Industrial wastewater desalination under uncertainty in coal-chemical eco-industrial parks

Liu Huang, Dongliang Wang, Chang He, Ming Pan, Bingjian Zhang, Qinglin Chen, Jingzheng Ren

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


This work proposes a stochastic multi-scenario model for the robust design of industrial wastewater desalination under uncertainty. For fully accommodating the diverse nature of wastewater variability, multiple uncertain design parameters consisting of salt concentration, flowrate, and inlet temperature of wastewater are taken into account for the realization of uncertainty. A three-step stochastic strategy for data processing including uncertainty characterization and quantification, data sampling, and data propagation is developed to generate a proper size of feeding scenarios. The detailed process model of the dual-stage reverse osmosis is incorporated in the optimization model for minimizing the expected specific production cost. Finally, we illustrate the applicability and effectiveness of the proposed stochastic multi-scenario model with an example from a coal-chemical eco-industrial park.

Original languageEnglish
Pages (from-to)370-378
Number of pages9
JournalResources, Conservation and Recycling
Publication statusPublished - Jun 2019


  • Data processing strategy
  • Reverse osmosis
  • Robust design
  • Uncertainty
  • Wastewater desalination

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Economics and Econometrics

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