Artificial neural network for mechanism identification and process prediction of the nanoclusters deagglomeration in the in-line HSM

Abstract

A multiple-input multiple-output artificial neural network (ANN) for the mechanism identification of nanoclusters deagglomeration and the prediction of particle size distribution (PSD) evolution of nanocluster in deagglomeration process of in-line high shear mixer (HSM) was successfully established. It can successfully realize the description of nanoclusters deagglomeration process within single or multiple processing batches. Besides, this ANN model also can realize the accurate prediction of the d32 of fine and coarse agglomerates, and the fines fraction, at different operating conditions and structural parameters, within the relative error of 10 % and 20 % respectively. Meanwhile, 3D profiles of PSD versus different operating conditions and structural parameters were output by the established ANN model, which were useful for parameter selection of in-line HSM to realize efficient production of nanoparticles suspension. The successful establishment of this ANN model provides an effective, flexible, and advanced intelligent method for guiding the parameter selection of in-line HSM, for realizing the controllable and efficient production of the high-end nanoparticle products with specific particle characteristics.