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

Standardization of Optimum Conditions for the Production of Glucosamine Hydrochiloride from Chitin

The study entitled standardization of optimum conditions for the production of glucosamine hydrochloride from chitin. Shellfish processing industries around the world turn out a significant quantity of head and shell as industrial waste. The waste must be removed immediately to prevent the contamination to the processing environment. The technique that are available for their disposal include ocean dumping, incineration or disposal of landfill sites. In this thesis the techniques and methods are used to process glucosamine hydrochloride from crustacean processing waste. Chitin is a nitrogenous polysaccharide, which is white, hard, inelastic, found in outer skeleton of insects, crabs, shrimp and lobsters and in the internal structures of other invertebrates. Glucosamine can be considered as a nutraceutical product by virtue of its properties. It is important for healthy skin, and plays a major role in the healing of surgical incisions and skin wounds. Deproteinisation of shrimp shell had significant effect on quality of chitin. Demineralization is also influences chitin quality. Solvents used for glucosamine hydrochloride affects the final yield and purity.

Beckmann rearrangement of E,E-cinnamaldoxime on rare earth exchanged (Ce3+, La3+, and RE3+) HFAU-Y zeolites: An efficient green process for the synthesis of isoquinoline

In this paper, a novel application of solid acid catalysts in the Beckmann rearrangement of E,E-cinnamaldoxime in the synthesis of an important heterocyclic compound; isoquinoline is reported. E,E-Cinnamaldoxime under ambient reaction conditions on zeolite catalysts underwent Beckmann rearrangement to produce isoquinoline in yields of ca. 86–95%. Cinnamonitrile and cinnamaldehyde were formed as by-products. LaH-Y zeolite produces maximum amount of the desired product (yield 95.6%). However, the catalysts are susceptible for deactivation due to the basic nature of the reactants and products, which neutralize the active sites. H-Y zeolite is more susceptible (22% deactivation in 10 h) for deactivation compared to the cerium-exchanged counterpart (18% deactivation in 10 h). Thus, the optimal protocol allows isoquinoline to be synthesised in excellent yields through the Beckmann rearrangement of cinnamaldoxime. The reaction is simple, effective, does not involve any other additives, and environmentally benign.

Process Optimization for Mass Production of Marine Yeast Candida sp. S 27 and its Nutritional Characterization

The main source of protein for human and animal consumption is from the agricultural sector, where the production is vulnerable to diseases, fluctuations in climatic conditions and deteriorating hydrological conditions due to water pollution. Therefore Single Cell Protein (SCP) production has evolved as an excellent alternative. Among all sources of microbial protein, yeast has attained global acceptability and has been preferred for SCP production. The screening and evaluation of nutritional and other culture variables of microorganisms are very important in the development of a bioprocess for SCP production. The application of statistical experimental design in bioprocess development can result in improved product yields, reduced process variability, closer confirmation of the output response to target requirements and reduced development time and overall cost.The present work was undertaken to develop a bioprocess technology for the mass production of a marine yeast, Candida sp.S27. Yeasts isolated from the offshore waters of the South west coast of India and maintained in the Microbiology Laboratory were subjected to various tests for the selection of a potent strain for biomass production. The selected marine yeast was identified based on ITS sequencing. Biochemical/nutritional characterization of Candida sp.S27 was carried out. Using Response Surface Methodology (RSM) the process parameters (pH, temperature and salinity) were optimized. For mass production of yeast biomass, a chemically defined medium (Barnett and Ingram, 1955) and a crude medium (Molasses-Yeast extract) were optimized using RSM. Scale up of biomass production was done in a Bench top Fermenter using these two optimized media. Comparative efficacy of the defined and crude media were estimated besides nutritional evaluation of the biomass developed using these two optimized media.

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

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.

Thermal degradation studies on Edible Oil during Deep Fat Frying Process

Deep fat frying process is one of the widely followed cooking practices throughout the world. Cooking oils serve as a medium for frying food for transferring heat and makes fried food tasty and palatable. Frying process is a most complex process involving numerous physicochemical changes which are complicated to understand. Frying leads to thermal degradation of oil through thermo-oxidation, hydrolysis, and polymerization. Hydrolysis results in formation of free fatty acids whereas oxidation process produces hydroperoxides and small molecular carbonyl compounds. This whole process leads to the formation of polar compounds and degradation of antioxidants that further degrades frying oil. Eventually, through mass transfer process these degradation products accumulate into fried food and reduce the nutritional quality of both oil and food. Thus, the frying process is of research interest calls for detailed systematic study which is chosen for the present study. The primary objective of this study is to understand the mechanism of degradation and characterization ofdegraded products which helps in arriving at the limits for frying oil utilization in terms of number of frying cycles. The mechanistic studies and the knowledge on the degraded products help to understand the way to retard the deterioration of oil for stability and enhancement of frying cycles. The study also explores the formation of the predominant polar compounds and their structural elucidation through mass spectrometry. Oxidation of oil is another important factor that ignites the degradation phenomena. One of the best ways to increase thermal stability of any oil is addition of potent antioxidants. But, most of the natural and synthetic antioxidants are unstable and ineffective at frying temperatures. Therefore, it is necessary to screen alternative antioxidants for their activity in the refined oils which are devoid of any added antioxidants. In this context, this study discussed the efficacy of several natural and synthetic antioxidants to retard the formation of polar compounds and thermooxidation during prolonged frying conditions. Similarly, the advantage of blending of two different oils to improve the thermal stability was explored. The present study brings out the total picture on the type of degradation products formed during frying and the ways of retarding the determination to improve upon the stability of the oil and enhancement of frying cycles.