The reliability of gas path components (compressor, burners and turbines) of a gas turbine is generally high, when compared with those of other systems. However, in case of forced stops, downtime is usually high, with a relatively low availability. The purpose of condition monitoring and fault diagnostics is to detect, isolate and evaluate (i.e., to estimate quantitatively the extent) defects within a system. One effective technique could provide a significant improvement of economic performance, reduce operating costs and maintenance, increase the availability and improve the level of safety achieved. However, conventional analytical techniques such as gas path analysis and its variants are limited in their engine diagnostic, due to several reasons, including their inability to effectively operate in the presence of noise measures, to distinguish anomalies of component from a failure sensor, to preserve the linearity in the relations between parameters of gas turbines and to manage the sensors range to achieve accurate diagnosis. In this paper, the approach of a diagnostic scenario to detect faults in the gas path of a gas turbine has been presented. The model provides a large-scale integration of artificial neural networks designed to detect, isolate and evaluate failures during the operating conditions. The engine measurements are considered as input for the model, such as the speed, pressure, temperature and fuel flow rate. The output supplies any changes in the sensor or in the efficiency levels and flow rate, in the event of fault components. The diagnostic method has the ability to evaluate anomalies of both multiple components and multiple sensors, within the range of operating points. In the case of components failures, the system provides diagnostic changes in efficiency and flow rate that can be interpreted to determine the nature of the physical problem. The technique has been applied in different operating conditions by comparing the results obtained with the solutions provided by linear and nonlinear analysis.

The reliability of gas path components (compressor, burners and turbines) of a gas turbine is generally high, when compared with those of other systems. However, in case of forced stops, downtime is usually high, with a relatively low availability. The purpose of conditions monitoring and faults diagnostics is to detect, isolate and evaluate (i.e., to estimate quantitatively the extent) defects within a system. One effective technique could provide a significant improvement in economic performance, reduce operating costs and maintenance, increase the availability and improve the level of safety achieved. However, conventional analytical techniques such as gas path analysis and its variants are limited in their engine diagnostic, due to several reasons, including their inability to effectively operate in the presence of noise measures, to distinguish anomalies of component from a failure sensor, to preserve the linearity in the relations between parameters of gas turbines and to manage the sensors range to achieve accurate diagnosis. In this paper, the approach of a diagnostic scenario to detect faults in the gas path of a gas turbine has been presented. The model provides a large-scale integration of artificial neural networks designed to detect, isolate and evaluate failures during the operating conditions. The engine measurements are considered as input for the model, such as the speed, pressure, temperature and fuel flow rate. The output supplies any changes in the sensor or in the efficiency levels and flow rate, in the event of fault components. The diagnostic method has the ability to evaluate both anomalies of multiple components or multiple sensors, within the range of operating points. In the case of components failures, the system provides diagnostic changes in efficiency and flow rate, which can be interpreted to determine the nature of the physical problem. The technique has been applied in different operating conditions by comparing the results obtained with the solutions provided by linear and nonlinear analysis.

Jatropha curcas is a plant with a variety of potential and ecological applications. The seeds of this plant contain a high amount of oil that can be used to obtain a better quality of alternative fuel biodiesel. But the Jatropha plants are seriously affected by the mosaic virus (Begomovirus) that is carried by infected vector whiteflies. It severely affects the Jatropha plants by causing leaf damage, yellowing leaves and sap drainage. In particular, it attacks its fruits considerably reducing the production of seeds. In this paper, we formulate a model for the dynamics of this disease and its possible control via insecticide spraying. We identify the parameters that are most important for vector-borne disease control. Pontryagin minimum principle is employed to minimize the cost of spraying. The findings indicate that the optimal spraying policy does not require insecticide application during the first ten days of the epidemic outbreak and that instead the spraying must be continued for the following three months to eradicate the disease.

Underground coal mining-induced land subsidence has large impacts on different components of natural environment such as changing the morphology of land settlements and soil characteristics, interrupting the hydrologic environment, damaging different structures, disordering the chain of social environment and so on. In view of these consequences, primarily this research recognized and estimated subsidence in field which compared with empirically predicted maximum depth and type of subsidence in the area. Secondly, the current situations of various environmental components were studied under intensive field investigations. From the field, it has observed that more than one square kilometer land settlements subsided with a depth at the center of subsidence is about 1 m. In fact, the depth of subsidence decreased gradually toward the virgin area, hence formed a trough shape structure which is more or less matched with the empirically predicted trough subsidence around the mine. The present noticeable impact of subsidence on different structures such as mud wall, brick wall of house, road, yard, land and others are deformed very slight to slight, and very few cases are severe. In the case of water environment, this study recognized that the whole trough area appeared as a big nonnatural lake/water reservoir where the water quality analysis revealed that major cations and anions excluding pH values are slightly below the standard limit suggested the degradation of water quality; then, water level data analysis reflected that the water level lowered considerably with respect to its previous state. These scenarios also directly or indirectly supported by the soil permeability analysis even as the permeable properties of soil reduced than the virgin area which might be interrupted the natural water recharge condition as a result depleted the groundwater level. Moreover, from the community consultations around the mines, it is realized that the impact of subsidence on social environment is noticeable and tormenting for future. In conclusion, the research provides recommendations for planning and management of subsidence and safe environment which ultimately be helpful for sound production of coal from the mine for present and future.

This study assessed the effect of vegetative filter strip (VFS) in removal of suspended sediment (SS), total nitrogen, total phosphorus and Escherichia coli (E. coli) in overland flow to improve receiving water quality standards. Four and half kilograms of cowpat manure was applied to the model pasture 14 m beyond the edge of vegetated filter strip (VFS) comprising 10-m Napier grass draining into 20-m Kikuyu grass (VFS II), 10-m Kikuyu grass draining into 20-m Napier grass (VFS III) and native grass mixture of Couch–Buffel (VFS I-control). Overland flow water samples were collected from the sites at positions 0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25 and 30 m along the length of VFSs. E. coli removal by Napier grass VFS was on the order of log unit, which provided an important level of protection and reduced surface-flow concentrations of E. coli to below the 200 (CFU 100 mL⁻¹) recommended water quality standards, but not for nutrients and SS. The Napier grass showed highest efficiency (99.6 %), thus outperforming both Kikuyu grass (85.8 %) and Couch–Buffel grasses VFS (67.9 ± 4.2 %) in removing E. coli from overland flow. The low-level efficiency of native Couch–Buffel grasses in reducing E. coli in overland flow was because of preferential flow. Composition and design of VFS was instrumental and could be applied with a high potential of contracting the uncertainty in improving water quality standards through mass reduction of SS, nutrients and E. coli load in watersheds.

The current study was intended to isolate and characterize the plant growth-promoting properties and pesticide (chlorpyripfos) tolerance ability of microbial strains for sustainable vegetable productions. Plant growth-promoting microorganism is a group of microbial consortia to improve crop growth and yield by various direct mechanisms, e.g. nitrogen fixation, phosphate solubilization, production of plant growth hormones, ammonia and siderophore, and indirect mechanisms, e.g. production of antibiotic, hydrogen cyanide to help as biocontrolling agent. Totally, 50 microbes were isolated from soils of vegetable field. Out of 50, 14 strains were selected on the basis of morphological, growth biochemically characters (e.g. gram staining, amylase, cellulase, catalase and citrate test) and plant growth-promoting properties. All strains have ability of ammonia production. Out of 14 strains, IESDV2, IESDV3, IESDV4, IESDV11, IESDV12 and IESD28 were found significant increase in production of IAA, ammonia and phosphate solubilization than others. Strains IESDV3 and IESDV11 showed tolerant at 6 μl/ml concentration of chlorpyrifos which is three-time increase concentration of recommended dose (2 μl/ml). Others strains IESDV12, IESDV13 and IESDV28 were also showed resistance or tolerance at different concentrations of pesticide 2, 4, 6 and 20 μl/ml. These strains also showed plant growth-promoting rhizobacteria activities. Therefore, the main result of this study is to screen and identify that three strains (IESDV3, IESDV11, IESDV12 and IESDV28) can be used as effective pesticide-tolerant plant growth-promoting microbial consortia for vegetable production under sustainable agriculture at eastern Uttar Pradesh.

The current study was aimed at optimizing the fermentation conditions for efficient ethanol production from biologically pretreated paddy straw. The yeast strain Saccharomyces cerevisiae LN1 showed highest fermentation efficiency at pH 5.0 and temperature 30 °C. Paddy straw pretreated with fungus Myrothecium roridum LG7 was saccharified with indigenous holocellulase from Aspergillus niger SH3 producing total sugar yield of 26.14 mg/ml with 19.23 mg/ml of glucose. Enzymatic hydrolysate was then fermented using S. cerevisiae LN1 to observe the effect of nutrient supplementation (yeast extract, MgSO₄·7H₂O and (NH₄)₂SO₄) on ethanol production. Higher ethanol was produced from saccharified material fermented without supplementation of any nutrient source. With the scale-up of ethanol production under optimized conditions in 7L bioreactor, 4.46 g/l of ethanol was produced with fermentation efficiency of 47.2 %. TLC of enzymatic hydrolysate confirmed the presence of p-coumaric acid, ferulic acid, vanillic acid, gallic acid and many other aromatic compounds and inhibitors in the saccharified material which limit fermentation efficiency of yeast strain. Thus, optimization of fermentation conditions can lead to development of a cost-effective process for efficient ethanol production, exploitation of which also requires removal of aromatic compounds and inhibitors which may hinder the ethanol production efficiency.