TY - JOUR
T1 - Modeling of bioprocesses via MINLP-based symbolic regression of S-system formalisms
AU - Forster, Tim
AU - Vázquez, Daniel
AU - Cruz-Bournazou, Mariano Nicolas
AU - Butté, Alessandro
AU - Guillén-Gosálbez, Gonzalo
N1 - Publisher Copyright:
© 2022
PY - 2023/2
Y1 - 2023/2
N2 - Mathematical modeling helps guide experiments more effectively, support process monitoring and control tasks, stabilize product quality, increase consumer safety, or ease specific decision-making tasks for subject matter experts. However, constructing accurate process models can be challenging, especially with bioprocesses, due to complex metabolic mechanisms and data scarcity. This work proposes a method for building models combining a mass balance backbone with a canonical kinetic representation, i.e., the S-system formalism. The model structure and parameters that best describe the studied system are automatically identified by solving a mixed-integer nonlinear programming (MINLP) problem. Following an incremental approach, the integration of ordinary differential equations is avoided. Numerical examples show that our method performs similarly to models based on artificial neural networks, outperforming them in some cases while providing an analytical, closed-form model. Such expressions can be more easily interpreted and optimized in existing algebraic modeling systems.
AB - Mathematical modeling helps guide experiments more effectively, support process monitoring and control tasks, stabilize product quality, increase consumer safety, or ease specific decision-making tasks for subject matter experts. However, constructing accurate process models can be challenging, especially with bioprocesses, due to complex metabolic mechanisms and data scarcity. This work proposes a method for building models combining a mass balance backbone with a canonical kinetic representation, i.e., the S-system formalism. The model structure and parameters that best describe the studied system are automatically identified by solving a mixed-integer nonlinear programming (MINLP) problem. Following an incremental approach, the integration of ordinary differential equations is avoided. Numerical examples show that our method performs similarly to models based on artificial neural networks, outperforming them in some cases while providing an analytical, closed-form model. Such expressions can be more easily interpreted and optimized in existing algebraic modeling systems.
KW - Artificial neural network
KW - Bioprocess
KW - Low sized dataset
KW - MINLP
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85145859115&partnerID=8YFLogxK
U2 - 10.1016/j.compchemeng.2022.108108
DO - 10.1016/j.compchemeng.2022.108108
M3 - Article
AN - SCOPUS:85145859115
SN - 0098-1354
VL - 170
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 108108
ER -