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project.bib
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@article{holligerMethaneProductionFullScale2017,
title = {Methane {{Production}} of {{Full-Scale Anaerobic Digestion Plants Calculated}} from {{Substrate}}'s {{Biomethane Potentials Compares Well}} with the {{One Measured On-Site}}},
author = {Holliger, Christof and {Fruteau de Laclos}, H{\'e}l{\`e}ne and Hack, Gabrielle},
year = {2017},
month = jun,
journal = {Frontiers in Energy Research},
volume = {5},
pages = {12},
issn = {2296-598X},
doi = {10.3389/fenrg.2017.00012},
abstract = {Biomethane potential (BMP) tests are used to determine the amount of methane that can be produced from organic materials in order to design different components of fullscale anaerobic digestion (AD) plants such as size of the digesters and units exploiting the produced biogas. However, little is known on how well BMPs compare with biogas production from the same organic materials in full-scale installations. In this study, two AD plants were chosen to carry out such comparisons, a dry AD plant treating green waste from urban areas and food waste from restaurants and supermarkets, and a liquid AD plant treating waste sludge from wastewater treatment and seven additional organic wastes. The BMPs of multiple samples of the individual organic materials collected during a period of 7\textendash 9 months were determined. Separate tests of mixtures of organic materials confirmed that the BMP of the mixtures can be calculated by adding the BMPs of the individual materials. The weekly methane production during the investigated periods was calculated from the full-scale installation data on the feeding of the digesters and the BMPs of each substrate fed into the digesters and compared with the weekly methane production measured on-site. The latter was calculated from the most accurately measured entity, either the electricity or the volume of purified biomethane injected into the grid. The weekly methane production rates calculated from BMPs and the one measured on-site were very similar and followed the same pattern. Some exceptions could be explained by, e.g., an overload of the full-scale installation. The measured weekly methane production accounted for 94.0 {$\pm$} 6.8 and 89.3 {$\pm$} 5.7\% of the calculated weekly methane production for the wet and dry AD plant, respectively. For 26 out of 29 weeks, the calculated weekly methane production overestimated the measured one in the case of the wet AD plant and for 37 out of 39 weeks for the dry AD plant. Based on these results, it is proposed using an extrapolation coefficient of 0.8 to 0.9 to estimate the methane production of full-scale AD plants from BMPs of the substrates to be digested and their specific organic loads.},
langid = {english},
file = {/Users/dareen/Zotero/storage/C4GINGMH/Holliger et al. - 2017 - Methane Production of Full-Scale Anaerobic Digesti.pdf}
}
@manual{R-bookdown,
type = {Manual},
title = {Bookdown: {{Authoring}} Books and Technical Documents with {{R}} Markdown},
author = {Xie, Yihui},
year = {2022},
note = {https://github.com/rstudio/bookdown}
}
@article{shiDairyProcessingSludge2021,
title = {Dairy Processing Sludge and Co-Products: {{A}} Review of Present and Future Re-Use Pathways in Agriculture},
shorttitle = {Dairy Processing Sludge and Co-Products},
author = {Shi, W. and Healy, M.G. and Ashekuzzaman, S.M. and Daly, K. and Leahy, J.J. and Fenton, O.},
year = {2021},
month = sep,
journal = {Journal of Cleaner Production},
volume = {314},
pages = {128035},
issn = {09596526},
doi = {10.1016/j.jclepro.2021.128035},
abstract = {The dairy industry is one of the largest global producers of wastewater and generates huge volumes of dairy processing sludge (DPS). There are two main types of DPS, lime-treated dissolved air floatation sludge and biochemically-treated activated sludge. These sludge types may also be converted to STRUBIAS (STRUvite, BIochar, AShes) products which have potential as fertilizers, secondary feedstocks for phosphate fertiliser granules, and soil amendments. A small number of studies indicate that these products have variable nutrient and metal contents, which differ across sludge and STRUBIAS product types. This is due to many factors such as the type of dairy plants, wastewater treatment process and production technologies. Although such products are land applied, the phosphorus (P) and nitrogen (N) fertilizer equivalency value (FEV) are often unknown and not factored into application rates, and therefore need study under field conditions (across soil and crop types). This review identifies a need to quantify antimicrobial drugs, hormones, pesticides, disinfectants, persistent organic pollutants (POPs), microplastics and nano-particles in all DPS and STRUBIAS types. Where detected, testing should follow the transfer of these contaminants to the soil, crop and water continuum. Further knowledge in the areas identified would enable both agronomic and environmental goals to be met and promote higher uptake of DPS and STRUBIAS re-use in agriculture.},
langid = {english},
file = {/Users/dareen/Zotero/storage/BKUESB2M/Shi et al. - 2021 - Dairy processing sludge and co-products A review .pdf}
}
@phdthesis{beckerMethodologyThermoEconomicOptimization2012,
type = {{PhD}},
title = {Methodology and {Thermo}-{Economic} {Optimization} for {Integration} of {Industrial} {Heat} {Pumps}},
url = {https://doi.org/10.5075/epfl-thesis-5341},
abstract = {This thesis presents a systematic methodology, based on pinch analysis and process integration
techniques, to integrate heat pumps into industrial processes. The main goals are to decrease the
energy consumption and the corresponding operating costs, and therefore to increase the energy
efficiency of an industrial process.
The objective of this thesis is to identify heat pump opportunities, and to optimize simultaneously
the energy conversion and utility system of a process. The process and utility integration is realized,
using mixed integer linear programming (MILP). In order to find systematically the optimal
operating conditions of heat pumps, an optimization framework combining linear and non-linear
optimization methods is presented. Technologically feasible heat pumps are collected in a data base
and proposed to the process. A multi-objective optimization, combined with process integration
methods, gives the possibility to systematically identify optimal heat pump sizing and positioning
solutions.
Pinch analysis is a promising tool, however several limits have been discovered, and the basic
methodology has been extended to obtain more realistic solutions.
The first extension gives the possibility to integrate heat exchange restrictions due to industrial
constraints (e.g. safety reasons, or long distances). A methodology, based on the decomposition into
sub-systems, is developed to include heat exchange restrictions. The penalty of these heat exchange
restrictions can be decreased by integrating intermediate heat recovery loops, which transfer heat
indirectly between two sub-systems. A further developed extension, which enables to define subsystems
at di↵erent levels, makes the approach very flexible and useful for many di↵erent application
cases.
The second extension is realized to integrate multi-period and multi-time slice problems. The main
focus is the integration of storage units, to enable the heat recovery between di↵erent time slices.
The interest in heat pumps can be increased when integrating storage units, because their working
hours and profitability increase.
The methodology and its extensions are tested and validated with several real industrial case studies.
It is shown that there is a great potential for industrial heat pumps.},
language = {English},
school = {ÉCOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE},
author = {BECKER, Helen Carla},
year = {2012},
}
@inproceedings{dardorROSMOSEWebbasedOptimization2023,
address = {Las Palmas de Gran Canaria},
title = {{ROSMOSE}: {A} {Web}-based {Optimization} {Tool} to {Aid} {Decision}-making for the {Design} and {Operation} of {Industrial} and {Urban} {Energy} {Systems}},
url = {https://infoscience.epfl.ch/record/303487},
abstract = {Energy efficiency is crucial for the sustainable operation of all industrial and urban sectors. However, practicing engineers have seldom access to open-source tools that can readily evaluate and compare scenarios in terms of energy consumption, cost, and environmental impact. In this work, a web-based tool called ROSMOSE is proposed for assessing the energy efficiency of systems and comparing available integration options. The ROSMOSE engine has been conceived as a high-level reporting language used to exploit the capabilities of the OSMOSE platform. This platform is an optimization framework that takes input process data and uses it to compute and graphically represent minimum energy requirement, pinch temperatures, and grand composite curves of industrial processes. The tool is then used to select suitable technologies that satisfy the energy demands, while minimizing costs. The Rmarkdown engine on which it is built allows the integration of various software and tools, such as database handling, process modelling suites, and optimization solvers, along with data visualization and reporting into any kind of format. ROSMOSE tool can be made available for the scientific community and industrial partners. As a case study, this paper discusses the application of ROSMOSE for the energy integration and total site optimization of a dairy process consisting of milk treatment, cream, and cheese production. Other integration options, such as heat pumps, biogas, and soft drinks production, are proposed to enhance the process economics and reduce environmental impact. As a result, 70 - 75\% energy savings and up to 90\% CO2 emissions reduction are identified.},
language = {en},
booktitle = {{ECOS} 2023},
author = {Dardor, Dareen and Flórez-Orrego, Daniel and Terrier, Cedric and Ribeiro, Meire Ellen and Lopez, Michel and Maréchal, François},
month = jun,
year = {2023},
file = {Dardor et al. - ROSMOSE A Web-based Optimization Tool to Aid Deci.pdf:/Users/dareen/Zotero/storage/KN8LEWX5/Dardor et al. - ROSMOSE A Web-based Optimization Tool to Aid Deci.pdf:application/pdf},
}
@book{goffDairyScienceTechnology,
title = {Dairy {Science} and {Technology} {eBook}},
abstract = {This e-book is about the study of milk and milk-derived food products from a food science perspective. It focuses on the biological, chemical, physical, and microbiological aspects of milk itself, and on the technological (processing) aspects of the transformation of milk into its various consumer products, including beverages, fermented products, concentrated and dried products, butter and ice cream. The first section includes a brief history of the dairy industry, production and consumption data and an overview of milk composition.},
author = {Goff, Douglas and Hill, Arthur and Ferrer, Mary Ann},
}
@book{xie2015,
title = {Dynamic Documents with {{R}} and Knitr},
author = {Xie, Yihui},
year = {2015},
edition = {Second},
publisher = {{Chapman and Hall/CRC}},
address = {{Boca Raton, Florida}},
note = {ISBN 978-1498716963}
}
@incollection{YOO2015587,
title = {OsmoseLua – An Integrated Approach to Energy Systems Integration with LCIA and GIS},
editor = {Krist V. Gernaey and Jakob K. Huusom and Rafiqul Gani},
series = {Computer Aided Chemical Engineering},
publisher = {Elsevier},
volume = {37},
pages = {587-592},
year = {2015},
booktitle = {12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering},
issn = {1570-7946},
doi = {https://doi.org/10.1016/B978-0-444-63578-5.50093-1},
url = {https://www.sciencedirect.com/science/article/pii/B9780444635785500931},
author = {Min-Jung Yoo and Lindsay Lessard and Maziar Kermani and François Maréchal},
keywords = {process system engineering, energy integration, LCA, Lua, OSMOSE},
abstract = {In this paper, we present our recent work on the implementation of an Energy Systems Integration platform allowing the modeling of Energy Systems integrating LCI (Life Cycle Inventory), LCIA (Life Cycle Impact Assessment) and GIS (Geographical Information Systems) data as modeling parameters and variables included in an industrial process model. Based on our previous experience in methodologies and models of Energy Systems Integrations, we developed a new generation of the platform using the script language Lua. The main motivation for choosing Lua was to radically improve the performance of the system, which was the main drawback of the existing Matlab-based system, while proposing a more convenient way of describing the modeling elements and their combination. The second objective of our work was to integrate LCI and LCIA aspects as a generic part of the Energy Systems modeling methodology. We are currently working on the process data mapping algorithm in order to match the appropriate Unit process and Elementary flows identifiers used in the ecoinvent v3 database. Thirdly, by having the possibility of importing GIS databases (in csv format), some coordination data, such as longitude and latitude, can be directly included as Energy System elements’ location parameters. This possibility improves the efficiency in modeling the energy integration of urban systems.}
}
@incollection{florez-orregoHeatPumpingRenewable2023,
title = {Heat pumping and renewable energy integration for decarbonizing brewery industry and district heating},
volume = {52},
isbn = {978-0-443-15274-0},
url = {https://linkinghub.elsevier.com/retrieve/pii/B9780443152740505072},
language = {en},
urldate = {2023-11-16},
booktitle = {Computer {Aided} {Chemical} {Engineering}},
publisher = {Elsevier},
author = {Flórez-Orrego, Daniel and Domingos, Meire Ribeiro and Maréchal, François},
year = {2023},
doi = {10.1016/B978-0-443-15274-0.50507-2},
pages = {3177--3182},
}