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A simulated verification and validation of the neural-network rate-function (NNRF) approach to modeling the nonlinear dynamic systems is provided. The NNRF modeling scheme utilizes some a priori process knowledge and experimental data to develop a dynamic neural-network model. Based on the obtained neural-network model, an optimal temperature trajectory was computed via the two-step method to drive a batch free-radical polymerization reaction to a prescribed molecular weight distribution (MWD). Evaluation of the quality of the end product suggests that the proposed NNRF modeling approach can be applied in dynamic modeling of a complex and nonlinear reaction system.

Highly dispersed silver nanoparticles could be used as catalysts, as staining pigments for glasses and ceramics, as antimicrobial materials, in surface-enhanced Raman spectroscopy, as transparent conductive coatings, in electronics, etc. Consequently, the versatility of such particles provides strong incentives to the development of a systematic way to produce their dispersions. In this work, the optimization of the synthesis of nanosized silver particles by chemical reduction using formaldehyde in aqueous solution was studied. Effects of the possible processing variables such as the reaction temperature T, the mole ratio of [formaldehyde]/[AgNO3], [NaOH]/[AgNO3], the weight ratio of PVP/AgNO3, and the molecular weight (MW) of protective agent PVP (Polyvinyl−pyrrolidone) were considered. The data-driven model on the basis of the 44 designed experimental runs provided us the optimal conditions for closely achieving the product with the specified mean particle size and conversion of silver nitrate.

We propose an integrated method for optimization and control of semibatch reactors. Based on the desired control objective, dynamic programming is applied to obtain optimal operating trajectories. Yield optimization is assured for a real plant by tracking model-dependent optimal trajectories according to the proposed modified globally linearizing control (MGLC) structure. The behavior of the proposed MGLC structure is predictable and reliable, with tuning parameters based on the proposed tuning method if the manipulated variables are not constrained.

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