Batch Process - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Batch Process

The Experts below are selected from a list of 129765 Experts worldwide ranked by ideXlab platform

Furong Gao – One of the best experts on this subject based on the ideXlab platform.

  • Batch Process Modeling and Monitoring With Local Outlier Factor
    IEEE Transactions on Control Systems and Technology, 2019
    Co-Authors: Jinlin Zhu, Youqing Wang, Donghua Zhou, Furong Gao

    Abstract:

    Batch Processes are commonly involved by a succession of working phases with implicit non-Gaussian behaviors. Besides, in most cases, Batch-to-Batch Processes also show similar but yet not identical running trajectory variations. To deal with these issues, this paper introduces a systematic analysis flowchart based on local outlier factor (LOF) for monitoring multiphase Batch Processes. First, a step-wise phase dividing algorithm is proposed with LOF to conduct phase dividing for a better understanding of Batch Process. Afterward, we develop the multiphase LOF for similar Batch data modeling and then fault detection. The fault isolation method is proposed, where variable contributions with LOFs are induced, also with the analysis of isolability. The developed method is validated on a numerical example and the fed-Batch fermentation benchmark Process, both of which are compared with the multiphase principal component analysis method. Results demonstrate the feasibility and superiority of the proposed method.

  • two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
    Chemical Engineering Science, 2018
    Co-Authors: Shumei Zhang, Furong Gao, Chunhui Zhao

    Abstract:

    Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.

  • an iterative two step sequential phase partition itspp method for Batch Process modeling and online monitoring
    Aiche Journal, 2016
    Co-Authors: Yan Qin, Chunhui Zhao, Furong Gao

    Abstract:

    Operating at different manufacturing steps, multiphase modeling and analysis of the Batch Process are advantageous to improving monitoring performance and understanding manufacturing Processes. Although many phase partition algorithms have been proposed, they have some disadvantages and cause problems: (1) time sequence disorder, which requires elaborate post-treatments; (2) a lack of quantitative index to indicate transition patterns; and (3) tunable parameters that cannot be quantitatively determined. To effectively overcome these problems, an iterative two-step sequential phase partition algorithm is proposed in the present work. In the first step, initial phase partition results are obtained by checking changes of the control limit of squared prediction error. Sequentially, the fast search and find of density peaks clustering algorithm is employed to adjust the degradation degree and update the phase partition results. These two steps are iteratively executed until a proper degradation degree is found for the first phase. Then, the remaining phases are Processed one by one using the same procedure. Moreover, a statistical index is quantitatively defined based on density and distance analysis to judge whether a Process has transitions, and when the transition regions begin and end. In this way, the phases and transition patterns are quantitatively determined without ambiguity from the perspective of monitoring performance. The effectiveness of the proposed method is illustrated by a numerical example and a typical industrial case. Several typical phase partition algorithms are also employed for comprehensive comparisons with the proposed method. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2358–2373, 2016

Chunhui Zhao – One of the best experts on this subject based on the ideXlab platform.

  • Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
    IEEE Transactions on Industrial Electronics, 2019
    Co-Authors: Shumei Zhang, Chunhui Zhao

    Abstract:

    In order to provide more sensitive monitoring results, the time dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for Batch Processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and Process dynamics is developed for Batch Processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the Process dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep Process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding Process, which is a typical multiphase Batch Process.

  • two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
    Chemical Engineering Science, 2018
    Co-Authors: Shumei Zhang, Furong Gao, Chunhui Zhao

    Abstract:

    Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.

  • pseudo time slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and Batch Process monitoring
    Industrial & Engineering Chemistry Research, 2017
    Co-Authors: Shumei Zhang, Chunhui Zhao, Shu Wang, Fuli Wang

    Abstract:

    Multiphase characteristics and uneven-length Batch duration have been two critical issues to be addressed for Batch Process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven Batch Processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The pr…

Shumei Zhang – One of the best experts on this subject based on the ideXlab platform.

  • Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly
    IEEE Transactions on Industrial Electronics, 2019
    Co-Authors: Shumei Zhang, Chunhui Zhao

    Abstract:

    In order to provide more sensitive monitoring results, the time dynamics and steady-state operating conditions should be separately monitored by distinguishing time information from the steady-state counterpart. However, it is a more challenging task for Batch Processes because they vary from phase to phase presenting multiple steady states and complex dynamic characteristics. To address the above issue, a concurrent monitoring strategy of multiphase steady states and Process dynamics is developed for Batch Processes in this paper. On one hand, multiple local models are constructed to identify a steady derivation from the normal operating condition for different phases. On the other hand, based on the recognition that the Process dynamics can be considered to be irrelevant with the steady states, a global model is built to detect the dynamics anomalies by monitoring the time variations. Corresponding to alarms issued by different statistics, different operating statuses are indicated with meaningful physical interpretation and deep Process understanding. To illustrate the feasibility and efficacy, the proposed algorithm is applied to the injection molding Process, which is a typical multiphase Batch Process.

  • two directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with uneven lengths
    Chemical Engineering Science, 2018
    Co-Authors: Shumei Zhang, Furong Gao, Chunhui Zhao

    Abstract:

    Abstract In general, Batch Processes cover two-directional dynamics, in which the Batch-wise dynamics are related to different operation modes, while the time-wise variations correspond to different phases within each Batch. The problem of unevenness is common as a result of various factors, particularly in multimode Batch Processes. In order to address these issues, this paper proposes a two-directional concurrent strategy of mode identification and sequential phase division for multimode and multiphase Batch Process monitoring with the uneven problem. Firstly, pseudo time-slices are constructed in order to describe the Process characteristics regarding the sample concerned, which can preserve the local neighborhood information within a constrained searching range and effectively prevent the synchronizing problem caused by uneven lengths. Secondly, mode identification is conducted along the Batch direction and the phase affiliation is sequentially determined along time direction by determining the changes in variable correlations. The two-directional steps are implemented alternatively in order to identify the mode and phase information, which can also guarantee the time sequence within each mode. Thirdly, for online monitoring, the mode information and phase affiliation are simultaneously judged in real time for each new sample, from which the fault status is distinguished from the phase shift. The division results can indicate the critical-to-mode phases from which a certain mode begins to be separated into different sub-modes. In order to illustrate the feasibility and effectiveness of the proposed algorithm, it is applied to a multimode and multiphase Batch Process (namely an injection molding Process) with the uneven problem.

  • pseudo time slice construction using a variable moving window k nearest neighbor rule for sequential uneven phase division and Batch Process monitoring
    Industrial & Engineering Chemistry Research, 2017
    Co-Authors: Shumei Zhang, Chunhui Zhao, Shu Wang, Fuli Wang

    Abstract:

    Multiphase characteristics and uneven-length Batch duration have been two critical issues to be addressed for Batch Process monitoring. To handle these issues, a variable moving window-k nearest neighbor (VMW-kNN) based local modeling, irregular phase division, and monitoring strategy is proposed for uneven Batch Processes in the present paper. First, a pseudo time-slice is constructed for each sample by searching samples that are closely similar to the concerned sample in which the variable moving window (VMW) strategy is adopted to vary the searching range and the k nearest neighbor (kNN) rule is used to find the similar samples. Second, a novel automatic sequential phase division procedure is proposed by similarity evaluation for local models derived from pseudo time-slices to get different irregular phases and ensure their time sequence. Third, the affiliation of each new sample is real-time judged to determine the proper phase model and fault status can be distinguished from phase shift event. The pr…