An acidic ionic liquid-conventional alkali-catalyzed biodiesel production process

A study was undertaken to prepare biodiesel via two-step process using ionic liquid as first step catalyst due to the unsuitability of using the straight alkaline-catalyzed transesterification of high FFA presented in crude palm oil (CPO). In the first step, esterification of the FFA presented in the CPO was carried out using butylimidazolium hydrogen sulfate (BIMHSO₄), in which the acid value was reduced from 6.93 to 1.02mg KOH/g and then, KOH-catalyzed transesterification was applied. The conversion rate of FFA attained 85.3% when 4.8 wt% of BIMHSO₄ was applied to the reaction system containing methanol to CPO ratio of 12: 1 reacted at 170 °C for 150min. The final yield in 97.3% revealed that the process proposed in this study could lead to an excellent biodiesel meeting the ASTM requirements. Furthermore, this new two-step catalysis process could solve the old conventional catalysis process drawbacks.     

Chaos control of a bounded 4D chaotic system

This paper is concerned with the problem of optimal and adaptive control for controlling chaos in a novel bounded four-dimensional (4D) chaotic system. This system can display hyperchaos, chaos, quasiperiodic and periodic behaviors, and may have a unique equilibrium, three equilibria and five equilibria for the different system parameters. An optimal control law is designed for the novel bounded chaotic system, based on the Pontryagin minimum principle. Furthermore, we propose Lyapunov stability conditions to control the new bounded 4D chaotic system with unknown parameters by a feedback control approach. Numerical simulations are presented to show the effectiveness of the proposed chaos control scheme.     

A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

     

Fabrication and characterization of microencapsulated PA with SiO<Subscript>2</Subscript> shell through sol–gel synthesis via sodium silicate precursor

In the present study, the microencapsulated phase change material with palmitic acid as core and inorganic SiO₂ shell was successfully fabricated by a sol–gel method in alkaline medium via sodium silicate precursor. The chemical compositions, crystalloid phase, microstructure and morphology of PA@SiO₂ microcapsule were studied by Fourier transform infrared spectroscopy, X-ray diffractometer, scanning electron microscopy and transmission electron microscopy. Differential scanning calorimetry and thermogravimetric analysis were used to determine the thermal properties and thermal stability of microcapsules, respectively. According to the XRD and FT-IR results, all the characteristic peaks of PA and SiO₂ were observed and there is no chemical reaction between them. Scanning electron microscopy images indicated that the microcapsule synthesized in pH 11 had a perfect spherical shape with smooth surfaces compared with other samples, and transmission electron microscopy images confirm that the PA have been well encapsulated by SiO₂. Differential scanning calorimetry analysis showed that the microcapsules indicated similar phase change behaviors as those of pristine PA, which melt at 67.2?°C with a latent heat of 111.2 J/g and freezing at 56.5?°C with a latent heat of 103.2 J/g. TGA analysis indicated that the thermal stability of the PA was also improved due to the protection of SiO₂ shell toward the encapsulated PA.     

Grain Refinement by Cyclic Displacive Forward/Reverse Transformation in Fe-High-Ni Alloys

A novel method for grain refinement of martensite structures was proposed, in which transformation strain is accumulated by cyclic displacive forward and reverse transformations. This method can refine martensite structures in an Fe-18Ni alloy because a high density of austenite dislocations is introduced by a displacive reverse transformation in addition to an inheritance of dislocations in body-centered cubic martensite into austenite during cyclic transformation. The addition of a small amount of carbon accelerates structure refinement significantly, which results in the formation of ultra-fine-grained structures after ten cycles.

     

An empirical study of software release notes

Release notes are an important source of information about a new software release. Such notes contain information regarding what is new, changed, and/or got fixed in a release. Despite the importance of release notes, they are rarely explored in the research literature. Little is known about the contained information, e.g., contents and structure, in release notes. To better understand the types of contained information in release notes, we manually analyzed 85 release notes across 15 different software systems. In our manual analysis, we identify six different types of information (e.g., caveats and addressed issues) that are contained in release notes. Addressed issues refer to new features, bugs, and improvements that were integrated in that particular release. We observe that most release notes list only a selected number of addressed issues (i.e., 6-26 % of all addressed issues in a release). We investigated nine different factors (e.g., issue priority and type) to better understand the likelihood of an issue being listed in release notes. The investigation is conducted on eight release notes of three software systems using four machine learning techniques. Results show that certain factors, e.g., issue type, have higher influence on the likelihood of an issue to be listed in release notes. We use machine learning techniques to automatically suggest the issues to be listed in release notes. Our results show that issues listed in all release notes can be automatically determined with an average precision of 84 % and an average recall of 90 %. To train and build the classification models, we also explored three scenarios: (a) having the user label some issues for a release and automatically suggest the remaining issues for that particular release, (b) using the previous release notes for the same software system, and (c) using prior releases for the current software system and the rest of the studied software systems. Our results show that the content of release notes vary between software systems and across the versions of the same software system. Nevertheless, automated techniques can provide reasonable support to the writers of such notes with little training data. Our study provides developers with empirically-supported advice about release notes instead of simply relying on adhoc advice from on-line inquiries.     

Experimental investigation on the mean drop size and drop size distribution in an L-shaped pulsed sieve-plate column

Vertical pulsed extraction columns cannot be employed in applications with height limitations. On the other hand, the horizontal extraction columns have low throughput, which affects their applicability in industrial applications. Therefore, there is a need to design a new type of extractors for such circumstances. In this paper, an experimental study on drop sizes has been implemented in a novel L-shaped pulsed sieve-plate extraction column in the absence and presence of mass transfer. Moreover, new correlations are developed for prediction of the mean drop size and size distribution using the log-normal probability density function.