Conceptual Framework for Near-to-site Waste Cycle Design

Steingrímsson, Jón Garðar; Seliger, Günther

doi:10.1016/j.procir.2014.06.014

Procedia CIRP

2014

DOI: 10.1016/j.procir.2014.06.014

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Conceptual Framework for Near-to-site Waste Cycle Design

Jón Garðar Steingrímsson¹,

Günther Seliger²

Abstract: The manufacturing industry is under pressure to reduce the emission of solid and liquid refuse as well as waste gas from plants into the environment. Technologies that reutilise these wastes can be referred to as cycle technologies. To assess which of these technologies to adopt is a challenge, since hardly any methods to evaluate the sustainability benefits exist. A conceptual framework for near-to-site variations of cycle technological design has been developed. Suitable structure and characteristics for ini… Show more

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Cited by 2 publications

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Machine learning-based forecasting of potability of drinking water through adaptive boosting model

et al. 2022

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Water is an indispensable requirement for life for health and many other purposes, but not all water is safe for consumption. Thus, various metrics, such as biological, chemical, and physical, could be used to determine the quality of potable water for use. This study presents a machine learning-based model using the adaptive boosting technique with the ability to categorize and evaluate the quality rate of drinking water. The dataset for the study was adopted from Kaggle. Consequently, an experimental analysis of the different machine learning techniques (ensemble) was carried out to create a generic water quality classifier. The results show that the forecast accuracy of the logistic regression model (88.6%), Chi-square Automatic Interaction Detector (93.1%), XGBoost tree (94.3%), as well as multi-layered perceptron (95.3%) improved by the presented ensemble model (96.4%). The study demonstrates that the use of ensemble model presents more precision in predicting water quality compared to other related algorithms. The use of the model presented in this study could go a long way to enhance the regulation of water quality and safety and address the gaps in conventional prediction approach.

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Machine learning-based forecasting of potability of drinking water through adaptive boosting model

et al. 2022

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A Holistic Sustainability Framework for Waste Management in European Cities: Concept Development

et al. 2018

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Waste management represents a challenge for public authorities due to many reasons such as increased waste generation following urban population growth, economic burdens imposed on the municipal budget, and nuisances inevitably caused to the environment and local inhabitants. To optimize the system from a sustainability perspective, moving the transition towards a more circular economy, a better understanding of the different stages of waste management is necessary. A review of recently developed sustainability frameworks for waste management showed that no single framework captures all the instruments needed to ultimately provide a solid basis for comprehensive analyses of the potential burdens associated with urban waste management. Bearing this limitation in mind, the objective of this research is to propose a conceptual and comprehensive sustainability framework to support decision-making in waste management of European cities. The framework comprises a combination of methods capable of identifying future strategies and scenarios, to assess different types of impacts based on a life cycle perspective, and considers the value of waste streams, the actors involved, and possible constraints of implementing scenarios. The social, economic, environmental, technical and political domains are covered, and special attention is paid to impacts affecting foremost the local population.

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Conceptual Framework for Near-to-site Waste Cycle Design

Cited by 2 publications

References 12 publications

Machine learning-based forecasting of potability of drinking water through adaptive boosting model

Machine learning-based forecasting of potability of drinking water through adaptive boosting model

A Holistic Sustainability Framework for Waste Management in European Cities: Concept Development

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