Ensuring long-term safe and efficient operation of industrial processes relies on real-time identification of abnormal operating conditions. However, industrial processes frequently switch among ...diverse operating conditions and face harsh production environments. As a result, some extreme cases exist in historical abnormal samples can mask some slight anomalies, making them show similar process dynamics to normal operating conditions. To address this issue, this study proposes a global-local slow-feature-analysis-based convolutional neural network (GLSFA-CNN). A global slow feature analysis (SFA) model extracts coarse-scale slow features at a macroscopic level to distinguish anomalies with different process dynamics, while a local SFA algorithm extracts real-time and fine-scale slow features at a microscopic level to identify anomalies with similar process dynamics. By combining global and local slow features, anomalies with similar or different dynamics can be simultaneously identified. The one-dimensional convolutional neural network (1-D-CNN) is then used to automatically extract deep features from the global-local slow features and identify abnormal operating conditions. The industrial experiment shows that the proposed method outperforms other traditional methods and achieves high anomaly identification accuracy for the industrial process with switching conditions and extreme cases.
Online and accurate estimation of key performance indicators (KPI) is the foundation for operational optimization of a chemical process. However, a chemical process usually consists of multiple ...reactors, and the factors influencing KPI are spatially distributed in the long process flow. In addition, due to the distinct time lags between KPI and each reactor, temporal relationships among KPI and its influence factors are a mixture of short‐term and long‐term relationships. In this regard, a deep distributed KPI estimator with a self‐attention mechanism is proposed in this paper. First, considering the process topology, a cascaded long short‐term memory network is developed to simulate the process topology and capture the short‐term effects. Then, to extract the long‐term dependencies, a de‐noise self‐attention layer is employed to model interactions of all the influence factors explicitly and dynamically. Lastly, the proposed method is compared with typical state‐of‐the‐art methods using real industrial data. The comparison results illustrate the performance and effectiveness of the proposed KPI estimation method.
For an industrial process, the estimation of feeding composition is important for analyzing production status and making control decisions. However, random errors or even gross ones inevitably ...contaminate the actual measurements. Feeding composition is conventionally obtained via discrete and low-rate artificial testing. To address these problems, a feeding composition estimation approach based on data reconciliation procedure is developed. To improve the variable accuracy, a novel robust M-estimator is first proposed. Then, an iterative robust hierarchical data reconciliation and estimation strategy is applied to estimate the feeding composition. The feasibility and effectiveness of the estimation approach are verified on a fluidized bed roaster. The proposed M-estimator showed better overall performance.
BackgroundThe prevalence of varying degrees of post-stroke dysfunctions commonly found in an increasing number of young and middle-aged stroke patients, has hindered them from returning to work ...quickly, and caused serious socioeconomic burdens. Return-to-work self-efficacy is an important predictor of returning to work, and the assessment of which may provide guidance for promoting patients to return to work. However, there is no scale measuring the return-to-work self-efficacy of Chinese stroke patients.ObjectiveTo develop a Chinese version of the Return-to-work Self-efficacy Questionnaire (RTW-SE) by translating the English version of the RTW-SE, then assess its reliability and validity in young and middle-aged Chinese stroke patients.MethodsBy use of forward and backward translation of the English version of the RTW-SE, the Chinese version of the scale was developed. Then the scale was tested in a sample of 130 cases, and was analyzed for item analysis and exploratory factor analysis. Then, the scale was r
Returning to work is an important sign of recovery and returning to normal life for patients. Most patients have a strong desire to return to work, however, their confidence is low. Return-to-work ...self-efficacy is not only a reflection of patients' confidence in returning to work but also an important predictor of his readiness to get back to work. Based on the concept and meaning of return-to-work self-efficacy, this study introduces its related theoretical models, summarizes the contents, scoring criteria, validity and reliability of related assessment tools, and conducts a comparative analysis of the tools, to provide Chinese rehabilitation care workers with evidence contributing to the selection of an appropriate return-to-work self-efficacy assessment tool.
Discrete and delayed laboratory analyses of product quality restrict the operational optimization of industrial processes. However, it is challenging to build an accurate online estimation model for ...product quality because of complex process dynamics, multiple working conditions, and multi-rate characteristics. Therefore, a multimode mechanism-guided product quality variable estimation model is proposed in this study. First, representative features are extracted from high-dimensional and redundant process variables via both feature engineering and deep learning to describe the internal reaction state. Then, the representative features are used to partition the data samples which are used to train the multi-mode long short-term memory (LSTM) network to increase the adaptability of the estimation model. Finally, the LSTM units are cascaded to learn the variation in the quality variable against time to handle the multi-rate problem. The mechanism models are placed in parallel with the LSTM units to guide the learning process. The estimation model utilizes production data, mechanism knowledge and working condition information, which increases model interpretability and adaptability. A zinc fluidized bed roaster is used to illustrate the proposed estimation approach. The simulation results demonstrate the feasibility and effectiveness of the proposed multi-rate estimation approach.
The roasting temperature is critical for enhancing product quality, reducing air pollution, and ensuring the long-term operation of the zinc roasting process. However, optimizing the roasting ...temperature is challenging due to complex reaction mechanisms, feed composition fluctuations, and the coupling relationship with downstream processes. In this article, a two-level decision-making system for co-optimization of the roasting temperature is proposed. In the first level, a fuzzy synthetic evaluation model with a variable-weight degradation degree is established to accurately evaluate the operating performance of the zinc roasting process. The evaluation results are used to design the basic setting rules that provide the basic temperature setting values. In the second level, the concept of a temperature-adjustable margin is introduced via sensitivity analysis of the process model to evaluate the optimality of two roasters in the zinc roasting process. Based on the temperature-adjustable margin, the collaborative setting rules are designed to reasonably allocate the basic setting value to the two zinc roasters for optimizing the operating performance of the zinc roasting process. Finally, an industrial case study is presented to demonstrate the effectiveness of the proposed two-level decision-making system.
Because of the increasing complexity and nonlinearity of industrial processes, nonlinear model predictive control (NMPC) has been rapidly developed owing to its fast response and robustness. However, ...the complicated optimization process of NMPC limits its application. Hence, this paper proposes an NMPC method that is compatible with nonlinear modeling and concise online control. First, an elastic autoregressive fuzzy neural network (EAFNN) is proposed under reasonable assumptions. The EAFNN exhibits strong parameter identification and structure optimization capabilities because of its autoregressive layer and elastic mechanism. Second, the EAFNN is adaptively simplified into a linear model based on the real-time working condition information during online control. Third, based on a simplified model, NMPC provides an explicit solution without complex optimization procedures. Finally, numerical simulations and roasting process experiments are conducted. Experimental results show that the proposed method exhibits superior control performance and computational complexity compared with other methods, thereby verifying its effectiveness and superiority. The source code for EAFNN-MPC is publicly available at: https://github.com/553318570/EAFNN_MPC.git.
•An improved fuzzy neural network is proposed with higher nonlinear representation.•An elastic mechanism is introduced to learn the optimal model structure.•The control are based on a simplified model for reducing computational burden.•The method can stabilize the roasting temperature and handle interference.
Timely and accurate detection of abnormal working conditions can ensure stability, improve production efficiency and reduce pollution of an industrial process. However, the production data of an ...industrial process has non-Gaussian and time-varying characteristics due to the diverse feed composition and complex reaction mechanisms. To address the above issue, an improved online principal component analysis (PCA) algorithm based on the selective model update is proposed in this study. First, considering the non-Gaussian nature of the process data, a local outlier factor-based (LOF) abnormality detection logic is used to replace the T2 and squared prediction error (SPE) statistics in traditional PCA algorithms. Then, to adapt to the time-varying characteristics of the process data, an approximate linear dependence (ALD) algorithm is used to evaluate the independent degree between the new sample and training samples. Only those samples containing new information are used to update the monitoring model, which can improve model performance and reduce the frequency of online updates. The zinc roasting process (ZRP) is used as an example to illustrate the proposed approach. Industrial data collected from a ZRP is used to demonstrate the performance of the ALD-based LOF-PCA method in the early detection of two typical abnormal working conditions in the ZRP.
In the zinc oxide rotary volatile kiln (ZORVK), an optimal temperature field is essential to balance the strong conflict between zinc recovery rate and carbon emissions. However, the complex and ...diverse temperature distribution modes make it challenging to quickly obtain optimization results under intricate controllability constraints and multi-conflict production objectives. In this study, a novel constrained multi-objective deep reinforcement learning (CMODRL) approach for temperature field optimization of the ZORVK is proposed. First, an evaluation metric called the uncontrollable factor is designed to quantify the controllability of the temperature field. Then, a dynamic penalty method in deep reinforcement learning (DRL) is proposed to handle the controllability constraint, in which the penalty coefficient is dynamically adjusted according to the training loss. After that, the Chebyshev scalarization function is introduced as an action selection mechanism in DRL. Finally, the CMODRL is developed by integrating the dynamic penalty and Chebyshev scalarization function into the multi-objective deep reinforcement learning (MODRL) framework. As a result, for any given preference between the two production objectives, the proposed method can rapidly get the Pareto-optimal solution fulfilling the constraint. Moreover, the optimization efficiency of the MODRL-based algorithm is forty times higher than that of the multi-objective genetic algorithm, which serves better for practical optimization problems.
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