7 resultados para Brams, Steven J.: The win-win solution
em Cochin University of Science
Resumo:
The primary objective of this investigation has been to develop more efficient and low cost adhesives for bonding various elastomer combinations particularly NR to NR, NR/PB to NR/PB, CR to CR,NR to CR and NR to NBR.A significant achievement of the investigation was the development of solventless and environment friendly solid adhesives for NR to NR and NR/PB to NR/PB particularly for precured retreading. Conventionally used adhesives in this area are mostly NR based adhesive strips in the presence of a dough. The study has shown that an ultra accelerator could be added to the dough just before applying it on the tire which can significantly bring down the retreading time resulting in prolonged tire service and lower energy consumption. Further latex reclaim has been used for the preparation of the solid strip which can reduce the cost considerably.Another significant finding was that by making proper selection of the RF resin, the efficiency and shelflife of the RFL adhesive used for nylon and rayon tire cord dipping can be improved. In the conventionally used RFL adhesive, the resin once prepared has to be added to the latex within 30 minutes and the RFL has to be used after 4 hours maturation time maximum shelf life of the RFL dip solution being 72 hours. In this study a formaldehyde deficient resin was used and hence more flexibility was available for mixing with latex and maturing. It also has a much longer shelf life. In the method suggested in this study, formaldehyde donors were added only in the rubber compound to make up the formaldehyde deficiency in the RFL. The results of this investigation show that the pull through load by employing this method and the conventional method are comparable. This study has also shown that the amount of RF resin with RFL adhesive can be partially replaced by other modifying agents for cost reduction.Cashew nut shell liquid (CNSL) resin can be employed for improving the bonding of dipped nylon and rayon cord with NR.Since CNSL resin cannot be added in the dip solution since it is not soluble in water, it was added in the rubber compound. The amount of wood rosin in the rubber compound can be reduced by using CNSL resin.Another interesting result of the investigation was the use of CR based adhesive modified with chlorinated natural rubber for CR to CR bonding. Addition of chlorinated natural rubber was found to improve sea water resistance of CR based adhesive. In the bonding of a polar rubber like nitrile rubber or polychloroprene rubber to a non polar rubber like natural rubber, an adhesive based on polychloroprene rubber was found to be effective.
Resumo:
To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.
Resumo:
One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.
Resumo:
The thesis presents the results of the investigations on the crystallisation ‘behaviour, detect structure end electrical properties of certain organic crystals---phthslic snhydride end potsssiun scid phthalate Hollow crystals of phthalic snhydride were grown from vapour. the norpholog of these hollow crystals were studied in detail and s. mechanism for their growth has been proposed. A closed crystal—vapour system was used to study the basal plane growth of the whiskers and the sequential growth, observed, confirmed the mechanism suggested for hollow crystals. The dendritic crystals of phthslic enhydride were grown, both iron the melt and solution. The observed morphologies of these dendrites ere described. Bpherulites of phthalic anhydride have been grown by the artificial initiation of nucleation, from melt and solution. The variation of the substructure oi’ these spherulites with the growth tenperature wee investigated. The spherulitic filll having ribbon substructure were etched to reveal dislocations. A mechanism for the formation of the observed etch pattern has been suggested. the slip occurring in these ribbons were studied and the results are presented
Resumo:
The objective of this thesis is to study the time dependent behaviour of some complex queueing and inventory models. It contains a detailed analysis of the basic stochastic processes underlying these models. In the theory of queues, analysis of time dependent behaviour is an area.very little developed compared to steady state theory. Tine dependence seems certainly worth studying from an application point of view but unfortunately, the analytic difficulties are considerable. Glosod form solutions are complicated even for such simple models as M/M /1. Outside M/>M/1, time dependent solutions have been found only in special cases and involve most often double transforms which provide very little insight into the behaviour of the queueing systems themselves. In inventory theory also There is not much results available giving the time dependent solution of the system size probabilities. Our emphasis is on explicit results free from all types of transforms and the method used may be of special interest to a wide variety of problems having regenerative structure.
Resumo:
The thesis relates to the investigations carried out on Rectangular Dielectric Resonator Antenna configurations suitable for Mobile Communication applications. The main objectives of the research are to: - numerically compute the radiation characteristics of a Rectangular DRA - identify the resonant modes - validate the numerically predicted data through simulation and experiment 0 ascertain the influence of the geometrical and material parameters upon the radiation behaviour of the antenna ° develop compact Rectangular DRA configurations suitable for Mobile Communication applications Although approximate methods exist to compute the resonant frequency of Rectangular DRA’s, no rigorous analysis techniques have been developed so far to evaluate the resonant modes. In this thesis a 3D-FDTD (Finite Difference Time Domain) Modeller is developed using MATLAB® for the numerical computation of the radiation characteristics of the Rectangular DRA. The F DTD method is a powerful yet simple algorithm that involves the discretimtion and solution of the derivative form of Maxwell’s curl equations in the time domain.
Behavioural Competency Management with special reference to Commercial Banks headquartered in Kerala
Resumo:
This study aims to analyze, compare and contrast the behavioral competency of officials in commercial banks headquartered in Kerala. This is done by analyzing the soft skills/behavioral skills possessed by an individual employee in both clerical and managerial levels and the means adopted to enhance their said skills in near future. The study was conducted with the objective of analyzing the behavioral competency of the managers and clerical staff in the commercial banks headquartered in Kerala. The researcher has gone through the available literature with respect to employee competency, job satisfaction and employee performance evaluation to formulate the problem and conceptualize the framework of the study. The study concluded that the competency of the employees differs from one bank to the other but strengthening the employees’ competency is the only possible solution by which the banks can determine their future growth prospects. Only through competency, banks can achieve high level of performance especially under the globalised situation.