Optimal temperature policies for catalytic transport reactors under periodic operation

The influence of inlet gas concentration cycling on the optimal temperature policy of catalytic transport reactors is studied theoretically. The model considered is based on plug flow of gas and catalyst particles with negligible interand intra-particle diffusional resistances and concentration dependent deactivation kinetics. To utilise the concentration of the reactant and the activity of the deactivating catalyst fully a proper temperature sequence along the reactor is needed. Thus, a general optimal temperature policy using the continuous minimum principle is derived for the reactor under periodic operation. The model equations are solved analytically for gas concentration, activity and temperature profiles. Resonance behaviour (maximum in conversion with pulse width) is obtained using the optimal temperature policy for certain sets of parameters. The effects of activation energy groups, reaction and deactivation constant groups and inlet temperature on the optimal temperature policy under periodic operation are evaluated. In all cases an improvement in conversion with the optimal temperature policy under periodic operation over that with an isothermal policy under periodic operation is obtained. A suboptimal policy, comprising constant temperature over different reactor sections, which is useful for implementation purposes is also discussed.     

Yield optimization for complex reactor systems

For complex reactions, the choice of reactor type is important for attaining optimum product yields. Strategies for specifying reactor types have beenIn this paper, product yield in a Van de Vusse reaction scheme has been studied for a wide range of the rate constants, in a reactor system consisting     

A robust kinematic hardening rule for cyclic plasticity with ratchetting effects

Summary A robust kinematic hardening rule is proposed which appropriately blends the deviatoric stress rate rule and the Tseng-Lee rule in order to satisfy both the experimental observations made by Phillips et al. [1]–[5] and the nesting of the yield surface to the limit surface. The work presented in Part I is confined to the theoretical formulation of kinematic hardening rule with limited correlation to experimental results. A more general expression for the plastic modulus is proposed. The expressions proposed by McDowell and by Dafalias can be obtained as a special case of the proposed expression. An additional parameter is introduced that reflects the dependence of the plastic modulus on the angle between the deviatoric stress rate tensor and the direction of the limit backstress relative to the yield backstress.     

On the electrochemical nature of hot-corrosion attack in Ni-Cr alloys

Hot-corrosion attack on gas-turbine components has been a problem of practical importance for over two decades. The nature and the extent of corrosion are influenced by the type of contaminants, the alloy constituents, and temperature. The hot-corrosion process is quite complex. There is controversy regarding the role of alloying elements, and whether the process is chemical or electrochemical in nature. In this investigation, an attempt has been made to investigate the role of Al and Ti in influencing the hot-corrosion attack of Ni-20 wt. % Cr alloys in molten Na₂SO₄-NaCl mixtures. It was found that Ni-Cr-Al alloys undergo a localized, heavy-blister attack under certain experimental conditions which is electrochemical in nature. An electrochemical model has been proposed which can satisfatorily explain the nature and kinetics of the localized blister corrosion.     

Prediction of urban stormwater quality using artificial neural networks

There are a vast number of complex, interrelated processes influencing urban stormwater quality. However, the lack of measured fundamental variables prevents the construction of process-based models. Furthermore, hybrid models such as the buildup-washoff models are generally crude simplifications of reality. This has created the need for statistical models, capable of making use of the readily accessible data. In this paper, artificial neural networks (ANN) were used to predict stormwater quality at urbanized catchments located throughout the United States. Five constituents were analysed: chemical oxygen demand (COD), lead (Pb), suspended solids (SS), total Kjeldhal nitrogen (TKN) and total phosphorus (TP). Multiple linear regression equations were initially constructed upon logarithmically transformed data. Input variables were primarily selected using a stepwise regression approach, combined with process knowledge. Variables found significant in the regression models were then used to construct ANN models. Other important network parameters such as learning rate, momentum and the number of hidden nodes were optimized using a trial and error approach. The final ANN models were then compared with the multiple linear regression models. In summary, ANN models were generally less accurate than the regression models and more time consuming to construct. This infers that ANN models are not more applicable than regression models when predicting urban stormwater quality.     

Mixed Nb<Subscript>2</Subscript>O<Subscript>5</Subscript>:MoO<Subscript>3</Subscript> (95:5 and 85:15) thin films and their properties for electrochromic device applications

Nb₂O₅:MoO₃ (95:5 and 85:15) thin films were deposited onto glass and fluorine doped tin oxide coated glass substrates at 100 and 300 °C by RF magnetron sputtering technique. The physical and electrochromic properties of the films were studied. XRD result reveals that deposited films were amorphous. The XPS study confirms the compositional purity and the presence of Nb⁵⁺ and Mo⁶⁺ in the deposited film. Surface morphological study shows platelet like features of deposited film. The average transmittance of the film is varied between 91 and 85 %. Photoluminescence study exhibits three characteristic emission peaks and confirms the better optical quality of deposited film. Raman spectra show the LO–TO splitting of Nb–O stretching of the deposited film. Electrochromic behavior of the deposited films characterized by cyclic voltammetry using 0.5 M LiClO₄·PC and 0.5 M H₂SO₄ electrolyte solutions show all the films are having better reversibility and reproducibility in their electrochemical analysis.     

Cross‐polarization reduction of microstrip antenna using microwave absorbers

In this article, a metamaterial inspired microwave absorber is used to reduce the cross‐polarization (XP) level of the radiated wave in microstrip antenna (MSA). A microwave absorber unit‐cell has been analyzed and implemented to reduce the cross polarization (XP) level in a single element and a 2 × 2 microstrip patch array antennas. The antennas have been designed on a FR‐4 substrate of thickness 0.8 mm at 10.1 GHz center frequency. The 2 × 2 patch array antenna with and without the absorbers have been experimentally verified for the S11 parameter, the radiation pattern, and the XP suppression in H‐plane and a good comparison has been found.     

Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection

Tolerance specification is an important part of mechanical design. Design tolerances strongly influence the functional performance and manufacturing cost of a mechanical product. Tighter tolerances normally produce superior components, better performing mechanical systems and good assemblability with assured exchangeability at the assembly line. However, unnecessarily tight tolerances lead to excessive manufacturing costs for a given application. The balancing of performance and manufacturing cost through identification of optimal design tolerances is a major concern in modern design. Traditionally, design tolerances are specified based on the designer’s experience. Computer-aided (or software-based) tolerance synthesis and alternative manufacturing process selection programs allow a designer to verify the relations between all design tolerances to produce a consistent and feasible design. In this paper, a general new methodology using intelligent algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection is presented. The problem has a multi-criterion character in which 3 objective functions, 3 constraints and 5 variables are considered. The average fitness factor method and normalized weighted objective functions method are separately used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.     

Evolutionary multi objective optimization for rule mining: a review

Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.