An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants

Abstract

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework. In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.

Implications:

A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-off between coal purchase cost and health risk.

Acknowledgment

The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestion.

Additional information

Notes on contributors

C. Dai

C. Dai is a doctor and X.H. Cai and H.C. Guo are professors in the College of Environmental Science and Engineering at Peking University, Beijing, China.

Y.P. Cai

Y.P. Cai is a professor in the State Key Laboratory of Water Environment Simulation at Beijing Normal University, Beijing, China.

Q. Tan

Q. Tan and G.H. Huang are professors, and W. Sun is a doctor in the Institute for Energy, Environment and Sustainable Communities at University of Regina, Regina, Saskatchewan, Canada.