20.500.12556/RUL-170300 Self-optimization of Claisen-Schmidt condensation in an automated microflow reaction system using machine learning An efficient self-optimization system was developed for managing chemical reactions in a plug flow reactor, aiming to minimize reactant and intermediate concentrations while maximizing product yield. The approach was demonstrated using a Claisen–Schmidt condensation between 2-methoxybenzaldehyde and acetone. Kinetic parameters, including activation energy, pre-exponential factors, and reaction orders, were determined and integrated into mass balance equations to predict final reactant and product concentrations. The self-optimization system autonomously adjusted flow rates, achieving experimental results with a deviation of ±10 % from theoretical predictions. Compared to classical methods, which first determine kinetic parameters in batch systems, this system significantly reduces the time required to reach optimal conditions. Additionally, it minimizes chemical consumption, enhancing both environmental sustainability and economic efficiency. This work highlights the potential of self-optimization in chemical reaction engineering, offering a faster and more resource-efficient alternative to conventional optimization approaches. Claisen-Schmidt condensation kinetic parameters self-optimization machine learning Claisen-Schmidtova kondenzacija kinetični parametri samooptimizacija strojno učenje true false true Angleški jezik Slovenski jezik Članek v reviji 2025-07-03 10:23:11 2025-07-03 10:23:12 2025-07-05 03:57:17 0000-00-00 00:00:00 2025 0 0 12 str. Vol. 201, art. 109261 Oct. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 66.095.3:004.85 0098-1354 10.1016/j.compchemeng.2025.109261 240904963 RAZ_Pucihar_Urh_2025.pdf RAZ_Pucihar_Urh_2025.pdf 1 156E98C54F6DC6D819D24465AC969C9A 12130ea953219a30d6e7c594dae3d63a7c2436f16b4a65ac87b512eddc4b5351 6fde96fa-57e5-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=212283 https://www.sciencedirect.com/science/article/pii/S0098135425002650 1 0f5095b3-57e5-11f0-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=212282 Fakulteta za kemijo in kemijsko tehnologijo Fakulteta za računalništvo in informatiko 0 0 0