Reducing cycle times in manufacturing and supply chains by input and service rate smoothing

A key performance parameter of a manufacturing network or supply chain is its cycle time; the time that a typical item spends in the network. A previous simulation study on a semiconductor assembly and test facility showed that cycle times could be reduced by having smooth input and service rates. This suggested that there is a “cycle time principle” that, for a system with a specified throughput or input rate, the shortest cycle times are obtained when the input and service rates do not vary over time. We prove that this principle is true for the M/G/1 and M/M/s queueing systems and Jackson networks. The analysis involves establishing several results on the concavity of waiting time probabilities and the convexity of expected waiting times and queue lengths, as functions of input and service rates. These results also have natural uses in other optimization problems.     

Parallel transport: a new transport layer paradigm for enabling Internet quality of service

The transport layer in the network protocol stack serves as a liaison between the application and the underlying network. Any quality of service provided by the network thus has to be effectively translated by the transport layer protocol in order to be enjoyed by the applications. In this article, we argue for a fundamental rethinking of the transport layer design to facilitate such QoS delivery. We identify the key requirement for a QoS enabling transport layer protocol as the ability to effectively handle multiplicity in terms of user differentiation levels, network resources, and service models. However, TCP, the transport layer protocol predominantly used in the Internet, is unable to support such multiplicity due to its single-state design. We extend TCP to a parallel transport layer protocol called parallel TCP (pTCP) that can tackle the different dimensions of multiplicity, and hence enable varying classes of QoS to applications. We discuss the applicability of pTCP in three specific domains with different levels of network support for QoS, and present simulation results substantiating our arguments.     

Control of power factor correcting boost converter withoutinstantaneous measurement of input current

This paper proposes a new control method for the constant-frequency control of power factor correcting boost power converter using a sinewave template modulated PWM signal which eliminates the need for instantaneous measurement of the line current for the switching control of the boost converter. The control strategy is based on the notion that the line current can be forced to trace a deterministic waveform such as a sinusoid by considering the implicit model of the sinewave in the boost converter controller structure. The modulating sinewave template is generated using the line voltage, the boost converter output voltage and the load current. The paper provides the analysis and the design of the controller and presents simulation and implementation results to demonstrate its effectiveness     

Esterification of valeric acid over PTA supported mesoporous Al-SBA-15 as efficient solid acid catalysts

Journal of Porous Materials - Valeric acid can be produced by selective hydrogenation of biomass-derived levulinic acid. The present work aims to synthesize ethyl valerate (EV), a fuel, fuel...     

A study on development of cognitive support features in recent ontology visualization tools

Ontologies are currently emerging as representation techniques for overlapping compatibility context domains. The continuing need for more effective information retrieval has lead to the creation of the notions of the semantic web and personalized information management. Subsequently, the need for effective ontology visualization for design, management and browsing has arisen. Several ontology visualization tools have come out to strengthen the users’ cognitive support. The primary goal of this paper is to present a survey on recently implemented ontology visualization tools and their contributions in the enrichment of users’ cognitive support. This work also presents the preliminary results of an evaluation of three visualization tools to determine the suitability of each method for end user applications where ontologies are used as browsing aids.     

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.     

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.