Simultaneous robust data reconciliation and gross error detection through particle swarm optimization for an industrial polypropylene reactor

Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants – An industrial case study

“…A steady-state identification algorithm examines key process variables and identifies when the operation is in steady-state. If the system is in steady state the collected data are validated [40,41]. The validated data are used to update the models of the compressors.…”

On-line identification of fermentation processes for ethanol production

“…Due to its capacity of handling multimodal, nonlinear, and discontinuous objective functions in high-dimensional problems with much smaller computational effort and simpler implementation, the PSO method has proved itself as a robust and effective tool for solving complex optimization problems. It has also been successfully used in important industrial applications [83,84]. The model and parameter estimation procedure have both been implemented in MATLAB [85].…”

Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes

“…The particle filtering (PF) technique, which is served as a general filter in the 13 nonlinear and non-Gaussian state-space systems, was recently applied to DDR problems (Chen 14 et al, 2008). However, it was restricted to the use of process state-space models, and it was not 15 able to deal with inequality constraints, such as lower and upper bounds on the states (Bai et al, 16 2007; Nicholson et al, 2013). 17 18 In the study of nonlinear dynamic processes, Liebman et al (1992) and later Ramamurthi et al 19 (1993) formulated the nonlinear dynamic data reconciliation (NDDR) problem and proposed 20 solution strategies by neglecting the random noise disturbances in the state transition equations.…”