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- 1Artificial Neural Network
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A Hierarchical Constrained Reinforcement Learning for Optimization of Bitumen Recovery Rate in a Primary Separation Vessel
Download2020-01-01
Shafi, Hareem, Velswamy, Kirubakaran, Ibrahim, Fadi, Huang,Biao
This work proposes a two-level hierarchical constrained control structure for reinforcement learning (RL) with application in a Primary Separation Vessel (PSV). The lower level is concerned with servo tracking and regulation of the interface level against variances in ore quality by manipulating...
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A Long-Short Term Memory Recurrent Neural Network Based Reinforcement Learning Controller for Office Heating Ventilation and Air Conditioning Systems
Download2017-08-18
Yuan wang, Kirubakaran Velswamy, Biao Huang
Energy optimization in buildings by controlling the Heating Ventilation and Air Conditioning (HVAC) system is being researched extensively. In this paper, a model-free actor-critic Reinforcement Learning (RL) controller is designed using a variant of artificial recurrent neural networks called...
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2008
Poor control of hot strip mill loopers degrades strip width and gauge, and may even lead to mill breakdowns due to instability. In this study, dynamics of the looper-strip system and the control challenges it poses are discussed, and covariance control theory is applied to variance control design...
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2015-07-02
The Hybrid tank pilot plant was designed in Process Control Laboratory (PCL) of the University of Alberta at 2009. In this report, we try to construct a model for this process. Using the physical behavior of the plant, it is possible to have the nonlinear first principle dynamic model of that...
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2010-03-30
Guay, M., Fraleigh, L.M., Forbes, J.F.
Real-time optimization systems have become a common tool, in the continuous manufacturing industries, for improving process performance. Typically, these are on-line, steady-state, model-based optimization systems, whose effectiveness depends on a large number of design decisions. The work...