Pool Boiling Heat Transfer in Diluted Water/Glycerol Binary Solutions

Pool boiling heat transfer in water/glycerol binary solutions has been experimentally investigated on a horizontal rod heater. The experiments have been performed at various concentrations (zero to 35% mass glycerol) and heat fluxes up to 92 kW m^?2 at atmospheric pressure. The experimental values of boiling heat transfer coefficient have been compared to main existing correlations. It has been shown that the various predictions are significantly inconsistent. Based on the high difference between relative volatilities of water and glycerol, a simple model has been proposed to predict the boiling heat transfer coefficient. The applicability of this model is limited to low concentrations of glycerol and medium/low heat fluxes; however, the predictions are accurate. The proposed model is anticipated to be extendable to other binary systems in which the vapor pressure of one constituent is considerably higher when compared to the other component.     

Evolutionary fuzzy intelligent system for multi-objective supply chain network designs: an agent-based optimization state of the art

Supply chain network designing and programming is a momentous issue that many practitioners have focused on and contributed numerous novelties for this prompt. This paper puts forward a fuzzy multi-agent system according to which compatible with the decision makers’ interests and environmental survey, identifies the parameters of the mathematical model. An embedded optimization party including evolutionary-based optimizer intelligent agents, obtains non-dominated potential solutions. The output of these optimizer agents during the calibration process is an underpinning for evaluating the performance of the party. The system makes the policy of optimization complying with the results evaluation as well as the decision makers’ elaborated desires. Afterwards, in step with this policy, it sets a pool from obtained Pareto Fronts and aggregates them to extract a set of the best individuals. It interactively represents this set to the decision makers and catches their desired circumstance amongst these optional solutions. Proposing the network graph and program—which its generic morphography is determined—for decision makers is contrived as the system last stage. The main competencies of this system could be contemplated regarding the facts that it interactively fulfills the decision makers’ utilities relying on its robustness in optimization, self-tuning, training loop, ambient intelligence and consciousness toward the changes in environment.     

A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts

Despite their capability in monitoring the variability of the processes, control charts are not effective tools for identifying the real time of such changes. Identifying the real time of the change in a process is recognized as change-point estimation problem. Most of the change-point models in the literature are limited to fixed sampling control charts which are only a special case of more effective charts known as variable sampling charts. In this paper, we develop a general fuzzy-statistical clustering approach for estimating change-points in different types of control charts with either fixed or variable sampling strategy. For this purpose, we devise and evaluate a new similarity measure based on the definition of operation characteristics and power functions. We also develop and examine a new objective function and discuss its relation with maximum-likelihood estimator. Finally, we conduct extensive simulation studies to evaluate the performance of the proposed approach for different types of control charts with different sampling strategies.     

Two-machine robotic cell scheduling problem with sequence-dependent setup times

In this paper, we introduce a new and practical two-machine robotic cell scheduling problem with sequence-dependent setup times (2RCSDST) along with different loading/unloading times for each part. Our objective is to simultaneously determine the sequence of robot moves and the sequence of parts that minimize the total cycle time. The proposed problem is proven to be strongly NP-hard. Using the Gilmore and Gomory (GnG) algorithm, a polynomial-time computable lower bound is provided.     

Design and analysis of carbon nanotube FET based quaternary full adders

CMOS binary logic is limited by short channel effects, power density, and interconnection restrictions. The effective solution is non-silicon multiple-valued logic (MVL) computing. This study presents two high-performance quaternary full adder cells based on carbon nanotube field effect transistors (CNTFETs). The proposed designs use the unique properties of CNTFETs such as achieving a desired threshold voltage by adjusting the carbon nanotube diameters and having the same mobility as p-type and n-type devices. The proposed circuits were simulated under various test conditions using the Synopsys HSPICE simulator with the 32 nm Stanford comprehensive CNTFET model. The proposed designs have on average 32% lower delay, 68% average power, 83% energy consumption, and 77% static power compared to current state-of-the-art quaternary full adders. Simulation results indicated that the proposed designs are robust against process, voltage, and temperature variations, and are noise tolerant.     

Fuzzy Community Detection Model in Social Networks

In this paper, a fuzzy graph clustering model is presented to identify overlapping communities in a complex network. A center‐based fuzzy clustering model is developed based on the possibilistic c‐means clustering model, and the distance measure is defined based on the similarity to the clusters’ centers. The performance of the clustering process is evaluated by intra and intercluster density. In addition, experimental results from two artificially generated networks and two real‐world networks (social interactions between karate club members and a part of the twitter network) indicate the new model's performance.     

A constraint programming model for the scheduling of JIT cross-docking systems with preemption

In this paper, a scheduling problem of minimizing the total of the earliness, tardiness and the number of preemption for the outbound trucks on a cross-dock system is considered. This problem, which is known to be NP-hard, is compatible with the concepts of just-in-time (JIT) production and supply chain management. A new multi-criteria model, with non-linear terms and integer variables, which cannot be solved efficiently for large sized problems, is proposed. This paper also shows how to map a JIT cross-dock model to a constraint satisfaction problem (CSP) and integer programming (IP). To solve the model for real size applications, a genetic algorithm (GA) is applied. Finally, a computational experiment is carried out to analyze the performances of CSP, GA and IP models with respect to modeling capability, solution quality and time.     

Prediction model for determining the optimum operational parameters in laser forming of fiber-reinforced composites

Composite materials are widely employed in various industries, such as aerospace, automobile, and sports equipment, owing to their lightweight and strong structure in comparison with conventional materials. Laser material processing is a rapid technique for performing the various processes on composite materials. In particular, laser forming is a flexible and reliable approach for shaping fiber-metal laminates (FMLs), which are widely used in the aerospace industry due to several advantages, such as high strength and light weight. In this study, a prediction model was developed for determining the optimal laser parameters (power and speed) when forming FML composites. Artificial neural networks (ANNs) were applied to estimate the process outputs (temperature and bending angle) as a result of the modeling process. For this purpose, several ANN models were developed using various strategies. Finally, the achieved results demonstrated the advantage of the models for predicting the optimal operational parameters.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00304-3     

Powdered Activated Carbon And Ozone-Assisted Activated Sludge Treatment for Removal of Toxic Organic Compounds

Industrial wastewater effluent standards in the United States are being established in terms of both conventional parameters like BOD and specific organic compounds called priority pollutants. Some of these compounds are difficult to treat biologically and the removal mechanisms may include biodegradation, stripping, sorption, and/or combinations of these removal mechanisms. Biorefractory organic compounds that are resistant or difficult to biodegrade, toxic, and/or are inhibitory to microbial growth may present problems in biological treatment systems. Powdered activated carbon addition and chemical oxidation with ozone-assisted biological treatment systems have been investigated with some of these compounds. This paper includes data from controlled activated sludge systems, systems receiving powdered activated carbon, and systems with preozonation assist. Complete mass balances, including influent, effluent, waste sludge and off-gas analyses of the specific compounds were conducted to determine the actual fate of these compounds.