INVESTIGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY

The thesis entitled INVESTIDGATIONS ON THE RECOVERY OF TITANIUM VANADIUM AND IRON VALUES FROM THE WASTE CHILORIDE LIQUORS OF TITANIA INDUSTRY embodies the results of the investigations carried out on the solvent extraction separation of iron (III) vanadium(V) and titanium (IV) chlorides from the waste chloride liquors of titanium minerals processing industry by employing tributylphosphate (TBT) as an extractant. The objective of this study is to generate the knowledge base to achieve the recovery of iron, vanadium and titanium cvalues from multi- metal waste chloride liquors originating from ilmenite mineral beneficiation industries through selective separation and value added material development

Reinforcement Learning Approaches to Power System Scheduling

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.

An Integrated Hydro geological Study of the Muvattupuzha River Basin, Kerala, India

The present investigation on the Muvattupuzha river basin is an integrated approach based on hydrogeological, geophysical, hydrogeochemical parameters and the results are interpreted using satellite data. GIS also been used to combine the various spatial and non-spatial data. The salient finding of the present study are accounted below to provide a holistic picture on the groundwaters of the Muvattupuzha river basin. In the Muvattupuzha river basin the groundwaters are drawn from the weathered and fractured zones. The groundwater level fluctuations of the basin from 1992 to 2001 reveal that the water level varies between a minimum of 0.003 m and a maximum of 3.45 m. The groundwater fluctuation is affected by rainfall. Various aquifer parameters like transmissivity, storage coefficient, optimum yield, time for full recovery and specific capacity indices are analyzed. The depth to the bedrock of the basin varies widely from 1.5 to 17 mbgl. A ground water prospective map of phreatic aquifer has been prepared based on thickness of the weathered zone and low resistivity values (<500 ohm-m) and accordingly the basin is classified in three phreatic potential zones as good, moderate and poor. The groundwater of the Muvattupuzha river basin, the pH value ranges from 5.5 to 8.1, in acidic nature. Hydrochemical facies diagram reveals that most of the samples in both the seasons fall in mixing and dissolution facies and a few in static and dynamic natures. Further study is needed on impact of dykes on the occurrence and movement of groundwater, impact of seapages from irrigation canals on the groundwater quality and resources of this basin, and influence of inter-basin transfer of surface water on groundwater.

Thermal Degradation of Short Nylon -6 Fiber-Reinforced Styrene Butadiene Rubber Composite

The thermal properties of short Nylon-6 fiber-reinforced Styrene butadiene rubber (SBR) composites were studied by Thermogravimetric Analysis (TGA). The effect of epoxy-based bonding agent on thermal degradation of the gum and the composites was also studied. The thermal stability of the SBR was enhanced in the presence of Nylon-6 fibers and the stability of the composites increased in the presence of bonding agent. The epoxy resin did not significantly change the thermal stability of SBR gum vulcanizate. Results of kinetic studies showed that the degradation of SBR and the short nylon fiber-reinforced composites with and without bonding agents followed first-order kinetics.

Thermal degradation of short polyester fiber polyurethane elastomer composite

The thermal degradation of short polyester fiber reinforced polyurethane composites with and without different bonding agents has been studied by thermogravimetric analysis . It was found that degradation of the polyurethane takes place in two steps and that of the composites takes place in three steps. With the incorporation of 30 phr of fiber in the matrix , the onset of degradation was shifted from 230 to 238 ° C. The presence of bonding agents in the virgin elastomer and the composite gave an improved thermal stability . Results of kinetic studies showed that the degradation of polyurethane and the reinforced composites with and without bonding agents follows first -order reaction kinetics

Thermal degradation of short kevlar fibre thermoplastic polyurethane composite

The thermal degradation of short kevlar fibre-thermoplastic polyurethane (TPU) composites has been studied by Thermogravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). TGA showed that the thermal degradation of TPU takes place in two steps with peak maxima (T1max and T2ma,) at 383°C and 448°C, respectively. In the presence of 10-40 phr of short kevlar fibres, T1_ and T2max were shifted to lower temperatures. The temperature of onset of degradation was increased from 245 to 255°C at 40 parts per hundred rubber (phr) fibre loading. Kinetic studies showed that the degradation of TPU and kevlar-TPU composite follows first-order reaction kinetics. The DSC study showed that there is an improvement in thermal stability of TPU in the presence of 20 phr of short kevlar fibres.

The Steady-State and Transient Characteristic Analysis of Thermal Fixing a Photorefractive Hologram Grating in Lithium Niobate

The transient interaction between a refraction index grating and light beams during simultaneous writing and thermal fixing of a photorefractive hologram is investigated. With a diffusion- and photovoltaic-dominated carrier transport mechanism and carrier thermal activation (temperature dependent) considered in Fe:LiNbO3 crystal, from the standpoint of field-material coupling, the theoretical thermal fixing time and the space-charge field buildup, spatial distribution, and temperature dependence are given numerically by combining the band transport model with mobile ions with the coupled-wave equation

Preparation of high surface area TiO (anatase) by thermal hydrolysis 2 of titanyl sulphate solution

Highly crystalline, ultra fine TiO (anatase) having high surface area has been prepared by thermal hydrolysis of titanyl sulphate 2 solution and characterized using B.E.T surface area measurements, XRD and chemical analysis. The dependence of surface area on concentration of staffing solution, temperature of hydrolysis, duration of boiling and calcination temperature were also studied. As the boiling temperature, duration of boiling and calcination temperature increased, the surface area of TiO formed decreased significantly. 2 On increasing calcination temperature, the crystallite size of TiO also increased and gradually the phase transformation to rutile took 2 place. The onset and completion temperatures of rutilation were 700 and 10008C, respectively