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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.

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In this article, the modelling and optimization of five operational process parameters involving initial concentration, adsorbent dosage, contact time, temperature and pH of the solution as it affects the treatment of aqueous solution contaminated with methylene blue, a heterocyclic aromatic compound, on chitosan sourced from African Snail Shell were studied using response surface methodology (RSM) and artificial neural network (ANN) techniques coupled with genetic algorithm. The single and interactive effects of the variables were examined by way of analysis of variance (ANOVA). A comparison of the model techniques was done and an evaluation was carried out with some selected error functions. Both modelling and optimization tools performed creditably well. However, the hybrid ANN-GA proved to be a superior modelling and optimization technique with excellent generalization ability which gave an average absolute deviation between the experimental and predicted data of both response variables considered. The insightful relative importance of the process variables based on the renowned Garson and Olden’s algorithm methods coupled with step by step approach initiated in the Matlab environment were equally investigated. The findings from this study revealed in clear terms that pH and initial concentrations were the most influential parameters and the maximum value of 99.28% of methylene blue removed at optimum conditions affirmed that the chitosan adsorbent is viable for the treatment of effluents from the textile industry.

The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay.  The process variables considered were surfactant concentration, , activation time, ,  activation temperature, , and chitosan dosage, .  The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent than those of ANN for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, , amongst others and lower error functions’ values than those obtained using ANN for both responses.  When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface pot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best.  The ANFILS predicted optimal conditions were = 116.00 mg/L, = 2.06 h, = 81.2oC and = 5.20 g.  Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively.  Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.