Project Description

Reducing waste and effective reuse/recycling of waste medicines are two critical challenges faced by pharmaceutical manufacturing to build a sustainable manufacturing system. Excessive waste production results in high environmental impact while inappropriate disposal of waste medicines ends up in waterways eventually leading to antibiotic resistance development in human body [1]. Motivated by the need of reducing waste, pharmaceutical manufacturing systems are undergoing a transition from batch to continuous manufacturing systems [2]. The continuous production process allows the manufacturing plants to be of reduced footprint and modular design. These design features allow manufacturing to be closer to the consumer and facilitate seamless end-to-end integration of manufacturing networks which can allow a distributed manufacturing model utilizing the strength of IoTs and cyber-physical systems [3]. However, full scale implementation of continuous manufacturing plants with distributed manufacturing paradigm for creating such circular supply chains for repurposing and recycling waste medicines has not been realized [4]. As models do not exist that connect single-scale plants to the larger network it makes it impossible to account for feedback interactions between single-scale process innovations and existing processes across the manufacturing network in order to optimize resource utilization towards circular supply chains. The project goal is to enable the design of zero waste future pharmaceutical manufacturing networks at a macroscale using an integrative, multidisciplinary approach focused on novel process chemistry and separation methods, macroscale modeling of manufacturing networks to integrate new processes at scale.

The postdoctoral fellow will work in a highly multidisciplinary team to create novel theory, multiscale process/optimization models and computational tools for the design of such zero-waste future pharmaceutical manufacturing networks. The major approach will involve integration of systems models such as Life Cycle Analysis (LCA), network models based on Physical Input-Output Tables (PIOTs) [5] to map pharmaceutical manufacturing networks for identifying design of distributed manufacturing, along with design of new modular process models at single scale. Other novel approaches including machine learning and IoT driven manufacturing will also be explored.

Start Date

Jan 2025 (earlier start feasible)

Postdoc Qualifications

PhD in Chemical Engineering
Knowledge of Process Systems Engineering, Environmental Challenges, Optimization, Machine Learning  

Co-advisors

Gintaras Reklaitis : reklaiti@purdue.edu
Zoltan Nagy : znagy@purdue.edu
Shweta Singh : singh294@purdue.edu
Vaneet Aggarwal : vaneet@purdue.edu

Bibliography

Sim et al, “Occurrence and distribution of pharmaceuticals in wastewater from households, livestock farms, hospitals and pharmaceutical manufactures.”, Chemosphere, Vol 82, Issue 2, 2011, pages 179-186
2. L. Hirsh_eld, E. I_cten, A. Giridhar, Z. Nagy, and G. Reklaitis, Real-Time Process Management Strategy for Dropwise Additive Manufacturing of Pharmaceutical Products," Journal of Pharmaceutical Innovation, vol. 10, no. 2, pp. 140{155, 2015.

3. L. Monostori, B. K_ad_ar, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, and K. Ueda, \Cyber-physical systems in manufacturing," CIRP Annals, vol. 65, no. 2, pp. 621{641, Jan. 2016. [Online].
http://www.sciencedirect.com/science/article/pii/S0007850616301974
4. J. S. Srai, C. Badman, M. Krumme, M. Futran, and C. Johnston, Future supply chains enabled by continuous processing opportunities and challenges. May 20-21, 2014 Continuous Manufacturing Symposium," Journal of Pharmaceutical Sciences, vol. 104,no. 3, pp. 840{849, Mar. 2015}

5. Vunnava, V.S.G, and Singh, S. “Integrated mechanistic engineering models and macroeconomic input-output approach to model physical economy for evaluating the impact of transition to a circular economy” Energy & Environmental Science, 2021, 14, 5017-5034