Optimization of Azare low-grade barite beneficiation: comparative study of response surface methodology and artificial neural network approach.

ABSTRACT: This study examined the efficacy of response surface methodology (RSM) and artificial neural network (ANN) optimization approaches on barite composition optimization from low-grade Azare barite beneficiation. The Box-Behnken Design (BBD) and Central Composite Design (CCD) approaches were used as RSM methods. The best predictive optimization tool was determined via a comparative study between these methods and ANN. Barite mass (60-100 g), reaction time (15-45 min) and particle size (150-450 μm) at three levels were considered as the process parameters. The ANN architecture is a 3-16-1 feed-forward type. Sigmoid transfer function was adopted and mean square error (MSE) technique was used for network training. Experimental data were divided into training, validation and testing. Batch experim

SUBMITTER: Popoola LT 

PROVIDER: S-EPMC10123164 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature