Time series analysis of coal mine accident experience

This study investigates several forecasting techniques that can be useful to mine safety managers for studying mine accident rate behavior. Three time series models were studied for extrapolation of accident rates. These models are applied to historical accident incidence data from a coal mine. Further, a method is presented for evaluating the three models for the selection of an appropriate model. For this particular mine application, it is concluded that the more complex Box-Jenkins ARMA model as well as first order autoregressive model do not give better results than the simple exponential smoothing model. However, when the random variations or autocorrelations in the accident experience rates between periods are different, the models may predict differently. As such, specific models must be developed for each mine on the basis of statistical analysis of the mine accident experience data over time. Moreover, the importance of incorporating human judgement to interpret the results of statistical forecasting cannot be overemphasized. Integration of policy or operating changes, which may impact mine safety performance, with statistical forecasting techniques is essential to arrive at a realistic prediction of future performance.     

Biosynthesis of Poly(3-hydroxybutyrate) from Cheese Whey by <Emphasis Type="Italic">Bacillus megaterium</Emphasis> NCIM 5472

Microbial polyhydroxyalkonate such as homopolyester of poly(3-hydroxybutyrate) (PHB) was produced from cheese whey by Bacillus megaterium NCIM 5472. Due to their numerous potential industrial applications, the focus was given to competently enhance the amount of PHB produced. The amount of PHB produced from whole cheese whey, and ultrafiltered cheese whey was first compared, and after observing a rise in PHB production by using ultrafiltered cheese whey, cheese whey permeate was chosen for further analysis. The presence of PHB was then confirmed by GCMS. Since the main aim of the study was to increase the amount of PHB produced through batch fermentation, various process parameters like time, pH, C/N ratio, etc. were optimized. After optimization, it was found that B. megaterium NCIM 5472 was capable of accumulating 75.5% of PHB of its dry weight and a PHB yield of 8.29 g/L. The chemical structure of the polymer was further analyzed by using FTIR and NMR spectroscopy methods. Also, the physical and thermal properties were studied by using Differential scanning calorimetry and Thermogravimetric analysis. It was found that the polymer produced had excellent thermal stability, thus allowing the possibility to exploit its properties for industrial purposes such as adhesives, packaging materials, etc.     

Vertical characterization of soil contamination using multi-way modeling – A case study

This study describes application of chemometric multi-way modeling approach to analyze the dataset pertaining to soils of industrial area with a view to assess the soil/sub-soil contamination, accumulation pathways and mobility of contaminants in the soil profiles. The three-way (sampling depths, chemical variables, sampling sites) dataset on heavy metals in soil samples collected from three different sites in an industrial area, up to a depth of 60 m each was analyzed using three-way Tucker3 model validated for stability and goodness of fit. A two component Tucker3 model, explaining 66.6% of data variance, allowed interpretation of the data information in all the three modes. The interpretation of core elements revealing interactions among the components of different modes (depth, variables, sites) allowed inferring more realistic information about the contamination pattern of soils both along the horizontal and vertical coordinates, contamination pathways, and mobility of contaminants through soil profiles, as compared to the traditional data analysis techniques. It concluded that soils at site-1 and site-2 are relatively more contaminated with heavy metals of both the natural as well as anthropogenic origins, as compared to the soil of site-3. Moreover, the accumulation pathways of metals for upper shallow layers and deeper layers of soils in the area were differentiated. The information generated would be helpful in developing strategies for remediation of the contaminated soils for reducing the subsequent risk of ground-water contamination in the study region.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Leaching studies for tin recovery from waste e-scrap

Printed circuit boards (PCBs) are the most essential components of all electrical and electronic equipments, which contain noteworthy quantity of metals, some of which are toxic to life and all of which are valuable resources. Therefore, recycling of PCBs is necessary for the safe disposal/utilization of these metals. Present paper is a part of developing Indo-Korean recycling technique consists of organic swelling pre-treatment technique for the liberation of thin layer of metallic sheet and the treatment of epoxy resin to remove/recover toxic soldering material. To optimize the parameters required for recovery of tin from waste PCBs, initially the bench scale studies were carried out using fresh solder (containing 52.6% Sn and 47.3% Pb) varying the acid concentration, temperature, mixing time and pulp density. The experimental data indicate that 95.79% of tin was leached out from solder material using 5.5M HCl at fixed pulp density 50g/L and temperature 90°C in mixing time 165min. Kinetic studies followed the chemical reaction controlled dense constant size cylindrical particles with activation energy of 117.68kJ/mol. However, 97.79% of tin was found to be leached out from solder materials of liberated swelled epoxy resin using 4.5M HCl at 90°C, mixing time 60min and pulp density 50g/L. From the leach liquor of solder materials of epoxy resin, the precipitate of sodium stannate as value added product was obtained at pH 1.9. The Pb from the leach residue was removed by using 0.1M nitric acid at 90°C in mixing time 45min and pulp density 10g/L. The metal free epoxy resin could be disposed-of safely/used as filling material without affecting the environment.     

Reserve design to optimize functional connectivity and animal density

Ecological distance-based spatial capture–recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner.     

Synthesizing habitat connectivity analyses of a globally important human-dominated tiger-conservation landscape

As ecological data and associated analyses become more widely available, synthesizing results for effective communication with stakeholders is essential. In the case of wildlife corridors, managers in human-dominated landscapes need to identify both the locations of corridors and multiple stakeholders for effective oversight. We synthesized five independent studies of tiger (Panthera tigris) connectivity in central India, a global priority landscape for tiger conservation, to quantify agreement on landscape permeability for tiger movement and potential movement pathways. We used the latter analysis to identify connectivity areas on which studies agreed and stakeholders associated with these areas to determine relevant participants in corridor management. Three or more of the five studies’ resistance layers agreed in 63% of the study area. Areas in which all studies agree on resistance were of primarily low (66%, e.g., forest) and high (24%, e.g., urban) resistance. Agreement was lower in intermediate resistance areas (e.g., agriculture). Despite these differences, the studies largely agreed on areas with high levels of potential movement: >40% of high average (top 20%) current-flow pixels were also in the top 20% of current-flow agreement pixels (measured by low variation), indicating consensus connectivity areas (CCAs) as conservation priorities. Roughly 70% of the CCAs fell within village administrative boundaries, and 100% overlapped forest department management boundaries, suggesting that people live and use forests within these priority areas. Over 16% of total CCAs’ area was within 1 km of linear infrastructure (437 road, 170 railway, 179 transmission line, and 339 canal crossings; 105 mines within 1 km of CCAs). In 2019, 78% of forest land diversions for infrastructure and mining in Madhya Pradesh (which comprises most of the study region) took place in districts with CCAs. Acute competition for land in this landscape with globally important wildlife corridors calls for an effective comanagement strategy involving local communities, forest departments, and infrastructure planners.