Exfoliated Graphitic Carbon Nitride for the Fast Adsorption of Metal Ions from Acid Mine Drainage: A Case Study from the Sungun Copper Mine

Mine Water and the Environment - Graphitic carbon nitride (g-C3N4) was easily synthesized from melamine and subsequently sonicated to create exfoliated g-C3N4. Samples were characterized with...     

Estimation of research reactor core parameters using cascade feed forward artificial neural networks

The pattern of the core reload program is very important for an optimize use of research reactors. Reactor safety issues and economic efficiency should be considered during pattern studies. In order to find the best core pattern for a research reactor, its reloading program should be solved as a multi-objective and constrained optimization problem. If considered objective functions of the optimization problem can be estimated in very short time, the optimal fuel reloading pattern can be used effectively. In this research a very fast estimation system for suggested core parameters has been developed using cascade feed-forward type of artificial neural networks (ANNs). Four main core parameters are suggested to optimize reactor core adequately. And also to get larger thermal fluxes in the desired irradiation box, a new flexible method was selected. A Software package has been developed to prepare and reform required data for ANNs training. The gradient descent method with momentum weight/bias learning rule has been used to train ANNs. To get the best conditions for considered ANNs training a vast study has been performed. It includes the effects of variation of hidden neurons, hidden layers, activation functions, learning and momentum coefficients, and also the number of training data sets on the training and simulation results. Some experimental convergence criteria are used to study them. A comparison selection rule has been used to adjust desirable conditions. Final training and simulation results show that developed ANNs can be trained and estimate suggested core parameters of research reactors very quickly. It improves effectively pattern optimization process of core reload program.     

Development of a novel graphene oxide-blended polysulfone mixed matrix membrane with improved hydrophilicity and evaluation of nitrate removal from aqueous solutions

In this study, four types of mixed matrix membranes were fabricated using polysulfone (as the base polymer) and different contents of graphene oxide (GO) nanosheets (as modifier) through wet phase inversion method. Based on the amounts of GO (0, 0.5, 1, and 2?wt%), the synthesized membranes named as M1, M2, M3, and M4, respectively. The membranes characteristics were evaluated using FE-SEM, FT-IR, and water contact angle measurements. In addition, the performance of the prepared membranes was investigated in terms of basic parameters: filtrate water flux, nitrate removal efficiency, and antifouling properties. Results showed significant improvements of the characteristics of modified membranes with GO. Accordingly, the permeability and hydrophilicity were enhanced and water flux was considerably improved. At operating pressure of 4?bar and nitrate concentration of 110?mg/L, the removal efficiency for unmodified membrane (M1) was 15.5% and for modified M2, M3, and M4 membranes were 22.78%, 39.12%, and 41.37%, respectively. In addition, the results of flux recovery ratio (FRR) showed that the anti-fouling properties of the GO modified membranes were improved due to the increase in membrane surface hydrophilicity.     

Economic impacts and challenges of China’s petroleum industry: An input–output analysis

It is generally acknowledged that the petroleum industry plays an important role in China’s national economic and social development. The direct, indirect, and induced impacts of China’s petroleum industry are analyzed in this study by using the Input-Output approach. The study also considers the main challenges that China’s economy might face in the future. The research results suggest the following: (1) The total economic impacts coefficients on output, given each unit of final demands change in extraction of petroleum and processing of petroleum, are 1.9180 and 3.2747 respectively, and the corresponding economic impacts coefficients on GDP are 1.0872 and 0.9001 respectively; (2) Extraction of petroleum has a more direct impact on GDP, while processing of petroleum has a greater effect on the total output; (3) Extraction of petroleum’s total economic impacts coefficients on both output and GDP have remained stable in recent years after a period of long decline; processing of petroleum’s total economic impacts coefficient on output is steadily increasing; (4) Import uncertainty, the likelihood of rising oil prices, and net oil exports caused by items manufactured with petroleum products (i.e. “Made in China” goods) are the main challenges the petroleum industry will cause for China’s overall economy.     

An experimental investigation to consider thermal methods efficiency on oil recovery enhancement

To study the profound impact of reservoir characteristics on flow regime in two‐phase condition, the effect of saturation and capillary pressure (Pc–S) should be taken into consideration in the porous medium. The purpose of this extensive experimental investigation is to inject the foaming agent and nitrogen gas, which is produced by the foam generator after waterflooding in a fractured reservoir to select the best optimum scenario. It has been elaborated that nitrogen of lower density and lower compressibility would provide a secondary gas cap at the top of the cores which causes to mobilize more oil volume in the unswept zones. The rupturing of foam considerably influences this phenomenon in the high permeable layers that have led the oil of low permeable layers to be mobilized in the presence of nitrogen.     

STABILITY PREDICTION OF TITANIUM MILLING WITH DATA DRIVEN RECONSTRUCTION OF PHASE-SPACE

Significant research effort has been carried out in the detection of chatter, which is one of the main barriers against titanium milling. State-of-the-art techniques are unable to satisfy requirements of industry in terms of in-process chatter detection. The present study reports the use of sensor-signal driven reconstructed phase space attractors combined with image correlation as a solution of chatter prediction during milling of titanium in industry. The method uses Poincaré sections of reconstructed phase space attractor as patterns to identify the onset of chatter in the apparently random behavior of vibrations in the milling process. Image correlation of Poincaré sections indicates the onset of chatter in the milling process.     

Toward learning autonomous pallets by using fuzzy rules, applied in a Conwip system

Nowadays, material planning and control strategies are becoming continuously complex tasks spanning from individual plants to logistic networks. In fact, this is the consequence of increasing intricacy in product variants and their respective convolution in networks’ structures. Customers ask for specific products with individual characteristics that force companies for more clever performances by more flexibility. For doing so, the existing planning and control systems, which work based on central monitoring and controlling, show some limitations for organizing every operation on time or in the right time. Therefore, in the recent decade, a great attention is put on decentralized control and, to some extent, autonomy. This paper tries to investigate the possibility of combining this new research paradigm with existing strategies in production logistics, in order to improve material handling and control task according to material flow criteria. To show this, an exemplary plant after decoupling point out of a logistic network is considered for simulation and analysis. This combines Conwip system with learning autonomous pallets’ concept in a discrete event simulation model. Several decentralized control scenarios are experimented and compared together. Here, the learn methodology is brought to pallets based on fuzzy rules and advantage of closed loop systems.     

Prediction of fatigue life in cold expanded Al-alloy 2024-T3 plates used in double shear lap joints

To predict fatigue crack initiation and fatigue crack growth lives in cold expanded double shear lap joints a numerical method has been employed. The total estimated fatigue lives were compared with available experimental fatigue test results for plain hole and cold expanded hole specimens of Al 2024-T3 in double shear lap joints. Three-dimensional finite element simulations have been performed in order to obtain the created residual stresses field due to cold expansion and subsequent far field longitudinal loading in the double shear lap joint. The obtained stress and strain distributions from the finite element analyses were employed to predict stress concentration factors to calculate fatigue crack initiation and fatigue crack growth lives using AFGROW computer code. The predicted fatigue lives demonstrate that there is a good agreement between the proposed method and experimental fatigue test results.