Thermodynamics of Aluminium in Molten Zinc with the use of Electrochemical Sensors

In the present study the thermodynamics of aluminium in molten Zn‐Al was determined at the temperatures 470, 490 and 520°C with the use of electrochemical sensors. Cell and sensor techniques were used. The following concentration cell was used: Al (pure reference) |Al³⁺ (in fused salt) | [Al]_{in zinc}. It was found that the activity of aluminium shows positive deviation from Raoult's law. The partial heat of solution of aluminium at temperatures between 470 and 520°C for X_Al = 0.004 was determined as = 21882 J. The dilute solution of aluminium in zinc in the range of 0≤X_Al≤0.044 obeys Henry's law. The Wagner's first order interaction coefficient e^Al_Al was determined.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.

     

Application of ANFIS and MLR models for prediction of methane adsorption on X and Y faujasite zeolites: effect of cations substitution

Neural Computing and Applications - In this work, cationic (Mg2+, Ca2+, Sr2+, and Ba2+) substitution in X and Y faujasite zeolite structures and their effects on capacity of zeolites for methane...     

A computational intelligence‐based approach for short‐term traffic flow prediction

This paper proposes alternative approaches for the prediction of short‐term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5‐ and 30‐min traffic flow rates. New LGP‐ and MLP‐based prediction equations are derived for the traffic flow rates in the 5‐ and 30‐min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht‐Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP‐based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.     

Characterization of motion power loss of off-road wheeled robot in a slippery terrain

For the first time in realm of power study of off-road wheeled robots, this study deals with motion power loss due to slippage of robot wheels traversed on slippery terrain. For this purpose, effects of slippery terrain type (solid balls with diameter of 0.0127, 0.0254, and 0.0508 m), tire air pressure (20.68, 34.47, and 55.16 kPa), and robot forward speed (0.17, 0.33, and 0.5 m/s) on the power loss were characterized. Derived results proved that the increasing effect of slippery terrain type on the power loss was dominant (1.08 and 3.21 times) than that of robot forward speed and tire air pressure, respectively. Meanwhile, the increasing effect of robot forward speed on the power loss was prevailed (2.98 times) than that of tire air pressure. Hence, to minimize the power loss of the robot traversed on each type of slippery terrain, adjustment of robot forward speed should be considered as first priority. A comparison between motion power loss (43.60–249.40 W) and provided motion power for the robot (136–436.37 W) implies that 12.93–75.44% of provided motion power was wasted by slippage of the robot wheels on slippery terrains. Overall, the analytical results obtained in this study lead to open a new prospection for comprehending of the power loss trends of off-road wheeled robots traversed on slippery terrains. As slippery terrain composed of solid balls, the results can be especially utilized for final phase of unloading robotic operations of catalyst handling procedure in process towers and reactors of oil, gas, petrochemical, and chemical industries.     

Threshold prediction for detecting rare positive samples using a meta-learner

Pattern Analysis and Applications - Threshold-moving is one of the several techniques employed in correcting the bias of binary classifiers towards the majority class. In this approach, the...     

Toward a practical face recognition system: robust alignment and illumination by sparse representation

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.     

A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

Complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.     

Rigorous modeling of gypsum solubility in Na–Ca–Mg–Fe–Al–H–Cl–H2O system at elevated temperatures

Precipitation and scaling of calcium sulfate have been known as major problems facing process industries and oilfield operations. Most scale prediction models are based on aqueous thermodynamics and solubility behavior of salts in aqueous electrolyte solutions. There is yet a huge interest in developing reliable, simple, and accurate solubility prediction models. In this study, a comprehensive model based on least-squares support vector machine (LS-SVM) is presented, which is mainly devoted to calcium sulfate dihydrate (or gypsum) solubility in aqueous solutions of mixed electrolytes covering wide temperature ranges. In this respect, an aggregate of 880 experimental data were gathered from the open literature in order to construct and evaluate the reliability of presented model. Solubility values predicted by LS-SVM model are in well accordance with the observed values yielding a squared correlation coefficient (R ²) of 0.994. Sensitivity of the model for some important parameters is also checked to ascertain whether the learning process has succeeded. At the end, outlier diagnosis was performed using the method of leverage value statistics to find and eliminate the falsely recorded measurements from assembled dataset. Results obtained from this study indicate that LS-SVM model can successfully be applied in predicting accurate solubility of calcium sulfate dihydrate in Na–Ca–Mg–Fe–Al–H–Cl–H₂O system over temperatures ranging from 283.15 to 371.15 K.