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.