Microwave-Assisted Pretreatment of Sago Palm Bark

Three types of microwave-assisted diluted solvents were employed using 0.1 N H₂SO₄ (MSA), 0.1 N NaOH (MSH), and 0.01 N NaHCO₃ (MSB). These solvents were evaluated as possible pretreatment routes for sago palm bark (SPB) with their effects on the pretreated substrate. A variety of analyses, consisting of fiber analysis, energy dispersive X-ray spectroscopy (EDX), X-ray fluorescence spectrometer (XRF), X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscope (SEM), and high-performance liquid chromatography (HPLC), were performed to understand the pretreatment effects on the chemical and physical characteristics of SPB and pretreatment liquor. The thermal analysis has revealed that higher hemicellulose degradation was also found in MSA pretreatment. In the analyses of the pretreatment liquid for the extracted monomeric sugar, a higher amount of glucose was found (9 mg/g) using MSH pretreatment and the highest xylose level was found (4 mg/g) using MSA pretreatment. The analysis of the formation of inhibitors has shown that acetic acid was only found in the MSH pretreatment.     

Assessing the kinetic model of hydro-distillation and chemical composition of Aquilaria malaccensis leaves essential oil

This study aimed to model the kinetic of hydro-distillation of Aquilaria malaccensis leaves oil in order to understand and optimize the extraction process. In addition, this study, for the first time, aimed to identify the chemical compositions of the A. malaccensis leave-oil. By assessing both first-order kinetic model and the model of simultaneous washing and diffusion, the result indicated that the model of simultaneous washing and diffusion better describes the hydro-distillation mechanism of the essential oil from A. malaccensis leaves.The optimum time, solid to liquid ratio, and the heating power for extracting the highest amount of essential oil were found to be around 3 h, 1:10(g·ml-1), and 300 W respectively. Yellow essential oil with a strong smell and a yield of 0.05 v/w was extracted by hydro-distillation Clevenger apparatus. Chemical compounds of the essential oil were analyzed using gas chromatography–mass spectroscopy(GC/MS), which resulted in identification of 42 compounds that constitute 93% of essential oil. Among the identified components,Pentadecanal(32.082%), 9-Octadecenal,(Z)(15.894%), and Tetradecanal(6.927%) were the major compounds.Considering the fact that all the identified major components possess pesticidal properties, A. malaccensis leaves can be regarded as a promising natural source for producing pesticides.     

Location allocation modeling for healthcare facility planning in Malaysia

Malaysia has seen tremendous growth in the standard of living and household per capita income. The demand for a more systematic and efficient planning has become increasingly more important, one of the keys to achieving a high standard in healthcare. In this paper, a Maximal Covering Location Problem (MCLP) is used to study the healthcare facilities of one of the districts in Malaysia. We address the limited capacity of the facilities and the problem is formulated as Capacitated MCLP (CMCLP). We propose a new solution approach based on genetic algorithm to examine the percentage of coverage of the existing facilities within the allowable distance specified/targeted by Malaysian government. The algorithm was shown to generate good results when compared to results obtained using CPLEX version 12.2 on a medium size problem consisting of 179 nodes network. The algorithm was extended to solve larger network consisting of 809 nodes where CPLEX failed to produce non-trivial solutions. We show that the proposed solution approach produces significant results in determining good locations for the facility such that the population coverage is maximized.     

Alternative for Rapid Detection and Screening of Pork,Chicken, and Beef Using Dielectric Properties in the Frequency of 0.5 to 50 GHz

Dielectric property at high microwave frequency region has been utilized for possible rapid detection and screening of different types of meat, especially for halal authentication. This investigation focused on both raw and sterilized (processed) beef, pork, and chicken samples. Dielectric response that consists of dielectric constant and dielectric loss factor was measured over the range of 0.5 to 50 GHz. All raw and sterilized meat samples could be differentiated by the dielectric values. Two distinct peaks were observed only for both raw and sterilized pork samples at the frequency around 7.43 and 31.19 GHz. These peaks can potentially be linked to compounds that exist only in pork such as DNA, microbes, enzyme, proteins, amino acids, and many others. Dielectric values for sterilized samples were lower than raw samples due to molecule structural changes that occurred in the samples. The dielectric results promise a great potential of utilizing dielectric properties as a rapid on-site detection approach prior to subsequent laborious analysis.     

Development of a hybrid PSO–ANN model for estimating glucose and xylose yields for microwave-assisted pretreatment and the enzymatic hydrolysis of lignocellulosic biomass

In this paper, two artificial intelligent systems, the artificial neural network (ANN) and particle swarm optimization (PSO), were combined to form a hybrid PSO–ANN model that was used to improve estimates of glucose and xylose yields from the microwave–acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on pretreatment parameters. ANN is a powerful tool capable of determining the relationship between the desired input and output data while PSO was used as a robust population-based search algorithm to optimize the performance of the ANN model. Specifically, it was used to determine the optimum number of neurons in the hidden layer and the best value of the learning rate of the ANN model. The optimization method includes minimizing the fitness function mean absolute error that was found to be 0.0176. The PSO algorithm suggested an optimum number of neurons in the hidden layer as 15 and a learning rate of 0.761 these consequently used to construct the ANN model. After constructing the hybrid PSO–ANN model, the performance of the intelligent system was examined by determining the regression coefficient (R ²) for estimating the experimental values of glucose and xylose and compared to the results from a response surface methodology (RSM) model. The results of R ² of the hybrid PSO–ANN model for glucose and xylose were 0.9939 and 0.9479, respectively, while the RSM model results for the same sugars were 0.8901 and 0.8439. This analysis reveals that the hybrid PSO–ANN model offers a higher degree of accuracy in comparison with the more commonly used RSM model.

     

Optimization and modeling of the performance of polydimethylsiloxane for pervaporation of ethanol−water mixture

Response surface methodology was used to optimize the performance of pervaporation of ethanol aqueous solution using polydimethylsiloxane hollow-fiber membrane. The effects of four operating conditions, that is, the feed temperature (30–50°C), the feed flow rate (10–50 L/h), ethanol concentration (5–20 wt%), and the vacuum pressure (10–50 KPa) on the membrane selectivity and the total flux of permeation were investigated with response surface methodology. The results showed that a quadratic model was suggested for both selectivity and total flux showing a high accuracy with R² = 0.9999 and 0.9995, respectively. The developed models indicated a significant effect of the four studied factors on both selectivity and total flux with some significant interactions between these factors. The optimum selectivity was 15.56, achieved for a feed temperature of 30°C, feed flow rate of 10 L/h, ethanol concentration of 15 wt%, and a permeate pressure of 10.74 KPa whereas the optimum total flux was 1833.66 g/m².h was observed for at a feed temperature of 50°C, a feed flow rate of 50 L/h, ethanol concentration of 15 wt%, and a permeate pressure of 49.38 KPa.     

Review of technologies for oil and gas produced water treatment

Produced water is the largest waste stream generated in oil and gas industries. It is a mixture of different organic and inorganic compounds. Due to the increasing volume of waste all over the world in the current decade, the outcome and effect of discharging produced water on the environment has lately become a significant issue of environmental concern. Produced water is conventionally treated through different physical, chemical, and biological methods. In offshore platforms because of space constraints, compact physical and chemical systems are used. However, current technologies cannot remove small-suspended oil particles and dissolved elements. Besides, many chemical treatments, whose initial and/or running cost are high and produce hazardous sludge. In onshore facilities, biological pretreatment of oily wastewater can be a cost-effective and environmental friendly method. As high salt concentration and variations of influent characteristics have direct influence on the turbidity of the effluent, it is appropriate to incorporate a physical treatment, e.g., membrane to refine the final effluent. For these reasons, major research efforts in the future could focus on the optimization of current technologies and use of combined physico-chemical and/or biological treatment of produced water in order to comply with reuse and discharge limits.     

The effect of acetylation on the crystallinity of BC/CNTs nanocomposite

BACKGROUND: Bacterial cellulose (BC) synthesized by Acetobacter xylinum has been shown to be a potential matrix for impregnating carbon nanotubes (CNTs) to produce bacterial cellulose/carbon nanotubes (BC/CNTs) nanocomposite. The objective of this study was to investigate the crystal structure of BC/CNTs nanocomposite after acetylation treatment. Approximately 0.05 w/v% CNTs were added onto continuously developed BC. Then, the BC/CNTs nanocomposite was acetylated heterogeneously. RESULTS: The crystallinity characteristic of formed BC/CNTs acetate was analyzed using an X‐ray diffractometer (XRD). The crystallization index (CrI) of BC/CNTs acetate was better than BC acetate, i.e. 97:3 and 93:7, respectively. The crystallite size of BC microfibrils in BC/CNTs sheet was reduced because of the impregnated CNTs. However, the reduction of crystallite size for BC/CNTs acetate was lower than that of BC acetate showing that the existence of CNTs within BC micofibrils had protected the BC crystal structure from further destruction by acetylating agent. CONCLUSION: CNTs is a powerful substance that acted as a void filler between BC crystalline structure and protected the nano‐biocomposite from total degradation by the acetylation process. Copyright © 2011 Society of Chemical Industry     

Influence of catalytic particle size on the performance of fluidized-bed chemical vapor deposition synthesis of carbon nanotubes

In this paper, the impacts of catalytic particle size on the overall reactor performance for carbon nanotubes (CNTs) production using a fluidized-bed chemical vapor deposition (FBCVD) process have been studied. Six different particle size fractions (10-20 μm, 20-53 μm, 53-75 μm, 75-100 μm, 100-200 μm, and 200-300 μm) were selected. It was observed that the smaller the catalytic particle diameter, the greater the carbon deposition efficiency and the greater CNT synthesis selectivity. The 10-20 μm catalytic particles exhibited 30% higher carbon deposition efficiency than the 200-300 μm catalytic particles. The selectivity toward CNTs formation was also approximately 100%. These observations could be explained by the fact that when the diameter of the catalytic particle gets smaller, the breakthrough capacities during frontal diffusion will be bigger due to a shorter diffusion path length within the particle. Moreover, the fine particles ensured high interstitial velocity which subsequently enhances the heat and mass transfer, and consequently improves the CVD reaction.