1.0 Introduction
Alumina ore is a white powder produced mainly from the mineral called
bauxite (Hosseini et al., 2011). It is a very important ore in the
mining industry from which aluminum is produced. Aluminum is in high
demand all over the world today due to some peculiar properties it has.
For example, its hardness and strength with good wear and resistance to
chemical attack even at a very high temperature has made it a good
choice for critical structural applications (Olaremu, 2005). Aluminum is
also in high demand in the production of electrical materials due to its
excellent conductive ability. Due to the high demand for this important
metal, there has been need to search for alternative raw materials other
than bauxite from which it can be produced (Al-Zarhani and Abdul-Majid,
2009). Many Nigerian clays have been identified to be very rich in
alumina content (Ogbuagu, et al., 2007; Ajemba and
Onukwuli, 2012; Orugba et al., 2014; Udochukwu et al., 2019).
Acid leaching has been an outstanding method of obtaining alumina from
clays (Al-Zarhani and Abdul-Majid, 2009). In the dissolution of alumina
from the clays using acids or alkalis, it is important to investigate
the process parameters in order to obtain their optimum conditions to
enhance efficient recovery, in which case, the optimization of the
dissolution process becomes very important.
Optimization is used to determine the most appropriate value of
variables under given conditions. The primary focus of using
optimization techniques is to measure the maximum or minimum value of a
function depending on the circumstances. The Response surface
methodology (RSM) has been in use in the chemical and process industries
for the purpose of either producing high quality products or operating
the process in a more cost effective manner and ensuring the process
operates in a more stable and reliable way (Sudamalla et al., 2012). The
Response Surface Methodology has also been applied to different
processes for achieving its optimization using experimental designs
(Gunawan and Suhendra, 2008; Alam et al., 2007; Narayana et al., 2011).
Ajemba et al., (2012) performed the optimization of alumina dissolution
from Ukpor clay in tetra-oxosulphate (vi) acid using the Response
Surface Methodology and obtained an optimum yield of 97.23% at the
leaching conditions of calcination temperature of
729.540C; leaching temperature of
103.250C; acid concentration of 2.93mol/l;
solid/liquid ratio of 0.027g/ml and stirring speed of 436.34 rpm. Orugba
et al., (2014) studied the process modeling of sulphuric acid leaching
of Iron from a local Nigerian clay using the Response Surface
Methodology and obtained the iron yield of 84.7% at calcinations
temperature of 650oC; leaching temperature of
70.02oC; acid concentration of
1.89mol/cm3; liquid-solid ratio of 10.67 and stirring
speed of 379.80rpm. Ohale et al., (2017) used Artificial Neural Network
(ANN) and Response Surface Methodology based on a
25_1 fractional factorial design as tools for
simulation and optimization of the dissolution process for a Nigerian
local clay and obtained an optimal response of 81.45% yield of alumina
at 4.6 M sulphuric acid concentration, 214 min leaching time, 0.085 g/ml
dosage and 214 rpm stirring speed. Onukwuli et al., (2018) performed the
process optimization of hydrochloric acid leaching of iron from Agbaja
Clay using the Response Surface Methodology and obtained iron yield of
85.13 % at calcinations temperature of 800oC,
leaching temperature of 53.7oC, acid concentration
(HCl) of 2.34 mol/cm3, liquid to solid ratio of
9.90cm3/g and stirring speed of 250 rpm.
In this study, the most significant factors as well as their possible
interactions which influence the overall efficiency of the hydrochloric
acid leaching of alumina from the local clay will be investigated using
the Surface Response Methodology to obtain predictive model for the
dissolution process from the data obtained from few experiments through
regression analysis.