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<p>Oil spills in water have led to many environmental problems and hence resulting in the search for low-priced and eco-friendly sorbents. The raw as well as nanoparticle impregnated Vigna subterranea husks (RVSH and NVSH respectively) were used for the treatment of the oil spilled water surfaces. The sorbents characterizations were achieved with SEM-EDX, XRD, FTIR, BET and TGA analysis. A response surface methodology (RSM) using Box-Behnken design (BBD) and machine learning (ML) optimization algorithm using multilayer perceptron (MLP) artificial neural network (ANN) was utilized in the optimization study of the sorption process. Among the isotherm models employed in the analysis, Langmuir fitted best the experimental data with utmost monolayer sorption capacities (gg<sup>− 1</sup>) of 3.49 and 5.71 respectively achieved for RVSH and NV<b>S</b>H. The kinetics showed that the best suited model was pseudo-second-order when compared with others at equilibrium sorption time of 70&#xa0;min for RVSH and 50&#xa0;min for NVSH. The thermodynamics proved that the uptake process was nonspontaneous for RVSH, and feasible and spontaneous for NVSH. It also showed physico-chemical process as well as an arbitrary rise in particulate movement at the crude oil-sorbent boundary for both RVSH and NVSH. Optimization results showed that pH of 4, concentration of 6.65&#xa0;g/L, dosage of 0.5&#xa0;g, time of 70&#xa0;min, and a temperature of 303&#xa0;K led to oil removal efficiency of 83.35% by NVSH. Regeneration as well as reusability were relatively better in NVSH and as a result more acceptable for management of polluted waters especially those with petroleum.</p>

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Response surface methodology and artificial neural network optimization of petroleum sorption from polluted waters using Mn2O3 nanoparticle-impregnated Vigna subterranea husks

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Cynthia N. Ibeto,
  • Ikeokwu C. Ogbu,
  • Chinwe A. Nwanya,
  • Chukwuma O. B. Okoye,
  • Christian E. Enyoh

摘要

Oil spills in water have led to many environmental problems and hence resulting in the search for low-priced and eco-friendly sorbents. The raw as well as nanoparticle impregnated Vigna subterranea husks (RVSH and NVSH respectively) were used for the treatment of the oil spilled water surfaces. The sorbents characterizations were achieved with SEM-EDX, XRD, FTIR, BET and TGA analysis. A response surface methodology (RSM) using Box-Behnken design (BBD) and machine learning (ML) optimization algorithm using multilayer perceptron (MLP) artificial neural network (ANN) was utilized in the optimization study of the sorption process. Among the isotherm models employed in the analysis, Langmuir fitted best the experimental data with utmost monolayer sorption capacities (gg− 1) of 3.49 and 5.71 respectively achieved for RVSH and NVSH. The kinetics showed that the best suited model was pseudo-second-order when compared with others at equilibrium sorption time of 70 min for RVSH and 50 min for NVSH. The thermodynamics proved that the uptake process was nonspontaneous for RVSH, and feasible and spontaneous for NVSH. It also showed physico-chemical process as well as an arbitrary rise in particulate movement at the crude oil-sorbent boundary for both RVSH and NVSH. Optimization results showed that pH of 4, concentration of 6.65 g/L, dosage of 0.5 g, time of 70 min, and a temperature of 303 K led to oil removal efficiency of 83.35% by NVSH. Regeneration as well as reusability were relatively better in NVSH and as a result more acceptable for management of polluted waters especially those with petroleum.