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Soft Sensors in the Intelligent Process Industry
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Soft Sensors in the Intelligent Process Industry

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

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 4794

Special Issue Editors

Dr. Francisco Alexandre Andrade Souza

E-Mail Website
Guest Editor
Department of Analytical Chemistry & Chemometrics, Radboud University, 6525 AJ Nijmegen, The Netherlands
Interests: Process control; Industry 4.0; Applied Machine Learning; Soft sensors
Prof. Dr. Maria Gabriella Xibilia

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, Contrada Di Dio, Vill. S. Agata , 98166 Messina, Italy
Interests: system identification; soft sensors; soft computing; machine learning; neural networks; nonlinear control; complex systems; industrial automation; process monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft-sensors are inferential data models that use easy-to-measure data (from physical sensors) to predict difficult-to-measure variables online. These difficult-to-measure variables are commonly associated with the quality of the process and often cannot be automatically measured at all or can only be measured at high costs, sporadically, or with high delays. Soft-sensors benefit many industrial processes, enabling the online monitoring of process quality, increasing performance, and positively impacting process sustainability. At the same time, soft-sensors pose many specific challenges in all design steps and due to the nature of industrial data. These challenges have led to increased heterogeneous modeling strategies for soft sensors in the last few years.

This special session aims to connect the researchers who work with soft-sensors, to share their recent advances, address innovative solutions, paradigms, and emerging issues. Also, there is a specific scope for soft sensor applications, intending to increase the community’s visibility and share potential benefits of adopting soft-sensor in the process industry.

Dr. Francisco Alexandre Andrade Souza
Prof. Dr. Maria Gabriella Xibilia
Guest Editors

Manuscript Submission Information

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Keywords

  • Soft-sensors applications
  • Data pre-processing
  • Variable and time-lag selection
  • Adaptive soft sensors
  • Hybrid soft-sensors
  • Just-in-time learning soft-sensors
  • Soft-sensor maintenance
  • Advanced soft sensors (deep learning, transfer learning, semi-supervised, etc.)

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Published Papers (2 papers)

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15 pages, 3146 KiB  
Article
Generalizability of Soft Sensors for Bioprocesses through Similarity Analysis and Phase-Dependent Recalibration
by Manuel Siegl, Manuel Kämpf, Dominik Geier, Björn Andreeßen, Sebastian Max, Michael Zavrel and Thomas Becker
Sensors 2023, 23(4), 2178; https://doi.org/10.3390/s23042178 - 15 Feb 2023
Cited by 3 | Viewed by 1956
Abstract
A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic [...] Read more.
A soft sensor concept is typically developed and calibrated for individual bioprocesses in a time-consuming manual procedure. Following that, the prediction performance of these soft sensors degrades over time, due to changes in raw materials, biological variability, and modified process strategies. Through automatic adaptation and recalibration, adaptive soft sensor concepts have the potential to generalize soft sensor principles and make them applicable across bioprocesses. In this study, a new generalized adaptation algorithm for soft sensors is developed to provide phase-dependent recalibration of soft sensors based on multiway principal component analysis, a similarity analysis, and robust, generalist phase detection in multiphase bioprocesses. This generalist soft sensor concept was evaluated in two multiphase bioprocesses with various target values, media, and microorganisms. Consequently, the soft sensor concept was tested for biomass prediction in a Pichia pastoris process, and biomass and protein prediction in a Bacillus subtilis process, where the process characteristics (cultivation media and cultivation strategy) were varied. High prediction performance was demonstrated for P. pastoris processes (relative error = 6.9%) as well as B. subtilis processes in two different media during batch and fed-batch phases (relative errors in optimized high-performance medium: biomass prediction = 12.2%, protein prediction = 7.2%; relative errors in standard medium: biomass prediction = 12.8%, protein prediction = 8.8%). Full article
(This article belongs to the Special Issue Soft Sensors in the Intelligent Process Industry)
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25 pages, 3257 KiB  
Article
Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling
by Tiago Dias, Rodolfo Oliveira, Pedro M. Saraiva and Marco S. Reis
Sensors 2022, 22(10), 3734; https://doi.org/10.3390/s22103734 - 13 May 2022
Cited by 5 | Viewed by 1870
Abstract
The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods [...] Read more.
The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions. Full article
(This article belongs to the Special Issue Soft Sensors in the Intelligent Process Industry)
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