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The operating performance assessment of industrial processes becomes increasingly important in manufacturing production. A novel operating performance assessment method based on performance-relevant kernel independent component analysis is proposed here for nonlinear and non-Gaussian processes. The proposed method accounts for the comprehensive economic index, and the objectives are simultaneously to maximize the non-Gaussianity of independent components as well as the correlations between them and the comprehensive economic index. When applying it to online assessment, it demonstrates stronger robustness and higher sensitivity than the traditional methods do, which is attributed to its capacity in highlighting the performance-relevant variation information in modeling. Furthermore, both the performance grades and the conversions can be evaluated, which enhances the interpretability of the results. For the nonoptimality, the variable contributions are used to find the possible cause. Finally, the efficiency of the proposed method is illustrated by a case of gold hydrometallurgy process. © 2019 Elsevier Ltd

The operating performance assessment of industrial processes becomes increasingly important in manufacturing production. A novel operating performance assessment method based on performance-relevant kernel independent component analysis is proposed here for nonlinear and non-Gaussian processes. The proposed method accounts for the comprehensive economic index, and the objectives are simultaneously to maximize the non-Gaussianity of independent components as well as the correlations between them and the comprehensive economic index. When applying it to online assessment, it demonstrates stronger robustness and higher sensitivity than the traditional methods do, which is attributed to its capacity in highlighting the performance-relevant variation information in modeling. Furthermore, both the performance grades and the conversions can be evaluated, which enhances the interpretability of the results. For the nonoptimality, the variable contributions are used to find the possible cause. Finally, the efficiency of the proposed method is illustrated by a case of gold hydrometallurgy process. © 2019 Elsevier Ltd

The operating performance assessment of industrial processes becomes increasingly important in manufacturing production. A novel operating performance assessment method based on performance-relevant kernel independent component analysis is proposed here for nonlinear and non-Gaussian processes. The proposed method accounts for the comprehensive economic index, and the objectives are simultaneously to maximize the non-Gaussianity of independent components as well as the correlations between them and the comprehensive economic index. When applying it to online assessment, it demonstrates stronger robustness and higher sensitivity than the traditional methods do, which is attributed to its capacity in highlighting the performance-relevant variation information in modeling. Furthermore, both the performance grades and the conversions can be evaluated, which enhances the interpretability of the results. For the nonoptimality, the variable contributions are used to find the possible cause. Finally, the efficiency of the proposed method is illustrated by a case of gold hydrometallurgy process. © 2019 Elsevier Ltd

Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods., QC 20181114

Batch processes have been widely applied in industry. Quality prediction plays an important role in batch process quality control. Multiple phases within a batch have correlated contributions to final qualities. So, previous phases should be considered in the quality-regression modeling for the current phase. In this paper, a new phase-based recursive statistical quality regression method is proposed for the quality prediction of multiphase batch processes. First, within each batch, main phases are obtained by a basic process analysis. Second, by analyzing phase characteristics, phases which have significant impacts on final qualities are identified as critical-to-quality phases. Then, a recursive quality regression algorithm is proposed using each quality residual of the regression models built in those critical-to-quality phases. Besides, phase characteristics are represented by the average trajectories of process variables within each phase. Quality prediction is performed at each sample point for online prediction. The application to a typical multiphase batch process, injection molding process, illustrates the feasibility and performance of the proposed algorithm. © 2017 IEEE.

Batch processes have been widely applied in industry. Quality prediction plays an important role in batch process quality control. Multiple phases within a batch have correlated contributions to final qualities. So, previous phases should be considered in the quality-regression modeling for the current phase. In this paper, a new phase-based recursive statistical quality regression method is proposed for the quality prediction of multiphase batch processes. First, within each batch, main phases are obtained by a basic process analysis. Second, by analyzing phase characteristics, phases which have significant impacts on final qualities are identified as critical-to-quality phases. Then, a recursive quality regression algorithm is proposed using each quality residual of the regression models built in those critical-to-quality phases. Besides, phase characteristics are represented by the average trajectories of process variables within each phase. Quality prediction is performed at each sample point for online prediction. The application to a typical multiphase batch process, injection molding process, illustrates the feasibility and performance of the proposed algorithm. © 2017 IEEE.

Batch processes have been widely applied in industry. Quality prediction plays an important role in batch process quality control. Multiple phases within a batch have correlated contributions to final qualities. So, previous phases should be considered in the quality-regression modeling for the current phase. In this paper, a new phase-based recursive statistical quality regression method is proposed for the quality prediction of multiphase batch processes. First, within each batch, main phases are obtained by a basic process analysis. Second, by analyzing phase characteristics, phases which have significant impacts on final qualities are identified as critical-to-quality phases. Then, a recursive quality regression algorithm is proposed using each quality residual of the regression models built in those critical-to-quality phases. Besides, phase characteristics are represented by the average trajectories of process variables within each phase. Quality prediction is performed at each sample point for online prediction. The application to a typical multiphase batch process, injection molding process, illustrates the feasibility and performance of the proposed algorithm. © 2017 IEEE.