Browsing by Author "Majekodunmi, Olukayode Titus"

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Influence of Ca2+ Ions on Freshly Precipitated Caco3 Particles
(Izmir Institute of Technology, 2019-07) Majekodunmi, Olukayode Titus; Özdemir, Ekrem
The objective of this study was to develop a method to synthesize CaCO3 nanoparticles from a chemical precipitation reaction under ambient and high supersaturation conditions. Equimolar CaCl2 and Na2CO3 solutions were reacted in a tubular reactor at a constant rate. The particles growth inhibition was attempted by dispersing the reaction mixture in a continuously stirred Ca(OH)2 solution. This procedure separated the nucleation phase from the growth inhibition process, and was conducted without pH and composition control. The possibility of impeding the CaCO3 particles overgrowth was explored at different precipitants and Ca(OH)2 concentrations. Their effects on the particles morphology, colloidal stability and specific surface area were studied. Although rapidlysettling particles were produced at precipitants concentration of 100 mM, colloidally stable CaCO3 nanoparticles were obtained at concentrations ≤75 mM. Additive Ca2+ ions, provided by the Ca(OH)2 solutions, inhibited the crystals growth by adsorbing irreversibly on the growth sites. The synthesized particles were as much as 95% smaller than those obtained when pure H2O was used instead. Ca2+ ions concentration and amount of precipitated particles were observed to be important factors for monodispersity and high growth inhibition. Monodisperse and stable nanoparticles were synthesized at low reactants concentration and/or precipitates volume. Vaterite phase was observed in the particles obtained when pure H2O was used as the growth-inhibiting solution. However, the presence of additive Ca2+ ions effected the crystallization of pure calcite, regardless of Ca(OH)2 or precipitants concentration, reaction mixtures retention time in the tubular reactor, volume of precipitates, and the growth-inhibiting solutions initial pH.
Citation - WoS: 19
Citation - Scopus: 29
Modeling of an Activated Sludge Process for Effluent Prediction—a Comparative Study Using Anfis and Glm Regression
(Springer Verlag, 2018-09) Araromi, Dauda Olurotimi; Majekodunmi, Olukayode Titus; Adeniran, Jamiu Adetayo; Salawudeen, Taofeeq Olalekan
In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson’s correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.