Jyh-Shyong Chang

Optimization of nanosized silver particles synthesis via sequential pseudo-uniform design method

A data-driven model based optimization on the synthesis of nanosized silver particles by chemical reduction using formaldehyde in aqueous solution was studied in this work. Effects of the possible processing variables such as the reaction temperature T, the mole ratios of [formaldehyde]/[AgNO3] and [NaOH]/[AgNO3], PVP/AgNO3, and the molecular weight of protective agent PVP (polyvinyl-pyrrolidone) were considered. The colloid dispersion products were mainly characterized for its mean particle size and conversion of silver nitrate. The identified model based on the 44 designed experiments can provide us the optimal conditions for achieving (a) the minimum mean particle size (29 nm) with conversion (48%), (b) the desired targets (mean particle size, 39nm and conversion, 97%), and (c) the desired targets (mean particle size, 32nm and conversion, 85%) closely. To accomplish the objectives of this work, the fractional factorial design was first applied to screen the insignificant factor [NaOH]/[AgNO3]. By the contrast experiment done at the near-optimal condition for achieving the minimum particle size of the product the PVP with MW (10,000) was chosen. A resulting 3 significant factors problem were then solved by the developed sequential pseudo-uniform design (SPUD) method.

Implementing operating trajectories in an exothermic chemical batch reactor by a self-tuning single neuron controller

A self-tuning single neuron controller was developed and applied to track operating trajectories in an exothermic chemical batch reactor. By self-tuning only the slope of the non-linear saturated function b, the adopted single neuron controller can track a given operating trajectory closely by merely observing the process output error. Experimental results reveal the applicability of such a self-tuning single neuron controller for tracking a given trajectory in batch reactors.