The DOE Water Cycle Pilot Study

A Department of Energy (DOE) mutli-laboratory Water Cycle Pilot Study (WCPS) investigated components of the local water budget at the Walnut River Watershed in Kansas to study the relative importance of various processes and to determine the feasibility of observational water budget closure. An extensive database of local meteorological time series and land surface characteristics was compiled. Numerical simulations of water budget components were generated and, to the extent possible, validated for three nested domains within the Southern Great Plains; the DOE Atmospheric Radiation Measurement/Cloud Atmospheric Radiation Testbed (ARM/CART), the Walnut River Watershed (WRW), and the Whitewater Watershed (WW), Kansas A 2-month Intensive Observation Period (IOP) was conducted to gather detailed observations relevant to specific details of the water budget, including fine-scale precipitation, streamflow, and soil moisture measurements not made routinely by other programs. Event and seasonal water isotope (delta 18O, delta D) sampling in rainwater, streams, soils, lakes, and wells provided a means of tracing sources and sinks within and external to the WW, WRW, and the ARM/CART domains. The WCPS measured changes in leaf area index for several vegetation types, deep groundwater variations at two wells, and meteorological variables at a number of sites in the WRW. Additional activities of the WCPS include code development toward a regional climate model with water isotope processes, soil moisture transect measurements, and water level measurements in ground water wells

A neural network and a genetic algorithm for multiobjective scheduling of semiconductor manufacturing plants

Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple objectives to be satisfied simultaneously (maximization of workstation utilization and minimization of waiting time and storage, for instance). In this study, we propose a methodology based on an artificial neural network technique, for computing the various objective functions, embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment. A discrete event simulator, developed and validated in our previous works, serves here to feed the neural network. Six criteria related to both equipment (facility average utilization) and products (average cycle time (ACT), standard deviation of ACT, average waiting time,Work In Process and total storage) are chosen as significant performance indexes of the workshop. The optimization variables are the time between campaigns and the release time of batches into the plant. An industrial size example is taken as a test bench to validate the approach

Identification of Recharge Processes in Groundwater in Hard Rock Aquifers of Madurai District Using Stable Isotopes

Stable isotopes of H and O are the integral parts of water molecule and serve as ideal tracers to understand the recharge processes in groundwater. Hence, a study has been conducted in hard rock aquifers of Madurai District of Tamilnadu to identify the recharge processes using stable isotopes. A total of 54 groundwater samples were collected representing the entire district from various lithounits during post monsoon. Samples were analysed for pH, EC, Ca2+, Mg2+, Na+, K+, Cl− HCO3−, SO42−, PO43−, H4SiO4, F−, δ18O and δD. Cl− and HCO3− were the dominant ions in groundwater samples. Average values of Cl− and HCO3− ranged from 247 and 244 mg/L in fissile hornblende biotite gneiss, 262 and 268 mg/L in Charnockite, 75 and 185 mg/L in quartzite, 323 and 305 mg/L in granite, 524 and 253 mg/L in floodplain alluvium rock types. Geochemical signatures of groundwater were used to identify the chemical processes that control hydrogeochemistry. Interpretation of δ18O and δD indicates recharge from the meteoric water in charnockite, quartzite, granite and some samples of fissile hornblende biotite gneiss. It is also inferred that recharge take place from evaporated water in floodplain alluvium and fissile hornblende biotite gneiss