Iterative real-time optimization of a reductive amination process in a thermomorphic multiphase system.

In this paper, we discuss the optimization of the operation of a reductive amination (RA) reaction process in a miniplant without an accurate process model using iterative real-time optimization (RTO). The rhodium-catalyzed RA of undecanal with diethylamine produces a tertiary amine from a long-chain aldehyde and is performed in a thermomorphic multiphase system (TMS) to recover and reuse the expensive catalyst efficiently. An iterative RTO method called modifier adaptation with quadratic approximation (MAWQA) is used in combination with guaranteed model adequacy (GMA) to drive the RA process to its optimum iteratively. MAWQA utilizes online measurements to overcome model deficiencies. GMA ensures that the model used in MAWQA satisfies the model adequacy conditions. The optimal operating conditions of the RA process in the miniplant were identified during an experimental run of the plant, thereby validating the applicability and efficiency of MAWQA with GMA. The results illustrate the benefits of process optimization using iterative RTO methods without accurate process models. • Online optimization of a reductive amination process in a mini-plant is performed in the presence of model uncertainty. • An iterative real-time optimization method, modifier adaptation with quadratic approximation is used for optimization. • The issue of undesired input oscillations is avoided by ensuring model adequacy in MAWQA using guaranteed model adequacy. • Experimental results show a smooth convergence to the process optimum. [ABSTRACT FROM AUTHOR]