On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

Memetic algorithm with Preferential Local Search using adaptive weights for multi-objective optimization problems

Evolutionary multi-objective optimization algorithms are generally employed to generate Pareto optimal solutions by exploring the search space. To enhance the performance, exploration by global search can be complemented with exploitation by combining it with local search. In this paper, we address the issues in integrating local search with global search such as: how to select individuals for local search; how deep the local search is performed; how to combine multiple objectives into single objective for local search. We introduce a Preferential Local Search mechanism to fine tune the global optimal solutions further and an adaptive weight mechanism for combining multi-objectives together. These ideas have been integrated into NSGA-II to arrive at a new memetic algorithm for solving multi-objective optimization problems. The proposed algorithm has been applied on a set of constrained and unconstrained multi-objective benchmark test suite. The performance was analyzed by computing different metrics such as Generational distance, Spread, Max spread, and HyperVolume Ratio for the test suite functions. Statistical test applied on the results obtained suggests that the proposed algorithm outperforms the state-of-art multi-objective algorithms like NSGA-II and SPEA2. To study the performance of our algorithm on a real-world application, Economic Emission Load Dispatch was also taken up for validation. The performance was studied with the help of measures such as Hypervolume and Set Coverage Metrics. Experimental results substantiate that our algorithm has the capability to solve real-world problems like Economic Emission Load Dispatch and is able to produce better solutions, when compared with NSGA-II, SPEA2, and traditional memetic algorithms with fixed local search steps.     

Microwave sintering of copper–graphite composites

Microwave processing is a distinctive and alternative technique when compared with the available processes such as material synthesis, sintering, and processing utilizing the conventional heating sources. Owing to microwave–material molecular interaction, microwave heating is of internal and faster. This results in improved quality of the product with time and energy savings. Metal at its bulk form reflects microwaves; however in its powder form, it couples with microwaves. This work emphasizes on the development of copper–graphite metal matrix composite for electrical sliding contact applications through microwave hybrid heating (2.45 GHz, 3.2 kW). The fabricated composites were tested for their mechanical properties such as porosity, relative density and hardness. Microstructural aspects were studied through SEM.     

Lithium difluoro(oxalate)borate‐based novel nanocomposite polymer electrolytes for lithium ion batteries

BACKROUND: HF formation and poor thermal stability found in commercial lithium ion batteries comprising LiPF₆ (and other salts) have hampered the replacement of LiPF₆. Therefore, a new kind of electrolyte salt is necessary to replace the one commercially available. RESULTS: A novel lithium difluoro(oxalate)borate (LiDFOB)‐based nanocomposite polymer electrolyte has been prepared in a matrix of poly[(vinylidene fluoride)‐co‐(hexafluoropropylene)] (PVdF‐HFP). The electrolyte contains ethylene carbonate and diethyl carbonate as plasticizers and nanoparticulate Sb₂O₃ as a filler. Membranes obtained by a solution casting technique were characterized by AC impedance, thermogravimetry and tensile strength measurements and morphological studies. Membranes with 5 wt% Sb₂O₃ exhibit a room‐temperature conductivity of 0.298 mS cm^?1, and are thermally stable up to ca 130 °C. Furthermore, the nanocomposite membranes show a 125% increase in mechanical stability as compared to filler‐free membranes. The structural change from α to β phases was confirmed by Raman studies. CONCLUSION: One of the important advantages of using LiDFOB lies in its bulkier DFOB anion, which also acts as solid plasticizer, thus improving the basic requirements of the electrolyte, such as mechanical and thermal stabilities, as well ionic conductivity and with a lower filler content. The overcharge tolerance of LiDFOB salt at higher temperature is also to be noted, because of the oxalate moieties. Preliminary investigations confirmed the possibility of using Sb₂O₃ nanoparticle‐filled membranes in industry in the near future. Copyright © 2008 Society of Chemical Industry     

Machinability study of high speed steel for focused ion beam (FIB) milling process – An experimental investigation at micron/nano scale

Nowadays the attention is focused on machining of non-silicon materials for miniaturized devices. High speed steel (HSS) is a non-silicon tool material, which is used in metal cutting applications as well as in micro-medical applications. Focused ion beam (FIB) milling process is highly suited for the fabrication of micro tools and other micro devices manufactured from HSS material. This study investigates the machinability aspects of HSS for FIB milling process. Beam current, extraction voltage, angle of incidence, dwell time and percentage overlap between beam diameters are the FIB process parameters, which have been analyzed experimentally to optimize FIB milling process for maximum material removal rate and minimum surface roughness. Beam current is found as the most significant parameter for controlling the material removal rate and surface roughness.     

Tribological Performance of Microwave-Heat-Treated Copper–Graphite Composites

Copper–graphite composite is a tribological composite that can be used in sliding electrical contact applications requiring low friction and wear in addition to high electrical conductivity. The graphite powder (5 wt%) was mixed with the copper powder, and then composite was fabricated through powder metallurgy (P/M) route. P/M product generally requires secondary operations such as rolling, extrusion, etc. to improve their mechanical properties. Post-heat-treatment technique is also applicable to improve the properties of P/M components. Microwave-post-heat-treatment research studies are gaining momentum nowadays due to the improved quality of products with reduced time, energy, and associated cost. Microwave post-heat treatment of copper–graphite composites for different heat treating duration was carried out in a hybrid microwave heating setup. Microstructural studies were carried out using SEM with EDAX. Microwave-heat-treated samples exhibited reduced porosity, improved density, and hardness. In order to understand the friction and wear properties of microwave-heat-treated copper–graphite composites, pin-on-disk wear experiments were conducted. For comparison, untreated copper–graphite composites were also subjected to similar studies. Microwave-heat-treated samples exhibited reduced coefficient of friction and specific wear rate when compared to the untreated ones. The wear mechanism of untreated composites was observed to be plastic deformation characterized by large wear fragments, whereas the mechanism of heat-treated composite was delamination observed through peel off tribolayer.     

Knowledge-based workcell attribute oriented dynamic schedulers for flexible manufacturing systems

The economy of production in flexible manufacturing systems (FMS) depends mainly on how effectively the production is planned and how the resources are used. This requires efficient and dynamic factory scheduling and control procedures. This paper addresses two knowledge-based scheduling schemes (work cell attribute oriented dynamic schedulers WCAODSs) to control the flow of parts efficiently in real-time for FMS in which the part-mix varies continually with the planning horizon. The present work employs a hybrid optimisation approach in the generalised A1 framework. A genetic algorithm that provides an optimal combination of a set of priority dispatching rules, one for each work cell WC (WCwisepdr set), for each of the problem instances characterised by their WC attributes, is used for generating examples. The WC attributes reflect the information about the operating environment of each individual WC. Two inductive learning algorithms are employed to learn the examples, and scheduling rules are formulated as a knowledge base. The learning algorithms employed are: the Genetic CID3 (Continuous Interactive Dichotomister3 algorithm extended with genetic program for weight optimisation) and the Classification Decision Tree algorithm. The knowledge base obtained through the above learning schemes generates robust and effective schedules intelligently with respect to the part-mix changes in real-time, for makespan criteria. The comparison made with a GA-based scheduling methodology shows that WCAODSs provide solutions closer to the optimum.     

Engineering surface and development of a new DNA micro array chip

This paper proposes a new concept of the ‘engineering surface’, which extends the conventional idea of a functional surface by combining it with micro/nano manufacturing technology. Characteristic features and possibilities of the engineering surface are discussed in detail. This paper reviews studies on micro/nano fabrication technologies for advanced materials and evaluation technology for surface function. New fabrication technologies, micro machining and nano forming, are introduced, which will be basic manufacturing processes of the engineering surface. Also, a new surface evaluation technique is introduced for the surface energy of the nano fabricated surface. Design of a new DNA micro array chip is introduced as an example of applications of the engineering surface. Controllability of surface property by nano fabrication is studied.     

Hierarchical NiMoS and NiFeS Nanosheets with Ultrahigh Energy Density for Flexible All Solid‐State Supercapacitors

Highly flexible supercapacitors (SCs) have great potential in modern electronics such as wearable and portable devices. However, ultralow specific capacity and low operating potential window limit their practical applications. Herein, a new strategy for the fabrication of free‐standing Ni?Mo?S and Ni?Fe?S nanosheets (NSs) for high‐performance flexible asymmetric SC (ASC) through hydrothermal and subsequent sulfurization technique is reported. The effect of Ni²⁺ is optimized to attain hierarchical Ni?Mo?S and Ni?Fe?S NS architectures with high electrical conductivity, large surface area, and exclusive porous networks. Electrochemical properties of Ni?Mo?S and Ni?Fe?S NS electrodes exhibit that both have ultrahigh specific capacities (≈312 and 246 mAh g^?1 at 1 mA cm^?2), exceptional rate capabilities (78.85% and 78.46% capacity retention even at 50 mA cm^?2, respectively), and superior cycling stabilities. Most importantly, a flexible Ni?Mo?S NS//Ni?Fe?S NS ASC delivers a high volumetric capacity of ≈1.9 mAh cm^?3, excellent energy density of ≈82.13 Wh kg^?1 at 0.561 kW kg^?1, exceptional power density (≈13.103 kW kg^?1 at 61.51 Wh kg^?1) and an outstanding cycling stability, retaining ≈95.86% of initial capacity after 10 000 cycles. This study emphasizes the potential importance of compositional tunability of the NS architecture as a novel strategy for enhancing the charge storage properties of active electrodes.     

A genetic algorithm for scheduling flexible manufacturing systems

General job shop scheduling and rescheduling with alternative route choices for an FMS environment is addressed in this paper. A genetic algorithm is proposed to derive an optimal combination of priority dispatching rules pdrs (independentpdrs one each for one Work Cell WC), to resolve the conflict among the contending jobs in the Giffler and Thompson GT procedure. The performance is compared with regard to makes-pan criteria and computational time. The optimal WCwise-pdr is proved to be efficient in providing optimal solutions in a reasonable computational time. Also, the proposed GA based heuristic method is extended to revise schedules on the arrival of new jobs, and on the failure of equipment to address the dynamic operation mode of flexible manufacturing systems. An iterative search technique is proposed to find the best route choice for all operations to provide a feasible and optimal solution. The applicability and usefulness of the proposed methodology for the operation and control of FMS in real-time are illustrated with examples. The scope of the genetic search process and future research directions are discussed.