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

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  • Julia Tsai
  • Victoria Chen
  • M. Beck
  • Jining Chen

Abstract

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. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Julia Tsai & Victoria Chen & M. Beck & Jining Chen, 2004. "Stochastic Dynamic Programming Formulation for a Wastewater Treatment Decision-Making Framework," Annals of Operations Research, Springer, vol. 132(1), pages 207-221, November.
    • Handle: RePEc:spr:annopr:v:132:y:2004:i:1:p:207-221:10.1023/b:anor.0000045283.86576.62
    DOI: 10.1023/B:ANOR.0000045283.86576.62
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    Cited by:

    1. Jayne Lois San Juan & Carlo James Caligan & Maria Mikayla Garcia & Jericho Mitra & Andres Philip Mayol & Charlle Sy & Aristotle Ubando & Alvin Culaba, 2020. "Multi-Objective Optimization of an Integrated Algal and Sludge-Based Bioenergy Park and Wastewater Treatment System," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    2. Ariyajunya, Bancha & Chen, Ying & Chen, Victoria C.P. & Kim, Seoung Bum & Rosenberger, Jay, 2021. "Addressing state space multicollinearity in solving an ozone pollution dynamic control problem," European Journal of Operational Research, Elsevier, vol. 289(2), pages 683-695.
    3. Zehua Yang & Victoria C. P. Chen & Michael E. Chang & Melanie L. Sattler & Aihong Wen, 2009. "A Decision-Making Framework for Ozone Pollution Control," Operations Research, INFORMS, vol. 57(2), pages 484-498, April.
    4. Huiyuan Fan & Prashant K. Tarun & Victoria C. P. Chen & Dachuan T. Shih & Jay M. Rosenberger & Seoung Bum Kim & Robert A. Horton, 2018. "Data-driven optimization for Dallas Fort Worth International Airport deicing activities," Annals of Operations Research, Springer, vol. 263(1), pages 361-384, April.
    5. Yu, Shiwei & Gao, Siwei & sun, Han, 2016. "A dynamic programming model for environmental investment decision-making in coal mining," Applied Energy, Elsevier, vol. 166(C), pages 273-281.
    6. Dachuan Shih & Seoung Kim & Victoria Chen & Jay Rosenberger & Venkata Pilla, 2014. "Efficient computer experiment-based optimization through variable selection," Annals of Operations Research, Springer, vol. 216(1), pages 287-305, May.
    7. Elcin Koc & Cem Iyigun, 2014. "Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach," Journal of Global Optimization, Springer, vol. 60(1), pages 79-102, September.
    8. Natalia Jorquera-Bravo & Andrea Teresa Espinoza Pérez & Óscar C. Vásquez, 2022. "Toward a sustainable system of wastewater treatment plants in Chile: a multi-objective optimization approach," Annals of Operations Research, Springer, vol. 311(2), pages 731-747, April.
    9. Nikolaos E. Pratikakis & Matthew J. Realff & Jay H. Lee, 2010. "Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 57(3), pages 211-224, April.

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