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The biosorption process of Zn (II) ions in industrial wastewater was investigated using derived composite biosorbents from walnut and snail shells. Composite adsorbents were produced by activating walnut shell carbon (WSC) with phosphoric acid to obtain acid-treated walnut shell carbon (AWSC) and WSC and AWSC were independently impregnated on chitosan to produce walnut shell carbon impregnated on chitosan (WSCC) and acid-treated walnut shell carbon impregnated on chitosan (AWSCC) respectively. The removal efficiencies of Zn (II) ions from synthetic wastewater using the prepared adsorbents were determined. The effects of operational parameters on Zn (II) ions adsorption were investigated. The adsorption data of Zn (II) ions were analysed using Langmuir, Freundlich and Temkin isotherms. The Langmuir isotherm fitted the adsorption data excellently for the derived composite biosorbents, giving an indication of monolayer coverage on the derived composite biosorbents and the determination coefficients were close to unity. Also, the maximum adsorption capacities of 3.1104, 3.8052, 16.4474 and 17.6991 mg/g were obtained for WSC, AWSC, WSCC and AWSCC at pH=5, 1 g of adsorbent dosage, Zn (II) ions initial concentration of 30 mg/L, contact time of 2 h, agitation speed of 150 rpm, particle size of 60 BSS and temperature of 30°C. The kinetic modelling of Zn (II) ions adsorption showed that pseudo second-order kinetic model gave the best fit amongst the investigated kinetic models. The adsorption of Zn (II) ions on the prepared adsorbents was filmdiffusion controlled. The experimental results of this study showed that acid-treated walnut shell carbon impregnated on chitosan has the potential to be applied as alternative efficient low-cost biosorbent in the remediation of heavy metal contamination in wastewater. The thermodynamic parameters indicated that the adsorption of Zn (II) ions on the derived composite biosorbents was exothermic, endogonic, favourable, non-spontaneous with changes in enthalpy (H  , negative), entropy [ S  , nearly zero (though negative)], and Gibbs free energy (G  , positive), for all the prepared adsorbents.

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In the current investigation, chitin and chitosan are extracted from Callinectes amnicola (crab) and Penaeus notialis (shrimp) shell wastes using predetermined optimization conditions. The shrimp shell produces higher chitin yield (26.08%), higher chitosan yield (16.93%) and higher degree of deacetylation (DDA) of 89.73% than the yields of chitin (19.36%), chitosan (13.29%) and the DDA from crab shell (84.20%). The Fourier Transform Infrared (FTIR) and acid-base titration methods are used to obtain % DDA of the optimized chitosan. Insignificant deviations between the DDA values from both methods are obtained. The experimental FTIR bands and standards for the refined chitosan from crab and shrimp shell wastes are in excellent agreement. The physicochemical properties of the raw precursors, extracted chitin and chitosan (raw and refined/decolorized) are equally evaluated. The extracted chitin and chitosan are characterized using analytical techniques. The implication of this study is in the current drive to produce chitin and chitosan from the underutilized shell wastes of C. amnicola and P. notialis of Nigerian sources with a high yield and a high DDA. In this study, the P. notialis shell is a better alternative source of chitin and chitosan than C. amnicola shell.

A one-dimensional adiabatic mathematical model was developed for the riser reactor of an industrial residue fluid catalytic cracking unit (RFCCU). A seven-lump kinetic model was presented for the catalytic cracking of vacuum residue, taking cognisance of diffusion resistance, which is a departure from the general norm in the literature. Also, heat transfer resistance between the fluid and solid phases was incorporated into the energy balances for instantaneous and one-dimensional vaporization of feedstock. The developed model was a set of twelve coupled, highly non-linear and stiff ordinary differential equations, ODEs, which was numerically solved with an implicit MATLAB built-in solver, ode23t, designed deliberately for handling stiff differential equations to circumvent the problem of instability associated with explicit methods. An excellent agreement was achieved between the industrial RFCCU plant data and the simulated results of this study, with average absolute deviation being < ± 5% for instantaneous vaporization of feedstock in all cases investigated. Moreover, the simulated results revealed that half of the reactor was relatively redundant as this accounted for only 3% of the conversion. Hence, the findings of this study could be useful to the production practice for the Khartoum Refinery Company.

A mathematical model was developed for a diffusion–reaction process in a spherical catalyst pellet contained in a heterogeneous packed bed reactor. The model developed was solved to predict the effectiveness factor and also to perform sensitivity analysis for steam reforming of methanol on Cu/ZnO/Al2O3 catalyst a source of hydrogen fuel. The method of orthogonal collocation was used to solve the resulting differential equation. At temperature below 473 K the effect on intra-particle diffusion limitation is reduced to the minimum indicated by the effectiveness factor being almost equal to one but as the temperature increases above 473 K there is considerable increase in the diffusion limitation effect. The effects of thermal conductivity, diffusion coefficient, catalyst size and surface temperature on effectiveness factor for the reaction process were also considered. Result indicates that catalyst size of $$1.623\,\, \times \,\,10^{ - 4}$$1.623×10-4 m eliminates the effect of intra-particle diffusion resistance in the pellet. The variation of effectiveness factor with Thiele modulus, showing the asymptotic values, using power law and Langmuir–Hinshelwood–Hougen–Watson (LHHW) kinetics, was predicted. The two reaction kinetics had almost the same magnitude of effectiveness factor at different Thiele modulus which indicates that they can adequately predict the reaction process.

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