Data reconciliation for simulated flotation process |
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| Affiliation: | 1. Institute for High Performance Computing and Networking, CNR, Naples, Italy;2. University of Naples Federico II, Naples, Italy;1. School of Chemical Engineering and Technology, Xi''an Jiaotong University, Xi''an 710049, PR China;2. Shaanxi Key Laboratory of Energy Chemical Process Intensification, Xi''an Jiaotong University, Xi''an 710049, PR China;3. MOE Key Laboratory of Thermal Fluid Science and Engineering, Xi''an Jiaotong University, Xi''an 710049, PR China;4. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, Xi''an 710049, PR China |
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| Abstract: | This paper introduces a novel neural network-based technique called system balance-related autoassociative neural networks (SBANN) for steady state data reconciliation. This neural network has the same architecture as traditional feedforward neural networks but the main difference lies in the minimization of an objective function that includes process material and/or energy imbalance terms in addition to the traditional least-squares prediction term. Accordingly, this neural network with the system balance-related objective criterion is able to perform the two basic functions necessary for proper steady state data reconciliation: data smoothing to reduce the data variance and data correction to satisfy material and/or energy balance constraints. This novel technique is illustrated for data reconciliation of a simulated flotation circuit that is widely used in mineral processing. |
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