Fusing Data from Diverse Sources to Characterize Batch Reactions

1. Fusing Data from Diverse Sources to Characterize Batch Reactions Paul J. Gemperline East Carolina University R. Russell Rhinehart and Karen High Oklahoma State University April, 2002

2. fiber-optic probes Four channel fiber-optic UV/vis spectrometer Single channel NIR Systems 6500 w/ fiber-optic probe Laboratory instrument for in-situ characterization of batch reactions Four station automated reaction calorimeter • Instrument control • reagent addition • temperature control • stirring control • Data acquisition • UV/vis absorption • calorimetry • Data analysis • preprocessing • PCA • SMCR

3. Experimental design Esterification of salicylic acid with acetic anhydride • Experimental details - Circulator system: Julabo F25-HD - Reactor type: 50 ml glass reactor - Initial reactor charge: 25 ml acetic anhydride w/ H3PO4 catalyst - Reactant charge:6 to 9 g salicylic acid dosed at one time - Equitech CCD UV/Vis ATR probes

4. Esterification of salicylic acid with acetic anhydride • SMCR produced excellent results • SA has pure absorbance at 400 nm • Acetic anhydride does not absorb

5. Multivariate kinetic fitting • Multivariate kinetic fitting was implemented with two different response function for rate constant determination • (#1) Residual calculation using projection of modeled concentration profiles, C, into column-mode eigenvectors, U[1]. 1. E. Furusjo, Anal. Chim. Acta 373 (1) 1998, 83-94 • (#2) Residual calculation between the original mixture spectra, A, and the reconstructed spectra

6. Kinetic fitting with spectroscopic data • Assumed kinetic model: A  A* + B  C + D • Expected results were obtained • with more reactant, more product obtained • with higher temp, product formed faster B5 B1 B2 B3 B4

7. Why reaction calorimetry? • A valuable tool for evaluation of thermal hazards and process data for batch-type reactors • Availability of modern commercial instruments • Intensive properties vs. Extensive properties • Intensive properties: measured at any point in the system, and each has a uniform value throughout a system at equilibrium  ex: absorbance • Extensive properties: proportional to the mass of the system and obtained by a process of summation  ex: temperature, power, energy

8. AA+SA -> ASA + HOAcCalorimetry profiles • Persistent effort solved problem with lack of reproducibility in calorimetry results. • Non-isothermal operating conditions caused significant change in heat loss to jacket during course of the batch • Specialized code written to est. power flow to jacket. • Compensated reaction power profile still lacked reproducibility

9. Kinetic fitting of ASA calorimetry profiles • Power curves were resolved by kinetic fitting Qt= Q1(1 - e –k1t) + Q2 (1 - e –k2t) • Dose heat has fast rate • Reaction heat has slow rate

10. Rate constants from 7 batches • The rate of dissolution (top) is fast by lacks reproducibility due to the manual method of adding solid. • The rate of dissolution (top) is fast by lacks reproducibility due to the manual method of adding solid.

11. Heat of reaction and dose heat for 7 batches • The heat of reaction (top) should be independent of reaction temp. • The dose heat should be a linear function of reaction temperature. • In both cases, conditions are near the saturation point far and from the ideal case, e.g., infinite dilution. At these concentrations, non-ideal behavior is observed and Hs (reactants and products) is concentration dependent.

12. Experimental details Circulator system: Julabo F25-HD Reactor type: 50 mL glass reactor Initial charge: 3.5 g salicylic acid 15 mL glacial acetic acid 1 mL phosphoric acidReagent addition 0.5 mL acetic anhydride @ 0.33 mL/min. 10 additions @ 30 min intervals Calorimeter settings: Const temp power comp mode Jacket temp: 90oC Reactor temp: 90, 100 (shown) and 110oC First batch titration data: esterification of salicylic acid with acetic anhydride • UV/Vis spectra • Equitech CCD • 3 bounce ATR probe • Spectra recorded @ 30 s intervals

13. Profiles from batch titration • Composition profiles estimated from SMCR • Fast rate of reaction observed in early steps • Small amt product formed in early steps • Large reaction exotherm in early steps • No attempt was made to operate reaction under anhydrous conditions • Above observations consistent with hypothesis that water was present early in the batch.

14. Batch Titration Reactor SA + AA ASA + HA W + AA 2HA k1 Rxn 1: k2 Rxn 2: The process Reactor is filled with SA and AA is injected in the reactor

15. The equations

16. The model: Kinetic fitting of batch profiles • Algorithm written in MATLAB • Equations solved using Euler’s method • Model internally consistent • model includes: • 2 reactions • 4 optimization parameters(k1, k2, CW0, CAAin)

17. New batch data in non-reacting solvent: acetylation of salicylic acid • Reaction Conditions: • Temperature: 60°C • Solvent: Ethyl Acetate : 17 ml • Reagents: • SA: 3.5g • AA : added by titration method, 15 additions of 1ml AA with 20 min. stir time between additions

18. Kinetic fitting (cont’d) • New Solvent: • Ethyl Acetate • Non reacting • 1 reaction only • 2 optimization para.

19. Temperature jump response calorimetry profiles Temperature dependent response of the calorimeter was characterized by performing a temperature jump experiment with water. An approximate first order exponential model was assumed for correcting time-lags in energy profiles.

20. Fusing calorimetry data and spectroscopic data into one model • Use intensive & extensive data in one single model • Obtain in one step all the information required: • Constants of the reaction • Energy of the different reactions • Limitations: • Spectral measurements reflect instantaneous concentration changes • Temperature, power and energy measurements are lagged by the thermal inertia of the system and controller

21. Correction for time lag • To perform the kinetic fitting, the energy measurements must be corrected for the time lag • The time lag was approximated by with a first order lag fitted to spectroscopic measurements. • Tau was adjusted to minimize y residuals.

22. Demonstration of lag correction • Correction of time-lag gives profile that approximately matches instantaneous spectroscopic response

23. Conclusions and future plans • In-situ spectroscopic measurements coupled with calorimetry measurements can be used to determine relative yield, reaction rates, and reaction heats. • Many parameters can determined at one time • Add the model more information such as the power, temperature, etc. • Determine more parameters in the reaction • Study different kind of reactions • Study different types of spectroscopy • Much future work is needed to determine the usefulness of this approach

24. Acknowledgements • Students: • Bei Ma (M.S. graduate) • Eric Cash (M.S. candidate) • Shane Moore (M.S. candidate) • Mary Bosserman (B.S. candidate) • Enric Comas (vsiting Ph.D., Tarragona, Spain) • Industrial partner: • Dr. Dwight Walker and Frank TarzcynskiGlaxoSmithKline • Financial support: • Measurement and Control Engineering Center, a National Science Foundation University / Industry Research Center