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Intelligent Sensing for Sustainable Production Industries

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A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (26 March 2023) | Viewed by 9487

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Special Issue Editors

Dr. Marion McAfee

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Chief Guest Editor

School of Engineering and Design, Atlantic Technological University, F91 YW50 Sligo, Ireland
Interests: process monitoring and control - particularly of polymer processes; soft sensors; process spectroscopy; process modelling and optimisation; medical device manufacturing

Dr. Johannes D. Stigter

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Guest Editor

Biometris, Department of Mathematical and Statistical Methods, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands

Dr. Salem Gharbia

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Guest Editor

Department of Civil and Construction Engineering, Atlantic Technological University, F91 YW50 Sligo, Ireland
Interests: Water resources; climate change and integrated environmental systems modelling; environmental interventions using and adapting low-cost and high-end sensing technologies and numerical modelling

Special Issue Information

Dear Colleagues,

Over the last decade, there have been significant advances in intelligent sensing technologies which are having a major impact on the ability of many production industries to accurately monitor and control processes to improve efficiencies, reduce waste, lower environmental impact, improve product quality and to develop new products to improve the quality of life of citizens. Intelligent sensors utilize advanced signal processing, sensor fusion, mathematical models and learning algorithms to gain a better understanding of industrial processes and products and the factors that affect them. Intelligent sensing technologies can allow powerful information to be quickly and easily extracted from sensors such as low cost vision devices and sensors with highly complex responses such as spectroscopic methods which are difficult for human interpretation.

Examples of the transformative potential of intelligent sensing technologies for improving sustainability are rife across production sectors including agriculture, manufacturing, materials and chemical industries, textiles and food production.

This Special Issue will highlight recent and emerging research on concepts, methods, tools, and applications of intelligent sensing technologies for enhancement of sustainability across wide ranging industrial production sectors. This special issue aims to advance and promote the uptake of intelligent sensing approaches as an aid in accelerating the transition to sustainable industry.

Dr. Marion McAfee
Dr. Johannes D. Stigter
Dr. Salem Gharbia
Guest Editors

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. Sustainability 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 2400 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.

Keywords

Signal Processing, Control and Estimation
Soft Sensors for sustainable industrial processes
Model-based design for sustainable production
Intelligent sensing for food production and safety
System identification and control for sustainable production
Intelligent sensing for low cost sensor networks
Information and Sensor Fusion
The role of sensing for Sustainable Agriculture

Published Papers (4 papers)

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Research

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15 pages, 5652 KiB

Open AccessArticle

Sensor Data Fusion as an Alternative for Monitoring Chlorate in Electrochlorination Applications

by Edwin Ross, Martijn Wagterveld, Mateo Mayer, Hans Stigter, Bo Højris, Yang Li and Karel Keesman

Sustainability 2022, 14(10), 6119; https://doi.org/10.3390/su14106119 - 18 May 2022

Cited by 1 | Viewed by 1566

Abstract

As chlorate concentrations have been found to be harmful to human and animal health, governments are increasingly demanding strict control of the chlorate concentration in drinking water. Since there are no chlorate sensors available, the current solution is sampling and laboratory analysis. This is costly and time consuming. The aim of this work was to investigate Sensor Data Fusion (SDF) as an alternative approach, with a focus on chlorate formation in the electrochlorination process, and design an observer for the real-time estimation of chlorate. The pH, temperature and UV-a absorption were measured in real time. A reduced-order nonlinear model was derived, and it was found to be detectable. An Extended Kalman Filter (EKF), based on this model, was then used to estimate the chlorate formation. The EKF algorithm was verified experimentally and was found to be capable of accurately estimating chlorate concentrations in real time. Electrochlorination is an emerging and efficient method of disinfecting drinking water. Soft sensing of chlorate concentrations, as proposed in this paper, may help to better control and manage the process of electrochlorination. Full article

(This article belongs to the Special Issue Intelligent Sensing for Sustainable Production Industries)

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23 pages, 11269 KiB

Open AccessArticle

Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale

by Salem Gharbia, Khurram Riaz, Iulia Anton, Gabor Makrai, Laurence Gill, Leo Creedon, Marion McAfee, Paul Johnston and Francesco Pilla

Sustainability 2022, 14(7), 4037; https://doi.org/10.3390/su14074037 - 29 Mar 2022

Cited by 6 | Viewed by 2106

Abstract

Changes in streamflow within catchments can have a significant impact on agricultural production, as soil moisture loss, as well as frequent drying and wetting, may have an effect on the nutrient availability of many soils. In order to predict future changes and explore the impact of different scenarios, machine learning techniques have been used recently in the hydrological sector for simulation streamflow. This paper compares the use of four different models, namely artificial neural networks (ANNs), support vector machine regression (SVR), wavelet-ANN, and wavelet-SVR as surrogate models for a geophysical hydrological model to simulate the long-term daily water level and water flow in the River Shannon hydrological system in Ireland. The performance of the models has been tested for multi-lag values and for forecasting both short- and long-term time scales. For simulating the water flow of the catchment hydrological system, the SVR-based surrogate model performs best overall. Regarding modeling the water level on the catchment scale, the hybrid model wavelet-ANN performs the best among all the constructed models. It is shown that the data-driven methods are useful for exploring hydrological changes in a large multi-station catchment, with low computational cost. Full article

(This article belongs to the Special Issue Intelligent Sensing for Sustainable Production Industries)

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23 pages, 4394 KiB

Open AccessArticle

Multivariate Modeling of Mechanical Properties for Hot Runner Molded Bioplastics and a Recycled Polypropylene Blend

by David O. Kazmer, Davide Masato, Leonardo Piccolo, Kyle Puleo, Joshua Krantz, Varun Venoor, Austin Colon, Justin Limkaichong, Neil Dewar, Denis Babin and Cheryl Sayer

Sustainability 2021, 13(14), 8102; https://doi.org/10.3390/su13148102 - 20 Jul 2021

Cited by 9 | Viewed by 2224

Abstract

Four sustainable materials including a recycled polypropylene blend, polybutylene adipate terephthalate, and two grades of polylactic acid are compared to a reference isotactic polypropylene. Tensile specimens were produced using a two-cavity, hot runner mold with fully automatic cycles per standard industrial practices to investigate the effect of melt temperature, injection velocity, cycle time, and screw speed on the mechanical properties. Multiple regression and principal component analyses were performed for each of the materials. Results indicated that all the materials were readily processed using a hot runner, and the mechanical properties exhibited minimal variation. To the extent that losses in mechanical properties were observed, the results indicated that the losses were correlated with thermal degradation as independently characterized by thermal gravimetric analysis. Such losses can be minimized by reducing melt temperature and cycle time, leading to a reduction of the environmental impact of injection molding processes. Full article

(This article belongs to the Special Issue Intelligent Sensing for Sustainable Production Industries)

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Review

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33 pages, 1259 KiB

Open AccessReview

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

by Marion McAfee, Mandana Kariminejad, Albert Weinert, Saif Huq, Johannes D. Stigter and David Tormey

Sustainability 2022, 14(6), 3635; https://doi.org/10.3390/su14063635 - 19 Mar 2022

Cited by 12 | Viewed by 2659

Abstract

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (‘soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (‘sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems. Full article

(This article belongs to the Special Issue Intelligent Sensing for Sustainable Production Industries)

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