Operation simulation of a recycled electrochemical ozone generator using artificial neural network

The present work has focused on the modeling and simulation of a recycled ozone generator system via electrochemical oxidation of water. To produce ozone, a Pyrex glass electrochemical reactor, comprised of two separate half-cell by Nafion 117 membrane was applied. The applied anode and cathode electrodes were Ti/Sn-Sb-Ni and platinized titanium, respectively. The modeling and simulation of the reactor operation were done via artificial neural network (ANN) technique. In this regard, four important operational parameters (i.e. electrolyte concentration, applied voltage, flow rate and electrolysis time) and the generated ozone concentration were considered as the independent inputs and the network output, respectively. To find out the best model, six numbers of three-layered ANNs with different functions were constructed and optimized. Best simulation was related to a model, consist of Levenberg–Marquardt Back propagation learning algorithm (trainlm) and tangent sigmoid (tansig) as transfer function in the both hidden and output layers. Also, application of 10 hidden neurons and 80 iterations for the network calibration caused to satisfy the network training while overfitting was prevented. The K-fold cross-validation method, employed for the model evaluation, showed high correlation coefficient (0.9936) and low mean square error (3.58 × 10⁻⁴) for the testing data. Sensitivity analysis indicated order of relative importance the operational parameters on the ozone production as: time > [electrolyte] > voltage > flow rate.     

Design of a general point focusing crystal geometry for x-ray spectroscopy

A nearly exact general equation for geometrical angular deviations from the Bragg angle over entire curved crystal surfaces is derived using a toroidally curvilinear coordinate system and applied on the nine conventional crystal geometries. Although the derived formula confirms Wittry's results for the first five cases, it shows considerable differences for the more important cases, such as 45° point focusing, general point focusing, and Berreman geometries. The effective scattering areas for the mentioned cases have been derived, plotted, and interpreted. A point-to-point focusing crystal geometry is introduced, and it is shown that it approaches Wittry's and spherical plane-spherical Johansson geometries as θ(B) → 90° and θ(B) → 0°, respectively.     

Catalytic NO–H2–CO–O2 reactions over Pt-supported mesoporous yttrium oxide

Catalytic light-off of a stream of NO, H₂, CO in an excess O₂ has been studied over various metal oxides loading 1 wt% Pt. Because a low-surface area Y₂O₃ (<5 m² g⁻¹) was found to exhibit the highest de-NO_x activity, a mesoporous Y₂O₃ was then synthesized from an yttrium-based surfactant mesophase templated by dodecyl sulfate , which was anion-exchanged by acetate (AcO = CH₃COO⁻). The product showed a 3-D mesoporosity with a large surface area (396 m² g⁻¹) and the Pt-supported catalyst achieved much improved light-off characteristics suitable for the low-temperature de-NO_x in the presence of CO and excess O₂.     

Manufacturing cell performance improvement: a simulation study

This paper reports on the study of the “underbody front”-automated welding cell at Opel Belgium, a major automobile manufacturer of General Motors International Operations. It employs the use of simulation in an experimental design framework to identify potential improvements in average daily output through management of buffer sizes at key buffer locations within the cell. Many practical applications of animated computer simulation stop at the modeling and displaying of the process under study. Simulation as a tool for process reengineering or enhancement can only reach its full potential if incorporated in a comprehensive statistical study, so as to attain statistically significant results. The paper also reports on the reactions of, and issues raised by, management when the experimental design methodology was presented as a tool for process enhancement and productivity improvement.     

In vitro antibacterial activity of ceftazidime,unlike ciprofloxacin,improves in the presence of ZnO nanofluids under acidic conditions

The development of antibiotic resistance among hospital pathogens has provided a great need for new antimicrobial agents. Zinc oxide nanoparticles (ZnO NPs) in combination with various antibiotics can act as a reducing agent for antibiotic resistance. The aim of this study was to investigate the influence and the mechanism of ZnO NPs on the antimicrobial activity of ciprofloxacin (CP) and ceftazidime (CAZ) against Enterococcus faecalis (E. faecalis) and Acinetobacter baumannii (A. baumannii) bacteria in acidic conditions (pH 5.5). ZnO NPs were synthesised using the solvothermal method and characterised. The MIC90 value of ZnO NPs against A. baumannii was 0.25 mg ml⁻¹ and its highest growth‐inhibitory activity was observed at 0.125 mg ml⁻¹ for E. faecalis. The Fourier transform infrared spectroscopy spectra of ZnO NPs treated with antibiotics showed the interaction between ZnO NPs and each of the two antibiotics. ZnO NPs at a sub‐inhibitory concentration had no effect on the antibacterial activity of CP and CAZ against E. faecalis and CP against A. baumannii. The action mechanism of ZnO NPs for enhancing the antibacterial efficacy of CAZ against A. baumannii was evaluated. ZnO NPs caused to increase in the antibacterial activity of CAZ against A. baumannii, possibly through the release of Zn²⁺ and increasing of membrane permeability.Inspec keywords: nanofluidics, antibacterial activity, drugs, nanoparticles, nanomedicine, microorganisms, pH, zinc compounds, II‐VI semiconductors, wide band gap semiconductors, semiconductor growth, X‐ray diffraction, light scattering, scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, DNA, molecular biophysics, biochemistry, atomic absorption spectroscopy, membranes, permeability, nanofabricationOther keywords: in vitro antibacterial activity, ceftazidime, nanofluids, acidic conditions, antibiotic resistance, hospital pathogens, antimicrobial agents, zinc oxide nanoparticles, reducing agent, antimicrobial activity, ciprofloxacin, Enterococcus faecalis bacteria, Acinetobacter baumannii bacteria, pH, solvothermal method, X‐ray diffraction, dynamic light scattering, ultraviolet‐visible spectrum, scanning electron microscopy, transmission electron microscopy, MIC90 value, growth‐inhibitory activity, Fourier transform infrared spectroscopy, subinhibitory concentration, reactive oxygen species measurement, DNA fragmentation, atomic absorption spectroscopy, SEM, membrane permeability, glycerol‐ammonium citrate. mixture, ZnO     

Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models

Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices. In the present study the abilities of hybrid wavelet-genetic programming [i.e. wavelet-gene-expression programming, WGEP] and wavelet-neuro-fuzzy (WNF) models for daily precipitation forecasting are investigated. In the first step, the single genetic programming (GEP) and neuro-fuzzy (NF) models are applied to forecast daily precipitation amounts based on previously recorded values, but the results are very weak. In the next step the hybrid WGEP and WNF models are used by introducing the wavelet coefficients as GEP and NF inputs, but no satisfactory results are produced, even though the accuracies increased to a great extent. In the third step, the new WGEP and WNF models are built; by merging the best single and hybrid models’ inputs and introducing them as the models inputs. The results show the new hybrid WGEP models are effective in forecasting daily precipitation, while the new WNF models are unable to learn the non linear process of precipitation very well.     

Combining algorithms for automatic detection of optic disc and macula in fundus images

This paper proposes an efficient combination of algorithms for the automated localization of the optic disc and macula in retinal fundus images. There is in fact no reason to assume that a single algorithm would be optimal. An ensemble of algorithms based on different principles can be more accurate than any of its individual members if the individual algorithms are doing better than random guessing. We aim to obtain an improved optic disc and macula detector by combining the prediction of multiple algorithms, benefiting from their strength and compensating their weaknesses. The location with maximum number of detectors’ outputs is formally the hotspot and is used to find the optic disc or macula center. An assessment of the performance of integrated system and detectors working separately is also presented. Our proposed combination of detectors achieved overall highest performance in detecting optic disc and fovea closest to the manually center chosen by the retinal specialist.     

Amylopectin Multiple Aldehyde Crosslinked Hydrogel as an Injectable and Self‐Healing Cell Carrier for Bone Tissue Engineering

Despite excellent processing and biological properties of gelatin for use as a cell carrier, none of the gelatin‐based hydrogel cell carriers reported to date offer all characteristics including quick formation, injectability, self‐healing, and durability, which are simultaneously required for an ideal system. Here, a gelatin‐based hydrogel with dynamic Schiff base linkages, so‐called “dynamic hydrogel,” as an injectable cell carrier consisting of gelatin and amylopectin multiple aldehyde (AMPA), with all the required characteristics is reported. Biocompatibility and osteoinductivity of the hydrogel are verified through the culture of human bone marrow‐derived mesenchymal stem cells (hBMSCs). As live/dead results show, hBMSCs are alive and highly viable ≈85–90% within the hydrogel after 5 days. According to bromodeoxyuridine cell proliferation assay, a significant increase in the number of the cells seeded in the hydrogel confirms its clinical significance for cell therapy. Most importantly, histological visualization using Mason's Trichrome staining indicates secretion of extracellular matrix around the cells loaded in the hydrogel and also expression level evaluation of the crucial osteogenic markers, confirms that the hydrogel can provide osteoinductive support for osteocyte differentiation of hBMSCs after 14 days. Therefore, this hydrogel provides more progress on the path toward bone tissue engineering and further treatment of bone diseases.     

Ethyl acetate as a bio-based solvent to reduce energy intensity and CO2 emissions of in situ bitumen recovery

We introduce ethyl acetate (EA), a bio-based chemical, as a potential solvent for bitumen recovery through comprehensive phase behavior and numerical simulation studies. Phase behavior and thermophysical properties of EA/live bitumen are measured at temperatures and pressures up to 190°C and 4 MPa, respectively. Experimental studies suggested that coinjection of EA with steam can reduce the bitumen viscosity by several orders of magnitude. Our numerical simulations show that coinjection of 2–8 mol% EA with steam can significantly reduce the steam-oil-ratio (SOR) by almost 0.9 units while increasing the bitumen production rate. This reduction in SOR can be translated to significant energy saving of ~2.2 GJ, emission reduction of ~120 kg of CO₂, and wastewater reduction of ~120 m³ per ton of the produced bitumen, which are almost 20–25% lower than the steam-assisted gravity drainage (SAGD) process.