Sorption of synthetic dyes on low-cost solid sorbents is a simple technique for their removal from wastewater. Recent initiatives in the sorption process have sought the use of activated carbon derived from agricultural wastes as it provides an attractive and cheaper alternative to commercial activated carbon, which is usually expensive. This research investigates the sorption kinetics and equilibrium of two synthetic cationic dyes, crystal violet and methylene blue from aqueous media using activated carbon prepared from an agro-waste, Millettia thonningii seed pods. Sorption experiments were carried out using the batch process. The kinetic data were analyzed using the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models while the equilibrium data were analyzed using the Langmuir, Freundlich, and Redlich–Peterson isotherm models. Nonlinear regression method was used to fit the data to the isotherm models in order to determine model parameters and the best-fit isotherms. Thus, three error functions; coefficient of determination, Chi-square statistic test, and the sum of error squares were applied to evaluate the sorption data. The pseudo-second-order model best described the sorption kinetics of both dyes while the Redlich–Peterson model described the equilibrium data the most, followed closely by the Freundlich isotherm model indicating a heterogeneous sorbent surface. The experimental results indicate that the agro-waste derived activated carbon is a viable adsorbent for the remediation of dye-contaminated water.

Background The use of active carbons derived from waste biomass as adsorbents in the remediation of wastewater remains a valuable and cost-effective technology when compared to the use of commercial active carbon for the same purpose. This research aims at using a 2-level full factorial design (FFD) to efficiently evaluate factors that influence the preparation of active carbon from the waste pods of the Dialium guineense seeds. The influence of three preparation factors (concentration of the activating agent, activation time, and type of activating agent) on the active carbon yield and its adsorption capacity for methylene blue were investigated. Based on the full factorial design, two regression models were developed to correlate the factors to the two responses. From an analysis of variance (ANOVA), the most significant factors influencing each response were identified. The active carbon preparation conditions were then optimized by maximizing both the active carbon yield and its adsorption capacity for Methylene Blue. The functional group and surface morphology of the active carbon prepared under the predicted optimum conditions was analyzed via Fourier Transform Infra-Red (FTIR) spectroscopy and Scanning Electron Microscopy (SEM) respectively. Results The results of this study revealed that the concentration of the activating agent had the most significant effect on the yield of the active carbon produced as well as on its adsorption capacity for methylene blue. The optimum preparation process conditions were found to be: concentration of activating agent 5M, activation time 30 min and activating agent, NaOH which resulted in an active carbon yield of 21.25%, and an adsorption capacity of 9.33509 mg MB per gram of active carbon. SEM and FTIR showed evidence of successful activation. Conclusion The preparation of Dialium guineense seed pods' active carbon is strongly influenced by concentration and type of activating agent used. Reliable statistical models based on the FFD proved to be useful in identifying factors that significantly influence the preparation of Dialium guineense seed pods' active carbon. The active carbon holds great potential for application in the elimination of hazardous synthetic dyes from wastewater and should be explored further.

An increasing research interest in the use of waste-derived activated carbon in the remediation of contaminated water as a renewable alternative to commercial activated carbon has been noted. This research evaluates the optimization of the preparation of activated carbon from Millettia thonningii seed pods for application in the removal of synthetic dyes from aqueous solutions. The Box-Behnken experimental design, a subset of response surface methodology is employed in this research as it provides an economical strategy to optimize the process using a minimal number of experiments. The effect of preparation factors such as activation temperature, activation time and impregnation ratio on the preparation process was evaluated and these conditions optimized by maximizing the activated carbon yield and the adsorption efficiencies of two synthetic dyes, Basic Violet 3 and Basic Blue 9. The optimal conditions established were activation temperature of 400 °C, activation time of 30 min and impregnation ratio of 2.0. The activated carbon yield, Basic Violet 3 and Basic Blue 9 adsorption efficiencies approached 39.12%, 83.25% and 91.05% respectively under these optimal experimental conditions. The optimized activated carbon was characterized via physicochemical and proximate analysis, Fourier transform infrared spectroscopy and scanning electron microscopy. Millettia thonningii seed pods activated carbon produced at the optimum activation conditions possessed desirable properties such as low ash content, low moisture content and suitable bulk density. The activated carbon also holds great potential for application in the removal of hazardous synthetic dyes from wastewater. Keywords Activated carbon • Optimization • Desirability function • Box-Behnken design • Millettia thonningii Abbreviations RSM Response surface methodology BBD Box-Behnken design°C Degrees centigrade OFAT One factor at a time BV3 Basic Violet 3 BB9 Basic Blue 9 MTSP Millettia thonningii seed pods MTSPAC Millettia thonningii seed pods activated carbon ANOVA Analysis of variance FTIR Fourier transform infrared A Activation temperature B Activation time C Impregnation ratio Y 1 Activated carbon yield Y 2 Basic Violet 3 adsorption efficiency Y 3 Basic Blue 9 adsorption efficiency D Desirability function DF Degrees of freedom Adj SS Adjusted sum of squares Adj MSS Adjusted mean of sum of squares