Artificial intelligence modeling of cadmium(II) biosorption using rice straw

• Published in: Applied water science Vol. 7; no. 2; pp. 823 - 831
• Main Authors: Nasr, Mahmoud, Mahmoud, Alaa El Din, Fawzy, Manal, Radwan, Ahmed
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2017; Springer Nature B.V
• Subjects: Aquatic Pollution; Comparative Law; Earth and Environmental Science; Earth Sciences; Hydrogeology; Industrial and Production Engineering; International & Foreign Law; Nanotechnology; Original Article; Private International Law; Waste Water Technology; Water Industry/Water Technologies; Water Management; Water Pollution Control; Rice straw biosorbent; Factorial design; Artificial intelligence; Cadmium(II) ions
• ISSN: 2190-5487; 2190-5495
• DOI: 10.1007/s13201-015-0295-x

The biosorption efficiency of Cd 2+ using rice straw was investigated at room temperature (25 ± 4 °C), contact time (2 h) and agitation rate (5 Hz). Experiments studied the effect of three factors, biosorbent dose BD (0.1 and 0.5 g/L), pH (2 and 7) and initial Cd 2+ concentration X (10 and 100 mg/L) at two levels “low” and “high”. Results showed that, a variation in X from high to low revealed 31 % increase in the Cd 2+ biosorption. However, a discrepancy in pH and BD from low to high achieved 28.60 and 23.61 % increase in the removal of Cd 2+ , respectively. From 2 3 factorial design, the effects of BD , pH and X achieved p value equals to 0.2248, 0.1881 and 0.1742, respectively, indicating that the influences are in the order X > pH >  BD . Similarly, an adaptive neuro-fuzzy inference system indicated that X is the most influential with training and checking errors of 10.87 and 17.94, respectively. This trend was followed by “pH” with training error (15.80) and checking error (17.39), after that BD with training error (16.09) and checking error (16.29). A feed-forward back-propagation neural network with a configuration 3-6-1 achieved correlation ( R ) of 0.99 (training), 0.82 (validation) and 0.97 (testing). Thus, the proposed network is capable of predicting Cd 2+ biosorption with high accuracy, while the most significant variable was X .