The use of artificial intelligence (AI) and life cycle analysis (LCA) tools for predicting the environmental performance of sustainable transport fuels
This thesis project aims to evaluate the environmental performance of transport fuels using a combined method of artificial intelligence (AI) and life cycle assessment (LCA). The project contributes to developing an integrated framework for sustainable transport fuels using a dynamic lifecycle and novel artificial intelligence (AI) approach.
There is an urgent need to substitute fossil-based fuels in the transport sector. Life cycle analysis (LCA) is a tool to evaluate the potential environmental impacts of alternative transport fuels. It is a comprehensive method for assessing all direct and indirect environmental impacts across the entire life cycle of a product system, from design, to materials acquisition to manufacturing, to use, and to final disposition. Artificial intelligence (AI) and Machine Learning (ML) methods encompass a wide variety of powerful data-driven techniques which have applicability for predicting resource use and environmental impacts.
In this project, students will evaluate the environmental performance of one of the selected alternative transport fuels (e.g., bioethanol, biodiesel, hydrogen), and use of artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models for the prediction of environmental impacts considering different scenarios and management options.
Task description
Tentative tasks for this project are:
- To perform inventory analysis on the selected alternative transport fuels
- To evaluate/compare the life cycle environmental impacts of transport fuel
- To determine the significant influencing factors/inputs in the life cycle in terms of environmental impacts and investigate their roles
- To identify potential hotspots and suggest possible measures to improve the environmental impact
- To model the lifecycle of transport fuel using ANNs or other appropriate data-driven methods
- To predict environmental impacts of transport fuels in different scenarios
- To contribute in developing integrated framework for sustainable transport fuels using a dynamic lifecycle and novel artificial intelligence (AI) approach
Criteria for evaluation
Critical criteria in the complete work and method development and metric for the final assessment are:
- Fulfilment of the ILOs for Master Thesis at KTH's ITM School;
- The student's initiative and independence in developing the overall research design;
- A critical and system perspective and critical discussion of the assumptions and results;
- Consideration of the literature.
- The ability to communicate the results of scientific work clearly and coherently.
If the work is of good quality and the student and project partners are interested, the research project will be designed to be suitable for a peer-reviewed publication in a high-quality journal.
Prerequisites
The analysis to be undertaken is interdisciplinary in nature, and requires some knowledge of alternative transport fuels, environmental assessment, data science and machine learning. Students should have an undergraduate degree in chemistry, biology, engineering, economics, or similar fields. Prior knowledge of the LCA and AI; Understanding of energy conversion technologies; Basic knowledge in energy modelling; Experiences in Python/MatLab will be an asset.
Track Specialization
Transformation of Energy System (TES)
Division/Department
Division of Energy Systems – Department of Energy Technology
Research areas:
- Energy Systems and Innovation
Circular Economy & Resource Efficiency
Start time: anytime soon (January/February 2023)
The student may choose to work individually or in pairs.
How to apply
Send an email expressing your interest on the topic to Dilip Khatiwada ([email protected]).
KTH Supervision and main contact
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Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review
- LIFE CYCLE SUSTAINABILITY ASSESSMENT
- Published: 02 March 2022
Cite this article
- Cecilia Silvestri ORCID: orcid.org/0000-0003-2528-601X 1 ,
- Luca Silvestri ORCID: orcid.org/0000-0002-6754-899X 2 ,
- Michela Piccarozzi ORCID: orcid.org/0000-0001-9717-9462 1 &
- Alessandro Ruggieri 1
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A Correction to this article was published on 24 March 2022
This article has been updated
The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.
Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.
The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.
Conclusions
The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation.
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Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis
Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals
Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis
Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster
Source: Authors’ elaboration. Notes: The graph shows the network visualization
Source: Authors’ elaboration. Notes: The graph shows the overlay visualization
Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field
Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field
Source: Authors’ elaboration
Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL
Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector
Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical
Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis
Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors
Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate
Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate
Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach
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Change history, 24 march 2022.
A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02038-9
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Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1
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DOI : https://doi.org/10.1007/s11367-022-02032-1
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