2026-10-13T09:06:23Zhttps://www.radar-service.eu/oai/OAIHandler

10.35097/u8a5wx8wr4b9rj482026-10-09T11:03:18Zradar4kit

https://dx.doi.org/10.35097/u8a5wx8wr4b9rj48 Jiokeng Dongmo, Marcel Kévin Simulation Data: Modeling, Observer and Controller Design for AZO particle Synthesis and Characterization Karlsruhe Institute of Technology 2025 Engineering dataset Dataset info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode Simulation data for the modeling, observer and model predictive control are summarized. Model predictive control is utilized to regulate the size of nanoparticles synthesized within a batch reactor under specific temperature, concentration, and pressure conditions. A custom model of the synthesis process is developed, focusing on integrating nucleation, growth, and aggregation phenomena of particles. This model is constructed with a combination of partial and ordinary differential equations, which are efficiently discretized to maintain accuracy and robustness while facilitating real-time implementation of the controller. This method employs dynamic mode decomposition with control to approximate the nonlinear system as a time-discrete linear system. The initial state, estimated from the measured nanoparticle concentration, serves to initialize the optimization problem solved at each time step. The objective is to minimize the disparity between the desired particle size and the actual size throughout the process by adjusting the power of heaters, thereby controlling the temperature within the reactor. Particulate System Modeling Population Balance Equation Dynamic Mode Decomposition Observer and Control Design for Particulate Systems https://publikationen.bibliothek.kit.edu/1000181834 application/x-tar