Influence of Vehicle Emission Control Systems on the Relationship Between Gasoline and Vehicle Exhaust Hydrocarbon Composition

Introduction. It is important to have models that adequately describe the relationship between the integral indicators of the functioning of the system with the particular indicators of the lower levels of management in complex control systems, in particular in RSChS. Traditional approaches based on normative models often turn out to be untenable due to the impossibility of covering all aspects of the functioning of such systems, as well as due to the high variability of the environment and the values of the set of target indicators. Recently, adaptive machine-learning models have proven to be productive, allowing build stable and adequate models, one of the variants of which is artificial neural networks (ANN), based on the solution of inverse problems using expert estimates. The relevance of the study lies in the development of compact models that allow assessing the effectiveness of the functioning of complex multi-level control systems (RSChS) in emergency situations, developing according to complex scenarios, in which emergencies of various types can occur simultaneously. Goals and objectives. The purpose of the article is to build and test the technology for creating compact models that are adequate to the system of indicators of the functioning of hierarchically organized control systems. This goal gives rise to the task of choosing tools for constructing the necessary models and sources of initial data. Methods. The research tools include methods for analyzing hierarchical systems, mathematical statistics, machine learning methods of ANN, simulation modeling, expert assessment methods, software systems for processing statistical data. The research is based on materials from domestic and foreign publications. Results and discussion. The proposed technology for constructing a neural network model of the effectiveness of the functioning of complex hierarchical systems provides a basis for constructing dynamic models of this type, which make it possible to distribute limited financial and other resources during the operation of the system according to a complex scenario of emergency response. Conclusion. The paper presents the results of solving the problem of constructing an ANN and its corresponding nonlinear function, reflecting the relationship between the performance indicators of the lower levels of the hierarchical control system (RSChS) with the upper level. The neural network model constructed in this way can be used in the decision support system for resource management in the context of complex scenarios for the development of emergency situations. The use of expert assessments as an information basis makes it possible to take into account numerous target indicators, which are extremely difficult to take into account in other ways. Keywords: emergency situations, hierarchical control system, efficiency, artificial neural network, expert assessments

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Preliminary sizing of voc emission control systems for paint booths

Metal Finishing ◽

10.1016/s0026-0576(00)80444-4 ◽

2000 ◽

Vol 98 (6) ◽

pp. 459-463

Author(s):

Robert E. Kenson

Keyword(s):

Control Systems ◽

Emission Control ◽

Voc Emission

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Effects of Biodiesel Operation on Light-Duty Tier 2 Engine and Emission Control Systems

SAE International Journal of Fuels and Lubricants ◽

10.4271/2008-01-0080 ◽

2008 ◽

Vol 1 (1) ◽

pp. 119-131 ◽

Cited By ~ 18

Author(s):

Marek Tatur ◽

Harsha Nanjundaswamy ◽

Dean Tomazic ◽

Matthew Thornton

Keyword(s):

Control Systems ◽

Emission Control ◽

Tier 2 ◽

Light Duty

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Evaluating Vehicle Emission Control Policies using on-Road Mobile Measurements and Continuous Wavelet Transform: a Case Study during the Asia-Pacific Economic Cooperation Forum, China 2014

10.5194/acp-2016-460 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ziqiang Tan ◽

Yanwen Wang ◽

Chunxiang Ye ◽

Yi Zhu ◽

Yingruo Li ◽

...

Keyword(s):

Wavelet Transform ◽

Air Quality ◽

Continuous Wavelet Transform ◽

Vehicle Emissions ◽

Emission Control ◽

Control Policy ◽

Asia Pacific ◽

Vehicle Emission ◽

Continuous Wavelet ◽

Asia Pacific Economic Cooperation

Abstract. Vehicle emissions are major sources of atmospheric pollutants in urban areas, especially in megacities around the world. Various vehicle emission control policies have been implemented to improve air quality. However, the effectiveness of these policies is unclear, due to a lack of systematic evaluation and sound methodologies. During the Asia-Pacific Economic Cooperation (APEC) Forum, China 2014, the Chinese government implemented the strictest vehicle emission control policy in the country's history, which provided an opportunity to evaluate its effectiveness, based on our recently developed method. To evaluate the vehicle emission reduction, we used a mobile research platform to measure the main air pollutants (PM2.5, black carbon (BC), SO2, CO, NOx and O3) on the 4th ring road of the city of Beijing, combined with a continuous wavelet transform method (CWT) to separate out "instantaneous emissions" by passing vehicles. The results suggested that our measurements captured the spatial distribution and variation of atmospheric pollutant concentrations on the 4th ring road. The "instantaneous concentration" decomposed by the CWT method represents on-road emissions better than other methods reported in the literature. With this method, we found that the daytime vehicle emission of CO and NOx decreased by 28.1 and 16.3 %, respectively, during the APEC period relative to the period before APEC, and by 39.3 and 38.5 %, respectively, relative to the period after APEC. The nighttime vehicle emissions of CO and NOx decreased by 56.0 and 60.7 %, respectively, during the APEC period relative to the period after APEC. Because vehicle emissions of NOx and CO contribute considerably to the total emissions of these pollutants in Beijing, the vehicle emission control policy implementation was extremely successful in controlling air quality during APEC 2014, China.

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