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Advanced Control Synthesis for Reverse Osmosis Water Desalination Processes
Author(s) -
Phuc Bui Duc Hong,
You SamSang,
Choi HyeungSix,
Jeong SeokKwon
Publication year - 2017
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143017x15054988926316
Subject(s) - desalination , reverse osmosis , control theory (sociology) , decoupling (probability) , robustness (evolution) , process engineering , parametric statistics , robust control , engineering , control system , control engineering , computer science , control (management) , membrane , mathematics , biochemistry , chemistry , genetics , statistics , electrical engineering , artificial intelligence , gene , biology
In this study, robust control synthesis has been applied to a reverse osmosis desalination plant whose product water flow and salinity are chosen as two controlled variables. The reverse osmosis process has been selected to study since it typically uses less energy than thermal distillation. The aim of the robust design is to overcome the limitation of classical controllers in dealing with large parametric uncertainties, external disturbances, sensor noises, and unmodeled process dynamics. The analyzed desalination process is modeled as a multi‐input multi‐output (MIMO) system with varying parameters. The control system is decoupled using a feed forward decoupling method to reduce the interactions between control channels. Both nominal and perturbed reverse osmosis systems have been analyzed using structured singular values for their stabilities and performances. Simulation results show that the system responses meet all the control requirements against various uncertainties. Finally the reduced order controller provides excellent robust performance, with achieving decoupling, disturbance attenuation, and noise rejection. It can help to reduce the membrane cleanings, increase the robustness against uncertainties, and lower the energy consumption for process monitoring.