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+
+
+
+ 20240502T170104-5d067b9107f5b334fa145dd9b07b1f6eaa2c84b1
+ 20240502170104
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 05
+ 2024
+
+
+ 9
+
+ 97
+
+
+
+ ETHOS.PeNALPS: A Tool for the Load Profile Simulation
+of Industrial Processes Based on a Material Flow Simulation
+
+
+
+ Julian
+ Belina
+ https://orcid.org/0000-0002-5878-2936
+
+
+ Noah
+ Pflugradt
+ https://orcid.org/0000-0002-1982-8794
+
+
+ Detlef
+ Stolten
+ https://orcid.org/0000-0002-1671-3262
+
+
+
+ 05
+ 02
+ 2024
+
+
+ 6358
+
+
+ 10.21105/joss.06358
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.11032663
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/6358
+
+
+
+ 10.21105/joss.06358
+ https://joss.theoj.org/papers/10.21105/joss.06358
+
+
+ https://joss.theoj.org/papers/10.21105/joss.06358.pdf
+
+
+
+
+
+ Framework for ecolabeling using discrete
+event simulation
+ Andersson
+ Proceedings of the 2012 symposium on emerging
+applications of m&s in industry and academia
+symposium
+ 9781618397874
+ 2012
+ Andersson, J., Johansson, B.,
+Berglund, J., & Skoogh, A. (2012). Framework for ecolabeling using
+discrete event simulation. Proceedings of the 2012 Symposium on Emerging
+Applications of m&s in Industry and Academia Symposium.
+ISBN: 9781618397874
+
+
+ AutoMod simulationssoftware - simulating
+reality
+ AutoMod Simulationssoftware
+ AutoMod Simulationssoftware.
+(21.03.2024). AutoMod simulationssoftware - simulating reality.
+https://automod.de/
+
+
+ Synthetic load profile generation for
+production chains in energy intensive industrial subsectors via a
+bottom-up approach
+ Binderbauer
+ Journal of Cleaner Production
+ 331
+ 10.1016/j.jclepro.2021.130024
+ 2022
+ Binderbauer, P. J., Kienberger, T.,
+& Staubmann, T. (2022). Synthetic load profile generation for
+production chains in energy intensive industrial subsectors via a
+bottom-up approach. Journal of Cleaner Production, 331, 1–14.
+https://doi.org/10.1016/j.jclepro.2021.130024
+
+
+ The shape of future electricity demand:
+Exploring load curves in 2050s germany and britain
+ Boßmann
+ Energy
+ 90
+ 10.1016/j.energy.2015.06.082
+ 2015
+ Boßmann, T., & Staffell, I.
+(2015). The shape of future electricity demand: Exploring load curves in
+2050s germany and britain. Energy, 90, 1317–1333.
+https://doi.org/10.1016/j.energy.2015.06.082
+
+
+ From system dynamics and discrete event to
+practical agent based modeling: Reasons, techniques,
+tools
+ Borshchev
+ International conference of the system
+dynamics society.
+ 2004
+ Borshchev, A., & Filippov, A.
+(2004). From system dynamics and discrete event to practical agent based
+modeling: Reasons, techniques, tools. International Conference of the
+System Dynamics Society.
+
+
+ Discrete event simulation of individual
+energy consumption for product-varieties
+ Kohl
+ Procedia CIRP
+ 17
+ 10.1016/j.procir.2014.01.088
+ 2014
+ Kohl, J., Spreng, S., & Franke,
+J. (2014). Discrete event simulation of individual energy consumption
+for product-varieties. Procedia CIRP, 17, 517–522.
+https://doi.org/10.1016/j.procir.2014.01.088
+
+
+ WITNESS simulation modeling
+software
+ Lanner Group Limited (Ed.).
+(19.03.2024). WITNESS simulation modeling software.
+https://www.lanner.com/en-gb/technology/witness-simulation-software.html
+
+
+ Manufacturing simulation and scheduling
+software | simio
+ Simio LLC (Ed.). (19.03.2024).
+Manufacturing simulation and scheduling software | simio.
+https://www.simio.com/applications/manufacturing-simulation-software/
+
+
+ Increasing the resolution of solar and wind
+time series for energy system modeling: A review
+ Omoyele
+ Renewable and Sustainable Energy
+Reviews
+ 189
+ 10.1016/j.rser.2023.113792
+ 2024
+ Omoyele, O., Hoffmann, M., Koivisto,
+M., Larrañeta, M., Weinand, J. M., Linßen, J., & Stolten, D. (2024).
+Increasing the resolution of solar and wind time series for energy
+system modeling: A review. Renewable and Sustainable Energy Reviews,
+189, 113792.
+https://doi.org/10.1016/j.rser.2023.113792
+
+
+ Petri nets
+ Peterson
+ ACM Computing Surveys
+ 3
+ 9
+ 10.1145/356698.356702
+ 0360-0300
+ 1977
+ Peterson, J. L. (1977). Petri nets.
+ACM Computing Surveys, 9(3), 223–252.
+https://doi.org/10.1145/356698.356702
+
+
+ Time series of useful energy consumption
+patterns for energy system modeling
+ Priesmann
+ Scientific data
+ 1
+ 8
+ 10.1038/s41597-021-00907-w
+ 2021
+ Priesmann, J., Nolting, L., Kockel,
+C., & Praktiknjo, A. (2021). Time series of useful energy
+consumption patterns for energy system modeling. Scientific Data, 8(1),
+148. https://doi.org/10.1038/s41597-021-00907-w
+
+
+ Classification and challenges of bottom-up
+energy system models - a review
+ Prina
+ Renewable and Sustainable Energy
+Reviews
+ 129
+ 10.1016/j.rser.2020.109917
+ 2020
+ Prina, M. G., Manzolini, G., Moser,
+D., Nastasi, B., & Sparber, W. (2020). Classification and challenges
+of bottom-up energy system models - a review. Renewable and Sustainable
+Energy Reviews, 129, 109917.
+https://doi.org/10.1016/j.rser.2020.109917
+
+
+ Simulating the energy consumption of machines
+in compound feed manufacturing for investment decisions
+ Rippel
+ Simulation in produktion und logistik
+2017
+ 2017
+ Rippel, D., Redecker, M. A., Lütjen,
+M., Decker, A., Freitag, M., & Thoben, K.-D. (2017). Simulating the
+energy consumption of machines in compound feed manufacturing for
+investment decisions. In S. Wenzel & T. Peter (Eds.), Simulation in
+produktion und logistik 2017 (pp. 79–88). kassel university
+press.
+
+
+ M-ProPlAn: E-mail
+ Rippel
+ Rippel, D. (25.03.2024). M-ProPlAn:
+E-mail (J. Belina, Ed.).
+
+
+ Arena simulation software | arena simulation
+software
+ Rockwell Automation (Ed.).
+(17.02.2024). Arena simulation software | arena simulation software.
+https://www.rockwellautomation.com/de-de/products/software/arena-simulation.html
+
+
+ SIMTER: A joint simulation tool for production
+development
+ Lind
+ 2009
+ Lind, S., Krassi, B., Johansson, B.,
+Viitaniemi, J., Heilala, J., Stahre, J., Vatanen, S., Fasth, Å., &
+Berlin, C. (2009). SIMTER: A joint simulation tool for production
+development. VTT Technical Research Centre of
+Finland.
+
+
+ Plant simulation software | siemens
+software
+ Siemens Digital Industries Software
+(Ed.). (15.03.2024). Plant simulation software | siemens software.
+https://plm.sw.siemens.com/en-US/tecnomatix/products/plant-simulation-software/
+
+
+ Gestaltungsmethodik für simulationsstudien in
+umplanungsprojekten zur energieeffizienzsteigerung in
+fabriken
+ Stoldt
+ 2019
+ Stoldt, J. (2019).
+Gestaltungsmethodik für simulationsstudien in umplanungsprojekten zur
+energieeffizienzsteigerung in fabriken [PhD thesis, Technische
+Universität Chemnit].
+https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa2-343579
+
+
+ A criteria-based database for research and
+applications of energy-oriented simulation in production and
+logistics
+ Stoldt
+ Simulation in produktion und logistik
+2021
+ 978-3-73697-479-1
+ 2021
+ Stoldt, J., Prell, B., Rabe, M.,
+Wenzel, S., & Thiede, S. (2021). A criteria-based database for
+research and applications of energy-oriented simulation in production
+and logistics. In J. Franke & P. Schuderer (Eds.), Simulation in
+produktion und logistik 2021 (pp. 93–102). Cuvillier Verlag.
+ISBN: 978-3-73697-479-1
+
+
+ AnyLogic: Simulation modeling software tools
+& solutions for business
+ The AnyLogic Company (Ed.).
+(19.03.2024). AnyLogic: Simulation modeling software tools &
+solutions for business.
+https://www.anylogic.com/
+
+
+ MATLAB
+ The MathWorks, Inc.
+ The MathWorks, Inc. (21.03.2024).
+MATLAB.
+https://de.mathworks.com/products/matlab.html
+
+
+ Energy efficiency in manufacturing
+systems
+ Thiede
+ 9783642259135
+ 2012
+ Thiede, S. (2012). Energy efficiency
+in manufacturing systems. Springer.
+ISBN: 9783642259135
+
+
+ Software milan: E-mail
+ Wohlgemuth
+ Wohlgemuth, V. (20.03.2024). Software
+milan: E-mail (J. Belina, Ed.).
+
+
+ Combining discrete event simulation and
+material flow analysis in a component-based approach to industrial
+environmental protection
+ Wohlgemuth
+ Environmental Modelling &
+Software
+ 11
+ 21
+ 10.1016/j.envsoft.2006.05.015
+ 2006
+ Wohlgemuth, V., Page, B., &
+Kreutzer, W. (2006). Combining discrete event simulation and material
+flow analysis in a component-based approach to industrial environmental
+protection. Environmental Modelling & Software, 21(11), 1607–1617.
+https://doi.org/10.1016/j.envsoft.2006.05.015
+
+
+
+
+
+
diff --git a/joss.06358/10.21105.joss.06358.jats b/joss.06358/10.21105.joss.06358.jats
new file mode 100644
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--- /dev/null
+++ b/joss.06358/10.21105.joss.06358.jats
@@ -0,0 +1,730 @@
+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+6358
+10.21105/joss.06358
+
+ETHOS.PeNALPS: A Tool for the Load Profile Simulation of
+Industrial Processes Based on a Material Flow Simulation
+
+
+
+https://orcid.org/0000-0002-5878-2936
+
+Belina
+Julian
+
+
+
+
+
+https://orcid.org/0000-0002-1982-8794
+
+Pflugradt
+Noah
+
+
+
+
+
+https://orcid.org/0000-0002-1671-3262
+
+Stolten
+Detlef
+
+
+
+
+
+
+
+Jülich Aachen Research Alliance, JARA-Energy, Jülich,
+Aachen, Germany
+
+
+
+
+Forschungszentrum Jülich GmbH, Institute of Energy and
+Climate Research – Techno-economic Systems Analysis (IEK-3), 52425
+Jülich, Germany
+
+
+
+
+RWTH Aachen University, Chair for Fuel Cells, Faculty of
+Mechanical Engineering, 52062 Aachen, Germany
+
+
+
+
+30
+9
+2023
+
+9
+97
+6358
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2022
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+Python
+Load Profile
+Industry
+Manufacturing
+Energy Simulation
+Industrial Production
+Materials Processing
+
+
+
+
+
+ Summary
+
ETHOS.PeNALPS (Petri Net Agent-based Load Profile Simulator) is a
+ Python library designed to simulate the load profiles of industrial
+ manufacturing processes for arbitrary energy carriers. It is part of
+ the
+ ETHOS
+ (Energy Transformation Pathway Optimization Suite). Load
+ profiles are time series of energy demand. The library models the
+ material flow of industrial processes and the activity of individual
+ machines during production. ETHOS.PeNALPS is capable of simulating
+ processes such as steel, paper, and industrial food production.
+ ETHOS.PeNALPS can model non-cyclic industrial production networks.
+
Figure
+ [fig:Main Component Overview]
+ shows the main conceptual objects of ETHOS.PeNALPS, which are:
+
+
+
Generic model objects
+
+
+
Material flow simulations
+
+
+
Production plans
+
+
+
Result load profiles
+
+
+
The user creates the process model based on generic simulation
+ objects. Once the user completes the process model, the model receives
+ a set of production orders to initiate the simulation. The simulation
+ generates a production plan that tracks the activity of each node to
+ fulfill the requested set of orders. Load profiles are then created
+ based on the activity in the production plan. The process steps’ load
+ profiles are modeled using a Petri net with an extensible number of
+ states that determine their activity and energy demand during
+ production.
+
+
The main components of ETHOS.PeNALPS are the generic
+ model objects, material flow simulation, production plan and load
+ profiles.
+
+
+
+
+ Statement of Need
+
Energy system models are tools that provide guidance on future
+ energy systems, which are currently undergoing significant changes to
+ due global efforts to reduce dependence on fossil fuels
+ (Prina
+ et al., 2020, p. 1). However, building long-term models with
+ high spatial and temporal resolution and transparent input data
+ remains a challenge
+ (Prina
+ et al., 2020, p. 12). For instance, historical load profiles
+ for the German industrial sector in 2015 are available
+ (Priesmann
+ et al., 2021, pp. 5–6), while load profiles for other regions
+ are not currently available. Furthermore, decarbonization efforts will
+ cause changes in the industrial sector, creating a need for load
+ profiles of future scenarios. To address the lack of sectoral load
+ profiles for the industry, Boßmann and Stafell
+ (2015,
+ p. 1321) demonstrated the use of a bottom-up approach.
+ Therefore, it is necessary to obtain load profiles of the industrial
+ processes that are part of the industrial sector. However, these
+ profiles are often unavailable for open research due to:
+
+
+
Companies’ efforts to protect commercial secrets;
+
+
+
Missing measurements;
+
+
+
Unstructured collection of energy data in companies;
+
+
+
Novelty of the industrial processes and their current lack of
+ implementation.
+
+
+
ETHOS.PeNALPS can support the creation of an energy system model by
+ providing load profiles for industrial processes. While many
+ industrial processes and their load profiles have been previously
+ simulated, most have not published load profiles and simulation
+ implementations under an open-source license. This creates a research
+ gap, despite similar work having already been done.
+
ETHOS.PeNALPS provides modeling capabilities to simulate load
+ profiles of individual production equipment and the logistics between
+ them in a network. Fluctuations of individual production equipment are
+ modeled using a deterministic Petri net of states. The level of detail
+ and temporal resolution of the load profile model depends on the
+ production process features, the level of detail in the process
+ description, and the available input data. To ensure the suitability
+ of a simulated load profile for each energy system model, it is
+ necessary to evaluate its temporal resolution. At lower temporal
+ resolutions, effects may occur that cannot be modeled using a
+ deterministic Petri net of machine states and average energy
+ consumption per state. Furthermore, the temporal resolution of energy
+ system models is constantly evolving. According to Prina et al.
+ (2020, p.
+ 10), a temporal resolution of one hour is considered high for
+ long-term energy system models. Currently, studies may require load
+ profiles with a resolution as low as one minute
+ (Omoyele
+ et al., 2024, pp. 12–13).
+
+
+ Method
+
There are four simulation modeling paradigms as shown in Figure
+ [fig:Simulation paradigms].
+ ETHOS.PeNALPS utilizes an agent-based approach for the nodes of a
+ material flow system. Currently, the most important nodes of the
+ material flow system, the process steps, contain a Petri net to model
+ their activity. The part of the ETHOS.PeNALPS simulation based on the
+ Petri net can be classified as a discrete event simulation. Borshchev
+ & Filippov
+ (2004)
+ and Thiede
+ (2012,
+ pp. 45–49) provide an introduction and comparison to these
+ paradigms.
+
+
Simulation paradigms for material flow simulations
+ (Thiede,
+ 2012, p. 47) adapted from
+ (Borshchev
+ & Filippov, 2004, p. 3).
+
+
+
+
The implementation as agents was chosen to improve the adaptability
+ and extensibility of the software. Thus, more specifics of a node or
+ even another simulation paradigm can be implemented. The
+ documentation
+ of ETHOS.PeNALPS contains a roadmap for the software. The
+ process model is generated from generic objects as shown in Figure
+ [fig:Main Component Overview].
+ The main components are the generic nodes that create and manage
+ material requests as agents. These nodes include
+
+
+
Source
+
+
+
Sink
+
+
+
Process step
+
+
+
Storage
+
+
+
Streams connect these nodes and determine the direction of material
+ flow in the simulation. Process chains combine sequentially dependent
+ nodes and streams. These process chains, whether multiple or single,
+ are integrated into a network level. A single network level model can
+ include multiple chains to represent parallel operation of similar
+ equipment. Multiple network levels can be used to model successive
+ production stages of the industrial process.
+
A network level consists of a source and a sink that define the
+ start and end points of the material within that level. To connect two
+ network levels, a shared storage is used to replace the source of one
+ network level and the sink of another. Each node functions as an agent
+ that manages material requests.
+
+
+
Sources only provide materials, while sinks only request
+ them.
+
+
+
Process steps and storages provide and request materials.
+
+
+
The simulation is initiated by creating the first request in the
+ sink from the production order. Requests are then passed upstream
+ until they reach the source of the network level. If a request cannot
+ be fulfilled in time, it can be modified within a chain to shift the
+ request to an earlier time and ensure that the deadline is always
+ met.
+
The behavior of a process step during request fulfillment is
+ determined by a sequence of states that are stored in a Petri net,
+ which is a state transition system consisting of places, transitions,
+ and arcs
+ (Peterson,
+ 1977). The states can be as simple as on or off switches or
+ constitute a complex network of states during production. The main
+ novelty of this method is the combination of these sub simulations for
+ each process step to model a complete industrial manufacturing
+ process. An example of toffee production is provided in the
+ ETHOS.PeNALPS
+ documentation to illustrate the method.
+
+
+ Other Tools
+
There are numerous publications on the simulation of energy
+ features of industrial processes. A collection is given by Stoldt
+ (2019,
+ pp. 69–73). However, many of these publications are limited to
+ the presentation of concepts and selected simulation results, without
+ implementation details. This lack of information creates a significant
+ overhead for new research.
+
Stoldt et al.
+ (2021)
+ present a comprehensive literature review on energy-oriented
+ simulations in production and logistics, covering 207 publications.
+ The article identifies the most relevant tools and simulation
+ architectures. The most relevant simulation architectures are the
+ discrete event simulation with integrated energy assessment, discrete
+ event simulation with separate energy simulation, continuous
+ simulation, agent-based simulation, one tool, different models and
+ coupling of models.
+
Stoldt et al.
+ (2021)
+ reported the most commonly used simulation tools include
+ PlantSimulation
+ (15.03.2024),
+ Anylogic
+ (19.03.2024),
+ Arena
+ (17.02.2024),
+ Matlab
+ (21.03.2024),
+ Automod
+ (21.03.2024),
+ Simio
+ (19.03.2024)
+ and Witness
+ (19.03.2024),
+ all of which are commercial tools. No open-source tools were found,
+ although self-developed tools were utilized. Many publications have
+ created extensions for commercial software. For instance, Kohl et al.
+ (2014)
+ developed an extension for the software PlantSimulation
+ (15.03.2024)
+ that maps measured load profiles to process states of manufacturing
+ equipment. However, the implementation of the extension has not been
+ published.
+
Additionally, Stoldt et al.
+ (2021)
+ identified some self-developed standalone tools, but no open-source
+ software was found. Well-documented open-source software and
+ simulation models enhance research reproducibility, leveraging new
+ findings without the need to re-implement state of the art concepts.
+ The licensing of the following software projects has been
+ investigated.
+
Wohlgemuth et al.
+ (2006)
+ developed the software “Milan” which is based on a discrete event
+ simulation. According to Wohlgemuth
+ (20.03.2024),
+ it was discontinued in 2015. Anderson et al.
+ (2012)
+ intended to develop the “EcoProIt tool” for conducting lifecycle
+ assessments based on discrete event simulation. No further information
+ could be found regarding the publication or licensing status of the
+ tool. The “SIMTER tool” was developed in the SIMTER research project
+ (Lind
+ et al., 2009) for combined environmental impact calculations
+ and discrete event simulation. However, information about licensing
+ and distribution is not available. Rippel et al.
+ (2017)
+ developed the “μ-ProPlAn framework”. Rippel
+ (25.03.2024)
+ stated via e-mail that the software has not been published and is no
+ longer executable due to a lack of maintenance. Binderbauer et al.
+ (2022)
+ published a software called “Ganymede” that simulates load profiles.
+ However, information about its licensing and distribution is not
+ available.