The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.
This study describes the properties of colloidal gold nanoparticles (AuNPs) with sizes of 20, 30 and 40 nm, which were synthesized using citrate reduction or seeding-growth methods. Likewise, the conjugation of these AuNPs to mouse anti-human IgG(4) (MαHIgG(4)) was evaluated for an immunochromatographic (ICG) strip test to detect brugian filariasis. The morphology of the AuNPs was studied based on the degree of ellipticity (G) of the transmission electron microscopy images. The AuNPs produced using the seeding-growth method showed lower ellipticity (G ≤ 1.11) as compared with the AuNPs synthesized using the citrate reduction method (G ≤ 1.18). Zetasizer analysis showed that the AuNPs that were synthesized using the seeding-growth method were almost monodispersed with a lower polydispersity index (PDI; PDI≤0.079), as compared with the AuNPs synthesized using the citrate reduction method (PDI≤0.177). UV-visible spectroscopic analysis showed a red-shift of the absorbance spectra after the reaction with MαHIgG(4), which indicated that the AuNPs were successfully conjugated. The optimum concentration of the BmR1 recombinant antigen that was immobilized on the surface of the ICG strip on the test line was 1.0 mg ml(-1). When used with the ICG test strip assay and brugian filariasis serum samples, the conjugated AuNPs-MαHIgG(4) synthesized using the seeding-growth method had faster detection times, as compared with the AuNPs synthesized using the citrate reduction method. The 30 nm AuNPs-MαHIgG(4), with an optical density of 4 from the seeding-growth method, demonstrated the best performance for labelling ICG strips because it displayed the best sensitivity and the highest specificity when tested with serum samples from brugian filariasis patients and controls.