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Soft Sensors: Inference and Estimation
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Soft Sensors: Inference and Estimation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (15 May 2019) | Viewed by 12967

Special Issue Editor

Dr. Vinay Prasad

E-Mail Website
Guest Editor
Assistant Professor, Department of Chemical and Materials Engineering, University of Alberta, 7th Floor ECERF, Room 7-084B, 9107 - 116 Street, Edmonton, AB T6G 2V4, Canada
Interests: process control; process estimation and optimization; fuel cells; multiscale modeling; heterogeneous catalysis

Special Issue Information

Dear Colleagues,

In cases where direct measurements are difficult to obtain, soft sensors are used to estimate these variables based on measurements of related variables; these soft sensors act as indirect measurements of the variable(s) of interest. Often, multiple indirect measurements are used along with data-driven or first-principles models to construct the soft sensor-based measurement. These inferential measurements often need to be calibrated in order to provide quantitative information of the variables of interest. Very often, soft sensors are used in conjunction with estimation algorithms to provide real-time updates of the states of the system.

This special issue focuses on advances in soft sensing with emphasis on the coupling of soft sensors with state and parameter estimation in an integrated inferential framework. The potential topics for this issue include, but are not limited to:

  • Multi-sensor data fusion for soft sensing
  • Advanced inferential control using soft sensors and estimation
  • Process monitoring, fault detection and diagnosis
  • The application of chemometrics in soft sensing
  • Semi-supervised learning for soft sensor calibration
  • Multi-rate and irregularly sampled soft sensing and estimation
  • Industrial implementation and applications of soft sensing

Dr. Vinay Prasad
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

16 pages, 3740 KiB  
Article
Optimal Design of Piezoelectric Cantilevered Actuators for Charge-Based Self-Sensing Applications
by Joël Bafumba Liseli, Joël Agnus, Philippe Lutz and Micky Rakotondrabe
Sensors 2019, 19(11), 2582; https://doi.org/10.3390/s19112582 - 6 Jun 2019
Cited by 6 | Viewed by 3482
Abstract
Charge-based Self-Sensing Actuation (SSA) is a cost and space-saving method for accurate piezoelectric based-actuator positioning. However, the performance of its implementation resides in the choice of its geometry and the properties of the constituent materials. This paper intends to analyze the charge-based SSA’s [...] Read more.
Charge-based Self-Sensing Actuation (SSA) is a cost and space-saving method for accurate piezoelectric based-actuator positioning. However, the performance of its implementation resides in the choice of its geometry and the properties of the constituent materials. This paper intends to analyze the charge-based SSA’s performances dependence on the aforementioned parameters and properties for a piezoelectric cantilever. A model is established for this type of Piezoelectric Actuator (PEA), and a multi-objective function is defined. The multi-objective function consists of the weighted actuator and sensor objective functions of the PEA. The analytical optimization approach introduced herein aims to assess the evolution of the defined multi-objective function across a defined set of geometrical parameters and material properties and highlights the existence of a subset of solutions for an optimal charge-based SSA’s implementation. The commercially-available finite element analysis software, COMSOL Multiphysics, is used on the parametric model of the given structure to validate the analytical model. Then, experiments are conducted to corroborate the numerical and analytical modeling and analysis. Full article
(This article belongs to the Special Issue Soft Sensors: Inference and Estimation)
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17 pages, 2846 KiB  
Article
Monitoring Chemical Processes Using Judicious Fusion of Multi-Rate Sensor Data
by Zhenyu Wang and Leo Chiang
Sensors 2019, 19(10), 2240; https://doi.org/10.3390/s19102240 - 15 May 2019
Cited by 14 | Viewed by 3220
Abstract
With the emergence of Industry 4.0, also known as the fourth industrial revolution, an increasing number of hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality and process safety. The accuracy of individual [...] Read more.
With the emergence of Industry 4.0, also known as the fourth industrial revolution, an increasing number of hardware and software sensors have been implemented in chemical production processes for monitoring key variables related to product quality and process safety. The accuracy of individual sensors can be easily impaired by a variety of factors. To improve process monitoring accuracy and reliability, a sensor fusion scheme based on Bayesian inference is proposed. The proposed method is capable of combining multi-rate sensor data and eliminating the spurious signals. The efficacy of the method has been verified using a process implemented at the Dow Chemical Company. The sensor fusion approach has improved the process monitoring reliability, quantified by the rates of correctly identified impurity alarms, as compared to the case of using an individual sensor. Full article
(This article belongs to the Special Issue Soft Sensors: Inference and Estimation)
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15 pages, 2076 KiB  
Article
Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty
by Iftikhar Ahmad, Ahsan Ayub, Nisar Mohammad and Manabu Kano
Sensors 2019, 19(7), 1626; https://doi.org/10.3390/s19071626 - 5 Apr 2019
Cited by 4 | Viewed by 3215
Abstract
Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the [...] Read more.
Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the process hinders the stability and efficiency. In this work, a quantitative analysis of the effect of uncertainty on the conversion efficiency of the entrained flow gasification is performed. A data-driven, i.e., ensemble, model of the process was developed to predict conversion efficiency of the process. Then sensitivity analysis methods, i.e., Sobol and Fourier amplitude sensitivity test, were used to analyze the effect of each individual process variables on conversion efficiency. For analyzing the collective impact of uncertainty in process variables on conversion efficiency, a non-intrusive polynomial chaos expansion (PCE) method was used. The PCE predicts probability distribution of the conversion efficiency. Reliability of the process was determined on the basis of percentage of the probability distribution falling within control limits. Measured data is used to derive the control limits for off-line reliability analysis. For on-line reliability analysis of the process, measured data is not available so a just-in-time method, i.e., k–d tree, was used. The k–d tree searches the nearest neighbor sample from a database of historical data to determine the control limits. Full article
(This article belongs to the Special Issue Soft Sensors: Inference and Estimation)
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20 pages, 1641 KiB  
Article
Student’s-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes
by Jingbo Wang, Weiming Shao and Zhihuan Song
Sensors 2018, 18(11), 3968; https://doi.org/10.3390/s18113968 - 15 Nov 2018
Cited by 12 | Viewed by 2592
Abstract
Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable that some measurements of industrial variables obtained through hardware sensors are incorrectly observed, recorded or imported into databases, resulting in the [...] Read more.
Because of multiple manufacturing phases or operating conditions, a great many industrial processes work with multiple modes. In addition, it is inevitable that some measurements of industrial variables obtained through hardware sensors are incorrectly observed, recorded or imported into databases, resulting in the dataset available for statistic analysis being contaminated by outliers. Unfortunately, these outliers are difficult to recognize and remove completely. These process characteristics and dataset imperfections impose challenges on developing high-accuracy soft sensors. To resolve this problem, the Student’s-t mixture regression (SMR) is proposed to develop a robust soft sensor for multimode industrial processes. In the SMR, for each mixing component, the Student’s-t distribution is used instead of the Gaussian distribution to model secondary variables, and the functional relationship between secondary and primary variables is explicitly considered. Based on the model structure of the SMR, a computationally efficient parameter-learning algorithm is also developed for SMR. Results conducted on two cases including a numerical example and a real-life industrial process demonstrate the effectiveness and feasibility of the proposed approach. Full article
(This article belongs to the Special Issue Soft Sensors: Inference and Estimation)
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