master thesis life cycle assessment

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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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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Received : 15 June 2021

Accepted : 16 February 2022

Published : 02 March 2022

DOI : https://doi.org/10.1007/s11367-022-02032-1

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