Abstract
Effluent trading to manage water pollution holds considerable potential for industries and policy makers alike. This paper proposes an optimization based approach to assist decision making in pollutant trading which is beyond heuristics. The optimization model, formulated as an Mixed Integer Linear Programming (MILP) problem, allows decision makers to incorporate water-shed and technology specific information in regulation development. The problem solution, as a consequence, suggests optimal approach to the industries to achieve the assigned load reduction targets. The basic model is extended to include health care costs to compare various decisions and also contribute towards decision making. The effect of uncertainty on the problem solutions is also analyzed by formulating a chance constrained programming problem as the extension of the original MILP problem. This ensures that no hotspots are created due to discharge uncertainty. The optimization model is implemented on a watershed level mercury pollution reduction case study. The results, indicating significant cost reductions due to trading, also emphasize the importance of considering watershed specific data in decision making. Health care cost is shown to be an important parameter for comparison and effects of uncertainty are observed to be more pronounced at tighter regulations.
| Original language | English (US) |
|---|---|
| Pages | 13603-13626 |
| Number of pages | 24 |
| State | Published - Dec 1 2005 |
| Event | 05AIChE: 2005 AIChE Annual Meeting and Fall Showcase - Cincinnati, OH, United States Duration: Oct 30 2005 → Nov 4 2005 |
Other
| Other | 05AIChE: 2005 AIChE Annual Meeting and Fall Showcase |
|---|---|
| Country/Territory | United States |
| City | Cincinnati, OH |
| Period | 10/30/05 → 11/4/05 |
ASJC Scopus subject areas
- Engineering(all)
