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Stochastic Modeling (stochastic + modeling)
Selected AbstractsDeterministic and Stochastic Modeling of Aquifer Stratigraphy, South CarolinaGROUND WATER, Issue 2 2000Russell B. Miller Deterministic and stochastic methods of three-dimensional hydrogeologic modeling are applied to characterization of contaminated Eocene aquifers at the Savannah River Site, South Carolina. The results address several important issues, including the use of multiple types of data in creating high-resolution aquifer models and the application of sequence-stratigraphic constraints. Specific procedures used include defining grid architecture stratigraphically, upscaling, modeling lithologic properties, and creating multiple equiprobable realizations of aquifer stratigraphy. An important question answered by the study is how to incorporate gamma-ray borehole-geophysical data in areas of anomalous log response, which occurs commonly in aquifers and confining units of the Atlantic Coastal Plain and other areas. To overcome this problem, gamma-ray models were conditioned to grain-size and lithofacies realizations. The investigation contributes to identifying potential pathways for downward migration of contaminants, which have been detected in confined aquifers at the modeling site. The approach followed in this investigation produces quantitative, stratigraphically constrained, geocellular models that incorporate multiple types of data from borehole-geophysical logs and continuous cores. The use of core-based stochastic realizations in conditioning deterministic models provides the advantage of incorporating lithologic information based on direct observations of cores rather than using only indirect measurements from geophysical logs. The high resolution of the models is demonstrated by the representation of thin, discontinuous clay beds that act as local barriers to flow. The models are effective in depicting the contrasts in geometry and heterogeneity between sheet-like nearshore-transgressive sands and laterally discontinuous sands of complex shoreline environments. [source] Stochastic Modeling of Affinity AdsorptionBIOTECHNOLOGY PROGRESS, Issue 3 2001John Hubble A stochastic model is described that allows surface proximity and packing effects to be incorporated into predictions of adsorption kinetics and equilibrium of affinity adsorption. Equilibrium predictions show that, depending on conditions chosen, the results obtained for equilibrium conditions can exhibit either a Freundlich- or a Langmuir-type relationship. Under conditions of surface density imposed adsorption constraints, the time taken for equilibrium to be reached increases as the "off" constant is decreased. This suggests that for resins having a high immobilized ligand density binding kinetics may be more highly limited by the "off" constant than by mass transfer limitations. [source] Stochastic modeling of particle motion along a sliding conveyorAICHE JOURNAL, Issue 1 2010Kevin Cronin Abstract The sliding conveyor consists of a plane surface, known as the track, along which particles are induced to move by vibrating the bed sinusoidal with respect to time. The forces on the particle include gravity, bed reaction force and friction. Because friction coefficients are inherently variable, particle motion along the bed is erratic and unpredictable. A deterministic model of particle motion (where friction is considered to be known and invariant) is selected and its output validated by experiment. Two probabilistic solution techniques are developed and applied to the deterministic model, in order to account for the randomness that is present. The two methods consider particle displacement to be represented by discrete time and continuous time random processes, respectively, and permits analytical solutions for mean and variance in displacement versus time to be found. These are compared with experimental measurements of particle motion. Ultimately this analysis can be employed to calculate residence-time distributions for such items of process equipment. © 2009 American Institute of Chemical Engineers AIChE J, 2010 [source] Stochastic modeling of usage patterns in a web-based information systemJOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 7 2002Hui-Min Chen Users move from one state (or task) to another in an information system's labyrinth as they try to accomplish their work, and the amount of time they spend in each state varies. This article uses continuous-time stochastic models, mainly based on semi-Markov chains, to derive user state transition patterns (both in rates and in probabilities) in a Web-based information system. The methodology was demonstrated with 126,925 search sessions drawn from the transaction logs of the University of California's MELVYL® library catalog system (www.melvyl.ucop.edu). First, user sessions were categorized into six groups based on their similar use of the system. Second, by using a three-layer hierarchical taxonomy of the system Web pages, user sessions in each usage group were transformed into a sequence of states. All the usage groups but one have third-order sequential dependency in state transitions. The sole exception has fourth-order sequential dependency. The transition rates as well as transition probabilities of the semi-Markov model provide a background for interpreting user behavior probabilistically, at various levels of detail. Finally, the differences in derived usage patterns between usage groups were tested statistically. The test results showed that different groups have distinct patterns of system use. Knowledge of the extent of sequential dependency is beneficial because it allows one to predict a user's next move in a search space based on the past moves that have been made. It can also be used to help customize the design of the user interface to the system to facilitate interaction. The group CL6 labeled "knowledgeable and sophisticated usage" and the group CL7 labeled "unsophisticated usage" both had third-order sequential dependency and had the same most-frequently occurring search pattern: screen display, record display, screen display, and record display. The group CL8 called "highly interactive use with good search results" had fourth-order sequential dependency, and its most frequently occurring pattern was the same as CL6 and CL7 with one more screen display action added. The group CL13, called "known-item searching" had third-order sequential dependency, and its most frequently occurring pattern was index access, search with retrievals, screen display, and record display. Group CL14 called "help intensive searching," and CL18 called "relatively unsuccessful" both had third-order sequential dependency, and for both groups the most frequently occurring pattern was index access, search without retrievals, index access, and again, search without retrievals. [source] Growth curve models for stochastic modeling and analyzing of natural disinfection of wastewaterENVIRONMETRICS, Issue 8 2006Wolfgang Bischoff Abstract This work is motivated by a study on the natural disinfection of wastewater in marine environment for ocean outfall systems without chlorination. In the study of the disinfection on wastewater in marine environment two natural factors, consisting of light intensity and salinity, one controllable factor, the volumetric mixing ratio of seawater to wastewater, and one random effect factor, the existence of predators, were investigated. Our problem and data are modeled by a growth curve model with an unknown random parameter that can be described by a mixed model with the factors mentioned above as covariates. For our model we determine the optimal variance estimations. Finally, we apply our model with these optimal estimated variance components to the data obtained from the real experiments. Copyright © 2006 John Wiley & Sons, Ltd. [source] Space,time modeling of rainfall dataENVIRONMETRICS, Issue 6 2004Luis Guillermo Coca Velarde Abstract Climate variables assume non-negative values and are often measured as zero. This is just the case when the rainfall level, in the dry season, is measured in a specified place. Then, the stochastic modeling demands the inclusion of a probability mass point at the zero level, and the resulting model is a mixture of a continuous and a Bernoulli distribution. In this article, spatial conditional autoregressive effects dealing with the idea that neighbors present similar responses is considered and the response level is modeled in two stages. The aim is to consider spatial interpolation and prediction of levels in a Bayesian context. Data on weekly rainfall levels measured in different stations at the central region of Brazil, an area with two well-marked seasons, will be used as an example. A method for comparing models, based on the deviance function, is also implemented. The main conclusion is that the use of space,time models improves the modeling of hydrological and climatological variables, allowing the inclusion of real life considerations such as the influence of other covariates, space dependence and time effects such as seasonality. Copyright © 2004 John Wiley & Sons, Ltd. [source] Data analytics and stochastic modeling in a semiconductor fabAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 1 2010Sugato Bagchi Abstract The scale, scope and complexity of the manufacturing operations in a semiconductor fab lead to some unique challenges in ensuring product quality and production efficiency. We describe the use of various analytical techniques, based on data mining, process trace data analysis, stochastic simulation and production optimization, to address these manufacturing issues, motivated by the following two objectives. The first objective is to identify the sub-optimal process conditions or tool settings that potentially affect the process performance and product quality. The second objective is to improve the overall production efficiency through better planning and resource scheduling, in an environment where the product mix and process flow requirements are complex and constantly changing. Copyright © 2010 John Wiley & Sons, Ltd. [source] | ||||||||||