14,358 research outputs found
Nonlinear process fault detection and identification using kernel PCA and kernel density estimation
Kernel principal component analysis (KPCA) is an effective and efficient technique for monitoring nonlinear processes. However, associating it with upper control limits (UCLs) based on the Gaussian distribution can deteriorate its performance. In this paper, the kernel density estimation (KDE) technique was used to estimate UCLs for KPCA-based nonlinear process monitoring. The monitoring performance of the resulting KPCA–KDE approach was then compared with KPCA, whose UCLs were based on the Gaussian distribution. Tests on the Tennessee Eastman process show that KPCA–KDE is more robust and provide better overall performance than KPCA with Gaussian assumption-based UCLs in both sensitivity and detection time. An efficient KPCA-KDE-based fault identification approach using complex step differentiation is also proposed
Dynamic latent variable modelling and fault detection of Tennessee Eastman challenge process
Dynamic principal component analysis (DPCA) is commonly used for monitoring multivariate processes that evolve in time. However, it is has been argued in the literature that, in a linear dynamic system, DPCA does not extract cross correlation explicitly. It does not also give the minimum dimension of dynamic factors with non zero singular values. These limitations reduces its process monitoring effectiveness. A new approach based on the concept of dynamic latent variables is therefore proposed in this paper for extracting latent variables that exhibit dynamic correlations. In this approach, canonical variate analysis (CVA) is used to capture process dynamics instead of the DPCA. Tests on the Tennessee Eastman challenge process confirms the workability of the proposed approach
Improved branch and bound method for control structure screening
The main aim of this paper is to present an improved algorithm of “Branch and
Bound” method for control structure screening. The new algorithm uses a best-
first search approach, which is more efficient than other algorithms based on
depth-first search approaches. Detailed explanation of the algorithms is
provided in this paper along with a case study on Tennessee–Eastman process to
justify the theory of branch and bound method. The case study uses the Hankel
singular value to screen control structure for stabilization. The branch and
bound method provides a global ranking to all possible input and output
combinations. Based on this ranking an efficient control structure with least
complexity for stabilizing control is detected which leads to a decentralized
proportional cont
Bidirectional branch and bound for controlled variable selection. Part III: local average loss minimization
The selection of controlled variables (CVs) from available measurements through
exhaustive search is computationally forbidding for large-scale processes. We
have recently proposed novel bidirectional branch and bound (B-3) approaches for
CV selection using the minimum singular value (MSV) rule and the local worst-
case loss criterion in the framework of self-optimizing control. However, the
MSV rule is approximate and worst-case scenario may not occur frequently in
practice. Thus, CV selection by minimizing local average loss can be deemed as
most reliable. In this work, the B-3 approach is extended to CV selection based
on local average loss metric. Lower bounds on local average loss and, fast
pruning and branching algorithms are derived for the efficient B-3 algorithm.
Random matrices and binary distillation column case study are used to
demonstrate the computational efficiency of the proposed method
Capacity Bounds for Two-Hop Interference Networks
This paper considers a two-hop interference network, where two users transmit
independent messages to their respective receivers with the help of two relay
nodes. The transmitters do not have direct links to the receivers; instead, two
relay nodes serve as intermediaries between the transmitters and receivers.
Each hop, one from the transmitters to the relays and the other from the relays
to the receivers, is modeled as a Gaussian interference channel, thus the
network is essentially a cascade of two interference channels. For this
network, achievable symmetric rates for different parameter regimes under
decode-and- forward relaying and amplify-and-forward relaying are proposed and
the corresponding coding schemes are carefully studied. Numerical results are
also provided.Comment: 8 pages, 5 figures, presented in Allerton Conference'0
Stability analysis of slug flow control
The threat of slugging to production facilities has been known since the 1970s. This undesirable flow phenomenon continues to attract the attention of researchers and operators alike. The most common method for slug mitigation is by choking the valve at the exit of the riser which unfortunately could negatively affect production. The focus, therefore, is to satisfy the need for system stability and to maximize production simultaneously. Active feedback control is a promising way to achieve this. However, due to the complexity of multiphase flow systems, it is a challenge to develop a robust slug control system to achieve the desired performance using existing design tools. In this paper, a new general method for multiphase flow system stability analysis was proposed. Active feedback control was observed to optimize slug attenuation compared with manual choking. The use of soft sensors was believed to be desirable for the practical implementation of the proposed control technique
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