Abstract
This paper presents an optimal integrated instrumentation sensor network design methodology for complex nonlinear chemical process plants using a Combinatorial Particle Swarm Optimiazation (CPSO) engine. No comprehensive sensor network design approach has been addressed yet in the literature to simultaneously incorporate cost, precision and reliability requirements for nonlinear plants. The presented approach attempts to accomplish this objective via enhancement of the estimation accuracy of the aimed instrumentation sensor network subject to desired cost, reliability and redundancy constraints. An Unscented Kalman Filter (UKF)-based data reconciliation algorithm has been developed to present evaluating measures through comparisions of the estimated and real variables in terms of Modified Root Mean Squared of Error (MRMSE), while CPSO maintains the provisions of the Network Fault Tolerence (NFT) including sensor netowrk reilability (R), strong and weak redundancy degrees (i.e., SRD and WRD). The developed CPSO engine searches in a diverse variety of possible sensor networks to adopt the most fitted one based on the imposed NFT and cost design constraints. The effective capabilities of the proposed design methodology has been illustrated in a simulated nonlinear Continuous Stirred Tank Reactor (CSTR) as a complex process plant benchmark.
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