Stochastic Dynamic Programming Formulation for a Wastewater Treatment Decision-Making Framework

• Published in: Annals of operations research Vol. 132; no. 1-4; pp. 207 - 221
• Main Authors: Tsai, Julia C.C., Chen, Victoria C.P., Beck, M. Bruce, Chen, Jining
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 01.11.2004
• Subjects: Chemical oxygen demand; Decision making; Decision theory; Design optimization; Dynamic programming; Infrastructure; Mars; Nitrogen; Operations research; Organic chemicals; Pathogens; Sludge; Stochastic models; Studies; Technology; Variables; Water treatment; A, Mid-Atlantic Ridge
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1023/B:ANOR.0000045283.86576.62

In this paper, a decision-making framework (DMF) based on stochastic dynamic programming (SDP) is presented for a wastewater treatment system, consisting of a liquid treatment line with eleven levels and a solid treatment line with six levels (Chen and Beck, 1997). A continuous-state SDP solution approach based on the OA/MARS method (Chen, Ruppert, and Shoemaker, 1999) is employed, which provides an efficient method for representing a wide range of possible influent conditions. The DMF is used to evaluate current and emerging technologies for the multi-level liquid and solid lines of the wastewater treatment system. At each level, one technology unit is selected out of a set of options which includes the empty unit. The DMF provides a comparison on possible technologies for screening which types of technologies may best be further developed in order for an urban wastewater infrastructure to be judged progressively more sustainable. The results indicate that one or a pair of technologies are dominant in each level. The cheap, lower-technology unit processes receive a mixed review. Some of them are selected as the most promising technology units while the others are not considered as good candidates. [PUBLICATION ABSTRACT]