Azizul Buang

Evaluation of Aerobic and Anaerobic Co-Digestion of Tetraselmis suecica and Oil Palm Empty Fruit Bunches by Response Surface Methodology

This study investigated co-cultivation of Tetraselmis suecica microalgae with Oil Palm empty fruit bunch (OPEFB) for anaerobic biomethane production and Palm oil mill effluent (POME) treatment. The highest specific biogas production (0.1162 m3 kg-1 COD day-1) and biomethane yield (3900.8 mL CH4 L-1 POME day-1) was achieved with microalgae at 2 mL mL-1 POME, and OPEFB at 0.12 g mL-1 POME. Without co-digestion of microalgae, higher specific biogas production (0.1269 m3 kg-1 COD day-1) but lower biomethane yield (3641.8 mL CH4 L-1 POME day-1) were observed. Second order polynomial model fits the data well with less than 5% error. Higher removal efficiency (62-95%) of COD, BOD, TOC and TN were achieved by aerobic and anaerobic treatment of POME with microalgae than without microalgal treatment after 3 and 7 days of hydraulic retention time (HRT).

Enhanced biosorption of transition metals by living Chlorella vulgaris immobilized in Ca-alginate beads

ABSTRACT In this study freely suspended and Ca-alginate immobilized C. vulgaris cells were used for the biosorption of Fe(II), Mn(II), and Zn(II) ions, from the aqueous solution. Experimental data showed that biosorption capacity of algal cells was strongly dependent on the operational condition such as pH, initial metal ions concentration, dosages, contact time and temperature. The maximum biosorption of Fe(II) 43.43, Mn(II) 40.98 and Zn(II) 37.43 mg/g was achieved with Ca-alginate immobilized algal cells at optimum pH of 6.0, algal cells dosage 0.6 g/L, and contact time of 450 min at room temperature. The biosorption efficiency of freely suspended and immobilized C. vulgaris cells for heavy metals removal from the industrial wastewater was validated. Modeling of biosorption kinetics showed good agreements with pseudo-second-order. Langmuir and D–R isotherm models exhibited the best fit of experimental data. The thermodynamic parameters (ΔG°, ΔH°, and ΔS°) revealed that the biosorption of considered metal ions was feasible, spontaneous and exothermic at 25–45°C. The SEM showed porous morphology which greatly helps in the biosorption of heavy metals. The Fourier transform infrared spectrophotometer (FTIR) and X-rays Photon Spectroscopy (XPS) data spectra indicated that the functional groups predominately involved in the biosorption were C–N, –OH, COO–, –CH, C=C, C=S and –C–. These results shows that immobilized algal cells in alginate beads could potentially enhance the biosorption of considered metal ions than freely suspended cells. Furthermore, the biosorbent has significantly removed heavy metals from industrial wastewater at the optimized condition.

Monoethanolamine (MEA) Wastewater Treatment Using Photo-Fenton Oxidation

Monoalkanolamine (MEA) is useful for scrubbing acidic gases such as carbon dioxide (CO2) in flue gas from oil and gas industries. However, small portion of MEA is carried out during the process and being discharged into the wastewater. The organic properties of MEA make it resilience to the conventional wastewater treatment. Photo-Fenton, a potential way of treating the MEA, is a method of producing hydroxyl radical from reaction of ferrous ion, Fe2+ and hydrogen peroxide, H2O2 with UV light. The hydroxyl radicals will then attack MEA and formed the degradation product. The scope of study is to treat synthetic MEA wastewater with the presence of UV light. Different concentration of ferrous ion and hydrogen peroxide is being tested. The removal efficiency is observed through the Total Organic Carbon (TOC) value of the. It is found that the percentage of TOC removal is higher with presence of UV light compared to without UV light. It is also found that for this project, the optimum concentration of ferrous ion is at 0.014M whereby for hydrogen peroxide, the optimum concentration is at 1.6M with both giving highest TOC removal at 99%.

Assessment of Human Factor Performance Using Bayesian Inference and Inherent Safety

Simply attributing incidents to human error is not adequate; human factors aspects should be investigated such that lessons are learnt and the true root causes are established in order to prevent recurrence. Whilst many petroleum and allied industry businesses have investigated and analyzed incidents – whether with major hazards or occupational injuries potential – human factors aspects are rarely addressed sufficiently. Therefore, this paper presents a hybrid methodology that combines a conventional Swiss Cheese model with Bayesian inference to predict the failure probability of human factors. An inherent safety concept associated with human factor is proposed and utilized as preventive measures to overcome the identified root causes. This approach is then applied to offshore safety assessment study. As a result, the failure probability of human factor can be monitored with time and the best preventive measure can be prioritized once human performance is degraded. It is proven that the approach has the ability to act as predictive tool that provides early warnings toward human deficiency. A preventive measure can then be taken to enhance the overall human performance and ultimately to reduce the likelihood of major incidents.