TY - GEN
T1 - Dynamic Life Cycle Assessment in Continuous Biomanufacturing
AU - Robinson Medici, Ada
AU - Boskabadi, Mohammad Reza
AU - Ramin, Pedram
AU - Mansouri, Seyed Soheil
AU - Papadokonstantakis, Stavros
PY - 2025
Y1 - 2025
N2 - This work introduces a Python-based interface that couples cradle-to-gate Life Cycle Assessment (LCA) with advanced process simulations in continuous biomanufacturing, resulting in dynamic process inventories and thus to dynamic LCA (dLCA). The open-source Brightway2.5 framework is used to dynamically track environmental inventories of the foreground process and LCA indica-tors (e.g. damage to ecosystems according to ReCiPE 2016) from the v3.10 cut-off ecoinvent da-tabase. The framework is applied to KTB1, a dynamic MATLAB–Simulink benchmark model of con-tinuous Lovastatin production. 580 data points are computed across four different 24-hour sce-narios. The difference between the hourly and the averaged foreground scenario is between 20-30%; a more pronounced deviation is observed when both background and foreground are aver-aged. The dLCA framework precisely identifies optimal periods for cleaner electricity usage, ena-bling future work on direct environmental feedback into process control and optimization for greener high-quality biomanufacturing.
AB - This work introduces a Python-based interface that couples cradle-to-gate Life Cycle Assessment (LCA) with advanced process simulations in continuous biomanufacturing, resulting in dynamic process inventories and thus to dynamic LCA (dLCA). The open-source Brightway2.5 framework is used to dynamically track environmental inventories of the foreground process and LCA indica-tors (e.g. damage to ecosystems according to ReCiPE 2016) from the v3.10 cut-off ecoinvent da-tabase. The framework is applied to KTB1, a dynamic MATLAB–Simulink benchmark model of con-tinuous Lovastatin production. 580 data points are computed across four different 24-hour sce-narios. The difference between the hourly and the averaged foreground scenario is between 20-30%; a more pronounced deviation is observed when both background and foreground are aver-aged. The dLCA framework precisely identifies optimal periods for cleaner electricity usage, ena-bling future work on direct environmental feedback into process control and optimization for greener high-quality biomanufacturing.
KW - Dynamic Life Cycle Assessment
KW - Continuous Biomanufacturing
KW - Python-Based Process Optimization
KW - Life Cycle Assessment
U2 - 10.69997/sct.193590
DO - 10.69997/sct.193590
M3 - Article in proceedings
T3 - Systems & Control Transactions
SP - 2530
EP - 2536
BT - Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35)
A2 - Van Impe, Jan
A2 - Léonard, Grégoire
A2 - Sheetal Bhonsale, Satyajeet
A2 - Polanska, Monika
A2 - Logist, Filip
PB - PSE Press
T2 - 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35)
Y2 - 6 July 2025 through 9 July 2025
ER -
