TY - GEN
T1 - Multistage Membrane Distillation for the Treatment of Shale Gas Flowback Water
T2 - Multi-Objective Optimization under Uncertainty
AU - Carrero-Parreño, Alba
AU - Onishi, Viviani C.
AU - Ruiz-Femenia, Rubén
AU - Salcedo-Díaz, Raquel
AU - Caballero, José A.
AU - Reyes-Labarta, Juan A.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017
Y1 - 2017
N2 - In this work, we analyze the effect of shale gas well data uncertainty on the multi-objective optimization of a multistage direct contact membrane distillation (DCMD) model. The uncertain parameters, flowrate and salt concentration of the flowback water, are modelled by a set of correlated scenarios. A bi-criterion stochastic MINLP was formulated to minimize the expected total annual cost (TAC) and its variability, controlled by the worst case (WC) risk management metric. The model was solved using a modified version of the sample average approximation (SAA) algorithm, which decomposes the original problem into two: a deterministic MINLP model and a stochastic NLP model. The solution is a set of Pareto curves, where the two global extreme solutions provide the DCMD designs that achieve the minimum expected TAC and the minimum WC, respectively. Furthermore, both designs are able to satisfy the zero liquid discharge (ZLD) requirement imposed in the outflow stream.
AB - In this work, we analyze the effect of shale gas well data uncertainty on the multi-objective optimization of a multistage direct contact membrane distillation (DCMD) model. The uncertain parameters, flowrate and salt concentration of the flowback water, are modelled by a set of correlated scenarios. A bi-criterion stochastic MINLP was formulated to minimize the expected total annual cost (TAC) and its variability, controlled by the worst case (WC) risk management metric. The model was solved using a modified version of the sample average approximation (SAA) algorithm, which decomposes the original problem into two: a deterministic MINLP model and a stochastic NLP model. The solution is a set of Pareto curves, where the two global extreme solutions provide the DCMD designs that achieve the minimum expected TAC and the minimum WC, respectively. Furthermore, both designs are able to satisfy the zero liquid discharge (ZLD) requirement imposed in the outflow stream.
KW - robust design
KW - shale gas flowback water
KW - stochastic optimization
KW - uncertainty
KW - worst case
KW - ZLD
UR - https://www.scopus.com/pages/publications/85041423313
U2 - 10.1016/B978-0-444-63965-3.50097-0
DO - 10.1016/B978-0-444-63965-3.50097-0
M3 - Conference contribution
AN - SCOPUS:85041423313
SN - 9780444639653
T3 - Computer Aided Chemical Engineering
SP - 571
EP - 576
BT - 27th European Symposium on Computer Aided Process Engineering
PB - Elsevier B.V.
ER -