Tjandra Setiadi

Adsorption and desorption of copper(II) ions onto garden grass

The garden grass (GG) was firstly used to remove copper(II) from water as bioadsorbent. From the results of characterisation, the GG had the merits of high specific surface area, significant adsorption sites and functional groups. Copper-adsorption significantly depends on the initial copper concentrations, contact time, pH, adsorbent doses, particle sizes and temperature. The positive values of ΔG° indicates that the adsorption of copper onto garden grass is non-spontaneous and values lies within the ranges of 4.452-13.660 kJ/mol for supporting physical adsorption. 0.1N H(2)SO(4) was found as suitable eluent, which could be used 5 cycles of adsorption-desorption. The data from adsorption and desorption equilibrium were well fitted by the Langmuir, SIPS and Redlich-Peterson isotherm models. The maximum adsorption and desorption capacities were 58.34 and 319.03 mg/g, respectively, for 1g dose. Adsorption and desorption kinetics could be described by the Pseudo-first-order model.

Flocculation study on spiral flocculator

Traditionally, the Jar Test has been used to assess the suitability of flocculation treatment for a given raw water. Unfortunately, the Jar Test suffers from a number of disadvantages. The new method of assessment of flocculation, Spiral Flocculator, has been claimed to be a better method than the Jar Test apparatus and a good method for continuous assessment of flocculation. This study compares the performance of Spiral Flocculator and Jar Test, and mathematical modeling of the Spiral Flocculator performance. The experiments conducted reveal that the Spiral Flocculator has a better effective energy dissipation compared to that of Jar Test. However, the size of flocs in Spiral Flocculator seems to be overestimated relative to the real flocculator and Jar Test.

Palm oil mill effluent treatment and utilization to ensure the sustainability of palm oil industries

The purpose of this study was to evaluate the current condition of palm oil mill effluent (POME) treatment and utilization and to propose alternative scenarios to improve the sustainability of palm oil industries. The research was conducted through field survey at some palm oil mills in Indonesia, in which different waste management systems were used. Laboratory experiment was also carried out using a 5 m(3) pilot-scale wet anaerobic digester. Currently, POME is treated through anaerobic digestion without or with methane capture followed by utilization of treated POME as liquid fertilizer or further treatment (aerobic process) to fulfill the wastewater quality standard. A methane capturing system was estimated to successfully produce renewable energy of about 25.4-40.7 kWh/ton of fresh fruit bunches (FFBs) and reduce greenhouse gas (GHG) emissions by about 109.41-175.35 kgCO2e/tonFFB (CO2e: carbon dioxide equivalent). Utilization of treated POME as liquid fertilizer increased FFB production by about 13%. A palm oil mill with 45 ton FFB/hour capacity has potential to generate about 0.95-1.52 MW of electricity. Coupling the POME-based biogas digester and anaerobic co-composting of empty fruit bunches (EFBs) is capable of adding another 0.93 MW. The utilization of POME and EFB not only increases the added value of POME and EFB by producing renewable energy, compost, and liquid fertilizer, but also lowers environmental burden.

Production of polyhydroxyalkanoate (PHA) by Ralstonia eutropha JMP 134 with volatile fatty acids from palm oil mill effluent as precursors

The highest volatile fatty acids (VFAs) concentration from palm oil mill effluent (POME) treated by anaerobic fermentation was achieved for a 1-day process when the main acids used were acetic, propionic and butyric acids. Polyhydroxyalkanoate (PHA) production with VFAs from POME as precursors in the fed-batch mode has advantages over batch mode, both in terms of its productivity and 3HV (3-hydroxyvalerate) composition in the produced polymer. With the fed batch, the productivity increased to 343% and contained more 3HV than those of the batch. The structures of the PHA were identified by different methods and they supported each other; the resulting products consisted of functional groups of 3HB (3-hydroxybutyrate) and 3HV.

Equilibrium and kinetic modelling of cadmium (II) biosorption by Dried Biomass Aphanothece sp. from aqueous phase

The Biosorption of cadmium (II) ions on dried biomass of Aphanothece sp.which previously grown in a photobioreactor system with atmospheric carbon dioxide fed input, was studied in a batch system with respect to initial pH, biomass concentration, contact time, and temperature. The biomass exhibited the highest cadmium (II) uptake capacity at 30oC, initial pH of 8.0±0.2 in 60 minute and initial cadmium (II) ion concentration of 7.76 mg/L. Maximum biosorption capacities were 16.47 mg/g, 54.95 mg/g and 119.05 mg/g at range of initial cadmium (II) 0.96–3.63 mg/L, 1.99–8.10 mg/L and 6.48–54.38 mg/L, respectively. Uptake kinetics follows the pseudo-second order model while equilibrium is best described by Langmuir isotherm model. Isotherms have been used to determine thermodynamic parameter process (free energy change, enthalpy change and entropy change). FTIR analysis of microalgae biomass revealed the presence of amino acids, carboxyl, hydroxyl, sulfhydryl and carbonyl groups, which are responsible for biosorption of metal ions. During repeated sorption/desorption cycles, the ratio of Cd (II) desorption to biosorption decreased from 81% (at first cycle) to only 27% (at the third cycle). Nevertheless, due to its higher biosorption capability than other adsorbent, Aphanothece sp appears to be a good biosorbent for removing metal Cd (II) ions from aqueous phase.

Application of An Adaptive Neuro Fuzzy Inference System (ANFIS) for a Class of Fed-Batch Fermentation Processes

Abstract This paper considers an implementation of a modified adaptive neuro fuzzy inference system (ANFIS) controller to find substrate feedrate for the growth of Saccharomyces cerevisiae in a fed-batch fermentation process from a given profile of output responses. The adaptive capability of the modified ANFIS is an advantage to handle nonlinear and uncertain dynamics of the fed-batch fermentation process. The hybrid learning algorithm of the modified ANFIS comprises two steps, i.e. forward pass by using fuzzy inference mechanism based on the neural architecture and Least-squares Estimator (LSE), and backward pass to update the premise parameters by the error a modified backpropagation algorithm.