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