Monte Carlo optimization for site selection of new chemical plants.
Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.
Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX 77710, USA.
Tianxing
T
Cai
Department of Computer Science, Lamar University, Beaumont, TX 77710, USA. Electronic address: swang3@my.lamar.edu.
Sujing
S
Wang
Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX 77710, USA. Electronic address: Qiang.xu@lamar.edu.
Qiang
Q
Xu
D000397:Air Pollution / Q000032:analysis; D002617:Chemical Industry; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods; D008962:Models, Theoretical; D009010:Monte Carlo Method; D018570:Risk Assessment / Q000379:methods; D014887:Weather
Air quality; Modeling; Monte Carlo simulation; Optimization; Plant site selection