A decision support system for eco-efficient biorefinery process comparison using a semantic approach

Enzymatic hydrolysis of the main components of lignocellulosic biomass is one of the promising methods to further upgrading it into biofuels. Biomass pre-treatment is an essential step in order to reduce cellulose crystallinity, increase surface and porosity and separate the major constituents of biomass. Scientific literature in this domain is increasing fast and could be a valuable source of data. As these abundant scientific data are mostly in textual format and heterogeneously structured, using them to compute biomass pre-treatment efficiency is not straightforward. This paper presents the implementation of a Decision Support System (DSS) based on an original pipeline coupling knowledge engineering (KE) based on semantic web technologies, soft computing techniques and environmental factor computation. The DSS allows using data found in the literature to assess environmental sustainability of biorefinery systems. The pipeline permits to: (1) structure and integrate relevant experimental data, (2) assess data source reliability, (3) compute and visualize green indicators taking into account data imprecision and source reliability. This pipeline has been made possible thanks to innovative researches in the coupling of ontologies, uncertainty management and propagation. In this first version, data acquisition is done by experts and facilitated by a termino-ontological resource. Data source reliability assessment is based on domain knowledge and done by experts. The operational prototype has been used by field experts on a realistic use case (rice straw). The obtained results have validated the usefulness of the system. Further work will address the question of a higher automation level for data acquisition and data source reliability assessment

Improving data identification and tagging for more effective decision making in agriculture

International audienc

Adopting Circular Economy Current Practices and Future Perspectives

The development of a closed-loop cycle is a necessary condition so as to develop a circular economy model as an alternative to the linear model, in order to maintain the value of products and materials for as long as possible. For this motive, the definition of the value must be demonstrated for both the environment and the economy. The presence of these analyses should be associated with the social dimension and the human component. A strong cooperation between social and technical profiles is a new challenge for all researchers. End of life of products attract a lot of attention, and the final output could be the production of technologies suitable for managing this waste

Ranking Of Sustainability Criteria For Industrial Symbiosis Applications Based On Anp

ALAKAS, Haci Mehmet/0000-0002-9874-7588WOS:000599463700004Enterprises have started to establish partnerships both to use their internal resources efficiently and to increase their environmental performance. Partnerships and interoperability of enterprises with different processes enable them to benefit more from their benefits. Moving towards the local and regional economy, these partnerships that increase environmental and own resources have created industrial symbiosis practices. Industrial ecology fields are established in these applications. Both environmental and economic gains can be achieved through the efficient use of resources by enterprises and the minimization of wastes. For the sustainability of these partnerships to be established by enterprises, they need to analyze the measures they take internally. In this study, the concept of industrial symbiosis and the criteria that are effective for the sustainability of these industrial symbiosis are evaluated. Analytical network process method is used. Thus, the industrial symbiosis infrastructures to be established by enterprises have been enabled to move strategically

Projecting Pathways to Food-Energy-Water Systems Sustainability through Ontology

The FEWsOnt ontology models major structural and dynamic concepts of the food-energy-water (FEW) systems from the complex system perspective by defining the emergent, nonlinear, and scale-invariant state transitions and behaviors of the network elements that result from natural and planned processes. The model represents the semantics of concepts such as security, footprint, challenge, risk, impact, and uncertainty in relation to governance and assessment of the level of sustainability of the FEW systems in varied domains of usage. The ontology will allow stakeholders working with the FEW systems' data to draw new inferences using semantic facts and discover insights and relationships among the systems' elements to make improved assessment and decisions toward sustainable growth. The knowledge-based model will lead users to optimize the tradeoffs and identify and prevent adverse changes to the FEW systems in relation to the interacting natural and social systems. The annotated terminology and formalized interactions in the ontology will facilitate the integration of the diverse FEW data types, improve communication among researchers, and help to reduce environmental stresses

Circular Economy and Sustainable Development: A Systematic Literature Review

Circular Economy put forth as an alternative to traditional linear model of extract-use-dispose along with the concept of Sustainable Development encompassing economic, environmental, and social aspects have garnered tremendous impetus among academics, practitioners and policymakers alike. The UN Sustainable Development Goals embraced by the member nations in 2015 based on the preceding Millenium Development Goals have been placed as the targets to be achieved as a part of holistic human development. In this backdrop, this paper examines the intersection of sustainability and circular economy with a focus on the three aspects of sustainable development, first the economic aspect by examining the relationship between GDP and circular economy, second the social economic aspect within the interaction of Circular Economy with Sustainable development and third the environmental-economical aspect by examining circularity and sustainability in waste management and waste valorisation. This paper achieves its objective through a systematic literature review of 1748 journal articles collected from Web of Science and SCOPUS database following PRISMA standards, network analysis of keywords, and manual review of texts. Four Research Questions are formulated: RQ1: What are the major emergent topics in Circular Economy and Sustainable Development and how are they related? RQ2: What is the relationship among CE and GDP in the CE and Sustainability? RQ3: What are the relationships between CE and Sustainability? RQ4: What are different use cases of valorisation of waste as CE tool, and can valorisation be sustainable? RQ1 is answered by presenting hotspot of research on Circular Economy and Sustainable Development through keywords occurrence network analysis using VosViewer. This study identifies three clusters and seven thematic areas of research, along with 25 most used keywords. RQ2 is attended through review of the relationship between economic growth (Gross Domestic Product) and Circular Economy and proposes based on the review that CE is still at its infancy. The paper also discusses the appropriateness of using GDP as a measure of sustainable development. This paper addresses RQ3 by examining the relationship between Circular Economy and Sustainable Development through review of literatures. The indicators used to measure CE and SD are also discussed and summarised. This review finds that achieving SDGs require greater effort, and that the present status of achievement is a bleak picture. Further, the role of waste management and potentiality of waste valorisation to aid in circular economy and sustainable development is analysed to answer RQ4. Though there are ample potential, however the recycle rate is very minimal to quench the required level of circularity. While CE and SD are related, CE cannot be a universal panacea to global challenges like emissions reduction, energy consumption, climate change, gender equality, poverty, well-being, environmental protection etc. even though the impact of CE to achieve SD can be substantial. The paper recommends avenues for future research and presents the conclusion of the study

Integration of BIM and Value Model for Sustainability Assessment for application in bridge projects

The integration of multicriteria decision-making (MCDM) methods with building information modeling (BIM) provides robust support for decision-making when assessing the sustainability of construction projects. Among the MCDM methods, the Integrated Value Model for Sustainability Assessment (MIVES) has proven to provide a representative and meaningful quantification of sustainability in different areas of civil engineering. Although previous studies have integrated BIM and MIVES, none automated the sustainability assessment process as BIM was only used to define the measurements of the MIVES indicators. This study accordingly developed a new method coupling the MIVES and BIM methodologies to realize automatic sustainability assessment of bridges. The proposed technique was validated using a real viaduct to evaluate sustainability and conduct a sensitivity analysis identifying the indicators with the most influence on sustainability performance. Critically, this methodology is not limited to the sustainability assessment of bridges as it can be readily adapted to other types of infrastructure.The authors are indebted to the projects PID2021-126405OB-C31, and PID2021-126405OB-C32 funded by MICIN/AEI/10.13039/501100011033/ and FEDER funds A way to make Europe.Peer ReviewedPostprint (published version

AgroPortal: a vocabulary and ontology repository for agronomy

Many vocabularies and ontologies are produced to represent and annotate agronomic data. However, those ontologies are spread out, in different formats, of different size, with different structures and from overlapping domains. Therefore, there is need for a common platform to receive and host them, align them, and enabling their use in agro-informatics applications. By reusing the National Center for Biomedical Ontologies (NCBO) BioPortal technology, we have designed AgroPortal, an ontology repository for the agronomy domain. The AgroPortal project re-uses the biomedical domain’s semantic tools and insights to serve agronomy, but also food, plant, and biodiversity sciences. We offer a portal that features ontology hosting, search, versioning, visualization, comment, and recommendation; enables semantic annotation; stores and exploits ontology alignments; and enables interoperation with the semantic web. The AgroPortal specifically satisfies requirements of the agronomy community in terms of ontology formats (e.g., SKOS vocabularies and trait dictionaries) and supported features (offering detailed metadata and advanced annotation capabilities). In this paper, we present our platform’s content and features, including the additions to the original technology, as well as preliminary outputs of five driving agronomic use cases that participated in the design and orientation of the project to anchor it in the community. By building on the experience and existing technology acquired from the biomedical domain, we can present in AgroPortal a robust and feature-rich repository of great value for the agronomic domain. Keyword

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae