The amount of palm oil mill residues increases rapidly and will become a severe problem in the future. One potential technique for alleviating this concerning environmental problem is to convert these residues into biochar by the pyrolysis process. Pyrolysis of three types of palm oil mill residues (namely, palm kernel shells, empty palm fruit bunches, and oil palm fibers) was conducted in a fixed bed reactor at 500 C and 2 L/min of nitrogen flow rate for 60 min. The optimization of biochar production was performed using the Box-Behnken design and analyzed using response surface methodology. The effects of three potential factors, including pyrolysis temperatures, nitrogen flow rates, and biomass particle sizes, were studied. The results showed that the highest biochar yield (44.91 wt%) was obtained from pyrolysis of palm kernel shells at 525 C with a nitrogen flow rate of 2 L/min and a particle size of 750 μm. Application of biochar produced from palm kernel shells for carbon dioxide capture was tested in a packed bed adsorber of 3.0 g of biochar sample by flowing 1,400 ppm of carbon dioxide in the gas feed mixture at 2.5 L/min. The capacity of the biochar sample for CO 2 adsorption was 0.46 mmol/g.
Anaerobic digestion is a highly complex process, particularly in co-digestion between poorly-defined, complex co-substrates like distillery wastewater, molasses, and crude glycerine. Thus, in this article, the authors tackled the problems by using Monod two-substrate with an intermediate (M2SI) model to represent accumulated biomethane evolution (ABE) obtained from the co-substrates, including easily degradable, slowly degradable substrates and intermediate. The M2SI model predictions were compared with the traditional Monod model's simulation results to clarify an outstanding of the present model in the aspect of modeling and control. Different behaviors of ABE curves from batch experiments were used to calibrate the M2SI model prediction with sensitivity analysis of the model parameters. It was found that the M2SI model gives a correct trend to describe the co-digestion process with multiple substrates and complex microbial activities with satisfactory fitting accuracy. At the same time, simple Monod kinetics have a good fit for dilute pure distillery wastewater, but the estimated microbial growth kinetics were counterintuitive. Therefore, the M2SI Model has a broader range of applications for co-digestion dealing with the complexity of multiple microbial activities to consume inherently complex or artificial co-substrates.
This work is an attempt to describe the dynamics of a two-stage industrial biogas plant using palm oil mill effluent (POME) and the mixture of POME with effluent from rubber factory (LTE), both at steady state and transient peroid before system failure accurred. One incident occurred in POME treatment plant when LFE bypassed its digesters and mixed together with palm-oil-mill wastewater due to no space in the existing latex wastewater ponds under water flooding during heavy raining period. The model was developed based on simplified ADM1 incorporating the effects of ALK/VFA and pH on the microbial growth. The model prediction for such scenario was in agreement with the actual data from the incident which occurred during November 2014. The Steady state simulation estimated that Ss reduced from 74,917 to 2856 mg/l at HRT 15 d which agreed well with the actual data. Dynamic simulation after adding LTE predicted that the Ss reduced to 20,300 at HRT 10.71 d which was the correct trend albeit rather imprecise. That was considered satisfactory for future operational purpose. This discrepancy was due to the difficulty in estimating many process parameters. In general the model demonstrates the usefulness of the ADM1 in describing behavior of an anaerobic wastewater treatment system from palm oil mill industry and can be used for the purpose of future design and operating of the existing plants.
Kiln drying of rubberwood lumbers is a complex transport phenomenon for realistic modeling and simulation. To decouple this complexity, researchers usually divide their research into two parts. The first one is single-lumber drying kinetics to describe how wood lumber responds to its surface conditions. Then they combine this drying kinetics with a lumped transport model or dispersion model or computational fluid dynamics. The mathematical models are then solved numerically to predict the industrial kiln drying behaviors. This work focuses on the drying kinetics of stacked rubberwood lumbers using hot air at different air velocity (0.5, 1.5, 2.5, 3.5, 4.0 m/s), relative humidity (6–67% relative humidity (RH)) and temperature (60–100 °C). The drying kinetics followed the conventional drying theory. However, the two drying periods, namely constant and falling rate (CRP and FRP), were not distinct. As the air velocity increased, the transition from CRP to FRP is faster. The middle of the transition period (at critical moisture content, CMC) moves closer to the fiber saturation point (FSP). The overall mass transfer coefficients in the falling rate period for stacked rubberwood drying were lower than those predicted by the Ananias correlation. Hence, a modified formula was proposed, representing the overall moisture transfer coefficients as a function of air velocity, temperature, relative humidity, and lumbers thickness for the range of variables under investigation satisfactorily. In general, the drying rate and the overall moisture transfer coefficient increased with increasing air velocity, drying temperature, and decreasing RH. Relative humidity directly affects the driving force of moisture transfer rate because higher RH is associated with higher equilibrium moisture content. A lumped parameter model for kiln drying was also developed. After being integrated with the estimated mass transfer coefficient, the model can predict the moisture profiles in lab-scale kiln drying satisfactory, although the model needs more validation data. These kinetic parameters and correlation for stacked rubberwood drying can be used in more complex models and process optimization in future research.
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