A fiber optic concentration sensor

A simple fiber optic concentration sensor based on the coupling of light f rom one fiber to another through a solution is discussed. The operational characteristics of the sensor are illustrated by taking the solutions of potassium permanganate and fast green dye as samples.The extrinsic type sensor described here shows linearity at lower concentrations.

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.

Polypropylene/metal oxide nanocomposites: fiber spinning and evaluation”

PP has been getting much attention over the years because it is a very durable polymer commonly used in aggressive environments including automotive battery casings, fuel containers etc. They are used to make bottles, fibers for clothing, components in cars etc. However, it has some shortcomings such as low dimensional and thermal stability. Materials such as metal oxides with sizes of the order 1–50 nm have received a great deal of attention because of their versatile applications in polymer/ inorganic nanocomposites, optoelectronic devices, biomedical materials, and other areas. They are stable under harsh process conditions and also regarded as safe materials to human beings and animals. In the present investigation, PP is modified by incorporating metal oxide nanoparticles such as ZnO and TiO2 by simple melt mixing method. Melt spinning method was used to prepare PP/metal oxide nanocomposite fibers. Various studies have been carried out on these composites and fibers. In the first part of the study, ZnO nanoparticles were prepared from ZnCl2 and NaOH in presence of chitosan, PVA, ethanol and starch. This is a simple and inexpensive method compared to other methods. Change in morphology and particle size of ZnO were studied. Least particle size was obtained in chitosan medium. The particles were characterized by using XRD, SEM, TEM, TGA and EDAX. Antibacterial properties of ZnO prepared in chitosan medium (NZO) and commercial zinc oxide (CZO) were evaluated using a gram positive and a gram negative bacteria

Development of optical fiber sensors for selected chemical and physical sensing applications

The objective of the preset work is to develop optical fiber sensors for various physical and chemical parameters. As a part of this we initially investigated trace analysis of silica, ammonia, iron and phosphate in water. For this purpose the author has implemented a dual wavelength probing scheme which has many advantages over conventional evanescent wave sensors. Dual wavelength probing makes the design more reliable and repeatable and this design makes the sensor employable for concentration, chemical content, adulteration level, monitoring and control in industries or any such needy environments. Use of low cost components makes the system cost effective and simple. The Dual wavelength probing scheme is employed for the trace analysis of silica, iron, phosphate, and ammonia in water. Such sensors can be employed for the steam and water quality analysers in power plants. Few samples from a power plant are collected and checked the performance of developed system for practical applications.

Synthesis, characterization and properties of Conductive elastomeric composites based on Polypyrrole and short Nylon-6 fiber

The search for new materials especially those possessing special properties continues at a great pace because of ever growing demands of the modern life. The focus on the use of intrinsically conductive polymers in organic electronic devices has led to the development of a totally new class of smart materials. Polypyrrole (PPy) is one of the most stable known conducting polymers and also one of the easiest to synthesize. In addition, its high conductivity, good redox reversibility and excellent microwave absorbing characteristics have led to the existence of wide and diversified applications for PPy. However, as any conjugated conducting polymer, PPy lacks processability, flexibility and strength which are essential for industrial requirements. Among various approaches to making tractable materials based on PPy, incorporating PPy within an electrically insulating polymer appears to be a promising method, and this has triggered the development of blends or composites. Conductive elastomeric composites of polypyrrole are important in that they are composite materials suitable for devices where flexibility is an important parameter. Moreover these composites can be moulded into complex shapes. In this work an attempt has been made to prepare conducting elastomeric composites by the incorporation of PPy and PPy coated short Nylon-6 fiber with insulating elastomer matrices- natural rubber and acrylonitrile butadiene rubber. It is well established that mechanical properties of rubber composites can be greatly improved by adding short fibers. Generally short fiber reinforced rubber composites are popular in industrial fields because of their processing advantages, low cost, and their greatly improved technical properties such as strength, stiffness, modulus and damping. In the present work, PPy coated fiber is expected to improve the mechanical properties of the elastomer-PPy composites, at the same time increasing the conductivity. In addition to determination of DC conductivity and evaluation of mechanical properties, the work aims to study the thermal stability, dielectric properties and electromagnetic interference shielding effectiveness of the composites. The thesis consists of ten chapters.

Studies On Vulcanization, Rheology And Reinforcement Of Natural Rubber Latex With Special Reference To Accelerator Combinations, Surface Active Agents And Gamma Irradiation

In the present work, studies on vulcanization, rheology and reinforcement of natural rubber latex with special reference to accelerator combinations, surface active agents and gamma irradiation have been undertaken. In vulcanization, the choice of vulcanization system, the extent and mc-zie of vulcanization and network structure of the vulcanizate are important factors contributing to the overall quality of the product. The vulcanization system may be conventional type using elemental sulfur or a system involving sulfur donors. The latter type is used mainly in the manufacture of heat resistant products. For improving the technical properties of the products such as modulus and tensile strength, different accelerator combinations are used. It is known that accelerators have a strong effect on the physical properties of rubber vulcanizates. A perusal of the literature indicates that fundamental studies on the above aspects of latex technology are very limited. Thereforea systematic study on vulcanization, rheology and reinforcement of natural rubber latex with reference to the effect of accelerator combinations, surface active agents and gamma irradiation has been undertaken. The preparation and evaluation of some products like latex thread was also undertaken as a part of the study. The thesis consists of six chapter

Preparation and characterization of micro and nano fiber reinforced natural rubber composites by latex stage processing

Use of short fibers as reinforcing fillers in rubber composites is on an increasing trend. They are popular due to the possibility of obtaining anisotropic properties, ease of processing and economy. In the preparation of these composites short fibers are incorporated on two roll mixing mills or in internal mixers. This is a high energy intensive time consuming process. This calls for developing less energy intensive and less time consuming processes for incorporation and distribution of short fibers in the rubber matrix. One method for this is to incorporate fibers in the latex stage. The present study is primarily to optimize the preparation of short fiber- natural rubber composite by latex stage compounding and to evaluate the resulting composites in terms of mechanical, dynamic mechanical and thermal properties. A synthetic fiber (Nylon) and a natural fiber (Coir) are used to evaluate the advantages of the processing through latex stage. To extract the full reinforcing potential of the coir fibers the macro fibers are converted to micro fibers through chemical and mechanical means. The thesis is presented in 7 chapters

“Studies on short polyester fiber - polyurethane elastomer composite with different interfacial bonding agents”

The work presented in this thesis is regarding the development and evaluation of new bonding agents for short polyester fiber - polyurethane elastomer composites. The conventional bonding system based on hexamethylenetetramine, resorcinol and hydrated silica was not effective as a bonding agent for the composite, as the water eliminated during the formation of the RF resin hydrolysed the urethane linkages. Four bonding agents based on MDI/'I‘DI and polypropyleneglycol, propyleneglycol and glycerol were prepared and the composite recipe was optimised with respect to the cure characteristics and mechanical properties. The flow properties, stress relaxation pattern and the thermal degradation characteristics of the composites containing different bonding agents were then studied in detail to evaluate the new bonding systems. The optimum loading of resin was 5 phr and the ratio of the -01 to isocyanate was 1:1. The cure characteristics showed that the optimum combination of cure rate and processability was given by the composite with the resin based on polypropyleneglycol/ glycerol/ 4,4’diphenylmethanediisocynate (PPG/GL/MDI). From the rheological studies of the composites with and without bonding agents it was observed that all the composites showed pseudoplastic nature and the activation energy of flow of the composite was not altered by the presence of bonding agents. Mechanical properties such as tensile strength, modulus, tear resistance and abrasion resistance were improved in the presence of bonding agents and the effect was more pronounced in the case of abrasion resistance. The composites based on MDI/GL showed better initial properties while composites with resins based on MDI/PPG showed better aging resistance. Stress relaxation showed a multistage relaxation behaviour for the composite. Within the-strain levels studied, the initial rate of relaxation was higher and the cross over time was lesser for the composite containing bonding agents. The bonding agent based on MDI/PPG/GL was found to be a better choice for improving stress relaxation characteristics with better interfacial bonding. Thennogravimetirc analysis showed that the presence of fiber and bonding agents improved the thennal stability of the polyurethane elastomer marginally and it was maximum in the case of MDI / GL based bonding agents. The kinetics of degradation was not altered by the presence of bonding agents

A Reinforcement Learning Algorithm to Economic Dispatch Considering Transmission Losses

Reinforcement Learning (RL) refers to a class of learning algorithms in which learning system learns which action to take in different situations by using a scalar evaluation received from the environment on performing an action. RL has been successfully applied to many multi stage decision making problem (MDP) where in each stage the learning systems decides which action has to be taken. Economic Dispatch (ED) problem is an important scheduling problem in power systems, which decides the amount of generation to be allocated to each generating unit so that the total cost of generation is minimized without violating system constraints. In this paper we formulate economic dispatch problem as a multi stage decision making problem. In this paper, we also develop RL based algorithm to solve the ED problem. The performance of our algorithm is compared with other recent methods. The main advantage of our method is it can learn the schedule for all possible demands simultaneously.

Reinforcement Learning approaches to Economic Dispatch problem

This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Mechanical Properties of Steel Fiber-Reinforced Concrete

This paper presents the results from an experimental program and an analytical assessment of the influence of addition of fibers on mechanical properties of concrete. Models derived based on the regression analysis of 60 test data for various mechanical properties of steel fiber-reinforced concrete have been presented. The various strength properties studied are cube and cylinder compressive strength, split tensile strength, modulus of rupture and postcracking performance, modulus of elasticity, Poisson’s ratio, and strain corresponding to peak compressive stress. The variables considered are grade of concrete, namely, normal strength 35 MPa , moderately high strength 65 MPa , and high-strength concrete 85 MPa , and the volume fraction of the fiber Vf =0.0, 0.5, 1.0, and 1.5% . The strength of steel fiber-reinforced concrete predicted using the proposed models have been compared with the test data from the present study and with various other test data reported in the literature. The proposed model predicted the test data quite accurately. The study indicates that the fiber matrix interaction contributes significantly to enhancement of mechanical properties caused by the introduction of fibers, which is at variance with both existing models and formulations based on the law of mixtures

Rice husk ash – A valuable reinforcement for high density polyethylene

This paper presents the results of a study on the use of rice husk ash (RHA) for property modification of high density polyethylene (HDPE). Rice husk is a waste product of the rice processing industry. It is used widely as a fuel which results in large quantities of RHA. Here, the characterization of RHA has been done with the help of X-ray diffraction (XRD), Inductively Coupled Plasma Atomic Emission Spectroscopy (ICPAES), light scattering based particle size analysis, Fourier transform infrared spectroscopy (FTIR) and Scanning Electron Microscope (SEM). Most reports suggest that RHA when blended directly with polymers without polar groups does not improve the properties of the polymer substantially. In this study RHA is blended with HDPE in the presence of a compatibilizer. The compatibilized HDPE-RHA blend has a tensile strength about 18% higher than that of virgin HDPE. The elongation-at-break is also higher for the compatibilized blend. TGA studies reveal that uncompatibilized as well as compatibilized HDPERHA composites have excellent thermal stability. The results prove that RHA is a valuable reinforcing material for HDPE and the environmental pollution arising from RHA can be eliminated in a profitable way by this technique.