Dynamic Optimization and Non‐linear Model Predictive Control to Achieve Targeted Particle Morphologies

Abstract

An event‐driven approach based on dynamic optimization and nonlinear model predictive control (NMPC) is investigated together with inline Raman spectroscopy for process monitoring and control. The benefits and challenges in polymerization and morphology monitoring are presented, and an overview of the used mechanistic models and the details of the dynamic optimization and NMPC approach to achieve the relevant process objectives are provided. Finally, the implementation of the approach is discussed, and results from experiments in lab and pilot‐plant reactors are presented.

Coupled stochastic simulation of the chain length and particle size distribution in miniemulsion radical copolymerization of styrene and N-vinylcaprolactam

“…Most modeling studies are also focused on a limited number of experimental variables and responses, typically the (co)monomer conversion and neglecting the PSD evolution. 12,18,19 Due to the high number of process variables, specially under emulsion conditions, it is recommended to apply multi-scale modeling in which at the micro-scale the interplay of chemistry and viscosity is grasped in a given reaction locus (e.g. polymer particle) and at the meso-scale interphase mass phenomena (e.g.…”

Transition to sustainable chemistry through digitalization

“…For example, BASF has reported a 30% reduction in pilot-scale batch cycle times of emulsion polymerization by introducing real-time optimization based on better reaction sensors and improved predictive models. 50 This time reduction translates into lower operational costs, higher plant throughput, and, with the quality-oriented process control, a reduction in off-spec product waste. In automotive manufacturing, one of the key benefits of digitalization that translates into supply chain optimization is an 80% increase in forecasting accuracy.…”

Inline Raman Spectroscopy of an Emulsion Copolymerization in an Industrial Pilot Plant Using Indirect Hard Modeling

“…For the control task, described in [20], only the mass fraction of styrene was considered. For the pilot plant operation, only HPLC measurements of styrene are available.…”

“…In total, four experiments were conducted. Three experiments were run with the optimized recipe as described in [20] and one was run with the standard recipe. In Fig.…”

“…Optimized semi-batch operation for desired particle morphology [18], [19] requires tight control as the particle morphology depends strongly on the conversion and reactor temperature. In [20], a closed-loop advanced process control for particle morphology is described using online Raman measurements for concentration predictions.…”