Artificial neural network (ANN) and Response Surface Methodology based on a 25â1 fractional factorial design were used as tools for simulation and optimisation of the dissolution process for Azaraegbelu clay. A feedforward neural network model with LevenbergâMarquard back propagating training algorithm was adapted to predict the response (alumina yield). The studied input variables were temperature, stirring speed, clay to acid dosage, leaching time and leachant concentration. The raw clay was characterized for structure elucidation via FTIR, SEM and X-ray diffraction spectroscopic techniques and the result indicates that the clay is predominantly kaolinite. Leachant concentration and dosage ratio were found to be the most significant process parameter with p-value of 0.0001. The performance of the ANN and RSM model showed adequate prediction of the response, with AAD of 11.6% and 3.6%, and R2 of 0.9733 and 0.9568, respectively. A non-dominated optimal response of 81.45% yield of alumina at 4.6Â M sulphuric acid concentration, 214Â min leaching time, 0.085Â g/ml dosage and 214Â rpm stirring speed was established as a viable route for reduced material and operating cost via RSM. Keywords: Alumina dissolution, ANN modelling, Azaraegbelu, Clay, RS

High-grade adsorbent (Fe–CS) was successfully synthesized from waste crab shells (CS) for the adsorptive removal of ammonia–nitrogen (A–N) from abattoir wastewater. Fe–CS was analyzed using Fourier transform infrared spectroscopy, x-ray diffraction, Scanning electron microscopy, and Thermogravimetric analysis. The characterization results indicated that Fe–CS possessed important qualities required for surface-driven chelation. The analysis of variance result showed that Fe–CS dosage (with f–value of 284.5) was the most effective process parameter of influence during A-N adsorption. The regression coefficients obtained from the process modeling illustrated the applicability of RSM (R² = 0.9799), ANN (R² = 0.9025), and ANFIS (R² = 0.9998) in predicting the A–N adsorptive removal, while the comparative statistical analysis established the superiority of ANFIS model over ANN and RSMs’ data prediction accuracy. The optimization result further demonstrated that ANFIS–GA predicted an optimum removal efficiency of 92.60% at pH of 6.5, a dosage of 2.2 g, A–N conc. of 18.8 mg/L, Temp. of 317 K, and adsorption time of 156 min. The mechanistic plot obtained from Weber–Morris model depicted that three regions were involved in the adsorption process. Boyd model parameters revealed that intraparticle diffusion controlled the process at low A–N concentration (A–N concentration ≤ 15 mg/L), while film diffusion dominated the adsorption process at concentrations higher than 15 mg/L. The average Gibbs free energy value ( $$- \Delta G^{0}$$- Δ G 0 = 3.08212 kJ/mol), enthalpy ( $$\Delta H^{0}$$Δ H 0 = 4.1150 kJ/mol), activation energy ( $$E_{A}$$E A = 3.7454 kJ/mol) and entropy ( $$\Delta S^{0}$$Δ S 0 = 22.9710 J/mol K) obtained from thermodynamic studies confirmed the spontaneous, endothermic, favorable and physical nature of the process.