A comparative optimization and modeling of ammonia–nitrogen adsorption from abattoir wastewater using a novel iron-functionalized crab shell

Abstract

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.

A review of the adsorption method for norfloxacin reduction from aqueous media

“…Thermodynamic studies provide data relating to temperature changes in a process. This information is exemplified by the values of thermodynamic parameters such as Gibbs free energy (ΔG o ), change in enthalpy (ΔH o ), and entropy (ΔS o ) [186] . These parameters portray the favor-ability, spontaneity, energy delivery (exothermic) or utilization (endothermic), and degree of randomness of any process under investigation [8] .…”

“…A significantly different trend was observed for ferrous sorbents [ 54 , 139 ], where NRFX uptake was highlighted to be unfavorable even though the process maybe spontaneous. This implies NRFX uptake by magnetic adsorbents would be triggered by an external influence [ 54 , 186 ]. Non-spontaneity was observed by the use of synthetic polymers (PS, PBS, PE) indicating that NRFX adsorption requires energy to promote the process [ 7 , 80 ].…”

“…Entropy changes (ΔS o ) signifies the degree of disorderliness (randomness) and also depicts the capacity of any adsorbent to remove the targeted adsorbate [186] . The entropy sign convention gives a hint of either increased randomness (ΔS o > 0) or reduced randomness (ΔS o < 0) of an adsorption process.…”

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

“…The advantage of ANFIS lies in the combination of neural network and fuzzy logic (Adedeji et al 2020;Karaboga and Kaya 2018). In theory, ANFIS may possess more advantages than ANN in predicting the adsorption of pollutants on biochar, and thus ANFIS is widely applied (Ohale et al 2022;Bisaria et al 2022). For example, the adsorption efficiency data of Cu(II) ions on biochar were constructed through laboratory batch studies, and the model efficiencies of ANFIS, ANN and MLR were compared (Wong et al 2020).…”

Agro-Industrial Wastewaters for Algal Biomass Production, Bio-Based Products, and Biofuels in a Circular Bioeconomy

“…Blood is one of the main dissolved contaminants, and its separation efficiency in processes is essential for ensuring lower effluent loads [29]. This effluent is characterized by high BOD, COD, high oil concentrations, total solids, total suspended solids, total nitrogen, and chlorides [30]. Nitrogen compounds are one of the main nutrients in wastewater; nitrates accelerate plant growth in water bodies and can be easily reduced to nitrites, threatening human health and the environment when treated incorrectly [31].…”