(249a) Optimization of a Wastewater Cotreatment Process for Blowdown and Produced Waters with Economic and Sustainability Analyses

Water is cycled in thermoelectric power plants and purged as cooling tower blowdown to alleviate contaminant accumulation. Fresh water is then used to regenerate the amount lost by the purge as well as additional evaporative losses. In the fracking process, high salinity wastewater is extracted from oil and gas wells during the process. As an inexpensive option, the produced water is disposed of via deep well injection. In contrast to the capital driven energy processes they accompany, lower effort is devoted to innovative and sustainable design practices for industrial wastewater treatment. This work evaluates the potential of mixing blowdown and produced waters to improve the overall process design. The concept is founded on exploiting complementary chemistry of contaminants to reduce the softening chemical demand in the combined process. The proposed cotreatment process design incorporates additional goals in reducing energy demand, increasing power plant water recycling, and producing saleable byproducts.

A cotreatment process model has been established using a combination of water treatment and chemical process simulators.^[1] The model utilizes rigorous thermodynamics to capture extensive reactions and equilibria occurring in the mixing and softening of the waters. This plays an integral role in maintaining high model fidelity but increases computational expense associated with process simulation. This developed model when employed for process optimization also results in a highly nonlinear problem that is limited by simulator capabilities, which require fixing the modular flowsheet structure. Simulation information is then exported to additional tools in CAPCOST^[2] and GREENSCOPE^[3] to evaluate the results using economic and sustainability correlations and metrics. These metrics can holistically evaluate the performance of the design but further burden the computational expense of the process framework. With variations in potential operating decisions and combinatoric options for the formulated process flowsheet, it becomes difficult to infer best design cases and mixing ratios of the wastewaters.

These challenges together pose a complex and computationally expensive optimization problem. This work presents optimization techniques and model reductions to accommodate sufficient model fidelity, optimization compatibility, and perspectives on multiple process objectives. A computationally efficient reduced process model is used to generate a large data set at varying conditions and designs. Using this set, a Kriging metamodel based on literature methods^[4] is generated and trained as a surrogate for optimization to minimize the levelized cost of water. Optimized conditions from the surrogate model are applied to the comprehensive process framework to extensively determine economic and sustainability metrics. The results are used to ultimately discuss the tradeoffs and viability of a more sustainably-focused cotreatment design.

References

[1] Barber, H. and Lima, F. V. (2021). “Modeling, Simulation and Optimization of a Synergistically Mixed Blowdown Water and Produced Water Wastewater Treatment Process”. Oral presentation in 2021 AIChE Annual Meeting, Boston, MA.

[2] Turton, R, Shaeiwitz. J. A., et al. (2018). CAPCOST: Analysis, Synthesis, and Design of Chemical Processes, 5th Edition. Prentice Hall, New Jersey.

[3] Ruiz-Mercado, G. J., Smith, R. L., & Gonzalez, M. A. (2012). Sustainability indicators for chemical processes: I. Taxonomy. Industrial & Engineering Chemistry Research, 51(5), 2309-2328.

[4] Caballero, J. A., & Grossmann, I. E. (2008). An algorithm for the use of surrogate models in modular flowsheet optimization. AIChE Journal, 54(10):2633–2650, 2008.