Browsing by Author "Oke, E.O."

Now showing 1 - 2 of 2
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    Analytical modeling of human exposure from short-term point source releases of aerosols from household spray products
    (Taylor and Francis, 2018) Adeniran, J.A.; Araromi, D.O.; Yusuf, R.O.; Oke, E.O.; Sonibare, J.A.
    Indoor air measurements of aerosols emitted from the use of household spray products are expensive and time-consuming. An analytical model was developed and incorporated into a graphical user interface to quantify exposure as a function of distance and time. The model considered point source dispersion with reflection from a wall and aerosol concentration decay due to air exchange rate and deposition. Aerosol concentration decreased with respect to increase in time and distance from the release point. There was no significant difference between the developed model and experimental results. The developed model could be used to adequately predict indoor human exposure to aerosols as a function of distance and time.
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    Kinetics and neuro-fuzzy soft computing modelling of river turbid water coag-flocculation using mango (Mangifera indica) kernel coagulant
    (2018) Oke, E.O.; Araromi, D.O.; Jimoda, L.A.; Adeniran, J.A.
    This study investigates kinetics and Adaptive Neuro-Fuzzy Modeling (ANFM) of river turbid water coagulation-flocculation (CF) process using mango kernel coagulant (MKC). CF experiments were performed using jar test apparatus and the process kinetic-transport parameters (coagulation rate constant, half-life time, and particle diffusivity) were determined using kinetic- transport models. Grid-partitioning neuro-fuzzy programming codes were written and implemented in Matlab 9.2 software environment for the development of neuro-fuzzy architecture. The ANFM input data include initial water pH, initial water turbidity, biocoagulant dosage, CF time, and turbidity removal percentage (TRP) as output data. Generalized bell membership function was optimally selected for fuzzification of input variables and a hybrid algorithm was considered for the learning method of input-output data with constant output membership type. The minimum turbidity (0.51 NTU) of treated water was achieved at pH 12 and coagulant dosage of 2.5mg/L with coagulation rate constant, half-life (t1/2) and particle diffusivity 0.0194 s 1, 10.01 min, and 7.267 10 14 m2/s, respectively. The correlation coefficient (R2) between the experimental and neuro-fuzzy predicted values was 0.9924 and the ratio (K) of training error to testing error was 0.68. Thus, this study shows that ANFM can be used as a reliable tool for modeling river water CF and kinetic-transport parameter results are useful in process design, optimization, and control.