Effect of Different Cosolvents on Transesterification of Waste Cooking Oil in a Microreactor

Biodiesel was prepared from waste cooking oil combined with methanol. The process was performed via transesterification in a microreactor using kettle limescale as a heterogeneous catalyst and various cosolvents under different conditions. n‐Hexane and tetrahydrofuran were selected as cosolvents to investigate fatty acid methyl esters (FAMEs). To optimize the reaction conditions, the main parameters affecting FAME% including reaction temperature, catalyst concentration, oil‐to‐methanol volumetric ratio, and cosolvent‐to‐methanol volumetric ratio were studied via response surface methodology. Under optimal reaction conditions and in the presence of the cosolvents n‐hexane and tetrahydrofuran, high FAME purities were achieved. Considering the experimental results, the limescale catalyst is a unique material, and the cosolvent method can reduce significantly the reaction time and biodiesel production cost.     

Removal of impurities from liquid solutions by vacuum desorption with solvent evaporation

Mass transfer with solvent evaporation in the vapor-liquid two-phase film evaporators used for the removal of undesirable impurities from liquid solutions at low pressure is studied. The average concentrations of solution components in the falling liquid film are determined. The most efficient operating conditions for impurity removal, in which the resistance to mass transfer is concentrated in the liquid phase, are found. Original Russian Text ? V.N. Babak, T.B. Babak, L.P. Kholpanov, 2008, published in Teoreticheskie Osnovy Khimicheskoi Tekhnologii, 2008, Vol. 42, No. 6, pp. 654–670.     

On the modal incremental dynamic analysis

In this article a new technique for the dynamic response of structures is investigated. This applied procedure can predict the approximate seismic performance of the structures and it is fast, inexpensive and results are reasonably acceptable. In fact, this novel method logically combines two different techniques, ‘incremental dynamic analysis (IDA)’ and ‘modal pushover analysis (MPA)’, presented by other researchers. This method will take advantage of both methodical ideas such as equivalent single degree of freedom of multi‐degree structures and the implementation of different scaled level of an earthquake record to the provided equivalent SDF structure. Using this procedure, simple approximate curves that present a realistic linear and non‐linear seismic behaviour of the structure due to the applied scaled level of earthquakes can easily be extracted. In this investigation, several four‐, eight‐ and 12‐storey structures are specified as the example models and are dynamically analysed. Next, three different scaled earthquakes, El Centro, Northridge and San Fernando, are applied to each example problem. The results of the presented technique, modal incremental dynamic analysis (MIDA), are then compared with the IDA method. Comparison of the results reveals good accuracy in building seismic demands evaluation. Finally, it is also shown that the MIDA method is simple enough to be carried out on most personal computers and the authors believe this technique will serve design engineers working in real design conditions. Copyright © 2005 John Wiley & Sons, Ltd.     

Agent-oriented techniques for network supervision

This paper presents the results of the Réseau futé (smart net) project, the goal of which is to use distributed AI and multi-agent techniques for network management and supervision. More precisely, these techniques have been applied to the partial automation of the dynamic processing (what is known about a network is always incomplete and can change at any time) of alarms and of various event notifications received by network management platforms. The system that we propose is able for example to automatically handle some alarms or to filter events of no-interest for a given operator. To achieve this goal, an assistant, or interface agent according to the model proposed by Patti Maes [MK93], has been realized. The goal of the assistant is first to learn, by observation, the behavior of the network supervision operator and second to reproduce such a behavior when the conditions in which the behavior has been learned are detected again. The learned information are stored using chronicles [Gha94]. A chronicle is a data-structure allowing programmers to represent sequences of events while taking temporal knowledge into account. Our assistant has been implemented and tested within Magenta which is a program, written in Smalltalk, that simulates (in a simplified way) a network management platform. This program respects roughly the gdmo and cmis standards.     

A new image encryption scheme with Feistel like structure using chaotic S-box and Rubik cube based P-box

Multimedia Tools and Applications - In this paper, a novel chaos-based dynamic encryption scheme with a permutation-substitution structure is presented. The S-boxes and P-boxes of the scheme are...     

A bio-inspired gelatin-based pH- and thermal-sensitive magnetic hydrogel for in vitro chemo/hyperthermia treatment of breast cancer cells

Gelatin (Gel)-based pH- and thermal-responsive magnetic hydrogels (MH-1 and MH-2) were designed and developed as novel drug delivery systems (DDSs) for cancer chemo/hyperthermia therapy. For this goal, Gel was functionalized with methacrylic anhydride (GelMA), and then copolymerized with (2-dimethylaminoethyl) methacrylate (DMAEMA) monomer in the presence of methacrylate-end capped magnetic nanoparticles (MNPs) as well as triethylene glycol dimethacrylate (TEGDMA; as crosslinker). Afterward, a thiol-end capped poly(N-isopropylacrylamide) (PNIPAAm-SH) was synthesized through an atom transfer radical polymerization technique, and then attached onto the hydrogel through “thiol-ene” click grafting. The preliminary performances of developed MHs for chemo/hyperthermia therapy of human breast cancer was investigated through the loading of doxorubicin hydrochloride (Dox) as an anticancer agent followed by cytotoxicity measurement of drug-loaded DDSs using MTT assay by both chemo- and chemo/hyperthermia-therapies. Owing to porous morphologies of the fabricated magnetic hydrogels according to scanning electron microscopy images and strong physicochemical interactions (e.g., hydrogen bonding) the drug loading capacities of the MH-1 and MH-2 were obtained as 72 ± 1.4 and 77 ± 1.8, respectively. The DDSs exhibited acceptable pH- and thermal-triggered drug release behaviors. The MTT assay results revealed that the combination of hyperthermia therapy and chemotherapy has synergic effect on the anticancer activities of the developed DDSs.     

The corrosion and corrosion-wear behaviour of plasma nitrided 17-4PH precipitation hardening stainless steel

Plasma surface nitriding of 17-4 PH martensitic precipitation hardening stainless steels was conducted at 350 °C, 420 °C and 500 °C for 10 h using a DC plasma nitriding unit, and the surface properties of the plasma surface engineered samples were systematically evaluated. Experimental results have shown that the surface properties of the plasma nitrided layers in terms of hardness, wear resistance, corrosion behaviour and corrosion-wear resistance are highly process condition dependent, and it is feasible to provide considerable improvement in wear, corrosion and corrosion-wear resistance of 17-4PH steel using optimised plasma treatment conditions. All three treatments can effectively improve the surface hardness and the sliding wear resistance under unlubricated conditions; high temperature (420 °C and 500 °C) treated materials revealed improved corrosion and corrosion-wear properties due to the formation of surface compound layers.     

Evolving Takagi–Sugeno fuzzy model based on switching to neighboring models

In this paper, we propose a new online identification approach for evolving Takagi–Sugeno (TS) fuzzy models. Here, for a TS model, a certain number of models as neighboring models are defined and then the TS model switches to one of them at each stage of evolving. We define neighboring models for an in-progress (current) TS model as its fairly evolved versions, which are different with it just in two fuzzy rules. To generate neighboring models for the current model, we apply specially designed split and merge operations. By each split operation, a fuzzy rule is replaced with two rules; while by each merge operation, two fuzzy rules combine to one rule. Among neighboring models, the one with the minimum sum of squared errors – on certain time intervals – replaces the current model.To reduce the computational load of the proposed evolving TS model, straightforward relations between outputs of neighboring models and that of current model are established. Also, to reduce the number of rules, we define and use first-order TS fuzzy models whose generated local linear models can be localized in flexible fuzzy subspaces. To demonstrate the improved performance of the proposed identification approach, the efficiency of the evolving TS model is studied in prediction of monthly sunspot number and forecast of daily electrical power consumption. The prediction and modeling results are compared with that of some important existing evolving fuzzy systems.