TY - JOUR
T1 - Simultaneous rational design of ion separation membranes and processes
AU - Rall, Deniz
AU - Schweidtmann, Artur M.
AU - Aumeier, Benedikt M.
AU - Kamp, Johannes
AU - Karwe, Jannik
AU - Ostendorf, Katrin
AU - Mitsos, Alexander
AU - Wessling, Matthias
PY - 2020
Y1 - 2020
N2 - Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules along with the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes the membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.
AB - Economically viable water treatment process plants for drinking water purification are a prerequisite for sustainable supply of safe drinking water in the future. However, modern membrane process development experiences a disconnect in this domain: the synthesis of the membrane and the design of the process are decoupled. We propose an optimization strategy to simultaneously design the performance of layer-by-layer nanofiltration membrane modules along with the separation process. This approach achieves overall optimal performance by extending the search space and thus exploiting synergies. Better separation performances at a lower cost as compared to conventional optimization strategies can be achieved. The key feature of this optimization framework is the integration of artificial neural networks. This machine-learning technique describes the membrane performance as a function of its synthesis protocol. We optimize the design problem rigorously by a deterministic global nonlinear optimization method. Thus, this framework yields membrane synthesis protocols and membrane processes that are optimally tailored to the desired separation task. In a showcase, the simultaneous membrane synthesis and process optimization design achieve immediately favorable results with lower impurities at comparable costs. The process investment and operation costs are compared to a state of the art commercially available membrane for nanofiltration.
KW - Artificial neural network
KW - Deterministic global optimization
KW - Hybrid modeling
KW - Layer-by-layer
KW - Nanofiltration
UR - http://www.scopus.com/inward/record.url?scp=85078308594&partnerID=8YFLogxK
U2 - 10.1016/j.memsci.2020.117860
DO - 10.1016/j.memsci.2020.117860
M3 - Article
AN - SCOPUS:85078308594
SN - 0376-7388
VL - 600
JO - Journal of Membrane Science
JF - Journal of Membrane Science
M1 - 117860
ER -