In recent years, pharmaceutical companies have been making a transition from traditional batch manufacturing to continuous production mode, due to the well-known advantages of continuous operation [1]. Even though continuous implementations already exist for many pharmaceutical unit operations [2-3], major limitations leave continuous end-to-end manufacturing lagging behind, especially regarding the isolation of the drug substance [4]. In this work we address the state-of-the-art challenges in continuous filtration and drying for upstream manufacturing through the modeling, design and optimization of a novel intensified filtration-drying carousel. The unit is based on the prototype manufactured by Alconbury Weston Ltd [5], which has already been successfully tested in an integrated continuous crystallization-filtration experimental framework [6-7]. The carousel consists of five cylindrical ports, where slurry loading, filtration, washing, deliquoring and drying are carried out simultaneously. After a fixed time interval, the carousel rotates, moving each port to the next processing position. In position 1 the slurry from the crystallizer is introduced into the port, while in positions 2-4 the filtration, washing and deliquoring steps are carried out, with the order designed by the operator. A final gas drying step is carried out in position 5, before the product is discharged. Strict conditions on the solvent and impurity content of the discharged dry cake must be satisfied. The introduction of the gas drying equipment is a novel feature of the carousel, which was not present in previous applications [5-7]. Operating the carousel requires deciding on the value of multiple decision variables, namely the pressure drop for filtration and deliquoring, the type and flowrate of the washing solvent and the flowrate and temperature of the gas flow for the drying step. Above all, due to the intrinsic carousel mechanism, the rotation time has an important impact on the final product quality. Hence, the total cycle time has to be regulated considering a trade-off between the residence time at the processing positions. All this considered, the traditional design, based on empirical knowledge, of the filtration-drying operating conditions is very challenging and time-consuming. For this reason, in this work we develop a comprehensive mathematical model for the unit, to investigate an optimal design for the operating conditions of the carousel. The employed equations are standard macroscopic and microscopic mass, energy and momentum balances. We consider the effect of the crystal size distribution of the solid phase coming from the crystallizer on the cake properties, such as porosity [8] and specific resistance [9]. Finally, upon validation with experimental data, we use the model for determining the optimal values of the decision variables of the carousel for different API and solvent systems. Future work will involve model-based online control of the carousel.
References
[1] Lee, S. L., T. F. OâConnor, X. Yang, C. N. Cruz, S. Chatterjee, R. D. Madurawe, C. M. V. Moore, L. X. Yu and J. Woodcock (2015). Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. J. Pharm. Innov., 10, 191â199.
[2] Teoh, S. K., C. Rathi, and P. Sharratt (2016). Practical Assessment Methodology for Converting Fine Chemicals Processes from Batch to Continuous. Org. Process Res. Dev., 20(2), 414â431.
[3] Su, Q., M. Moreno, C. Laird, Z. Nagy and G. Reklaitis (2019). A perspective on Quality-by-Control (QbC) in pharmaceutical continuous manufacturing, Comput. Chem. Eng., 125, 216â231.
[4] Simon, L. L., A. A. Kiss, J. Cornevin, and R. Gani (2019). Process engineering advances in pharmaceutical and chemical industries: digital process design, advanced rectification, and continuous filtration. Curr. Opin. Chem. Eng., 25, 114â121.
[5] Ottoboni, S., C.J. Price, C. Steven, E. Meehan, A. Barton, P. Firth, A. Mitchell and F. Tahir (2019). Development of a Novel Continuous Filtration Unit for Pharmaceutical Process Development and Manufacturing. J. Pharm. Sci., 108(1), 372â381.
[6] D. Acevedo, R. Peña, Y. Yang, A. Barton, P. Firth, and Z. K. Nagy (2016). Evaluation of mixed suspension mixed product removal crystallization processes coupled with a continuous filtration system. Chem. Eng. Process. Process Intensif., 108, 212â219.
[7] Liu, Y. C. , A. Domokos, S. Coleman, P. Firth, and Z. K. Nagy (2019). Development of Continuous Filtration in a Novel Continuous Filtration Carousel Integrated with Continuous Crystallization- Org. Process Res. Dev., 23(12), 2655â2665.
[8] Ouchiyama, N., and T. Tanaka (1986). Porosity estimation from particle size distribution. Ind. Eng. Chem. Fundam., 25(1), 125â129.
[9] Bourcier, D., J.P. Féraud, D. Colson, K. Mandrick, D. Ode, E. Brackx and F. Puel (2016). Influence of particle size and shape properties on cake resistance and compressibility during pressure filtration. Chem. Eng. Sci., 144, 176â187.