Self-Optimizing Control of the HDA Process

1. Self-Optimizing Control of the HDA Process • Outline of the presentation • Process description. • Self-optimizing control procedure. • Self-optimizing control of the HDA process. • Concluding remarks.

2. Process Description • Benzene production from thermal-dealkalination of toluene (high-temperature, non-catalytic process). • Main reaction: Toluene + H2→ Benzene + CH4 • Side reaction: 2·Benzene ↔ Diphenyl + H2 • Excess of hydrogen is needed to repress the side reaction and coke formation. • References for HDA process: • McKetta (1977) – first reference on the process; • Douglas (1988) – design of the process; • Wolff (1994) – discuss the operability of the process. • No reference about the optimization of the process for control purposes.

3. Purge (H2 + CH4) Compressor H2 + CH4 Toluene Quench Mixer FEHE Furnace PFR Separator Cooler CH4 Toluene Benzene Toluene Column Stabilizer Benzene Column Diphenyl Process Description

4. Self-Optimizing Control Procedure • Objective: Optimize operation • Find the optimum. • Implement the optimum (in practice). • Self-optimizing control: • Set point control which optimize the operation with acceptable loss. Loss = J – Jopt • Pure steady state considerations. • Stepwise procedure for evaluating the loss: • Degree of freedom analysis; • Cost function and constraints; • Identification of the most important disturbances (uncertainty); • Optimization; • Identification of candidate controlled variables; • Evaluation of loss; • Further analysis and selection.

5. 10 9 4 1 2 7 3 17 6 5 8 16 14 12 15 13 11 Self-Optimizing Control of the HDA ProcessSteady-state degrees of freedom

6. Self-Optimizing Control of the HDA ProcessCost Function and Constraints • The following profit is maximized (Douglas’s EP): (-J) = pbenDben – ptolFtol – pgasFgas – pfuelQfuel – pcwQcw – ppowerWpower - psteamQsteam + Σ(pv,iFv,i), i = 1,…,nc. • Where: • Qcw = Qcw,cooler + Qcw,stab + Qcw,ben + Qcw,tol; • Qsteam = Qsteam,stab + Qsteam,ben + Qsteam,tol; • Fv,i = Fpurge + Dstab,i + Btol,i, i = 1,…,nc. • Constraints during operation: • Production rate: Dben≥ 265 lbmol/h. • Hydrogen excess in reactor inlet: FH2 / (Fben + Ftol + Fdiph) ≥ 5. • Bound on toluene feed rate: Ftol ≤ 300 lbmol/h. • Reactor pressure: Preactor ≤ 500 psia. • Reactor outlet temperature: Treactor ≤ 1300 °F. • Quench outlet temperature: Tquencher ≤ 1150 °F. • Product purity: xDben ≥ 0.9997. • Separator inlet temperature: 95 °F ≤ Tflash ≤ 105 °F. • + some distillation recovery constraints • Manipulated variables are bounded.

7. Self-Optimizing Control of the HDA Process Identification of the Most Important Disturbances

8. Self-Optimizing Control of the HDA Process Optimization

9. 8 5 7 1 6 3 4 2 Self-Optimizing Control of the HDA ProcessOptimization • Active constraint control: • (1) Benzene product purity (lower bound); • (2) Recovery (benzene in feed/benzene in top) in stabilizer (lower bound); • (3) Loss (toluene in feed/toluene in bottom) in benzene column (upper bound); • (4) Loss (toluene in feed/toluene in top) in toluene column (upper bound); • (5) Toluene feed flow rate (upper bound); • (6) Separator inlet temperature (lower bound); • (7) Inlet hydrogen to aromatic ratio (lower bound); • (8) By-pass feed effluent heat exchanger (lower bound). • 9 remaining unconstrained degrees of freedom.

10. Self-Optimizing Control of the HDA ProcessIdentification of Candidate Controlled Variables • Candidate controlled variables: • Pressure differences; • Temperatures; • Compositions; • Heat duties; • Flow rates; • Combinations thereof. • 137 candidate controlled variables can be selected. • 17 degrees of freedom. • Number of different sets of controlled variables: • 8 active constraints (active constraint control) • What to do with the remaining 9 degrees of freedom? • Self-optimizing control implementation!!! • Still have many possibilities of single measurements:

11. Analysis of linear steady-state model from 9 u’s to 137 candidate outputs • Scale variables properly! • G: matrix with 9 inputs and 137 outputs • (Glarge)=37 • Select one output at the time: • Select output corresponding to largest singular value (essentially largest row sum) • “Control” this output by pairing it with an input (which does not matter for this analysis), and obtain new matrix with one input (and output) less • Final result: • (G9x9)=10 which is OK (“close” to 37) • Method is not optimal but works well

12. Self-Optimizing Control of the HDA Process • Linearized model before scaling:

13. Self-Optimizing Control of the HDA Process • Output scaling factors: • Input scaling factors: • Scaled matrix:

14. Self-Optimizing Control of the HDA Process • Output scaling factors:

15. Self-Optimizing Control of the HDA Process • Input scaling factors:

16. Self-Optimizing Control of the HDA Process • Linearized model after scaling:

17. Self-Optimizing Control of the HDA Process • Linearized model after scaling: • Linearized model before scaling:

18. more details in here about results • and also present RGA-method...

19. 13 17 8 10 9 15 5 7 1 16 6 14 12 3 4 2 11 Self-Optimizing Control of the HDA ProcessFurther Analysis and Selection • Minimum singular value analysis of G gives that we should control (i.e. keep constant) • (9) Hydrogen in reactor outlet flow; • (10) Methane in reactor outlet flow; • (11) Reboiler duty in benzene column; • (12) Condenser duty in toluene column; • (13) Compressor power; • (14) Separator feed valve opening; • (15) Separator vapor outlet valve opening; • (16) Separator liquid outlet valve opening; • (17) Purge valve opening.

20. Self-Optimizing Control of the HDA ProcessConcluding Remarks • Demonstration of a self-optimizing procedure. • The economy in the HDA process is rather insensitive to disturbance in the process variables. • A set of controlled variables is found from an SVD screening of the scaled linearized model.