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Predicting Freshwater Consumption

NSF Award:

Multiobjective Optimization Strategies for the Design of Sustainable Biofuel Processes  (Carnegie-Mellon University)

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Water is becoming a scarce natural resource. Predictions suggest that by 2025, two-thirds of the world's population will experience water stress. Adding to this burden, many chemical, biofuel and petroleum processes often consume significant amounts of water.

To accurately predict the minimum amount of fresh water consumed in energy-intensive chemical or biofuel processes, Ignacio Grossmann and student Linlin Yang of Carnegie Mellon University developed a simplified mathematical model. The major benefit of the proposed model is its ability to establish the absolute minimum amount of water required in a chemical process. This data will contribute significantly to the design of competitive sustainable process systems.

The model was developed as part of an NSF-sponsored project for the design of biofuel plants in which investigators determine the optimal trade-offs among economic investment, energy cost and water consumption. Detailed nonlinear models for water networks involving multiple contaminants have been developed in the past but they require the use of complex and computationally expensive global optimization techniques. The new model corresponds to a linear programming model that is easily solved, and that predicts the exact same value as the complex nonlinear model. 

Images (1 of )

  • global map showing projected water scarcity in 2025
  • schematis shows alternative designs for two process units and two treatment units
  • schematic incorporates water and heat targeting models to optimize process flow
Projected water scarcity in 2025.
International Water Management Institute
Schematic contains alternative design for two process and two treatment units.
Linlin Yang, Carnegie Mellon University
Incorporation of water and heat targeting models.
Linlin Yang, Carnegie Mellon University

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