Research Article: Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants

Date Published: March 24, 2016

Publisher: Public Library of Science

Author(s): Qing-chun Meng, Xiao-xia Rong, Yi-min Zhang, Xiao-le Wan, Yuan-yuan Liu, Yu-zhi Wang, Yongtang Shi.

http://doi.org/10.1371/journal.pone.0152057

Abstract

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996–2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Partial Text

Four types of mathematical methods are introduced in this section, namely the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model considering energy consumption, and an optimality condition analysis using the mathematical features of the multi-objective optimization model to design the optimal-pole algorithm and get an efficient collaborative scheme of reduction.

Tianjin City, an old and the largest coastal open city of China, has a continuous augment of energy consumption caused by rapid development of its industry-based economy. China’s Ministry of Environmental Protection once pointed out that unorganized emission phenomenon was serious in some industrial enterprises of Tianjin. The reason for selecting Tianjin for this research is because the energy consumption pattern in the city, which mainly consists of fossil fuels like coal, petroleum, and natural gas, is the typical pattern in most industrial cities, hence, Tianjin can be a representative in the empirical analysis of collaborative emission. We use data for Tianjin from 1996 to 2012 to verify the model. Because of the hysteretic nature of statistical yearbook issuing, the data from 2012 is the latest data acquired when we implemented this research.

This paper used multi-objective optimization to conduct mathematical modeling, theoretical analysis, and empirical research on emission reduction of sulfur dioxide, nitric oxides, and carbon dioxide, and acquired several comprehensive emission reduction schemes which place particular emphasis on different objectives. Ever since China issued measures on energy conservation, emission reduction, and total quantity control of pollutants such as sulfur dioxide, the government has been more motivated to solve problems such as managingair pollution, energy conservation, and emission reduction, and investments made by the government have been increasing consistently. These investments are mainly used for structural adjustment, technology improvement, and measures of fuel gas desulfurization and denitration to reduce the harm of industrial waste gas in the air quality to an extreme degree. Balancing the quantity of pollutants discharged and energy usage schemes in control investments under insufficient funds in order to reach the optimal emission reduction strategy is a problem demanding prompt discussion in the future. By implementing theemission reduction of sulfur dioxide, nitric oxide, and carbon dioxide, as well as accumulation of specific investment data, control investments can be incorporated into the model to solve the optimization model of emission reduction with investments considered.

 

Source:

http://doi.org/10.1371/journal.pone.0152057