Comparison of Estimation Capabilities of the Artificial Neural Network with the Wavelet Neural Network in Lipase-Catalyzed Synthesis of Triethanolamine-Based Esterquats Cationic Surfactant

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained.     

Prediction of macerals contents of Indian coals from proximate and ultimate analyses using artificial neural networks

Coal, a prime source of energy needs in-depth study of its various parameters, such as proximate analysis, ultimate analysis, and its biological constituents (macerals). These properties manage the rank and calorific value of various coal varieties. Determination of the macerals in coal requires sophisticated microscopic instrumentation and expertise, unlike the other two properties mentioned above. In the present paper, an attempt has been made to predict the concentration of macerals of Indian coals using artificial neural network (ANN) by incorporating the proximate and ultimate analysis of coal. To investigate the appropriateness of this approach, the predictions by ANN are also compared with conventional multi-variate regression analysis (MVRA). For the prediction of macerals concentration, data sets have been taken from different coalfields of India for training and testing of the network. Network is trained by 149 datasets with 700 epochs, and tested and validated by 18 datasets. It was found that coefficient of determination between measured and predicted macerals by ANN was quite higher as well as mean absolute percentage error was very marginal as compared to MVRA prediction.     

Modeling, analysis and optimization of aircyclones using artificial neural network, response surface methodology and CFD simulation approaches

The pressure drop is an important performance parameter to evaluate and design cyclone separators. In order to accurately predict the complex non linear relationships between pressure drop and geometrical dimensions, a radial basis neural network (RBFNN) is developed and employed to model the pressure drop for cyclone separators. The neural network has been trained and tested by experimental data available in literature. The result demonstrates that artificial neural networks can offer an alternative and powerful approach to model the cyclone pressure drop. Four mathematical models (Muschelknautz method “MM”, Stairmand, Ramachandran and Shepherd & Lapple) have been tested against the experimental values. The residual error (the difference between the experimental value and the model value) of the MM model is the lowest. The analysis indicates the significant effect of the vortex finder diameter D_x and the vortex finder length S, the inlet width b and the total height H_t. The response surface methodology has been used to fit a second order polynomial to the RBFNN. The second order polynomial has been used to get a new optimized cyclone for minimum pressure drop using the Nelder-Mead optimization technique. A comparison between the new design and the standard Stairmand design has been performed using CFD simulation. CFD results show that the new cyclone design is very close to the Stairmand high efficiency design in the geometrical parameter ratio, and superior for low pressure drop at nearly the same cut-off diameter. The new cyclone design results in nearly 75% of the pressure drop obtained by the old Stairmand design at the same volume flow rate.     

Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying

A hot air drying system equipped with real-time computer vision system was used to investigate the effects of drying variables on apple slices color changes. Drying experiments were conducted at drying air temperatures of 50–70 °C, drying air velocities of 1–2 m/s, and samples thicknesses of 2–6 mm. A multilayer perceptron (MLP) artificial neural network (ANN) was also used to correlate color parameters and moisture content of apple slices with drying variables and drying time. The effects of drying air temperature and sample thickness on color changes were dominated over the effect of drying air velocity. However, non-linear and somewhat complex trends were obtained for all color parameters as function of moisture content. The MLP ANN satisfactorily approximated the color and moisture variations of apple slices with correlation coefficient higher than 0.92. Therefore, the computer vision system supplemented with ANN can be used as a non-invasive, low cost, and easy method for fast and in-line assessing and controlling of foodstuffs color and moisture changes during drying.     

Evaluation of embeddable potential sensor for corrosion monitoring in concrete structures

Embeddable potential sensor based on MnO₂ was assembled and characterised in concrete. The stability, reversibility, polarisability and impedance characteristics have been studied with respect to known reference. The corrosion performance of reinforced steel with respect to MnO₂ sensor was monitored by different electrochemical techniques. Reversibility of MnO₂ sensor indicated that difference of ±5 mV between the forward and reverse scan indicates the better reversibility characteristics in concrete. The rebar potentials (E_R) of steel with respect to MnO₂ are −315 and −525 mV for passive and active conditions of rebar in concrete. The corrosion current from potentiodynamic polarisation and R_ct from a.c. impedance technique clearly differentiated the behaviour of steel embedded in chloride contaminated concrete (active condition) from uncontaminated concrete (passive condition) with respect to MnO₂ sensor. All these studies revealed that corrosion monitoring of steel in concrete using embedded MnO₂ as a better potential sensor for steel in concrete. In addition it is easy to fabricate for amenable miniaturisation, varied configuration as demanded for corrosion monitoring in concrete structures.     

Comparison between the artificial neural network,SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide

The aim of this study is to model the solubilities of solid aromatic compounds in supercritical carbon dioxide (SCCO₂) using feed-forward artificial neural network (ANN). Temperature, pressure, critical properties and acentric factor of each solute have been used as independent variables of ANN model. The parameters of multi-layer perceptron (MLP) network have been adjusted by back propagation learning algorithm using experimental data which have been collected from various literatures. In order to find the optimal topology of the MLP, different networks were trained and examined and the network with minimum absolute average relative deviation percent (AARD%), mean square error (MSE) and suitable regression coefficient (R²) has been selected as an optimal configuration. By this procedure a single hidden layer network composed of nineteen hidden neurons has been found as an optimal topology. Sensitivity error analyses confirmed that the optimal ANN can predict experimental data with an excellent agreement (AARD% = 4.99, MSE = 7.08 × 10⁻⁷ and R² = 0.99699). Capability of the proposed ANN model has compared with those published results which have obtained by SAFT combined with eight different mixing rules (one, two and three parameters mixing rules) and PRSV equation of state (EOS). The best presented overall AARD% for SAFT approach with one, two and three parameters mixing rules are 16.15, 12.32% and 7.65%, respectively while PRSV EOS showed AARD% of 21.10%. The results emphasize that the proposed ANN model can predict the solubilities of solid aromatic compounds in SCCO₂ more accurate than SAFT and PRSV EOS.     

Evaluation of electrical energy per order (EEO) with kinetic modeling on the removal of Malachite Green by US/UV/H2O2 process

The kinetics of decolorization of Malachite Green (MG) as a model cationic dye from textile industry, by US/UV/H₂O₂ process, was investigated with nonlinear regression analysis. The experimental results indicated that the decolorization kinetics of MG in this process fit well by pseudo-first order kinetics. With nonlinear regression analysis a model was developed for pseudo-first order constant (k_ap) as a function of operational parameters such as initial concentrations of H₂O₂ (25-600 mg l^− 1) and MG (1.82-9.87 mg l^− 1), temperature (294-307 K) and power density (0.049-0.16 W ml^− 1) as following:

     

Effects of dimethyl-ether (DME) spray behavior in the cylinder on the combustion and exhaust emissions characteristics of a high speed diesel engine

The purpose of this study was to analyze the exhaust emissions of DME fuel through experimental and numerical analyses of in-cylinder spray behavior. To investigate this behavior, spray characteristics such as the spray tip penetration, spray cone angle, and spray targeting point were studied in a re-entrant cylinder shape under real combustion chamber conditions. The combustion performance and exhaust emissions of the DME-fueled diesel engine were calculated using KIVA-3V. The numerical results were validated with experimental results from a DME direct injection compression ignition engine with a single cylinder.The combustion pressure and IMEP have their peak values at an injection timing of around BTDC 30°, and the peak combustion temperature, exhaust emissions (soot, NO_x), and ISFC had a lower value. The HC and CO emissions from DME fuel showed lower values and distributions in the range from BTDC 25° to BTDC 10° at which a major part of the injected DME spray was distributed into the piston bowl area. When the injection timing advanced to before BTDC 30°, the HC and CO emissions showed a rapid increase. When the equivalence ratio increased, the combustion pressure and peak combustion temperature decreased, and the peak IMEP was retarded from BTDC 25° to BTDC 20°. In addition, NO_x emissions were largely decreased by the low combustion temperature, but the soot emissions increased slightly.     

New developments in polymer science and technology using combination of ionic liquids and microwave irradiation

The purpose of this review is to provide appropriate details concerning the application of ionic liquids (IL)s associated with microwave-assisted polymer chemistry. From the viewpoint of microwave chemistry, one of the key significant advantages of ILs is their high polarity, which is variable, depending on the cation and anion and therefore can effectively be tuned to a particular application. Hence, these liquids offer a great potential for the innovative application of microwaves for organic synthesis as well as for polymer science. ILs efficiently absorb microwave energy through an ionic conduction mechanism, and thus are employed as solvents and co-solvents, leading to a very high heating rate and a significantly shortened reaction time. Since an IL-based and microwave-accelerated procedure is efficient and environmentally benign, we believe that this method may have some potential applications in the synthesis of a wide variety of vinyl and non-vinyl polymers. This review describes application of combination of ILs with microwave irradiation as a modern tool for the addition and step-growth polymerization as well as modification of polymers and it was compared with ILs alone and conventional polymerization method.     

Thermal precipitation or gelling behaviour of dissolved methylcellulose (MC) derivatives—Behaviour in water and influence on the extrusion of ceramic pastes. Part 1: Fundamentals of MC-derivatives

Methylcelluloses dissolved in water show a temperature dependent gelling behaviour. The gel temperatures depend mainly on the degree of substitution with methyl groups. The behaviour of methylcellulose containing pastes is of high importance in various applications. The paper describes the influence of the degree of substitution on the thermal characteristics of methylcelluloses in water and in ceramic pastes. The gelation temperature of the methylcellulose in both systems is increasing with decreasing degree of substitution. This enables a broader temperature window in the ceramic extrusion process. Extrusion near the gelation temperature normally leads to many defects in the extrudate. However, close to the gelation temperature the extruded profiles show more defects with methylcelluloses having a higher degree of substitution. Methylcelluloses having a low degree of substitution also enable a paste extrusion above the gelation temperature (up to 90 °C). This is not possible with currently commercially available methylcelluloses.     

A review on the mechanical and electrical properties of graphite and modified graphite reinforced polymer composites

Carbon materials particularly in the form of sparkling diamonds have held mankind spellbound for centuries, and in its other forms, like coal and coke continue to serve mankind as a fuel material, like carbon black, carbon fibers, carbon nanofibers and carbon nanotubes meet requirements of reinforcing filler in several applications. All these various forms of carbon are possible because of the element's unique hybridization ability. Graphene (a single two-dimensional layer of carbon atoms bonded together in the hexagonal graphite lattice), the basic building block of graphite, is at the epicenter of present-day materials research because of its high values of Young's modulus, fracture strength, thermal conductivity, specific surface area and fascinating transport phenomena leading to its use in multifarious applications like energy storage materials, liquid crystal devices, mechanical resonators and polymer composites. In this review, we focus on graphite and describe its various modifications for use as modified fillers in polymer matrices for creating polymer-carbon nanocomposites.     

Modifications of carbon for polymer composites and nanocomposites

The various forms of carbon used in composite preparation include mainly carbon-black, carbon nanotubes and nanofibers, graphite and fullerenes. This review presents a detailed literature survey on the various modifications of the carbon nanostructures for nanocomposite preparation focusing upon the works published in the last decade. The modifications of each form of carbon are considered, with a compilation of structure-property relationships of carbon-based polymer nanocomposites. Modifications in both bulk and surface modifications have been reviewed, with comparison of their mechanical, thermal, electrical and barrier properties. A synopsis of the applications of these advanced materials is presented, pointing out gaps to motivate potential research in this field.