Development of Bioreactors for Nitrifying Water In Closed System Hatcheries Of Penaeid And Non-Penaeid Prawns

In the present study the development of bioreactors for nitrifying water in closed system hatcheries of penaeid and non-penaeid prawns. This work is an attempt in this direction to cater to the needs of aquaculture industry for treatment and remediation of ammonia and nitrate in penaeid and non-penaeid hatcheries, by developing nitrifying bacteria allochthonous to the particular environment under consideration, and immobilizing them on an appropriately designed support materials configured as reactors. Ammonia toxicity is the major limiting factors in penaeid and non-penaeid hatchery systems causing lethal and sublethal effects on larvae depending on the pH values. Pressing need of the aquaculture industry to have a user friendly and economically viable technology for the removal of ammonia, which can be easily integrated to the existing hatchery designs without any major changes or modifications. Only option available now is to have biological filters through which water can be circulated for the oxidation of ammonia to nitrate through nitrite by a group of chemolithotrophs known as nitrifying bacteria. Two types of bioreactors have been designed and developed. The first category named as in situ stringed bed suspended bioreactor(SBSBR) was designed for use in the larval rearing tanks to remove ammonia and nitrite during larval rearing on a continuous basis, and the other to be used for nitrifying freshly collected seawater and spent water named as ex situ packed bed bioreactior(PBBR). On employing the two reactors together , both penaeid and non-penaeid larval rearing systems can be made a closed recirculating system at least for a season. A survey of literature revealed that the in situ stringed bed suspended reactor developed here is unique in its design, fabrication and mode of application.

Growth and characterisation of certain organic crystals

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

Microstrip band rejection filter using open loop resonator

Design of a compact microstrip band reject filter is proposed. The device consists of an Open Loop Rectangular Resonator (OLRR) coupled to a microstrip line. The transmission line has a U-bend which enhances the coupling with the OLRR element and reduces the size of the filter. The filter can be made tunable by mounting variable capacitance to the system. Simulated and experimental results are presented.

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.