Taifu Li

Optimized Relative Transformation Matrix Using Bacterial Foraging Algorithm for Process Fault Detection

Fault diagnosis of an aluminum electrolysis cell has long been a challenging industrial issue due to its inherent difficulty in extracting meaningful features from numerous nonlinear and highly coupled parameters. To solve this problem, this paper presents optimized relative transformation matrix (RTM) using bacterial foraging algorithm (BFA-ORTM). In particular, the operator of relative transformation is introduced to change the original variables in the spatial distribution and eigenvalues of the covariance matrix in the feature space. Then, optimization objective function on the comprehensive index φ, the squared prediction error (SPE), and Hotellings T-squared (T2) statistics are established. Furthermore, bacterial foraging algorithm is applied to obtain the optimized operator to facilitate extracting the representative principal components. Compared with traditional approaches, BFA-ORTM not only overcomes the drawback of losing feature after the normalization of nonlinear variables, but also improves the accuracy of fault diagnosis. Extensive experimental results on real-world aluminum electrolytic production process validated our proposed methods effectiveness.

Multi-Objective Bacterial Foraging Optimization Algorithm Based on Parallel Cell Entropy for Aluminum Electrolysis Production Process

Environment-friendly aluminum electrolysis production process has long been a challenging industrial issue due to its built-in difficulty in optimizing numerous highly coupled and nonlinear parameters. This paper presents a multi-objective bacterial foraging optimization (MOBFO) algorithm to find optimal solutions that maximize the current efficiency and minimize the energy consumption and the production of perfluorocarbons (PFCs). Our method can be viewed as an enhanced version of the bacterial foraging optimization (BFO) in solving multi-objective optimization (MOO) problems (MOPs). We first propose a task-oriented optimization framework and model, and then parallel cell entropy and its difference are introduced to evaluate the evolutionary status of the Pareto solutions in a new objective space called parallel cell coordinate system (PCCS). In particular, the Pareto-archived evolution approach (PAEA) and the adaptive foraging strategy (AFS) are applied to balance the convergence and diversity of the Pareto front in the optimization procedure. Compared with traditional approaches, MOBFO not only increases speed of convergence toward the Pareto front, but also improves the diversity of the obtained solutions. Extensive experiment results on numerous benchmark problems and real-world aluminum electrolysis production process validated our proposed methods effectiveness.

A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix

In the standard kernel partial least squares (KPLS), the mapped data in the feature space need to be centralized before extraction of new score vectors. However, each vector of the centralized variables is often uniformly distributed, and some original features that can reflect the contribution of each variable to fault diagnosis might be lost. As a result, it might lead to misleading interpretations of the principal components and to increasing the false alarm rate for fault detection. To cope with these difficulties, a novel data-driven framework using KPLS based on an optimal preference matrix (OPM) is presented in this paper. In fault monitoring, an OPM is proposed to change the distribution of the variable and to readjust the eigenvalues of the covariance matrix. To obtain the OPM, the objective function can be determined in terms of the squared prediction error and Hotellings T-squared (

A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

{T^2}

Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving

) statistics. Two optimization algorithms, genetic algorithm and particle swarm optimization algorithm, are extended to maximize effectiveness of the OPM. Compared with traditional methods, the proposed method can overcome the drawback of original features loss of the centralized mapped data in the feature subspace and improve the accuracy of fault diagnosis. Also, few extra computation costs are needed in fault detection. Extensive experimental results on both the Tennessee Eastman benchmark process and the case study of the aluminum electrolytic production process give credible fault diagnosis.

An improved feed-forward neural network based on UKF and strong tracking filtering to establish energy consumption model for aluminum electrolysis process

Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries) to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm) to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s) on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s) of out-of-control signal and even outperforms the conventional multivariate control scheme.

Characterization of a Stable Adaptive Calibration Model Using Near-Infrared Spectroscopy and Partial Least Squares with a Kalman Filter

Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved.

Modeling method based on iterative UKFNN pumping oil production process

The paper presents a modeling method about the energy consumption of aluminum electrolysis process based on a new neural network. The proposed neural network (NN) is built by combining two theories of unscented Kalman filtering (UKF) and strong tracking filtering (STF), which is shortened as STUKFNN in this study. Moreover, the new training algorithm and robustness analysis of the STUKFNN are presented. The final section of the paper shows an illustrative example regarding the application of the new training algorithm to estimate the technical energy consumption of the aluminum electrolysis process, compared with the modeling methods of back-propagation neural network (BPNN), extended Kalman filtering neural network (EKFNN) and unscented Kalman filtering neural network (UKFNN). The analysis and results show that the method improves the real-time tracking ability of dynamic interference in aluminum electrolysis process, and the accuracy of STUKFNN is better than the other three modeling methods. The average indicators MAE, MSE, R of the STUKFNN based on 30 runs are 15.4793, 1862.65 and 0.9966, respectively, which are all superior to other methods. The proposed method also shows better performance compared with UKFNN, EKFNN and BPNN by the proportion of relative error (RE) in the interval

HCN production process hybrid intelligence based on artificial neural networks and genetic algorithm

|{\mathrm{RE}}|<0.1\%