Application of Genetic Algorithms for the Design of Ozone Control Strategies

Abstract

Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying costeffective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models.We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chanceconstrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making. IMPLICATIONSState agencies have been charged with developing management strategies for tropospheric ozone which ideally will be cost-effective. Considering cost-effectiveness explicitly in the strategy design process, however, has proven quite challenging. The genetic algorithm-based optimization approach discussed here can be used to generate cost-effective control strategies that account for the temporal and spatial distribution of emissions, costs and removal efficiencies of control technologies, and the nonlinear chemistry characteristic of ozone formation. The application of genetic algorithms to this problem has the potential to make the design of ozone control strategies more efficient and the resulting strategies more cost-effective.

Discrete Element Modelling on a Cluster of Workstations

“…Our own studies since the late 1980s have shown that distributed solutions on non-dedicated, heterogeneous hardware are a practical approach in such diverse application areas as finite element analysis [21,22], vehicle routing and scheduling [23], and air quality optimisation and management [24].…”

A gaussian-box modeling approach for urban air quality management in a Northern Chinese City—II. pollutant emission abatement

“…Various studies were reported to address the air quality management problem by using an optimization approach such as linear programming, nonlinear programming, and integer programming. For example, Shih et al (1998) developed a linear programming model for optimal control of photochemical pollutants by minimizing the net present value of emission control costs from various emission sources while meeting the ambient air quality goals over the planning time periods; Loughlin et al (2000) developed a genetic algorithm-based optimization model for developing the urban-scale ozone control strategies by integrating a simple air quality model into the optimization process to represent ozone transport and chemistry; Ikeda et al (2001) examined the optimal emission control strategy by developing the mixed-integer linear programming model based on the estimated sulfur emission for China in 2010; Wang and Milford (2001) developed a stochastic optimization model to investigate the optimal control strategies of urban ozone for achieving a specified air quality target with a given reliability, while the uncertainties in air quality simulation model were considered; Ma and Zhang (2002) developed a stochastic programming model to define the total allowable pollutant discharge of SO2 in Yuxi City of China; Dutta et al (2003) developed a linear programming model to evaluate the impact of imposed maximum SO 2 emission limits on the operation and the profitability of a petroleum refinery in India in order to satisfy all relevant constraints due to the refinery configuration and operational limitations; Guariso et al (2004) integrated a large photochemical model (CALGRID) with a multiobjective mathematical program to evaluate the emission abatement action priorities in Lombardy of Northern Italy. Other related studies can be found in Fedra and Haurie (1999), Yu et al (2000), and Craig et al (2001).…”

How effective and efficient are multiobjective evolutionary algorithms at hydrologic model calibration?

“…There is a growing body of water resources literature (Horn and Nafpliotis, 1993;Ritzel et al, 1994;Cieniawski et al, 1995;Halhal et al, 1997;Loughlin et al, 2000;Reed et al, 2001;Erickson et al, 2002;Reed and Minsker, 2004) demonstrating the importance of multiobjective problems (MOPs) and evolutionary multiobjective solution tools. A key characteristic of MOPs is that optimization cannot consider a single objective because performance in other objectives may suffer.…”