Optimal Conditions of Paint Wastewater Coagulation With Gastropod Shell Conchiolin Using Response Surface Design and Artificial Neural Network-Genetic Algorithm

Abstract The potential of gastropod shell conchiolin (GSC) (a waste product of the deprotenization stage of chitosan production) as one of the alternatives to chemical coagulants has been explored for treatment of paint industrial wastewater (PW). The accuracy of response surface design (RSD) and the precision of artificial intelligence (AI) in predicting and optimizing the process conditions were harnessed in raising experimental design matrix and response optimization, respectively for the bench scale jar test coagulation experiment. PW was characterized using American public health association (APHA) standard methods. Extraction of conchiolin was done via alkaline extraction method. PW contains 2098mg/l total suspended solid (TSS) above discharge limit (1905mg/l). Fourier transform infrared (FTIR) spectrum of GSC revealed a broad N–H wagging band at 750 – 650 cm−1 indicating the presence of secondary amine linked to the presence of protein. Turbidity removal from PW via one factor at a time (OFAT) was found to be a function of pH, GSC dosage, temperature and time. Artificial neural network (ANN) response prediction shows 92% correlation with the response surface design (RSD) experimental result. The optimal conditions obtained via genetic algorithm (GA) for the response optimization at the best pH of 4 indicate optimal turbidity removal of 98% at GSC dosage, time and temperature of 4 g, 20 min and 45oC, respectively.

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Comparative analysis of the application of artificial neural network-genetic algorithm and response surface methods-desirability function for predicting the optimal conditions for biodiesel synthesis from chrysophyllum albidum seed oil

Journal of the Taiwan Institute of Chemical Engineers ◽

10.1016/j.jtice.2021.06.012 ◽

2021 ◽

Author(s):

Dominic Okechukwu Onukwuli ◽

Chizoo Esonye ◽

Akuzuo Uwaoma Ofoefule ◽

Rita Eyisi

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Comparative Analysis ◽

Response Surface ◽

Seed Oil ◽

Desirability Function ◽

Optimal Conditions ◽

Response Surface Methods ◽

Biodiesel Synthesis

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Response surface methodology-artificial neural network based optimization and strain improvement of cellulase production by Streptomyces sp.

Bioscience Journal ◽

10.14393/bj-v36n4a2020-48006 ◽

2020 ◽

Vol 36 (4) ◽

Author(s):

Ega Soujanya Lakshmi ◽

Manda Rama Narasinga Rao ◽

Muddada Sudhamani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Wild Strain ◽

Cellulase Activity ◽

Cellulase Production ◽

Optimal Conditions ◽

Zone Formation ◽

Artificial Neural

ABSTRACT Thirty seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, grampositive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on CMC agar plate after Gram’s iodine staining. Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG) and Ethyl methane sulphonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86 fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Effect of pH, temperature, duration of incubation, , and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxy Methyl Cellulase as a substrate are 55 oC of temperature, pH of 5.0 and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimised to 2 fold of the activity of the wild type by RSM and ANN.

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