Kinetics and neuro-fuzzy soft computing modelling of river turbid water coag-flocculation using mango (Mangifera indica) kernel coagulant

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, ha...

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Bibliographic Details
Published in:Chemical engineering communications 2019-02, Vol.206 (2), p.254-267
Main Authors: Oke, Emmanuel Olusola, Araromi, Dauda Olurotimi, Jimoda, Lukuman Adekilekun, Adetayo Adeniran, Jamiu
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Coagulants
Coagulation
Coagulation-flocculation
Correlation coefficients
Design optimization
Diffusivity
Dosage
Error analysis
Flocculation
Fuzzy logic
Half-life
Jar-test
Kernels
Kinetics
Machine learning
Mango kernel coagulant
Mangoes
Modelling
Neuro-fuzzy: Modeling
Process parameters
Soft computing
Transport
Turbidity
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Summary: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 2.5 mg/L with coagulation rate constant, half-life (t 1/2 ) and particle diffusivity 0.0194 s −1 , 10.01 min, and 7.267 × 10 −14 m 2 /s, respectively. The correlation coefficient (R 2 ) 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.
ISSN:0098-6445
1563-5201
DOI:10.1080/00986445.2018.1483351