(26b) Technoeconomic Optimisation, Antisolvent Selection and Comparative Environmental Evaluation for Continuous Paracetamol Crystallisation
- Conference: AIChE Annual Meeting
- Year: 2017
- Proceeding: 2017 Annual Meeting
- Group: Pharmaceutical Discovery, Development and Manufacturing Forum
- Time: Sunday, October 29, 2017 - 3:55pm-4:20pm
The need for cost-effective R&D methodologies brings process modelling and simulation to the forefront of detailed, knowledge-based process option evaluation. Furthermore, they are also pivotal towards evaluating alternative design parameters for existing or newly developed processes (Benyahia et al., 2012) and for developing control strategies, including model-predictive control (MPC) (Lakerveld et al., 2013; Mesbah et al., 2015).
Our recent publications have illustrated the use of systematic process modelling, plantwide simulation and detailed costing for technoeconomic evaluation of CPM processes (Jolliffe and Gerogiorgis, 2016). Moreover, we have employed nonlinear optimisation on the basis of rigorous process models to determine optimal design and operating parameters for key product separation operations (Jolliffe and Gerogiorgis, 2017a, 2017b). The present paper focuses on systematically evaluating a range of solvents, antisolvents and operating conditions for the crystallisation of paracetamol.
The solvents and antisolvents studied are those that are relatively benign and are most preferable to use, and for which thorough experimental solubility data have been compiled (Granberg and Rasmuson, 1999, 2000; Hojjati and Rohani, 2006). These substances are namely water, toluene, methanol, ethanol, 1-propanol, 2-propanol, 1-butanol, acetone, acetonitrile and ethyl acetate. Where data are not available, rigorous thermodynamic estimation tools (such as the UNIFAC and NRTL activity coefficient prediction methods) have been employed. The impact of crystallisation temperature and antisolvent addition ratio are also considered. The cases are formulated into a unified nonlinear optimisation (NLP) framework to determine optimal design variables and process conditions. In addition to technical metrics for evaluation such as product recovery, the sustainability of the different cases has been evaluated by monitoring quantitative green chemistry metrics, such as the E-factor. The use of the solvent and antisolvent in plug flow and Continuous Oscillatory Baffled Crystallisers (COBC) has relied on published models thereof, because these novel unit operations have been shown to offer promising scalability and excellent heat and mass transfer control (McGlone et al., 2015; Su et al., 2015). The solved optimisation cases illustrate the relative benefits and drawbacks of different combinations of solvent and antisolvent, in both technical and sustainability terms. Accordingly, the considered crystalliser designs and unit volume/size variations underscore the benefits of COBC technology, and its potential for production-scale implementation.
Baumann, M., Baxendale, I.R., 2015. The synthesis of active pharmaceutical ingredients (APIs) using continuous flow chemistry. Beilstein J. Org. Chem. 11, 1194â1219.
Benyahia, B., Lakerveld, R., Barton, P.I., 2012. A plant-wide dynamic model of a continuous pharmaceutical process. Ind. Eng. Chem. Res. 51, 15393â15412.
Granberg, R.A., Rasmuson, Ã .C., 2000. Solubility of paracetamol in binary and ternary mixtures of water + acetone + toluene. J. Chem. Eng. Data 45, 478â483.
Granberg, R.A., Rasmuson, Ã .C., 1999. Solubility of paracetamol in pure solvents. J. Chem. Eng. Data 44, 1391â1395.
Gutmann, B., Cantillo, D., Kappe, C.O., 2015. Continuous-flow technologyâA tool for the safe manufacturing of active pharmaceutical ingredients. Angew. Chem. Int. Ed. 54, 6688â6728.
Hojjati, H., Rohani, S., 2006. Measurement and prediction of solubility of paracetamol in waterâisopropanol solution. Part 1. Measurement and data analysis. Org. Process Res. Dev. 10, 1101â1109.
Ierapetritou, M., Muzzio, F., Reklaitis, G., 2016. Perspectives on the continuous manufacturing of powder-based pharmaceutical processes. AIChE J. 62, 1846â1862.
Jensen, K.F., 2017. Flow chemistryâMicroreaction technology comes of age. AIChE J. 63, 858â869. doi:10.1002/aic.15642
Jolliffe, H.G., Gerogiorgis, D.I., 2017a. Technoeconomic optimisation of a conceptual flowsheet for continuous separation of an analgaesic Active Pharmaceutical Ingredient (API). Ind. Eng. Chem. Res., in press.
Jolliffe, H.G., Gerogiorgis, D.I., 2017b. Technoeconomic optimisation and comparative environmental evaluation of continuous crystallisation and antisolvent selection for artemisinin recovery. Comput. Chem. Eng., in press.
Jolliffe, H.G., Gerogiorgis, D.I., 2016. Plantwide design and economic evaluation of two Continuous Pharmaceutical Manufacturing (CPM) Cases: ibuprofen and artemisinin. Comput. Chem. Eng. 91, 269â288.
Lakerveld, R., Benyahia, B., Braatz, R.D., Barton, P.I., 2013. Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant. AIChE J. 59, 3671â3685.
Mascia, S., Heider, P.L., Zhang, H., Lakerveld, R., Benyahia, B., Barton, P.I., Braatz, R.D., Cooney, C.L., Evans, J.M.B., Jamison, T.F., Jensen, K.F., Myerson, A.S., Trout, B.L., 2013. End-to-End continuous manufacturing of pharmaceuticals: integrated synthesis, purification, and final dosage formation. Angew. Chem. Int. Ed. 52(47): 12359â12363.
McGlone, T., Briggs, N.E.B., Clark, C.A., Brown, C.J., Sefcik, J., Florence, A.J., 2015. Oscillatory Flow Reactors (OFRs) for continuous manufacturing and crystallization. Org. Process Res. Dev. 19, 1186â1202.
Mesbah, A., Paulson, J.A., Lakerveld, R., Braatz, R.D., 2015. Plant-wide model predictive control for a continuous pharmaceutical process, in: 2015 American Control Conference (ACC), pp. 4301â4307.
Su, Q., Nagy, Z.K., Rielly, C.D., 2015. Pharmaceutical crystallisation processes from batch to continuous operation using MSMPR stages: Modelling, design, and control. Chem. Eng. Process. Process Intensif. 89, 41â53.