Understanding Contributions of Divalent Cations in Mineral Carbonation Using Artificial Neural Network

Abstract

The roles played by divalent cations (calcium, magnesium and iron) of rock minerals in the efficiency of mineral carbonation have been investigated. Statistical modeling with Artificial Neural Network (ANN) having configuration ANN[17-4-1] shows that carbonation efficiency largely increases as the quantity of calcium content increases. Averagely,there is approximately 5% rise in the original efficiency for 10% increase in the quantity of calcium. This changes to 3.4% and 1.6% increases in efficiency, relative to the original efficiency for 20% and 30% increases in calcium content, respectively. Iron content of minerals offers clear positive correlation to the carbonation efficiency. From the global average, there is approximately 17% rise in the original efficiency for 10% increase in the quantity of iron. This increases to 29% and 41% over the original efficiency for 20% and 30% increases in iron content, respectively.. The influence of magnesium was found to be mainly negatively correlated to carbonation efficiency, after exceeding an unknown threshold. The global average of the efficiency changes with magnesium content results in original efficiency rising by 2% at 10% quantity increase and then reduces by 3% and 9% for 20% and 30% increase in magnesium quantity, respectively, relative to the original efficiency. Thus, iron compounds are found to be most potent of the divalent cations in carbonation reaction while calcium and magnesium content should maintain a threshold ratio with silica content for improved efficiency.