scholarly journals Dynamic Decision Model for Amount of AlF3 Addition in Industrial Aluminum Electrolysis

2015 ◽  
Author(s):  
Shuiping Zeng ◽  
Fuwei Cui
Keyword(s):  
Decision Model ◽  
Aluminum Electrolysis ◽  
Dynamic Decision Model ◽  
Industrial Aluminum

Bayesian dynamic programming

1975 ◽  
Vol 7 (2) ◽  
pp. 330-348 ◽  
Author(s):  
Ulrich Rieder
Keyword(s):  
Dynamic Programming ◽  
Weak Convergence ◽  
Decision Model ◽  
Transition Probabilities ◽  
General State ◽  
Parameter Spaces ◽  
State Action ◽  
Total Rewards ◽  
Bayesian Dynamic Programming ◽  
Dynamic Decision Model

We consider a non-stationary Bayesian dynamic decision model with general state, action and parameter spaces. It is shown that this model can be reduced to a non-Markovian (resp. Markovian) decision model with completely known transition probabilities. Under rather weak convergence assumptions on the expected total rewards some general results are presented concerning the restriction on deterministic generalized Markov policies, the criteria of optimality and the existence of Bayes policies. These facts are based on the above transformations and on results of Hindererand Schäl.

CFD Simulation of Effect of Interphase Forces and Turbulence Models on Gas–Liquid Two-Phase Flows in Non-Industrial Aluminum Electrolysis Cells

JOM ◽  
2017 ◽  
Vol 69 (9) ◽  
pp. 1589-1599 ◽  
Author(s):  
Shuiqing Zhan ◽  
Jianhong Yang ◽  
Zhentao Wang ◽  
Ruijie Zhao ◽  
Jun Zheng ◽  
...  
Keyword(s):  
Cfd Simulation ◽  
Turbulence Models ◽  
Aluminum Electrolysis ◽  
Two Phase Flows ◽  
Two Phase ◽  
Electrolysis Cells ◽  
Industrial Aluminum

scholarly journals A Dynamic Decision Model of Technology Adoption under Uncertainty: Case of Herbicide-Resistant Rice

2005 ◽  
Vol 37 (1) ◽  
pp. 161-172 ◽  
Author(s):  
Mamane M. Annou ◽  
Eric J. Wailes ◽  
Michael R. Thomsen
Keyword(s):  
Technology Adoption ◽  
Decision Model ◽  
Profit Maximization ◽  
Maximization Problem ◽  
Red Rice ◽  
Herbicide Resistant ◽  
Programming Framework ◽  
Optimal Rotation ◽  
Dynamic Decision Model ◽  
Potential Tool

Herbicide-resistant (HR) rice technology is a potential tool for control of red rice in commercial rice production. Using anex antemathematical programming framework, this research presents an empirical analysis of HR rice technology adoption under uncertainty. The analysis accounts for stochastic germination of red rice and sheath blight to model a profit maximization problem of crop rotation among HR rice, regular rice, and soybeans. The results demonstrate that risk attitudes and technology efficiency determine adoption rates and optimal rotation patterns.

scholarly journals Diagnosing the Relative Impact of “Sneaks,” “Phantoms,” and Volatility in Sequences of Lagged Ensemble Probability Forecasts with a Simple Dynamic Decision Model

2011 ◽  
Vol 139 (2) ◽  
pp. 387-402 ◽  
Author(s):  
Justin G. McLay
Keyword(s):  
Monte Carlo ◽  
Cost Function ◽  
Decision Model ◽  
Substantial Reduction ◽  
Sequence Structure ◽  
Markov Transition ◽  
Probability Forecasts ◽  
Lag Times ◽  
Dynamic Decision Model ◽  
Monte Carlo Sequence

Abstract Monte Carlo simulation of sequences of lagged ensemble probability forecasts is undertaken using Markov transition law estimated from a reforecast ensemble. A simple three-state, three-action dynamic decision model is then applied to the Monte Carlo sequence realizations using a basket of cost functions, and the resulting expense incurred by the decision model is conditioned upon the structure of the sequence realizations. Findings show that the greatest average expense is incurred by “sneak” and “volatile” sequence structures, which are structures characterized by large and rapid increases in event probability at short lag times. These findings are simple quantitative illustration of the adage that large run-to-run variability of forecasts can be troublesome to a decision maker. The experiments also demonstrate how even small improvements in the amount of advance warning of an event can translate into a substantial reduction in decision expense. In general, the conditioned decision expense is found to be sensitive to sequence structure for a given cost function, to the parameters of a given cost function, and to the choice of cost function itself.

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