 meteorological monitoring stations in Xi 'an city and compared with the traditional intelligent optimization algorithm. The results show that the established VOCs optimal emission reduction model is scientific and feasible. Compared with the contrast algorithm, the SEIRS-CE algorithm in cloud environment can solve the VOCs optimal emission reduction problem with faster convergence speed, higher solution accuracy, lower probability of falling into local trap, with high concurrency, high security, high sharing, and provide reference for the joint prevention and control of VOCs among the government.

Key words: VOCs(volatile organic compounds), cloud computing, SEIRS infectious disease model, joint prevention and control, optimal emission reduction scheme

CLC Number:

TP301.6

Guangqiu Huang, Xixuan Zhao, Qiuqin Lu. Calculation of Optimal VOCs Emission Reduction Based on Improved SEIRS Model in Cloud Environment[J]. Journal of System Simulation, 2023, 35(3): 632-645.

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References 28

1	竹涛, 朱晓晶, 牛文凤, 等. 国内外挥发性有机物排放标准对比研究[J]. 矿业科学学报, 2020, 5(2): 209-218.
	Zhu Tao, Zhu Xiaojing, Niu Wenfeng, et al. Comparative Study of Domestic and Foreign Emission Standards for Volatile Organic Compounds[J]. Journal of Mining Science and Technology, 2020, 5(2): 209-218.
2	王伶瑞, 李海燕, 陈程, 等. 长三角北部沿海城市2018年大气VOCs分布特征[J]. 环境科学学报, 2020, 40(4): 1385-1400.
	Wang Lingrui, Li Haiyan, Chen Cheng, et al. Distributions of VOCs in a Coastal City in the Northern Yangtze River Delta During 2018[J]. Acta Scientiae Circumstantiae, 2020, 40(4): 1385-1400.
3	林燕芬, 段玉森, 高宗江. 基于VOCs加密监测的上海典型臭氧污染过程特征及成因分析[J]. 环境科学学报, 2019, 39(1): 126-133.
	Lin Yanfang, Duan Yusen, Gao Zongjiang. Typical Ozone Pollution Process and Source Identification in Shanghai Based on VOCs Intense Measurement[J]. Acta Scientiae Circumstantiae, 2019, 39(1): 126-133.
4	Cbabc D, Nva B, Jc E, et al. Gestational Exposure to Volatile Organic Compounds (VOCs) in Northeastern British Columbia, Canada: A Pilot Study[J]. Environment International (S0160-4120), 2018, 110: 131-138.
5	林旭, 严仁嫦, 金嘉佳, 等. 基于SOA和O₃生成潜势的杭州市PM_2.5和O₃协同控制[J]. 环境科学, 2022, 43(4): 1799-1807.
	Lin Xu, Yan Renchang, Jin Jiajia, et al. Coordinated Control of PM_2.5 and O₃ in Hangzhou Based on SOA and O₃ Formation Potential[J]. Environmental Science, 2022, 43(4): 1799-1807.
6	Klimont Z, Cofala J, Schöpp W, et al. Projections of SO₂, NOx, NH₃ and VOC Emissions in East Asia up to 2030[J]. Water, Air, and Soil Pollution (S0049-6979), 2001, 130(1): 193-198.
7	Streets D G, Waldhoff S T. Present and Future Emissions of Air Pollutants in China: SO₂, NOx, and CO[J]. Atmospheric Environment (S1352-2310), 2000, 34(3): 363-374.
8	刘扬, 王颖, 刘灏, 等. 基于WRF-Chem模拟验证的天水市主城区大气污染源排放清单[J]. 中国环境科学, 2022, 42(1): 32-42.
	Liu Yang, Wang Ying, Liu Hao, et al. Air Pollutants Emission Inventory for the Main Urban Area of Tianshui City Based on Verification by WRF-Chem Simulation[J]. China Environmental Science, 2022, 42(1): 32-42.
9	王燕军, 黄志辉, 唐祎骕, 等. 我国非道路移动源排放清单估算及技术减排潜力分析[J]. 环境与可持续发展, 2021, 46(4): 64-69.
	Wang Yanjun, Huang Zhihui, Tang Yisu, et al. Estimation on Non-road Mobile Source Emission Inventory in 2017 and Its Technological Reduction Potential Analysis[J]. Environment and Sustainable Development,2021, 46(4): 64-69.
10	陈天雷, 吴敏, 潘成珂, 等. 基于前体物多情景排放的兰州市2030年夏季臭氧预测[J]. 环境科学, 2022, 43(5): 2403-2414.
	Chen Tianlei, Wu Min, Pan Chengke, et al. Ozone Simulation of Lanzhou City Based on Multi-scenario Emission Forecast of Ozone Precursors in the Summer of 2030[J].Environmental Science, 2022, 43(5): 2403-2414.
11	谢放尖, 史之浩, 李婧祎, 等. 基于达标约束的南京市环境空气质量情景模拟[J]. 环境科学, 2019, 40(7): 2967-2976.
	Xie Fangjian, Shi Zhihao, Li Jingyi, et al. Scenario Simulation Study Constrained by the Ambient Air Quality Standards in Nanjing[J]. Environmental Science, 2019, 40(7): 2967-2976.
12	Zhang Y N, Xue L K, Li H Y, et al. Source Apportionment of Regional Ozone Pollution Observed at Mount Tai, North China: Application of Lagrangian Photochemical Trajectory Model and Implications for Control Policy[J]. Journal of Geophysical Research: Atmospheres (S2169-897X), 2021, 126(6): e2020JD033519.
13	Kim M J, Park R J, Ho C H, et al. Future Ozone and Oxidants Change under the RCP Scenarios[J]. Atmospheric Environment (S1352-2310), 2015, 101: 103-115.
14	Zhu J, Liao H. Future Ozone Air Quality and Radiative Forcing over China Owing to Future Changes in Emissions under the Representative Concentration Pathways (RCPs)[J]. Journal of Geophysical Research Atmospheres (S2169-897X), 2016, 121(4): 1978-2001.
15	杨丹丹, 王体健, 李树, 等. 基于空气质量模式和数学规划模型的城市PM_2.5达标策略——以临汾为例[J]. 中国环境科学, 2021, 41(8): 3493-3501.
	Yang Dandan, Wang Tijian, Li Shu, et al. Urban PM_2.5 Compliance Strategy Based on Air Quality and Mathematical Planning Model[J]. China Environmental Science, 2021, 41(8): 3493-3501.
16	陆秋琴, 何舒, 黄光球. 区域联防联控挥发性有机物(VOCs)最优减排方案研究[J]. 环境科学学报, 2021, 41(5): 1764-1773.
	Lu Qiuqin, He Shu, Huang Guangqiu. Research on the Best Emission Reduction Scheme for Regional Joint Prevention and Control of Volatile Organic Compounds (VOCs)[J]. Acta Scientiae Circumstantiae, 2021, 41(5): 1764-1773.
17	黄梦瑶, 黄丽达, 袁宏永, 等. 社交隔离对COVID-19的发展影响[J]. 清华大学学报(自然科学版), 2021, 61(2): 96-103.
	Huang Mengyao, Huang Lida, Yuan Hongyong, et al. Effects of Social Isolation on COVID-19 Trends[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(2): 96-103.
18	程穆阳. 高斯模型在中小城市多点源大气扩散模拟中的应用研究[D]. 哈尔滨: 哈尔滨师范大学, 2020.
	Cheng Muyang. Research on the Application of Gauss Model in Multi-point Atmospheric Diffusion Simulation of Small and Medium-sized Cities-A Case Study of Suihua City[D]. Harbin: Harbin Normal University, 2020.
19	张瑞锋, 李欣秋. 基于SWN-SEIRS模型的供应链金融信用风险传染测度研究[J]. 财经理论与实践, 2021, 42(2): 20-26.
	Zhang Ruifeng, Li Xinqiu. Research on Credit Risk Contagion Measure of Supply Chain Finance Based on SWN-SEIRS Model[J]. The Theory and Practice of Finance and Economics, 2021, 42(2): 20-26.
20	Kermack W O, Mckendrick A G. Contributions to the Mathematical Theory of Epidemics[J]. Proceedings of the Royal Society of London Series A (S0950-1207), 1927, A115: 700-721.
21	杨伟. 传染病动力学的一些数学模型及其分析[D]. 上海: 复旦大学, 2010.
	Yang Wei. Some Mathematical Models and Analysis of Infectious Disease Dynamics[D]. Shanghai: Fudan University, 2010.
22	Iisufescu M. Finite Markov Processes and Their Applications[M]. Wiley: Chichester, 1980.
23	黄光球, 陆秋琴. 具有跨物种多级传播特征的包虫病优化算法[J]. 计算机科学与探索, 2020, 14(6): 1054-1069.
	Huang Guangqiu, Lu Qiuqin. Hydatid Disease Optimization Algorithm with Multistage Cross-Species Transmission Characteristics[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(6): 1054-1069.
24	Li Y X, Shi B D, Pan X R. Ballistic Target Signal Separation Based on Differential Evolution Algorithm[J]. Journal of Physics: Conference Series (1742-6588), 2021, 1883(1): 012005.
25	Lim J Y, Kim T W, Wang X Y, et al. Evaluation of Compressive Strength of Sustainable Concrete Using Genetic Algorithm Assisted Artificial Neural Networks[J]. Materials Science Forum (S0255-5476), 2021, 1029: 83-88.
26	Bangyal W H, Hameed A, Alosaimi W, et al. A New Initialization Approach in Particle Swarm Optimization for Global Optimization Problems[J]. Computational Intelligence and Neuroscience (S1687-5265), 2021, 2021: 6628889.
27	Liu Yi, Feng Xuesong, Ding Chuanchen, et al. Electric Transit Network Design by an Improved Artificial Fish-Swarm Algorithm[J]. Journal of Transportation Engineering, Part A: Systems (S2473-2907), 2020, 146(8): 04020071.
28	张晓凤, 王秀英. 布谷鸟搜索算法综述[J]. 计算机工程与应用, 2018, 54(18): 8-16.
	Zhang Xiaofeng, Wang Xiuying. Survey of Cuckoo Search Algorithm[J]. Computer Engineering and Applications, 2018, 54(18): 8-16.

特征集合类型	变量名	产生方法
强壮居民集合	SR_s	L个居民的强壮度比当前居民i的强壮度高
普通居民集合	CR_s	L个居民的强壮度与当前居民i的强壮度无关
虚弱居民集合	WR_s	L个居民的强壮度是所有居民中最小的

操作	时间复杂度	最多循环次数
初始化	O(6n+5(n+1)N+2n²N)	1
计算S_i (t)， E_i (t)，I_i (t)， R_i (t)	O(7)	(G+N+3)N
S-S、 S-E、 E-E、 E-I、 E-S、 I-I、 I-R、 I-S、 R-R、 R-E、 R-S	O((N+5+4)nE₀/18)	(G+N+7)N
状态保持	O((1-5E₀/12)n)	(G+N+7)N
目标函数计算	O(n)~O(n²)	(G+N+7)N
生长算子	O(3n)	(G+N+7)N
结果输出	O(n)	1

名称	版本	要求
阿里云服务器	轻量应用服务器，系统镜像Windows Server 2008 R2	无
Python	Python 3.8.3， 64位	安装在云服务器上
Apache 服务器	Apache-2.4.46	安装在云服务器上
Python numpy包	numpy-1.19.3+mkl-cp38-cp38-win_amd64.whl	安装在Python中
Python CGI	无	在Apache服务器中配置

参数名称	取值依据
演化时期数G	算法迭代次数，由实际问题决定，G=800
居民数量N	试探解数量，设置过少容易陷入局部最优，设置过多会影响收敛效果，N=100
每个居民的特征数n	每个试探解的维数，在本文中n=13，对应13个气象监测点
患病概率E₀	算子调度概率或居民状态变化概率，E₀=0.01
施加影响的居民数L	决定了特征集合的维度与算法时间复杂度的大小，L=3
全局最优解误差ε	ε为趋于0的数，取值越小代表精确度越高，ε=0.001

日期	PM_2.5(μg/m³) u_1,1(0)	PM₁₀(μg/m³) u_1,2(0)	SO₂(μg/m³) u_1,3(0)	NO₂(μg/m³) u_1,4(0)	O₃(μg/m³) u_1,5(0)	CO(mg/m³) u_1,6(0)
11.01	27.0	108.0	11.0	51.0	100.0	0.7
11.02	35.0	97.0	13.0	57.0	87.0	1.0
…	…	…	…	…	…	…
11.29	80.0	99.0	9.0	49.0	64.0	1.0
11.30	92.0	110.0	7.0	51.0	86.0	1.4