Operating Performance Assessment Method Research Articles

Operating performance assessment method for industrial process with slowness principle-based LSTM network

Timely and accurate performance assessment and non-optimal regulation of industrial processes can effectively guarantee product quality. Most industrial processes are highly nonlinear and dynamic, so long short-term memory (LSTM) network is suitable for industrial performance assessment. However, in the network learning, the typical LSTM network focuses on the representation learning of input variables, lacks the representation of comprehensive economic indexes (CEI), and cannot selectively learn essential features, which increases the computational burden and easily mixes redundant information. Thus, a supervised slow feature analysis (SFA)-based LSTM (SSFALSTM) network is proposed for industrial operating performance assessment. By utilizing CEI information and SFA constraints, the network is guided to simultaneously learn features related to CEI and slow-changing features that reflect the inherent dynamics of the process. Further, cascade performance recognition model to construct the complete performance assessment framework. For the non-optimal performance, a reconstruction-based contribution plot method is proposed to identify the main cause variables and guide adjustment operations. Finally, the effectiveness of the proposed method is validated on the dense medium coal preparation process.

KPI-related operating performance assessment based on distributed ImRMR-KOCTA for hot strip mill process

With the development of modern industrial processes toward integration and complexity, industrial process operation monitoring is of great significance to ensure the plant safety, product quality, and operating efficiency. However, the inherent nonlinear, dynamic, and plant-wide characteristics make it difficult to evaluate the operating performance accurately. To handle this issue, a key performance indicator-related operating performance assessment method based on distributed improved minimal redundancy maximal relevance and kernel output-relevant common trend analysis (ImRMR-KOCTA) is proposed in this paper. First, by replacing mutual information with maximal information coefficient, the minimal redundancy maximal relevance is improved to describe the interdependencies between process variables and key performance indicators, and the correlated variables are retained in each subsystem. Second, based on kernel functions and outputrelevant common trend analysis, the assessment model is developed for describing the nonlinearity and dynamicity in each subsystem. Then, operating performance level is determined by Bayesian inference and predefined rules. Finally, a validation on a hot strip mill process is given to verify the effectiveness of the proposed method.

A new key performance indicator oriented industrial process monitoring and operating performance assessment method based on improved Hessian locally linear embedding

ABSTRACT The industrial process monitoring and operating performance assessment techniques are of great significance to ensure the safety and efficiency of the production and to improve the comprehensive economic benefits for the modern enterprises. In this paper, a new key performance indicator (KPI) oriented nonlinear process monitoring and operating performance assessment method is proposed based on the improved Hessian locally linear embedding (HLLE), in view of the problems of strong nonlinearity, high dimension and information redundancy in actual industrial process data. Firstly, in order to characterise the similarities of samples in both temporal and spatial dimensions, a new measurement, based on Finite Markov theory, is defined to replace the Euclidean distance in traditional HLLE. Secondly, by mining the relationships between process variables and the key performance indicator, the KPI oriented feature extraction method is developed. On this basis, the monitoring statistics is constructed and the corresponding control limit is determined for the real-time fault detection. After that, a new operating performance assessment approach based on sliding window Kullback–Leibler divergence is put forward to facilitate maintenance or adjustments. Finally, the proposed method is applied to the hot strip mill process, and the results show the effectiveness.

Performance-relevant kernel independent component analysis based operating performance assessment for nonlinear and non-Gaussian industrial processes

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.

Hierarchical Multiblock T-PLS Based Operating Performance Assessment for Plant-Wide Processes

With the pursuit of profit, the operating performance assessment of industrial processes has gradually attracted attention and concentration in modern industrial production. However, owing to the complexity of plant-wide processes, many existing operating performance assessment techniques are lacking in interpretation of the assessment result, and the cause identification is also difficult for the nonoptimal operating performance. In this study, a new operating performance assessment method is proposed for plant-wide processes based on hierarchical multiblock total projection to latent structures (T-PLS). The proposed approach divides the process variables into several conceptually meaningful blocks and incorporates block information into high level for operating performance assessment of plant-wide processes. With the new method, the assessment task can be greatly reduced and the interpretability of the assessment models are greatly improved. Once the process operating performance is nonoptimal, the resp...

Gaussian Process Regression and Bayesian Inference Based Operating Performance Assessment for Multiphase Batch Processes

Batch processes have been playing a significant role in modern industrial processes. However, even if the operating conditions are normal, the process operating performance may still deteriorate away from optimal level, and this may reduce the benefits of production, so it is crucial to develop an effective operating performance assessment method for batch processes. In this study, a novel operating performance assessment method of batch processes is proposed based on both Gaussian process regression (GPR) and Bayesian inference. It is committed to solving the challenges of multiphase, process dynamics and batch-to-batch uncertainty that are contained in most of batch processes. To characterize different dynamic relationships within each individual phase, multiple localized GPR-based assessment models are built first. Furthermore, the phase attribution of each new sample is determined, and two different identification results are obtained, i.e., a certain interval and a fuzzy interval between two adjacent...

Online process operating performance assessment and nonoptimal cause identification for industrial processes

Although industrial processes are usually operated at the optimal point in the early stage of the production, the operating performance may deteriorate with time due to process disturbances. In order to pursue optimal comprehensive economic benefit (CEB), online process operating performance assessment on optimality has become a key issue. However, a little work has been published in this research area. In this paper, a new online operating performance assessment and nonoptimal cause identification method for industrial process are proposed. The contributions of this paper can be summarized as follows: a novel performance-similarity-based online operating performance assessment method is proposed; total projection to latent structures (T-PLS) is applied to the area of process performance assessment for the first time; the online assessment results include not only the deterministic performance grades, but also the performance grade conversions which were not covered in the existing assessment method; when the assessment result is nonoptimal, a novel automatic nonoptimal cause identification strategy is developed based on variable contributions, which is meaningful for guiding the further production adjustment. Finally, the feasibility and efficiency of the proposed method are illustrated with a case of gold hydrometallurgical process.