Photoacoustic detection of two photon absorption in formaldehyde using pulsed dye laser

Absorption spectra of formaldehyde molecule in the gas phase have been recorded using photoacoustic (PA) technique with pulsed dye laser at various power levels. The spectral profiles at higher power levels are found to be different from that obtained at lower laser powers. Two photon absorption (TPA) is found to be responsible for the photoacoustic signal at higher laser power while the absorption at lower laser power level is attributed to one photon absorption (OPA) process. Probable assignments for the different transitions are given in this paper.

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.

Qualitative Air Flow Modelling and Analysis of Data Centre Air Conditioning as Multiple Jet Array

Data centre is a centralized repository,either physical or virtual,for the storage,management and dissemination of data and information organized around a particular body and nerve centre of the present IT revolution.Data centre are expected to serve uniinterruptedly round the year enabling them to perform their functions,it consumes enormous energy in the present scenario.Tremendous growth in the demand from IT Industry made it customary to develop newer technologies for the better operation of data centre.Energy conservation activities in data centre mainly concentrate on the air conditioning system since it is the major mechanical sub-system which consumes considerable share of the total power consumption of the data centre.The data centre energy matrix is best represented by power utilization efficiency(PUE),which is defined as the ratio of the total facility power to the IT equipment power.Its value will be greater than one and a large value of PUE indicates that the sub-systems draw more power from the facility and the performance of the data will be poor from the stand point of energy conservation. PUE values of 1.4 to 1.6 are acievable by proper design and management techniques.Optimizing the air conditioning systems brings enormous opportunity in bringing down the PUE value.The air conditioning system can be optimized by two approaches namely,thermal management and air flow management.thermal management systems are now introduced by some companies but they are highly sophisticated and costly and do not catch much attention in the thumb rules.

Nucleophilic addition of Nitrones to Electron deficient acetylenes and related studies

Nitrones or azomethine-N-oxides are important precursors for the synthesis of several heterocyclic systems. They belong to the allyl anion type 1,3-dipoles and possess unique structural features which make them extraordinarily useful synthons. They behave as 1,3-dipoles in 1,3-dipolar cycloaddition reactions and as electrophiles in reactions with organometallic reagents. These are the two basic reactions given by nitrones. Nitrones also act as ‘spin traps’ in which they react with short-lived radicals to furnish stable nitroxide radicals which can be detected and identified by electron paramagnetic resonance (EPR) spectroscopy. Recently SmI2 catalysed reductive cross-coupling reactions of nitrones have gained significant interest in which the reactions are initiated by single electron transfer (SET) to nitrones. Apart from these reactions, nitrones are also known to participate in reactions which are initiated by the nucleophilic attack of nitrone-oxygen. In our group, we have also explored the nucleophilic character of nitrones through various reactions. The results obtained enabled us to develop a novel two-step one-pot strategy for quinolines and indoles - the heterocycles renowned for their pharmacological applications, from nitrones and electron deficient acetylenes. Using dibenzoylacetylene and phenylbenzoylacetylene as dipolarophiles, we could introduce a desired functional group at a predetermined position of the quinolines or indoles to be synthesised. In this context, the thesis entitled “NUCLEOPHILIC ADDITION OF NITRONES TO ELECTRON DEFICIENT ACETYLENES AND RELATED STUDIES” portrays our attempt to expand the scope of our x novel synthetic protocol using ester functionalised acetylenes: dimethyl acetylenedicarboxylate (DMAD) and methyl propiolate. The thesis is organised in to five chapters. The first chapter briefly describes the different classes of reactions that nitrone functionality can tolerate. The research problem is defined at the end of this chapter. The second chapter describes the synthesis of different nitrones used for the present study. The optimisation and expansion of scope of the novel strategy towards quinoline synthesis is discussed in the third chapter. The fourth chapter portrays the synthesis of indole-3-carboxylates using the novel strategy. In the fifth chapter, the reaction of N-(2,6-dimethylphenyl) and N-(2,4,6-trimethylphenyl)nitrones are discussed. Here we also discuss the mechanistic reinvestigation of Baldwin’s proposal in the isoxazoline-oxazoline rearrangement. The major outcome of the work is given at the end of the thesis. The structural formulae, schemes, tables and figures are numbered chapter-wise since each chapter of the thesis is organized as an independent unit. All new compounds (except two compounds reported in fourth chapter) are fully characterised on the basis of spectral and analytical data and single crystal X-ray analysis on representative examples. Relevant references are included at the end of individual chapters.