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Infrastructures
Special Issues
Investigating Waste Behaviours for Reducing the Strain on Critical Infrastructures
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Investigating Waste Behaviours for Reducing the Strain on Critical Infrastructures

A special issue of Infrastructures (ISSN 2412-3811).

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 18342

Special Issue Editors

Dr. William Hurst

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University and Research, Building No. 201 (Leeuwenborch), Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Interests: data science; information technology; critical infrastructures; creative technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Kwabena Ebo Bennin

E-Mail Website
Guest Editor
Information Technology Group, Wageningen University and Research, Building No. 201 (Leeuwenborch), Hollandseweg 1, 6706 KN Wageningen, The Netherlands
Interests: empirical software engineering; machine learning and predictive modelling; explainable AI; smart metering
Dr. Ben Kotze

E-Mail Website
Guest Editor
Faculty of Engineering, Built Environment and Information Technology, Central University of Technology-Free State, Bloemfontein 9300, South Africa
Interests: robotics; AGV’s; microcontrollers; programming; AI; renewable technology; vision
Dr. Tonderayi Mangara

E-Mail Website
Guest Editor
Faculty of Engineering, Built Environment and Information Technology, Central University of Technology-Free State, Bloemfontein 9300, South Africa
Interests: smart metering; smart grid; energy; supply resilience and resilient infrastructure

Special Issue Information

Dear Colleagues,

Urbanisation is surging, with over half of the human population predicted to live in an urban environment in the near future. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order to create a sustainable society, there needs to be a change in both societal behaviours (e.g. reducing water/energy/food waste activities) and the technology currently in place (e.g. greater use of green energy, digital twins, preventative maintenance solutions and precision technology). The main challenges are that there is no in-depth knowledge or understanding of waste behaviour, a lack of technological solutions to address this and stark inequalities globally in current critical infrastructure systems. This special issue aims to invite research articles (including theoretical and applied works) that will further the understanding of waste behaviours, the reliability/availability and resilience of critical infrastructures, and the use of advanced technologies for reducing waste.

Areas of interest include the following:

  • Food, Water and Energy waste behaviour
  • Digital twins for infrastructure management
  • AR/VR for greater process awareness
  • Critical infrastructure optimisation and precision
  • Transmission / distribution network regimes
  • Component importance measures
  • Smart grid/smart metering
  • Preventative maintenance with artificial intelligence
  • Supply resilience and resilient infrastructure design
  • Green computing technologies for reduced emissions

Dr. William Hurst
Dr. Kwabena Ebo Bennin
Dr. Ben Kotze
Dr. Tonderayi Mangara
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. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • water, energy and food Network
  • digital twins
  • augmented/virtual reality
  • waste behaviours
  • interconnectivity
  • transmission / distribution network regimes
  • artificial intelligence and machine learning

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Published Papers (3 papers)

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Research

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18 pages, 1218 KiB  
Article
Solid Waste Image Classification Using Deep Convolutional Neural Network
by Nonso Nnamoko, Joseph Barrowclough and Jack Procter
Infrastructures 2022, 7(4), 47; https://doi.org/10.3390/infrastructures7040047 - 25 Mar 2022
Cited by 25 | Viewed by 10495
Abstract
Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used [...] Read more.
Separating household waste into categories such as organic and recyclable is a critical part of waste management systems to make sure that valuable materials are recycled and utilised. This is beneficial to human health and the environment because less risky treatments are used at landfill and/or incineration, ultimately leading to improved circular economy. Conventional waste separation relies heavily on manual separation of objects by humans, which is inefficient, expensive, time consuming, and prone to subjective errors caused by limited knowledge of waste classification. However, advances in artificial intelligence research has led to the adoption of machine learning algorithms to improve the accuracy of waste classification from images. In this paper, we used a waste classification dataset to evaluate the performance of a bespoke five-layer convolutional neural network when trained with two different image resolutions. The dataset is publicly available and contains 25,077 images categorised into 13,966 organic and 11,111 recyclable waste. Many researchers have used the same dataset to evaluate their proposed methods with varying accuracy results. However, these results are not directly comparable to our approach due to fundamental issues observed in their method and validation approach, including the lack of transparency in the experimental setup, which makes it impossible to replicate results. Another common issue associated with image classification is high computational cost which often results to high development time and prediction model size. Therefore, a lightweight model with high accuracy and a high level of methodology transparency is of particular importance in this domain. To investigate the computational cost issue, we used two image resolution sizes (i.e., 225×264 and 80×45) to explore the performance of our bespoke five-layer convolutional neural network in terms of development time, model size, predictive accuracy, and cross-entropy loss. Our intuition is that smaller image resolution will lead to a lightweight model with relatively high and/or comparable accuracy than the model trained with higher image resolution. In the absence of reliable baseline studies to compare our bespoke convolutional network in terms of accuracy and loss, we trained a random guess classifier to compare our results. The results show that small image resolution leads to a lighter model with less training time and the accuracy produced (80.88%) is better than the 76.19% yielded by the larger model. Both the small and large models performed better than the baseline which produced 50.05% accuracy. To encourage reproducibility of our results, all the experimental artifacts including preprocessed dataset and source code used in our experiments are made available in a public repository. Full article
(This article belongs to the Special Issue Investigating Waste Behaviours for Reducing the Strain on Critical Infrastructures)
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27 pages, 11551 KiB  
Article
A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating
by Daniel N. de Bruyn, Ben Kotze and William Hurst
Infrastructures 2021, 6(5), 67; https://doi.org/10.3390/infrastructures6050067 - 30 Apr 2021
Cited by 2 | Viewed by 2859
Abstract
Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented [...] Read more.
Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller. Full article
(This article belongs to the Special Issue Investigating Waste Behaviours for Reducing the Strain on Critical Infrastructures)
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Review

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19 pages, 1793 KiB  
Review
Critical Infrastructures: Reliability, Resilience and Wastage
by William Hurst, Kwabena Ebo Bennin, Ben Kotze and Tonderayi Mangara
Infrastructures 2022, 7(3), 37; https://doi.org/10.3390/infrastructures7030037 - 9 Mar 2022
Cited by 4 | Viewed by 3431
Abstract
By 2050, according to the UN medium forecast, 68.6% of the world’s population will live in cities. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order [...] Read more.
By 2050, according to the UN medium forecast, 68.6% of the world’s population will live in cities. This growth will place a strain on critical infrastructure distribution networks, which already operate in a state that is complex and intertwined within society. In order to create a sustainable society, there needs to be a change in both societal behaviours (for example, reducing water, energy or food waste activities) and future use of smart technologies. The main challenges are that there is a limited aggregated understanding of current waste behaviours within critical infrastructure ecosystems, and a lack of technological solutions to address this. Therefore, this article reflects on theoretical and applied works concerning waste behaviours, the reliability/availability and resilience of critical infrastructures, and the use of advanced technologies for reducing waste. Articles in the Scopus digital library are considered in the investigation, with 51 papers selected by means of a systematic literature review, from which 38 strains, 86 barriers and 87 needs are identified, along with 60 methods of analysis. The focus of the work is primarily on behaviours, barriers and needs that create an excess or wastage. Full article
(This article belongs to the Special Issue Investigating Waste Behaviours for Reducing the Strain on Critical Infrastructures)
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