Controllability comparison for extractive dividing-wall columns: ANN-based intelligent control system versus conventional control system

The focus of this work is the study of the extractive dividing wall column (EDWC) for separating the azeotropic mixture of dipropyl ether and 1-propyl alcohol with N, N-dimethylacetamide (DMAC) as the entrainer. Three separation sequences are investigated, including a conventional extractive distillation sequence (CEDS), EDWC and a pressure swing distillation sequence (PSDS). The static simulation results showed that the EDWC with DMAC as the entrainer is more economically attractive than CEDS and PSDS. Subsequently, a control structure CS1 based on a three-temperature control loop and a control structure CS2 with the vapor split ratio as the manipulated variable are investigated for the EDWC. Their dynamic control performances are evaluated by facing large feed flow rates and composition disturbances. The results showed that the CS1 can deal with feed flow rate disturbance effectively. However, the transient deviation is large and the settling time is too long when facing feed flow composition disturbances. The CS2 can quickly and effectively deal with feed flow rate and composition disturbances, and it can maintain the two products at high purity.

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Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment

Chuquin-Vasco,

Chicaiza-Sagal,

Calderón-Tapia

et al. 2024

AIMSE

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<abstract> <p>We developed an artificial neural network (ANN) to predict mole fractions in the extractive distillation of an n-hexane and ethyl acetate mixture, which are common organic solvents in chemical and pharmaceutical manufacturing. The ANN was trained on 250 data pairs from simulations in DWSIM software. The training dataset consisted of four inputs: Feed flow inlet (T1-F), Feed Stream Mass Flow temperature pressure (FM1-F), Make-up stream mass flow (FM2-MU), and ERC tower reflux ratio (RR-ERC). The ANN demonstrated the ability to forecast four output variables (neurons): Mole fraction of n-hexane in the distillate of EDC (XHE-EDC), Mole fraction of N-methyl-2 pyrrolidone in the bottom of EDC (XNMP-EDC), Mole fraction of ethyl acetate in the distillate of ERC (XEA-ERC), and Mole fraction of N-methyl-2 pyrrolidone in the bottom of ERC (XNMP-ERC).The ANN architecture contained 80 hidden neurons. Bayesian regularization training yielded high prediction accuracy (MSE = 2.56 × 10<sup>–7</sup>, R = 0.9999). ANOVA statistical validation indicated that ANN could reliably forecast mole fractions. By integrating this ANN into process control systems, manufacturers could enhance product quality, decrease operating expenses, and mitigate composition variability risks. This data-driven modeling approach may also optimize energy consumption when combined with genetic algorithms. Further research will validate predictions onsite and explore hybrid energy optimization technologies.</p> </abstract>

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Controllability comparison for extractive dividing-wall columns: ANN-based intelligent control system versus conventional control system

Cited by 3 publications

References 25 publications

Dynamic control of an energy-saving process with two extractive dividing-wall columns for separation of acetone/methanol/butanone/tert-butyl alcohol mixtures

Dynamic control of an energy-saving process with two extractive dividing-wall columns for separation of acetone/methanol/butanone/tert-butyl alcohol mixtures

Design and Control of Extractive Dividing Wall Column for Separating Dipropyl Ether/1-Propyl Alcohol Mixture

Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment

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