Geometrical model to calculate the consumption of sewing thread for 504 over-edge stitch

Sewing thread is one of the most important components of a sewn product that contributes significantly in the useful life of a product. Stitch class 504 is the one which is used in all types of sewn products. Its thread consumption is higher than class 300 and class 400. A mathematical model to predict the sewing thread consumption of stitch class 504 has been proposed in this paper. The model is based on the geometry of the stitch. The proposed model takes into account material thickness and stitch density. The model was validated by using 24 samples (with different material thickness and stitch densities). The accuracy of the model was found to be 99%. Sensitivity analysis revealed that stitch density has 62% effect and material thickness has 38% effect on thread consumption. The proposed model can predict the thread consumption accurately; therefore, it can be used for better estimation of required thread and encourage its better utilization in sewn product industry.     

Qualitative response of safety injection system for a large size break in the reactor coolant system cold leg

Safety injection system, accumulator injection system and residual heat removal system of CHASNUPP-1 were simulated using the computer code APROS. We observed the qualitative response of the simulated system during injection and re-circulation phases after LOCA. During rapid depressurization of SRC system due to leakage, these systems started coolant injection in the SRC system as per plant requirement. Different thermal-hydraulic parameters of the respective systems are presented and discussed. Results obtained are in good agreement with the reported document of the reference power plant.     

GA-SVR: a novel hybrid data-driven model to simulate vertical load capacity of driven piles

Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R²). In other words, GA-SVR with RMSE of 0.017 and R² of 0.980 has higher performance than SVR with RMSE of 0.035 and R² of 0.912, and linear regression model with RMSE of 0.079 and R² of 0.625.

     

Optimal Automatic Generation Control of Asynchronous Power Systems Using Output Feedback Control Strategy with Dynamic Participation of Wind Turbines

Abstract—This article presents the design of optimal output feedback automatic generation control regulators for an interconnected power system with dynamic participation of doubly fed induction generator based wind turbines. The power systems consist of plants with hydro-thermal turbines and are interconnected via parallel AC/DC links. Efforts have been made to propose optimal automatic generation control regulators based on feedback of output state variables, which are easily accessible and available for the measurement. The designed optimal output feedback automatic generation control regulators are implemented, and the system dynamic responses for various system states are obtained considering 1% load perturbation in one of the areas. The dynamic performance is compared with that obtained with optimal automatic generation control regulators designed using full state vector feedback. The pattern of closed-loop eigenvalues is also determined to test the system stability.     

A new hybrid approach for the solution of nonconvex economic dispatch problem with valve-point effects

Economic dispatch (ED) generally formulated as convex problem using optimization techniques by approximating generator input/output characteristic curves of monotonically increasing nature results in an inaccurate dispatch. The genetic algorithm has previously been used for the solution of problem for economic dispatch but takes longer time to converge to near optimal results. The hybrid approach is one of the methodologies used to fine tune the near optimal results produced by GA. This paper proposes new hybrid approach to solve the ED problem by using the valve-point effect. The approach we propose combines the genetic algorithm (GA) with active power optimization (APO) based on the Newton's second order approach (NSO). The genetic algorithm acts as a global optimizer giving near optimal generation schedule, which becomes the input for generation buses in APO algorithm. This algorithm acting as local search technique dispatching the generated active power of units for minimization of cost and gives optimum generation schedule. Three machines 6-bus, IEEE 5-machines 14-bus, and IEEE 6-mchines 30-bus systems have been tested for validation of our approach. Results of the proposed scheme compared with results obtained from GA alone give significant improvements in the generation cost showing the promise of the proposed approach.     

Structural and magnetic properties of erbium doped nanocrystalline Li–Ni ferrites

A series of nanocrystalline Li_0.25Ni_0.5Fe_2.25−xEr_xO₄ (x=0.00, 0.02, 0.06, 0.08, and 0.10) ferrite powders, having a cubic spinel crystal structure and a low value of coercivity, was synthesized by the sol–gel auto-combustion route. The structure, morphology and magnetic properties of the prepared nanoferrites were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and the magnetic property measurement system (MPMS). A well-defined single phase spinel structure is confirmed in all the samples by X-ray diffraction analysis. The lattice parameters of the samples increase slightly with increasing the erbium content. The crystallite size of the Er-doped samples is smaller than that of pure Li–Ni ferrite, and decrease regularly in the range of 36.0–14.5 nm. It has been observed that the magnetic properties of these ferrites are strongly influenced by the added erbium content. The magnetic measurements indicate that saturation magnetization (M_s) and coercivity (H_c) decrease gradually with the increase of Er content in the lattice.     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

Effect of transition metal doping on the sintering and electrochemical properties of GDC buffer layer in SOFCs

A dense Ce_0.9Gd_0.1O_2−d (GDC) interlayer is an essential component of the SOFCs to inhibit interfacial elemental diffusion between zirconia-based electrolytes (eg YSZ) and cathodes. However, the characteristic high sintering temperature of GDC (>1400°C) makes it challenging to fabricate an effective highly dense interlayer owing to the formation of more resistive (Zr,Ce)O₂ interfacial solid solutions with YSZ at those temperatures. To fabricate a useful GDC interlayer, we studied the influence of transition metal (TM) (Co, Cu, Fe, Mn, & Zn) doping on the sintering and electrochemical properties of GDC. Dilatometry data showed dramatic drops in the necking and final sintering temperatures for the TM-doped GDCs, improving the densification of the GDC in the order of Fe > Co > Mn > Cu > Zn. However, the electrochemical impedance data showed that among various transition metal dopants, Mn doping resulted in the best electrochemical properties. Anode supported SOFCs with Mn-doped, nano, and commercial-micron GDC interlayers were compared with regard to their performance and stability levels. Although all of the SOFCs showed stable performance, the SOFC with the Mn-doped GDC interlayer showed the highest power density of 1.14 W cm⁻² at 750°C. Hence, Mn-doped GDC is suggested for application as an effective diffusion barrier layer in SOFCs.