Project Description

Reducing waste and effective reuse/recycling of waste medicines are two critical challenges faced by pharmaceutical manufacturing. 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 has not been realized [4]. A critical gap to achieve this goal is lack of systems mapping of production, use and disposal networks for pharmaceuticals along with efficient algorithms for decision making at systems scale that drive the manufacturing towards waste reduction. Our goal is to fill this critical gap by 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. Finally, automated machine learning algorithms using multi-scale data from IoTs in continuous pilot plant at Purdue and developed systems models of whole manufacturing network will be established for decision making towards the goal of reducing waste and reuse/recycle waste medicines.

Start Date

February 2020

Postdoc Qualifications 

Postdoc should have PhD in Chemical Engineering/Industrial Engineering with skills in Programming (Python, Matlab) and Statistics. Exposure to systems modeling such as Network Science, Life Cycle Analysis and Macroeconomics is highly desired.

Co-advisors 

Shweta Singh
singh294@purdue.edu
Agricultural and Biological Engineering/Environmental and Ecological Engineering

Ginataras Reklaitis
reklaiti@ecn.purdue.edu
Chemical Engineering

References 

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

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.

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

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}

S. Singh, J. E. Compton, T. R. Hawkins, D. J. Sobota, and E. J. Cooter, A Nitrogen Physical Input-Output Table (PIOT) model for Illinois," Ecological Modelling, vol. 360, pp. 194{203, 2017