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The suggested state-driven approach inserts process states into a process model using process units, and separates the business rules from the process flow by regarding the states as the milestones which indicate where the rules are separated.
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The paper discusses process identification algorithms based on virtual models design using process data archives and knowledge bases.
The paper presents process identification algorithms based on virtual models design using process data archives and knowledge bases.
The paper presents process identification algorithms for trading based on virtual models design using process data archives and knowledge bases.
Process identification algorithms based on virtual models design using process data archives and knowledge bases are discussed.
The process model was developed using process simulator software (SuperPro Designer®) in order to evaluate the continuous production of starch and co-products using E-milling.
These price changes are converted to the analogous price increases for the consumer/producer goods in the DZ model using a process described in Diamond and Zodrow (2018).
In order to build the predictive controller the adaptation of the fuzzy model using dynamic process information is carried out.
A Process Performance Model is a specific type of Calibrated Predictive Model that uses Process Performance Baselines (at least 2) to establish and quantify the relations between Measures.
The selected parameters were used to develop stochastic process models using Ito process and mean reverting process for feed composition and kinetic parameter uncertainty.
The decision trees were sequentially added to the model using a "boosting" process that enhances the accuracy of prediction.
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