Water pollution has become a major issue in many countries, including Malaysia. Malaysia is one of the countries that suffers from this detrimental influence on water resource sustainability. Adsorption has been discovered to be a cost-effective and efficient method of removing contaminants such as pigments, dyes, and metal impurities. Many biomass-based adsorbent materials have been successfully used for the removal of dyes from aqueous solutions. In this study, the potential use of coconut dregs as the new biosorbent for the removal of Methylene Blue (MB) (basic dye) and Brilliant Red Remazol (BRR) (acidic dye) was investigated. The effects of adsorption time, adsorbent dosage, pH, and initial dye concentration on coconut dregs adsorption for MB and BRR dye were investigated using 2-Level Factorial Design of Design-Expert 7.1.5. The results indicated that the amount of dye adsorbed on the coconut dregs increased with increasing dye concentration, adsorbent dosage, and adsorption time. However, both MB and BRR dyes favor different pH for the adsorption process. The adsorption capacity of MB dye increased with increasing pH, while the adsorption capacity of BRR dye increased with decreasing pH. Removal of MB was optimum at pH 11, contact time of 240 min, a dosage of 0.25 g adsorbent, and an initial dye concentration of 50 mg/L. Meanwhile, for BRR dye, the optimum condition was pH 2, contact time of 180 min, the dosage of 0.25 g adsorbent, and an initial dye concentration of 50 mg/L. The equilibrium data for both dyes fitted very well with the Langmuir Isotherm equation giving a maximum monolayer adsorption capacity as high as 5.7208 mg/g and 3.7636 mg/g for Methylene Blue Dye and Brilliant Red Remazol dye, respectively. This study shows that coconut dregs can be one of the potential and low-cost biosorbents for the treatment of industrial dyes soon.
Red algae species, Euchema Spinosum (ES) in Malaysia possesses excellent biosorbent properties in removing dyes from aqueous solutions. In the present study, the experimental design for the biosorption process was carried out via response surface methodology (RSM-CCD). A total of 20 runs were carried out to generate a quadratic model and further analysed for optimisation. Prior to the evaluation, the characterisation study of the ES was performed. It was observed that the maximum uptake capacity of 399 mg/g (>95%) is obtained at equilibrium time of 60 min, pH solution of 6.9-7.1, dosage of 0.72 g/L and initial dye concentration of 300 g/L through statistical optimisation (CCD-RSM) based on the desirability function. It is demonstrated in the present study that the ANN model (R2=0.9994, adj-R2=0.9916, MSE=0.19, RMSE=0.4391, MAPE=0.087 and AARE=0.001) is able to provide a slightly better prediction in comparison to the RSM model (R2= 0.9992, adj-R2= 0.9841, MSE=1.95, RMSE=1.395, MAPE=0.08 and AARE=0.001). Moreover, the SEM-EDX analysis indicates the development of a considerable number of pore size ranging between 132 to 175 mm. From the experimental observations, it is evident that the ES can achieve high removal rate (>95%), indeed become a promising eco-friendly biosorptive material for MB dye removal.
Water treatment plants (WTPs) in Kuantan river basin abstracts water from the blue water source, which is the Kuantan river. Therefore, by accounting the blue water footprint (WFb), the overall water consumption for all five WTPs namely; Sungai Lembing, Bukit Sagu, Panching, Semambu, and Bukit Ubi can be obtained. In order to predict the value, Backpropagation method is the best method to be used due to the historical data obtained from the WFb accounting for all five WTPs above. The objective of this study is to predict the overall blue water consumption for water treatment plants located along Kuantan river basin using Backpropagation method in artificial neural network. In this study, WFb has been accounted throughout all water treatment plants by using reference from water footprint manual. Then, the WFb will undergo a series of testing using application in MATLAB software in order to predict the future value based on historical data from 2015 until 2016. As a result, the total WFb accounting obtained was 190,543,378.2 m3/day, while the total maximum capacity of the WTPs was 189,654,000 m3/day. Hence, the prediction value that kept increasing will not be able to cater the future demand due to unstoppable urbanization.
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