- 22 December, 2024
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- ONECSIR
| Designation: |
Senior Technical Officer (2)
|
| Email Id: | s_das[at]neeri[dot]res[dot]in |
| Qualification: |
B.Sc., Diploma in Electronics and telecommunication |
| Specialization: |
Electronics
|
| Address: |
Sub-Vertical 3A :Solid and Hazardous Waste Management, NEERI Nagpur
|
| Biodata: |
| Sr. No. | Publication Name |
|---|---|
| 1 |
An investigation on the predictability of thunderstorms over Kolkata, India using fuzzy inference system and graph connectivity
The purpose of this study was to develop a computing system (CS) with fuzzy membership and graph connectivity approach to estimate the predictability of thunderstorms during the pre-monsoon season (April–May) over Kolkata (22°32′N, 88°20′E), India. The stability indices are taken to form the inputs of the CS. Ten important stability indices are selected to prepare the input of the fuzzy set. The data analysis during the period from 1997 to 2006 led to identify the ranges of the stability indices through membership function for preparing the fuzzy inputs. The possibility of thunderstorms with the given ranges of the stability indices is validated with the bipartite graph connectivity method. The bipartite graphs are prepared with two sets of vertices, one set for three membership functions (strong, moderate and weak) with the stability indices and the other set includes the three membership functions for the probability of thunderstorms (high, medium and low). The percentages of degree of vertex (ΔG) are computed from a sample set of bipartite graph on thunderstorm days and are assigned as the measure of the likelihood of thunderstorms. The results obtained from graph connectivity analysis are found to be in conformity with the output of fuzzy interface system (FIS). The result reveals that the skill of graph connectivity is better and supports the FIS in estimating the predictability of thunderstorms over Kolkata during the pre-monsoon season. The result further reveals from the minimum degree of vertex connectivity that among the ten selected stability indices, only four indices: lifted index, bulk Richardson number, Boyden index and convective available potential energy, are most relevant for estimating the predictability of thunderstorms over Kolkata, India. |
| 2 |
Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)
Thunderstorms are well-known severe weather phenomena of the Gangetic West Bengal (GWB) region of India. The objective of the present study is to identify the ranges of Max_Z parameters of Doppler Weather Radar (DWR) associated with precipitating clouds that eventually grow into thunderstorms and to obtain a model to assess the predictability of thunderstorm and non-thunderstorm events with maximum possible accuracy during the pre-monsoon season (April–May) over the metropolis Kolkata (22.6°N; 88.4°E) enclosed within GWB (20–26°N, 85–91°E), India. The DWR imageries are analyzed to identify the stages of thunderstorm development. The survival of the fittest principle of genetic algorithm (GA) is implemented to find a suitable combination of the DWR Max_Z parameters; the reflectivity, distance of the first detected echo from Kolkata where the DWR is installed and the echo top height for the genesis of thunderstorms. The problem is posed as an optimization problem and the values of the parameters are converted into binary strings. The result reveals that the echoes with reflectivity between 44 and 48 dBZ at a distance of 250–300 km from Kolkata with echo top height between 13 and 15 km have the maximum possibility to grow into a thunderstorm. The artificial neural network (ANN) model is developed with the values of the Max_Z parameters optimized by GA as the inputs. The target of the ANN model is to forecast the type of the echo cells leading either to thunderstorm or non-thunderstorm events. The result further reveals that the ANN model with three hidden layers and one node in each layer is the most suitable model for estimating the likelihood of thunderstorm/non-thunderstorm events with mean absolute error (MAE) of 0.71/2.81. The result of the study is validated with the observation of India Meteorological Department. |
| 3 |
Enhanced arsenic removal from drinking water by iron-enriched aluminosilicate adsorbent prepared from fly ash
"The study deals with an efficient approach for the utilization of fly ash and mitigating one of the most severe drinking water problems caused due to arsenate. Iron enriched alumi- nosilicate adsorbent (IEASA) was synthesized using a novel method of alkali fusion of fly ash followed by ageing and hydrothermal curing. The raw material, intermediates, and final products were thoroughly characterized using powder X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, and particle size analysis. The charac- terization results suggested that the prepared adsorbent is highly crystalline with particle size of 500 nm. The IEASA was evaluated as an adsorbent for the removal of arsenate at initial concentration of 1 mg L, 1 by batch adsorption studies, which shows excellent removal efficiency for arsenate (above 99%) in wide pH range of 4–10 and in the presence of various interfering ions. The efficiency was also compared with synthetic zeolite, which shows negligible arsenate removal. Adsorption isotherms were plotted using the Langmuir and Freundlich models to compute the adsorption capacities. The adsorption capacity obtained from Langmuir isotherm was 0.592 mg g 1as compared to the adsorption capacity of 0.455 mg g 1calculated from kinetics data. Detailed kinetics studies were also carried which confirms that the adsorption kinetics follows pseudo-second-order and particle diffu- sion is the rate determining step. Water quality was evaluated before and after adsorption, which suggests the suitability of the adsorbent for the decontamination of arsenate from drinking water and other parameters also confirms that treated water is potable." |
| 4 |
Solar assisted alkali pretreatment of garden biomass: Effects on lignocellulose degradation, enzymatic hydrolysis, crystallinity and ultra-structural changes in lignocellulose
A comprehensive study was carried out to assess the effectiveness of solar assisted alkali pretreatment (SAAP) on garden biomass (GB). The pretreatment efficiency was assessed based on lignocellulose degradation, conversion of cellulose into reducing sugars, changes in the ultra-structure and functional groups of lignocellulose and impact on the crystallinity of cellulose, etc. SAAP was found to be efficient for the removal of lignin and hemicellulose that facilitated enzymatic hydrolysis of cellulose. FTIR and XRD studies provided details on the effectiveness of SAAP on lignocellulosic moiety and crystallinity of cellulose. Scanning electron microscopic analysis showed ultra-structural disturbances in the microfibrils of GB as a result of pretreatment. The mass balance closer of 97.87% after pretreatment confirmed the reliability of SAAP pretreatment. Based on the results, it is concluded that SAAP is not only an efficient means of pretreatment but also economical as it involved no energy expenditure for heat generation during pretreatmeny. |
View Biodata [0.36 MB]