Fuzzy Logic Modeling and Observers Applied to Estimate Compositions in Batch Distillation Columns

Abstract

In this chapter, the analysis and design of a fuzzy observer based on a Takagi-Sugeno model of a batch distillation column are presented. The observer estimates the molar compositions and temperatures of the light component in the distillation column considering a binary mixture. This estimation aims to allow monitoring the physical variables in the process to improve the quality of the distillated product as well as to detect failures that could affect the system performance. The Takagi-Sugeno fuzzy model is based on eight linear subsystems determined by three premise variables: the opening percentage of the reflux valve and the liquid molar composition of the light element of the binary mixture in the boiler and in the condenser. The stability analysis and the observer gains are obtained by linear matrix inequalities (LMIs). The observer is validated by MATLAB ® simulations using real data obtained from a distillation column to verify the observer's convergence and analyze its response under system disturbances.

Pressure-Driven Batch Distillation Optimal Control for Ethanol-Water Separation

“…13.5%, whereas, the ratio of distillate to feed flowrate (like "liquid ratio for batch distillation"), greater for even 43.75%. Heras-Heras-Cervantes et al, 7 a special case of fuzzy-logic control is introduced, ie. Takagi-Sugeno controller, significantly, among the three premise variables the reflux valve opening percentage is placed as recognized to influence all the states of the system together with light component compositions in condenser/reboiler.…”