Special Issue on Virtual (Soft) Sensors in Chemical Industries

Submission Deadline: Feb. 20, 2020

Please click the link to know more about Manuscript Preparation: http://www.ajche.org/submission

This special issue currently is open for paper submission and guest editor application.

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Special Issue Flyer (PDF)
  • Lead Guest Editor
    • Babak Ghanaati
      Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
  • Guest Editor
    Guest Editors play a significant role in a special issue. They maintain the quality of published research and enhance the special issue’s impact. If you would like to be a Guest Editor or recommend a colleague as a Guest Editor of this special issue, please Click here to complete the Guest Editor application.
    • Babak Ghanaati
      Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
    • Hamid Poorhabibi
      Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran
  • Introduction

    Industrial plants are usually heavily instrumented with a large number of sensors. The primary purpose of the sensors is to deliver data for process monitoring and control. In the context of process industry, these predictive models are called Inferential Sensors. Other common terms for predictive sensors in the process industry are soft sensor, virtual on-line analyzer as they are called in the Six-Sigma context and observer-based sensors.
    Prediction algorithms using inferential (soft) sensors have recently become very powerful tools to a wide array of real-world applications. The most common application of Inferential Sensors is the prediction of values which cannot be measured on-line using automated measurements. For example, we can use an adaptive learning machine to estimate bottom Benzene concentration in a distillation column as a common industrial unit or another variables related to chemical industries. The main machine could be considered Fuzzy system or Neural network. Conventional Learning of considered machine can be tested by real industrial processes or laboratory variables. The employed machine should be proportional to the variable used in the model. In the prediction mode, the results can confirm that the designed inferential sensor based on the proposed method is accurate, optimized and faster and novel in response for the process variations.
    Aims and Scope:
    1. Soft Sensor
    2. Machine Learning
    3. Virtual Adaptive Sensor
    4. Predictive Models
    5. Distillation Column
    6. Fuzzy System

  • Guidelines for Submission

    Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.

    Papers should be formatted according to the guidelines for authors (see: http://www.ajche.org/submission). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.

  • Published Papers

    The special issue currently is open for paper submission. Potential authors are humbly requested to submit an electronic copy of their complete manuscript by clicking here.