E Brugère

J-M Andanson

K Ballerat-Busserolles

The Use of IR Spectroscopy to Follow the Absorption of CO 2 in Amine media -Evaluation of the Speciation with Time

published or not. The documents may come     The Use of IR Spectroscopy to Follow the Absorption of CO 2 in Amine media -Evaluation of the Speciation with Time E Brugère, J-M Andanson, K Ballerat-Busserolles

Introduction

Carbon Capture and Storage (CCS) is a solid option for CO 2 mitigation in the atmosphere. One option is the CO 2 capture from post-combustion industrial effluents followed by utilization or storage in secured sites. Capture processes are generally based on selective absorption/desorption cycles of gas into aqueous solutions of alkanolamines [1]. The following scheme shows the principle of the process. In this treatment scheme, the industrial effluent is entering in the absorber at atmospheric pressure and at temperature close to 40°C. Due to favorable chemical reactions, and physical absorption, the CO 2 is selectively captured into the aqueous solution, while the other gases are rejected to the atmosphere. The CO 2 rich solvent is then transport to the stripper, where the loaded solution is heated up to 120°C using a countercurrent steam. The CO 2 is then desorbed from the solvent, separated, compressed and transported for further use, while the lean solvent is returned to the absorber. This last step is the most critical in the process cost, as it necessitates high energy consumption for heating up the system. The cost of CO 2 treatment using classical alkanolamine solutions as absorbent is prohibitive and is a clear limitation for the intensive use of this technology.

In order to provide optimized treatment processes, it is necessary to have a comprehensive understanding of the chemical and physical interactions taking place in the mixture. SIMODEX project is a joint program between ANR (Agence Nationale de la Recherche) in France and NSERC (Natural Sciences and Engineering Research Council of Canada) in Canada dealing with coupling experimental developments, molecular simulation and modeling to understand structure-properties relationships, and to develop transferable conclusions to optimize processes at low costs. The objectives of the project are to understand the complex equilibria taking place in the ternary water-alkanolamine-CO 2 system by studying the elementary mixtures starting with pure compounds and going through binary ones as shown in figure 2.

Figure 2: structuration of the SIMODEX project and amine family to be studied

This chapter will focus on the use of IR spectroscopy to quantify the speciation in solutions, in order to validate the thermodynamic models already used, and to give crucial information for molecular simulation developments. The mixtures chosen are binary systems containing initially only amine and CO 2 , where there is almost no publication in the literature. For this binary mixture the only reaction that may happen during the mixing process is the formation of the carbamate that can be written as following:

eq.1

As experimental issues happened when studying CO 2 -monoethanolamine (MEA) mixtures, such as high increase in viscosity or solid phase formation, a physical solvent, known for not interacting with amine or CO 2 was used. Then ethylene glycol was chosen to dilute the system. [2] More specifically, the speciation in mixtures containing MEA + ethylene glycol + CO 2 will be studied using IR-spectroscopy, and a new in situ cell designed for following absorption of gas in liquid mixtures versus time will be described and first promising results are presented.

Materials and Methods

Chemicals

Monoethanolamine (MEA) was purchased in TCI-europe (purity >99%), ethylene glycol is from Riedel and Haën (purity 99.5%) and CO 2 from air products (99.995%). The water quantities in liquids were verified using Karl Fisher measurements, and correspond to 300 ppm and 1000 ppm respectively for amine and glycol.

Sample preparation

As direct measurements of MEA+CO 2 mixtures were not possible due to increase of viscosity and solid phase generation, the MEA was first mixed with a CO 2 physical solvent, namely the ethylene glycol. A first concentration of 30% of MEA in ethylene glycol was chosen as this is comparable to the classical concentration of MEA in water when studying the solvents used in the process. Then a second concentration of 10% MEA in ethylene glycol was considered to reach more easily the complete stoichiometric reaction of the CO 2 on MEA.

Experimental device

The spectroscopic measurements, realized at atmospheric pressure and room temperature, were performed using a heatable Golden Gate TM accessory (Specac, Ltd., UK) with a diamond as ATR (Attenuated Total Reflection) crystal. The ATR-IR spectra were collected by means of Thermo Nicolet TM 380 FT-IR spectrometer with a DTGS (deuterated triglycine sulfate) detector. The spectra were obtained with a resolution of 4 cm -1 , 32 co-added scans and were recorded in the range of 4000 -500 cm -1 . The measurement of one spectrum takes around 50 seconds.

For the spectroscopic measurements under pressurized CO 2 a heatable Golden Gate TM accessory (Specac, Ltd., UK) equipped with a home-designed pressurized cell was used. Briefly, the Golden Gate TM was used to hold the ATR crystal with an incident angle of 45• below the liquid phase of interest. Some modifications on the design of the bridge were realized to support a customized sapphire cell as shown in figure 3. Two aluminum arms (1) were added to the bridge to increase the space between the thumb screw and the diamond in order to fit with the new high volume cell (2+4) and a stirring mechanism (3). The liquid cell was installed on the top of the heating plate of the Golden Gate TM as shown on figure 3. The internal volume of the cell is about 2 ml. The cell is composed of a sapphire cylinder surrounded by an inox block. Heating cartridges and PT100 resistances are inserted in the inox cylinder to control the temperature of the sample. Two windows are present in the inox cylinder, so the inside of the cell can be observed during the whole experiment. A Teflon ring was installed between the cell and the plate to prevent leaking.

The liquid is inserted using the injection valve (6) until the needed volume of liquid is obtained. About 1 mL of liquid is introduced in the cell. After reaching the temperature equilibrium, the gas is added using the gas injection valve, while the pressure is maintained constant using the high pressure syringe pump. As the magnetic stirrer is agitating the solution, the diffusion of the gas in the liquid starting immediately.

The IR spectra are then recorded every minute from the beginning of the experiment. When the evolution of the peaks is reduced, the spectra are measured every 5 minutes, until the signal becomes constant, or when the gas contained in the high pressure pump is consumed.

Results and discussion

Kinetic of absorption

A first kinetic experiment was realized using a liquid mixture containing initially MEA (10% w/w) and ethylene glycol (90% w/w). IR spectra of direct measurements are reported on the top figure 4. The mixture was introduced in the cell, and a first spectrum was realized. Then the valve isolating the CO 2 from the system was open and the gas started flowing in the head space of the cell. In order to interpret the results, the spectrum obtained for the binary mixture without gas (t=0 minute) was subtracted to the subsequent spectra as shown on the bottom figure 4. All positive peaks after subtraction are characteristics of new species appearing in the liquid during the sorption while negative peaks correspond to the disappearance of pre-existing species. In order to quantify the speciation versus time in such systems, it is necessary to determine a relation between the intensity of the IR signals and the quantity of each species.

Calibration of speciation

Sample preparation

In order to realize the calibration of the absorption of CO 2 in MEA as function of the loading charge, it was necessary to record spectra containing very well know concentrations of MEA and CO 2 . For that purpose, a mixture of MEA + ethylene glycol was prepared by weight. Then CO 2 was added above the liquid sample, for 12 hours, under a pressure of CO 2 of 0.2 MPa (2 bar), while the liquid phase was agitated using a magnetic stirrer. Then the mixture was returned to atmospheric pressure. The sample was then weighted, and the mass difference with the sample without CO 2 was considered as corresponding to the mass of CO 2 absorbed in the sample. This very concentrated sample was diluted using the initial mixture of MEA+Glycol, in different proportions to reach various CO 2 loading charges.

Spectra and results

The spectra obtained for the calibration are given in figure 5. The first graph shows the direct spectra obtained for mixtures at known loading charge, from =0 to 0.5 (mol of CO 2 / mol of aminewhile the second one present a zoom of the fingerprint region (1700 -750 cm -1 ), where most of the bands of organic compounds are regrouped. The red spectrum corresponds to the sample obtained for MEA+Glycol solution without CO 2 . As previously described, and in order to be able to analyze the results, the red spectrum was subtracted to the others as shown on the third graphic of figure 5. On this graph, 4 peaks have been identified and used to calibrate the amount of absorbed CO 2 . Peaks labelled from 1 to 3 correspond to peaks characteristic of vibration in carbamate molecules [3]. The peak 4 centered at 960 cm -1 is characteristic of non-protonated nitrogen of MEA [3]. When increasing CO 2 loading charge, the height of all those peaks increases. Peaks 1 to 3 are more and more positive while the peak 4 is more and more negative. This corresponds naturally to the generation of carbamate and consumption of non-protonated MEA following the equation 1 shown above. The signal corresponding the physisorbed CO 2 (band label 5 in figure 5 at around 2340 cm -1 ) has not been detected for the concentrations realized during the calibration.

In order to calibrate the system, it is necessary to relate the height (or the area) of each peak to the loading charge. The peak 4 was truncated on the left side due to peak displacements during the absorption. Then, for this peak we considered the height of the peak as the characteristic data. For peaks 1 to 3 it was possible to use the integration that reduces the uncertainty on the result. The curves representing area of peaks 1 to 3, and height of peak 4 versus loading charge are given in figure 7. From those calibrations, it is then possible to recalculate at any time of a kinetic experiment the quantity of each species formed when the loading charge is below 0.45. When the concentration of CO 2 is higher the increase of the IR signals are much slower, probably part of the CO 2 does not react but is physisorbed. Furthermore, it was not possible to prepare mixtures containing high amount of CO 2 physically absorbed in the binary mixture. For that purpose, CO 2 was then absorbed in pure ethylene glycol under higher pressure directly in the ATR-IR cell.

Physisorption

As it can be seen on figure 4, during the kinetic of the CO 2 sorption in the MEA-Ethylene glycol mixture, after one hour, it is possible to observe a small signal at around 2340 cm -1 corresponding to the -

asymmetric stretching vibration of the CO 2 physically absorbed in the mixture. In order to quantify this CO 2 , it is then necessary to calibrate this band. For that purpose, a mixture of ethylene glycol + CO 2 was measured at high pressure. On figure 7, the black curve show the signal obtained for pure ethylene glycol. Measurements have then been realized for mixtures of CO 2 + glycol, at 2.0 MPa, 35°C at different times. Two spectra obtained for different times are shown on figure 7 (red and blue spectra). Two phenomena have to be observed. The intensity of all bands corresponding to the ethylene glycol is decreasing with the sorption of CO 2 . These decreases of intensity are explained by the swelling of the solvent due to the presence of CO 2 . This phenomenon is well known and documented in the literature, particularly in the case of liquid [4,5].

When looking at absorbance close to 2340 cm -1 it is easy to observe an intense peak corresponding to the CO 2 physically absorbed in ethylene glycol. The rest of the spectra is identical with and without CO 2 , apart from the decreasing of the intensity of bands corresponding to the swelling of the ethylene glycol, meaning that no chemical reaction happened in those mixtures. The area of the CO 2 peak at 2340 cm -1 can then be directly associated to the solubility of CO 2 in ethylene glycol using literature value [6], at this temperature and pressure, the solubility of CO 2 in ethylene glycol is x CO2 = 0.0369±0.0006. Admitting that the absorption of CO 2 in ethylene glycol follow the Beer-Lambert law, a linear regression was determined between zero and this concentration to estimate the quantity of CO 2 absorbed in solutions of interest.

Full curve speciation

The evolution of absorptions of the characteristic bands of different species versus time shown in figure 5, were plotted in figure 8. Carbamate vibrations are shown in blue, while the vibration of the noneprotonated amine group of MEA is in red. The signals of the physically absorbed CO 2 in the MEA-Glycol mixture correspond to the black empty circle. On this figure one can see that the absorbance area for peaks corresponding to carbamate increases quickly during the 20 first minutes. The quantity of none protonated amine decreases synchronously to the appearance of carbamate. All the blue and the red curves reach a plateau after 30 minutes. Using the calibration curves shown before, at this time, the value of absorbance correspond to a loading charge of about 0.4. Parallel, during the first 40 minutes, no peak for physical absorption is observed. After this time, the intensity of the band starts to increase continuously. After 70 minutes the CO2 physically absorbed corresponds to a molar fraction of 0.002. With this graph, it is confirmed that the chemical reaction is the first step of the process, while the physical absorption starts only when most the nonprotonated amine has react with CO 2 to form carbamate and protonated amine.

Conclusion

In situ ATR-IR spectroscopy has been used successfully to follow the absorption of CO 2 in binary liquids containing MEA and ethylene glycol. The spectra obtained have shown peaks characteristic of carbamate formation, and none protonated amine consumption. After calibrating the signal, the area

(5)

of the peaks linked to carbamates, and the height of the peak corresponding the none protonated amine has been associated to an advancement of the reaction of formation of carbamate.

The same experiments will be carried out with amines with a skeleton similar to MEA, as shown on figure 2. The results obtained will allow evaluating the influence of the substituents on the carbons of the MEA on the chemical reaction leading to carbamate formation. It will be possible to compare the kinetic of the reaction as well as the final advancement of the reaction.

Aknowledgements

This work is part of the international project SIMODEX supported by ANR in France and NSERC in Canada. The authors also acknowledges the financial support received from the Agence Nationale de la Recherche of the French government through the program ''Investissements d'Avenir'' (16-IDEX-0001 CAP 20-25).

references

Fig 1 :

 1 Fig 1: Schematic view of the amine based post-combustion CO 2 capture process.

Figure 3 :

 3 Figure 3 : New experimental setup for gas absorption : 1. Aluminum arms; 2. sapphire cylinder; 3. magnetic stirrer; 4. inox cell, surrounding sapphire cylinder; 5. ATR-IR spectrometer; 6. liquid injection valve; 7 high pressure pump equipped with pressure transducer and gas injection valve.

Figure 4 :

 4 Figure 4 : IR spectra during the kinetic of the sorption of CO 2 in MEA-Ethylene glycol mixture (top) and MEA-Glycol-CO 2 after subtraction of the MEA-Glycol spectrum (bottom) at different time; 1-3 peaks : characteristic peaks for carbamates ; 4 : characteristic peak for non-protonated amine; 5 : asymmetric stretching vibration band for physically absorbed CO 2 . The spectra obtained during the first 10 minutes are the black ones. The red spectra are obtained for time from 10 to 40 minutes, while blue ones are for times higher than 40 minutes after the beginning of the CO 2 sorption.

Figure 5 :

 5 Figure 5: IR spectra for mixtures containing MEA (30%) ethylene glycol (70%), at different and well known loading charges from a = 0 up to a = 0.53. top figure: direct spectra; middle figure: zoom of the spectra in the range of 1700-750 cm -1 ; bottom figure: fingerprint region of the IR spectra of the MEA-ethylene glycol-CO 2 mixtures after subtraction of the MEA-ethylene glycol spectrum.

Figure 6 :

 6 Figure 6 : area (peak 1 to 3) and height (peak 4) of the IR signals characteristic to chemical reaction occurring in the mixture.

Figure 7 :

 7 Figure 7: IR spectra for pure ethylene glycol (black curve) and CO 2 in ethylene glycol (red and blue curve) at 20.0 bar and 35°C.

Figure 8 :

 8 Figure8: Area/height of the peaks versus time. Blue curves: area of characteristic peaks for carbamate (wavelength 1573, 1495 and 1385 cm -1 respectively for peaks 1 to 3); red curve: height of characteristic peak for non-protonated MEA (peak 4); black circle: area of characteristic peak for physically absorbed CO 2 (peak 5; signal multiply by 100 for visibility).


Abdelhadi Belfadel

Sebastian Hörl

Rodrigo Javier Tapia

Dimitra Politaki

Ibad Kureshi

Lorant Tavasszy

Jakob Puchinger

A Conceptual Digital Twin Framework for City Logistics

Keywords: digital twin, urban logistics, city logistics, on-Demand Logistics, system architecture

Urban logistics is one of the key elements of urban mobility planning. The use of real-time information systems in logistics operations generates an enormous amount of data, nowadays used mainly for the purpose of monitoring and control of large flows of goods. At the same time, urban planners, business stakeholders, and city administrators are in need of adaptive, data-driven decision support solutions to address today's urban logistics problems. Recently, digital twins have received a lot of attention to support advanced experimentation, simulation and decision-making for on-demand logistics operations. Questions still remain on how to realize these for urban logistics management in a mixed public-private stakeholder context. We argue that this lack of a specific framework for city logistics with a model library for data mergers, linking physical and virtual data exchange, can compromise the timely adoption of digital twin technology. We contribute to filling this gap by presenting a systematic review of the literature, proposing a conceptual framework for digital twin applications in urban logistics, and providing use case scenarios for their demonstration. Together, these should advance the technical implementation of digital twins in a sustainable city logistics context.

Introduction

Today's consumer trends require reactive logistics systems that deliver personalized products at a low cost. Urban freight transport is essential for cities, and the surge of on-demand logistics is putting a noteworthy strain on last mile delivery systems that are growing at a rapid rate through large-and small-scale consumer platforms [START_REF] Joerss | Parcel delivery. the future of last mile: Mckinsey & company report[END_REF]. However, the economic drivers to build more sustainable systems are weak [START_REF] Allen | Understanding the impact of e-commerce on last-mile light goods vehicle activity in urban areas: The case of london[END_REF]. Consequently, cities are facing the likely downside of this "uberisation" of logistics. There is some apprehension about how the actual business will develop and what approach will be taken by the major actors, such as the introduction of tiny storage facilities in neighborhoods or the physical internet development (ALICE-ETP, 2020). It is therefore impossible to reliably predict the most probable outcomes for the next few years and to propose feasible policies.

To this end, data-driven models should run in conjunction with real-world experiments to replicate results and predict the consequences of response actions. The concept of Digital Twins (DT) can be used effectively in this regard, as depicted in Figure 1. Technology enablers for building DTs include modelling, predictive analytics and decision-making methods, and the use of lifecycle-oriented knowledge with historical and real-time operational and city data. In the latest years, cities are already heading in this direction with a growing number of logistics living labs, which supplement the old paradigm of predict and provide [START_REF] Marcucci | Digital twins: A critical discussion on their potential for supporting policy-making and planning in urban logistics[END_REF]. Nevertheless, research on digital twins applied to transportation logistics is still very new and still developing [START_REF] Marcucci | Digital twins: A critical discussion on their potential for supporting policy-making and planning in urban logistics[END_REF]. The applications of digital twin frameworks are mainly in the context of product management, shop floor or production management (Zhuang et al., 2018;Haße et al., pp. 4-28, 2019), and the difficulty we face today is that the existing architectures are too generic to be used in logistics (Haße et al., pp. 4-28, 2019).

The lack of frameworks for model orchestration, data transformation and merging that connect the exchange of physical and virtual (simulation) data can compromise the adoption of digital twin technology for cities, which increasingly need to address city logistics problems with advanced data-driven solutions. Moreover, the complexity of the twinning for urban freight planning is influenced by long feedback loops that rely on multiple independent agents' interactions.

Therefore, we propose a comprehensive conceptual framework for a digital twin in urban logistics. With our framework, we address (1) urban and logistics modelers that study whole urban logistics systems or individual services who face the challenge of implementing their models in an integrated environment for decision-making, (2) public or private entities that strive to leverage their data by not only feeding individual models but maximizing their impact as part of such an integrated decision-making system, (3) developers of future digital twinning platforms, for which we provide a blueprint that already contains the major components to set up such a system. At the same time, we outline the specifics (for instance, in terms of time scales) that are relevant to the scope of urban logistics.

This work is an extended version of a previously published conference paper [START_REF] Belfadel | Towards a digital twin framework for adaptive last mile city logistics[END_REF]. We have extended our contribution as follows. First, we conduct a systematic literature review to thoroughly analyze existing work and put it in context. Second, we demonstrate in a generic business process how cities can leverage the targeted generic framework to create value case scenarios by using their specific models. For this, a generic model library that contains a set of models, when combined, allows the user to model different value cases, according to their needs. Third, we conceptualize a generic digital twin framework for urban logistics by proposing a general system architecture, investigating relevant modeling components, and providing a comprehensive list of existing open-source solutions. One of the main advantages of this framework are the different simulation model connections using connector functions. The models and connector functions are stored in a model library component, enhancing the continuous models' integration by adapting them to different use cases and city needs. Finally, we give use case examples for the proposed system.

The paper starts with a systematic literature review in Section 3. Afterwards, Section 4 focuses on the main components of the proposed architecture framework, the targeted generic business process, the meta-model for model categorization and each building block of the proposed framework as well as technical insights for its implementation. Section 5 combines the aspects presented in Section 4 to present use case examples for a digital twin in urban logistics. Finally, conclusions and future work are covered in Section 7.

Physical Assets

Urban & city planners

City administration

Business stakeholders

IoT

Data model

Models

Simulation

Digital Twin

Figure 1: A targeted digital twin environment for evaluating urban logistics policies

Background

Since its introduction, different authors have defined DTs in different terms. The original definition was proposed by [START_REF] Grieves | Origins of the digital twin concept[END_REF] who defines a DT as a digital replication of a physical system that is depicted as a distinct digital entity but related to the physical system upon demand. The core elements of a DT are: (1) real world, (2) virtual world and (3) the exchange of data/information flow between real and virtual worlds.

Many applications of digital twin frameworks are found mostly in the context of the product, shop floor, and production management (Zhuang et al., 2018;Haße et al., pp. 4-28, 2019). The first definition of the DT was forged by the NASA to mirror the life of its flying twin [START_REF] Shafto | Modeling, simulation, information technology & processing roadmap[END_REF]. From that moment on, several initiatives (ISO, 2021;[START_REF] Lopez | A conceptual model-based systems engineering (mbse) approach to develop digital twins[END_REF][START_REF] Bickford | Operationalizing digital twins through model-based systems engineering methods[END_REF][START_REF] Hatakeyama | Systems engineering "v" in a model-based engineering environment: Is it still relevant?[END_REF][START_REF] Qamsane | A methodology to develop and implement digital twin solutions for manufacturing systems[END_REF] tend to standardise and construct conceptual and DT platforms in manufacturing-oriented digital twin systems. One of the recent published standards is the ISO 23247 (ISO, 2021). It divides the conceptual digital twinning system into four layers. The bottom layer depicts the elements of manufacturing that are observable. This layer depicts the elements of the production floor that need to be modeled. Yet, the difficulty we face today is that existing architectures are either too generic or too specific (Haße et al., pp. 4-28, 2019) to be used in logistics due to the complexity of the twinning as mentioned in section 1.

City logistics is one of the essential elements of urban mobility planning. The growth of e-commerce activities is a trend that has been reinforced by the COVID-19 pandemic. The ultimate objectives of introducing DT in city logistics are to improve the operation and efficiency of parcel delivery; to reduce costs and externalities through forecasting and predictions of future states; and to support advanced decision-making through the entire logistics lifecycle, while also fostering stakeholder participation via reliable real-life information. Thus, the adoption of DT in city logistics aims to help stakeholders to take decisions about the future implications of a given policy, aiming at supporting their information and enforcing the decision process [START_REF] Marcucci | Digital twins: A critical discussion on their potential for supporting policy-making and planning in urban logistics[END_REF].

Forecasting both behavior and reactions to structural changes and policy measure implementations in urban freight transport requires two elements. [START_REF] Marcucci | Digital twins: A critical discussion on their potential for supporting policy-making and planning in urban logistics[END_REF] emphasize the usage of behavioral and simulation models to be used jointly when modelling such future behavior, especially when considering the public-private urban context.

DTs for urban logistics are evolved systems relying on three essential technical building blocks. First, relevant models for the physical counterpart under study are required. The models must be sufficiently detailed to meet the intended purpose. However, they should not be more detailed than necessary to avoid overfitting and support ease of use. It is worth mentioning that digital twins are not identical twins. As highlighted by [START_REF] Batty | Digital twins[END_REF], the notion of an exact mirror is "an idealization and aspiration that may never be achieved." Second, the object of the study must be described by evolving data as physical conditions tend to vary over time, whether it be traffic, air quality, or noise levels. Thus, informed policy decisions must be based on evolving datasets rather than point-in-time data collection that simply provides a snapshot of urban conditions at a given time. In this way, systemic changes over much larger time scales and patterns can be better understood. Third, like datasets, digital twins also require dynamic updates related to simulation models that update and evolve based on physical conditions [START_REF] Kaur | The convergence of digital twin, iot, and machine learning: transforming data into action[END_REF]. However, [START_REF] Kritzinger | Digital twin in manufacturing: a categorical literature review and classification[END_REF] and [START_REF] Sepasgozar | Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment[END_REF] identify the difference among a digital twin, a digital model and a digital shadow depending on the integration level.

Requirements identification and value case scenarios

To identify stakeholder needs, constraints and target scenarios, workshops were organized as part of a European project1 to engage stakeholders in real-world experiences from different European cities and to identify the expected outcomes of DTs and the impacts on city logistics of the proposed value case scenarios. A value case scenario is defined as a set of measures and innovation being applied in one of the living labs. The results of the workshops also fed the definition of the generic business process model described in section 4.2.

All the Living Labs (LLs) identified, contacted, and engaged stakeholders from different local organizations, and assessed expected outcomes of the DT. One of the main constraints to be considered is the handling of different types of scenarios, all accompanied by a different set of case-specific, open-source software applications, for example MATSim2 (a general multi-agent transportation simulation framework) or Visum3 (traffic planning software designed for transportation planners to provide cities with a developed macro transportation model). Together, these models form an open-source model library (MLib) that can be applied in future DTs of other cities. The ML comprises different types of models that allow the platform to twin different cities and be able to test the different project strategies and policies.

As mentioned earlier, various use cases for digital twins in urban logistics are developed. The LLs use case scenarios include the "do nothing" scenario and four future scenarios. The first is used as a baseline scenario to compare the impacts on externalities of implementing certain actions. The second refers to the actions that will be implemented as a result of combining a set of logistical measures defined from the strategies and trends identified from the literature [START_REF] Gatta | Planning with stakeholders: Analysing alternative off-hour delivery solutions via an interactive multi-criteria approach[END_REF]Verlinde et al., 2014). Once the LLs approved the preliminary set of scenarios, the future scenarios were described and used to assess sustainability impacts and identify opportunities and threats. The results of these two activities help validate the scenarios.

We briefly describe hereafter two targeted value case scenarios of the cities, that are considered as an input for this research work for the design of a generic digital twin framework for city logistics:

• Lyon value case: In the south-east of Lyon, most of the freight operators are located in a 20 km radius from the Confluence district. The current situation calls for new spots within the district to host freight operations, close to core areas. In the meantime, flows should be concentrated to cope with this organization and to ensure the integration of new modes and the reduction of externalities.

The Lyon LL will focus on parcel deliveries. It will include the use of an underground parking as a hub towards final customers through cargo bikes, autonomous droids and electric assisted handling tools. The goal is to reduce the number of trucks entering and parking in the district. For the public authority, the main KPI to be obtained from the Digital Twin is the fleet operating in the Confluence area and the emissions related to this traffic, while for the operators, the relevant KPIs are related to the delivery costs and fleet use. Regarding the targeted models, this LL scenario aims to use different simulation models such as MATSim or COPERT (for COmputer Program to calculate Emissions from Road Traffic -which is the EU standard vehicle emissions calculator).

• The Hague value case: The densification of the Central Innovation District (CID) and development of Binckhorst comes with the building of larger and higher department buildings. Besides the mobility of thousands of people, it brings logistical challenges such as "how to deal with the supply of goods to these buildings, such as groceries, parcels and furniture.". The project will allow testing innovative solutions to generate a sustainable inner-city logistic service for parcels. Important approaches for sustainable parcel logistics include crowd-shipping solutions, platform-based approaches and public consolidation places like mobility hubs. Many times, these are stand-alone solutions, however, with no or limited integration with other companies. This LL aims to innovate by creating improved connectivity between different services and across platforms as a new proposition, to create an overall more efficient and sustainable urban parcel delivery system. Similarly to Lyon, the public authority is interested in knowing how much CO2 is emitted in the situations with or without platform connectivity. The sustainable logistics companies interested in validating their sustainable proposal and, more importantly, to be able to know the costs and benefits of integrating their logistical operations. Regarding the targeted models, this LL scenario aims to use different simulation models such as MASS-GT (de Bok and [START_REF] De Bok | An empirical agent-based simulation system for urban goods transport (mass-gt)[END_REF][START_REF] De Bok | Application of an empirical multi-agent model for urban goods transport to analyze impacts of zero emission zones in The Netherlands[END_REF], which simulates the different aspects of urban logistics, externality measurement models such as COPERT [START_REF] Ntziachristos | COPERT: A European Road Transport Emission Inventory Model[END_REF]) and a noise model making use of a simplified methodology that will determine the percentage of residents in the study area exposed to noise emissions.

We follow the perspective of Wildfire (2018) and [START_REF] Batty | Digital twins[END_REF], where DTs are introduced as either being reactive or predictive. The first category describes models in which the real system is modeled closely and which operate on time frames that allow for near-real-time feedback of input signals in order to optimize its future states. Usually, this involves rapidly running models and focus on certain aspects of the system. In contrary, the second category can involve large interconnected, potentially computationally intensive modeling pipelines, which predict many aspects of the urban system on a rather long-term horizon. Such models allow observing the progression of the system given certain use case scenarios while taking into account complex interactions between system components, emergent behaviors and rebound effects.

Finally, the overall architecture work is built and consolidated using the TOGAF standard and its guidelines, which is an open, industry consensus framework for enterprise architecture (TOGAF, 2009). TOGAF provides an architecture development method and tools for assisting the acceptance and the production of architecture assets. It is founded on an iterative process driven by a reusable set of existing assets and best practices. The usage of this methodology ensures that the architecture is considered as a whole, taking into account business, information system, and technology architectures. This allows to provide a big-picture of the targeted generic DT framework for city logistics, its main business processes, the high-level architecture, and implementation aspects that are discussed later on in this paper.

Systematic Literature Review

This section aims to systematically evaluate examples, frameworks and tools that are proposed in the literature to construct, evaluate, implement and track pin-pointed policies in urban logistics. The evaluation results will be used for the conceptualization and consolidation of a conceptual digital twin framework for city logistics.

Research Methodology

In this paper, we conduct a systematic literature review (SLR; [START_REF] Snyder | Literature review as a research methodology: An overview and guidelines[END_REF][START_REF] Kitchenham | Guidelines for performing systematic literature reviews in software engineering 2[END_REF]Xiao and Watson, 2019) for the evaluation of recent research studies regarding the application of digital twin approaches in the research area of city logistics. The research subject, i.e., the conceptual boundaries, was defined based on the term "digital twin" and the related terms "IoT" in the smart city logistics context.

By including the sequential identification, screening, clustering, and evaluation of research-relevant studies in research-related areas, the SLR approach was selected because of its systematic, method driven, and replicable approach [START_REF] Booth | Systematic approaches to a successful literature review[END_REF].

The following Table 1 depicts the SLR protocol based on the recommendations of the Cochrane Handbook for Systematic Reviews of Interventions [START_REF] Higgins | Cochrane handbook for systematic reviews of interventions[END_REF] applied here.

SLR Protocol Content Main Question

How to manage the complexity of paralleling real-world events with digital twins, to support experimentation, simulation and decision-making for urban logistics operations? Objective

The objective is to search in the literature if examples, frameworks or tools are proposed to construct, evaluate, implement and track pin-pointed policies in urban logistics Inclusion Criteria (1) Studies that mention big data, digital twin, and, internet of things frameworks or tools applied in urban logistics;

(2) Studies that mention systematic analysis of the literature to collect city logistics data for better decision making;

(3) Studies that contain the keywords: IoT, Digital Twin, Decision making, Urban logistics, big data, urban or city policies Exclusion criteria (1) Study is not in English; (2) The paper does not mention any framework, tool, use case or literature review related to the digital twins applied to urban or last mile logistics Specific Questions Does the paper's main question involve the urban logistics area? Is cited in the paper a framework or tool for designing and assessing targeted urban logistics policies? Does the paper propose an approach to speed up transition through the usage of DT tools?

Table 1: SLR Protocol and content

The SLR further requires a definition of search criteria, databases, search terms, and publication period. In this study, we have used Google Scholar as the main source for the keyword search, because it was identified as the most comprehensive database for scientific publications in the areas of engineering and management science by referencing contributions published in other important databases in our research topic such as Web of Science and Science Direct.

In the first step, we identified the relevant literature for digital twin, urban logistics and smart city in the subject areas of engineering, business and management by screening the article title, abstract, and keywords. In this step, we included all kinds of document types and restricted them to the language English covering a period between 2015 and 2021. This step has lead to a result of 261 records. In the second step, we additionally focused on the studies that propose frameworks or tools of the digital twins, internet of things, and big data applied to city logistics for decision making. This has lead to a result of 133 records. In the third step, an evaluation of the papers is realized regarding the topic and our research objectives considering high-quality studies only. In sum, the final meta-search query was formulated as follows:

allintitle: ("city logistics" OR "urban logistics" OR "urban planning" OR "delivery" OR "last mile delivery") AND ("iot" OR "digital twin" OR "smart city")

As a result, we have identified 27 papers as the basis for the further research process.

Results

Based on the quality criteria for empirical research (validity, reliability, objectivity, and generalizability), we assured the quality of the research results by coding the identified studies with level "A" (high appropriateness), level "B" (medium appropriateness), or level "C" (low appropriateness).

The selection was conducted in two stages by three researchers. In the first phase, the selection was based on the title and abstract of the studies only. In the second phase, the full text of the study was evaluated by the research team. In addition, reliability was calculated by assessing significant differences in results. Articles without significant differences were directly included or excluded from the search process. Articles with significant differences were re-evaluated by the research team to obtain unambiguous research results.

Descriptive Analysis

In what follows, we analyze the relevant papers as identified above. Table 2 shows the distribution of the appropriateness of the identified studies, which was evaluated by screening the title and the abstract of the respective studies. Table 3 shows the distribution of document types based on the identified contributions. The following Figure 2 shows the development of the relevant research studies from 2015-2021. We notice that this topic is getting more and more importance starting from 2020 with more than 24 papers published in this period.

Type of document

Records

Content Analysis

We have grouped the selected contributions into 4 topics. The first topic includes papers in relation to the modeling in smart city logistics. The second topic discusses the usage of the digital twin in logistics. The third topic involves the contributions regarding the digital twin in smart cities. Finally, the last topic is about the usage of IoT for urban logistics.

In the first topic, [START_REF] Liu | Digital twin-driven approach for smart city logistics: The case of freight parking management[END_REF] introduce a freight parking management for last-mile delivery scenarios in a smart city. A graph is used to formulate the object relationships with properties that represent the elements constituting the physical infrastructure (in the sense of a digital twin), and an ontology is designed to add semantics to the structural description of the smart city logistics parking lot. The built model is implemented into an experimental digital twin platform that displays the different relationships between nodes, and offers a user interface to query the ontology store to find suitable parking places for the defined last mile delivery scenario taking into account relevant constraints.

In the topic of using digital twins in smart cities, [START_REF] Raes | Duet: A framework for building secure and trusted digital twins of smart cities[END_REF] propose an architecture that facilitates data integration and fusion for the physical and virtual data exchange in a smart-city digital twin environment. The authors propose a means to link the architecture with individual models and data to monitor and synchronize the state and behavior of the digital twin to perform various what-if analyses related to traffic, air quality or noise pollution through APIs. However, there is no possibility to create complex scenarios that combine and integrate several individual models. [START_REF] Lee | Digital twin for supply chain coordination in modular construction[END_REF] look at the use of digital twinning in the context of modular construction. They present an interconnected system between a building information management providing progress information from a construction site and a GIS-based logistics simulation to assess potential risks in delivering modular building blocks. They emphasize the cost generated by blocks arriving too early and the resulting need for exact just-in-time deliveries. By repeatedly synchronizing information from the construction and production site and by repeatedly routing risk-avoiding trajectories for the delivery trucks, the aim is to reduce those delays. In [START_REF] Schrotter | The digital twin of the city of zurich for urban planning[END_REF] a digital twin for the city of Zurich is presented. The authors propose an open 3D model of the city's building stock. It is continuously updated to allow for analysis and future planning applications. They emphasize their focus on the full life-cycle of data acquisition and updates within the system. Feedback loops are only envisioned on a conceptual and long-term planning level, but not in an automated way. Finally, after reflecting on the issues regarding the challenges and opportunities that IoT poses for smart cities, [START_REF] Silva | Urban planning and smart city decision management empowered by real-time data processing using big data analytics[END_REF] identify the need to create a flexible architecture that can allow reliable and efficient data management for real-time decision-making. The authors propose a three layer architecture and highlight the importance of having a strong data management component to handle the increasing volumes of information and making them usable. While their architecture focuses on the processing of big data, they lack an explicit modeling framework for the specific services offered, which can be an issue when dealing with urban logistics, where decisions on different time-scales interact.

Regarding the topic on the usage of digital twin in logistics, [START_REF] Pan | Digital-twin-driven production logistics synchronization system for vehicle routing problems with pick-up and delivery in industrial park[END_REF] introduce a digital twin to deal with the increasing complexity and uncertainty of delivery systems with a focus on production logistics in an industrial park setting, where they aim to connect different units and agents. The presented architecture consist on three layers: (1) a Physical object perception layer, where the data is ingested and processed; (2) the Virtual model simulation level, where the virtual imaging of the physical objects is done and; and (III), the Synchronization system control layer, where the evaluation of the execution status is performed. To meet the social and ecological challenges of today's city, it is necessary to put in place policies that respect the individual circumstances and specificities of the areas concerned. Therefore, [START_REF] Belfadel | Towards a digital twin framework for adaptive last mile city logistics[END_REF] introduce a conceptual framework for the usage of digital twins in lastmile city logistics to help to interpret the logistics network dynamics in the city and the outcomes of the introduction of specific innovations. They propose a first approach to digital twins deployment for city logistics.

Finally, the last topic of selected papers is about IoT for urban logistics. In (Wanganoo and Patil, 2020), a digital twin for last-mile parcel deliveries in the United Arabic Emirates is conceptualized. In this particular context, last-mile deliveries are difficult to implement as addresses are not given by zip code or street name. The authors proposed an integrated system by which parcels can be tracked using a QR code, which is connected to a GPS-based location that is transmitted the day or hours before the delivery by the customer.

Discussion

In the previously identified contributions from literature, models are intertwined with smart city domains (i.e., building, environment, health) receiving input from multiple data sources. Dynamic correspondence linking a digital twin platform with models and data makes it possible to monitor and synchronize the state and behavior of the digital twin with the physical environment being mirrored, however this link is mainly a one-way link going from the physical world to the digital objects that are considered as digital shadow [START_REF] Kritzinger | Digital twin in manufacturing: a categorical literature review and classification[END_REF]. Very few papers consider the bi-directional communication.

In addition, very few papers have considered the usage of digital twins for urban logistics. The depicted decision systems from the presented state of the art are not well specified regarding the added value that those systems will provide nor any description of ongoing experimentation or example that might improve decision making in the domain of urban logistics. To validate such architecture, [START_REF] Hopkins | Big data analytics and iot in logistics: a case study[END_REF] underline the importance of digital twinning in logistics and the need of use cases.

Finally, one big challenge to consider while designing our digital twin framework for urban city logistics is with regard to the twinning rate or what is considered as high and low-frequency time horizons by [START_REF] Batty | Digital twins[END_REF]. In urban logistics, several kind of data exposition have to be managed, spanning from a very low rate data such as time series data coming from sensors of daily traffic to socio demographic data that are updated once a year (or more).

Digital Twin Framework for City Logistics

Generic Business Process Model

This section presents and analyses the targeted generic business process enabling the exploitation of DTs for City Logistics. First, as mentioned in Section 2, we realized workshops with relevant city logistics stakeholders and defined relevant value cases and real-world scenarios (description of the context, together with the actors, territories and companies involved, the main problematics they are facing and the targeted goals). Then, we identified the common future workflow that is shared between all the value case scenarios, and that is generic enough to be used by any other city along with their specific simulation models (see example in Section 2 and related models). This is to support experimentation and decision-making and create value for all different stakeholders.

The generic business process is depicted in Figure 3. The first task (Data process, PD) manages the contextual information from the physical twins, and keeps the data updated during the whole lifecycle of the entire process. In addition, it enables to push updated contextual data to the linked physical twins, in case a stakeholder needs to update the state of the physical twin. The second pool (Execution Process, PE ) describes the workflow that manages the user tasks and model configuration. These tasks enable stakeholders to construct simulation scenarios, with a possibility to choose and configure simulation model properties linked to it. The specific task Monitoring initializes the data monitoring (if applicable in the modeled scenario) to check the received data from physical twins and restart the simulation when a certain configured threshold is reached. The third and last pool (Decision process, PD) describes the workflow managed by a decision system. It receives the outcomes of the simulation as a set of successful scenarios. Then the decision system selects the best strategies among produced scenarios. Finally, the stakeholder that has launched the simulation scenario is notified when a generation of a detailed evaluation report is produced. This to compare the produced KPIs (from simulation) with the ones calculated based on a refreshed contextual data of physical twins. It is important to mention the bidirectional communication between the digital twin and its physical counterpart that is materialized through the link between the outcome of the best scenario and the PD task that allows to publish the updates to the physical counterparts in the real world. As depicted in this business process, there is a need to manage and integrate several kinds of simulation models in a given scenario. Consequently, a model library with a flexible approach is needed to categorize the urban freight system models, describe them, and most importantly, provide a means to handle all the transformations needed to transform the outputs of a model into another one.

Conceptual Framework

Figure 4 presents the DT framework for urban logistics, which is divided into three main layers. The left layer (Physical World ) represents the sensors and external entities such as Sensors and IoT Entities, City Operational Data and exposed APIs. The middle layer (Data/Model Management System) consists of two main elements: the DIS (Data Ingestion System) and the MMS (Model Management System).

The DIS provides a solution to the need depicted by the PI task of the generic business process (see Figure 3) that manages the contextual information from the physical twins and exposes the twinned data through web APIs to the other framework's internal components. It is intended to ingest the contextual entities (through the Data Ingestion Manager and maintain the digital twins updated, managing bidirectional connections with the associated devices that are exposing the contextual data via the Device Since a digital twin needs a detailed model of its environment and its physical counterpart, DIS provides the ability to enhance the DT framework and related artifacts with descriptions that are based on semantic annotations or standard ontologies by means of the Semantic Manager component. See [START_REF] Anand | Genclon: An ontology for city logistics[END_REF] for an urban logistics ontology. This enables the use of business-oriented specific semantic models to provide a detailed and integrated view of all relevant artifacts, thanks to the use of a unified urban freight ontology that might be used by any model in a simulation scenario.

The MMS brings a response to the need illustrated by the PE and PD tasks (see Figure 3) to integrate multiple types of simulation models in a particular scenario. The MMS consists of several components, including the Model Library Manager, which provides an outline of all of the available models, as well as their specifications and the connections between the models, and the key performance indicators (KPIs) that are useful for decision making. Please note that all data provided to MMS components during the configuration or simulation phase comes from the Context Entity Manager which exposes all physical counterpart's data to MMS components via web APIs.

The Scenario Manager allows the registration of what-if scenarios and their related models and configuration (model parameters, contextual data and expected KPIs). The Model Integration and Orchestration aims to orchestrate the models by connecting the datasets and the outputs from one model to another based on the what-if scenario configuration. The Simulation Configuration component is intended to manage the setup of the simulation environment in terms of technical aspects (parameterization of a virtual machine, technical libraries, etc.) As for the Simulation Environment component, it is a virtual execution environment generated on the basis of the simulation configuration. containerization and virtualization to create dynamically an execution environment for every scenario in order to execute the simulation and to collect the outputs of the scenario.

Digital twins offer real-time communication among assets and systems. The challenge is to know how the data is then processed to provide true added value. In this context, the value added is provided by the KPIs that are tailored precisely to the intended application. For this purpose, the final component of the MMS is the Decision System which aims to decide what results of the performed what-if scenario are most likely to be achieved. It considers the key performance indicators of a defined scenario and then recommends the required interventions in the physical world via the DIS to reach the predicted outcome. This decision system constructs its knowledge using different inputs such as model outputs, scenario parameters, model parameters, contextual data and predicted outcomes.

The remaining component is the Storage system for handling general storage such as what-if scenarios, scenario configuration, models and related data, or an ontology store (DuCharme, 2013) related to the use of the semantic manager component.

Model Library and Meta-model

The multiple value case scenarios that are considered in the generic business process model needs a flexible approach to be able to accommodate them. For this, a generic model library (MLib) that contains a set of models that can represent different aspects of the urban freight system. The objective of the MLib is to store multiple models that, when combined, allow the user to model different value cases, according to their needs.

In this work, The MLib is conceived as collection of models, where different model types, languages and frameworks can be included. This means that the models included can be theory driven, data driven, AI based or any other type of model. The models in the MLib are connected with connector functions that handle all the transformations needed to transform the outputs of a model into another one. These connectors work in a 1-to-1 fashion, making explicit connections. This has the advantage of increased modularity of the models (the model implementation do not need to change much from their original form to be included), makes data transformation invisible to users and avoids defining an unified urban freight ontology that affects every model.

To assist the user in the model selection, the MLib is organized in the Meta-Model (M2) according to its role in a broader city logistics framework, where an instance of the M2 is a set of models that represents a value case. As highlighted by Wildfire (2018) and [START_REF] Batty | Digital twins[END_REF], the time horizon of the model should match the time horizon of the city activity it wants to replicate. Thus, the M2 is organized by the time scope of logistic decisions: Strategic, Tactical and Operational [START_REF] Langevin | Logistics Systems: Design and Optimization[END_REF][START_REF] Tavasszy | Modelling Freight Transport[END_REF] and by the resolution of the model: Zone or Parcel/Shipment level. Strategic level models are models that deal with long term decisions, such as demand trends and depot locations. Tactical level models define how the fulfilment processes are done. Finally, operational models deal with routing and delivery. This classification allows the user to choose the model that better represents their value case. Additionally, an extra category for models specifically designed to evaluate the outputs of the other models and convert them into KPIs that are useful for decision making. The current version of the M2 was developed according to the needs of several European cities of the aforementioned European project. The models in the MLib were further categorized according to their method, such as optimization, demand models, network models, Agent based models, impact assessment, and routing models. By being aware of the modelling methods used, the user can have more detailed knowledge and control of how the DT is replicating the targeted freight system. Figure 5 shows how the models are categorized.

Zone level

Framework Implementation and Technical Insights

This DT framework might be built on top of existing open source cloud computing technology. It can apply containerization techniques like Docker5 and Kubernetes6 for use case model integration. Regarding the cloud environment, several cloud solutions exist in the market. For example, the hybrid stack could be provided by Cloudify7 , can integrate cloud and local environments. Other existing platforms are OpenStack8 , Cloud Foundry9 , and Apache CloudStack10 . Classical approaches adopt the idea of deploying full virtual machines (KVM11 ).

For the Data and Model Management System, a job scheduler can be employed over Docker/Kubernetes APIs, with a solution to handle data ingestion such as Kafka backbone, along with an open source framework for creating and managing digital twins in the IoT environment such as Eclipse DITTO12 or Fiware Orion Context Broker13 for the Context Entity Manager. For the Decision System (component of the Model Management System) one can employ Bayesian inference techniques coded in Python. Regarding the Simulation Configuration and Simulation Environment components, solutions for IT automation to configure systems, deploy software and orchestrate IT tasks might be considered such as Ansible14 , SLRUM15 and KVM.

For the connection of the physical world with the data and model management system, there are several front-end with open source solution for creating customizable dashboards (APIs) such as Grafana 16 technology tools, that is widely used to compose observability dashboards with metrics, logs, and application data.

For the Storage System, different solutions may be considered. For example, because sensor data can be produced very quickly, a traditional database may reach its limits in terms of processing speed and size rather quickly, requiring a different approach to managing this type of data. In this situation, big data solutions are required to achieve the necessary speed and scalability. Time series database such as InfluxDB17 , or document oriented database like MongoDB18 may be used for the querying and storage.

Component

Use case example

In the following, we elaborate a use case that is based on the proposed digital twin architecture. As before, we base our considerations on the distinction between predictive and reactive models made by [START_REF] Batty | Digital twins[END_REF] and Wildfire (2018). The former describes a detailed modeling of the real system to predict or perform a scenario-based analysis of the (longer term) future with focus on complexity and a systemic perspective. The latter describes a sufficiently accurate model that can be run quickly in order to engage in a near real-time exchange with sensors and actuators in the real world.

The use case considers the management of an Urban Consolidation Center (UCC). Whenever upstream logistics operators intend to deliver goods in a specified area in the city, they need to pass these goods though the Urban Consolidation Center, which, in turn, organizes the last mile delivery in the area. The delivery can be performed, for instance, using cargo-bikes or future electric delivery robots. The goal is to minimize emissions by making use of sustainable means of transport and aggregating flows from multiple upstream operators.

Following the dichotomy of reactive and predictive digital twins, the UCC operator is interested (1) in efficiently dispatching its vehicles and (2) making sure that longer term needs such as increasing the vehicle fleet can be detected well in advance. The individual UCC case should be understood as being integrated in a larger urban logistics digital twin with other use cases, data interfaces and models. However, already the individual case allows us to demonstrate the interaction of our architecture components on a realistic example.

The general business case for the UCC is shown in Figure 6. It includes three models:

• The VRP model describes a solver for Vehicle Routing Problems (VRPs). Its first input is a list of transport goods that need to be picked up at the UCC and delivered to their final destination. Its second input is a list of vehicles with their current location and charge (if they have already picked up some good). Third, the model may make use of information on the travel times in the underlying road network. The task of the VRP solver is to find relevant routes through the network and ideal sequences of pick-up and delivery stops. The solution may be based on different objectives, such as minimizing the incurred delays for the deliveries or minimizing the driven distance (and costs). • The Traffic model is a network model that is used to predict travel times in the urban road network. Its inputs are current and historical vehicle trajectories through the network that may be considered with different weights. The goal is to fuse the trajectory information (locations and timestamps) to obtain an accurate prediction of travel times in the next few minutes or hours. The model should be able, for instance, to detect unusual levels of congestion on special occasions and, thus, intelligently adjust its predictions.

• The Demand model is used to perform longer term predictions. Its input are historical daily records of processed goods flows. Using this information, the model is able to predict general and seasonal trends on the total daily transport demand for the UCC, but also demand surges for specific periods of the year (such as Christmas or Black Friday).

Each model takes a specific role in the digital twin. The Traffic model is an auxiliary component which is constantly fed information to observe traffic and provide valuable information to the VRP model. The VRP model itself observes the current state of the system in terms of active vehicles and current demand. If new transport requests arrive, the model should react to them and integrate them into the planned vehicle itineraries. Finally, the Demand model makes use of historical information to perform longer term predictions of the daily parcel demand and test (using another instance of the VRP model) whether the future demand can be served with the planned vehicle fleet. It, hence, takes the role of future fault detection. All models are managed by the MMS component through its Model Integration and Orchestration which is able to configure those models given their individual input and output data streams and connect them where necessary. The input data and output data is obtained from and send to the Storage system component of the digital twin platform.

The models receive and send information through various interfaces that are managed by the DIS component that keeps the digital twin updated. In the specific example, we defined two APIs and a push service:

• The Shipment API manages the interaction with the upstream operators. They are asked to announce incoming flows to the UCC well in advance so that they can be processed without delays. Using the Context Entity Manager component that keeps the digital twin model update, the upstream operator communicates (and potentially later updates) information on the predicted arrival time at the UCC and the list of last-mile item that need to be delivered (item identifier, destination address, optionally, desired delivery time). In the real world, each item (parcel) should be tagged using a QR code so that the on-site operators can assign them an internal delivery identifier.

• The Fleet API manages the interaction with the UCC's vehicle fleet. Through the Context Entity Manager, the vehicles submit frequently their current positions. This allows to construct the vehicle trajectory by saving the positions and their respective timestamps (vehicle identifier, location, timestamp). Besides the location endpoint, there is an endpoint that allows the vehicle operators (or the vehicles themselves in the case of robots) to confirm that they have picked up or delivered a delivery. For that, they communicate the respective event (pickup / dropoff) and the delivery identifier.

• Finally, there is the Task Push Service to which all UCC vehicles are connected. The service can update the vehicles trajectories by sending them a sequence of tasks that can be pickup or idle (which always happen at the UCC) or dropoff (which happens at the delivery destination). For a sequence of N jobs described by (stop type, delivery identifier), the vehicle receives a list of N -1 routes that define the movements in the road network. While these are highly important for an automated distribution service, human drivers might divert from these proposed routes.

When looking at the business process in Figure 6, one starting point is an incoming shipment from an upstream operator. The Delivery process (in blue) describes how deliveries are managed by the (reactive) digital twin. First, one or multiple incoming shipments are announced by the upstream operators through the Shipment API and monitored by the Data Ingestion System to keep the digital twin updated. The existing data can be stored by the Storage System. Every five minutes, the Context Entity Manager can check whether new shipments have been announced. If this is the case, new instances of the VRP model are configured and started by the Model Management System. Note that in the current case, multiple alternative scenarios are created by obtaining a distance-minimizing solution and one that minimizes delivery delays for the goods that were assigned a desired delivery time. Based on KPIs that are calculated based on the outcomes, the Decision System component chooses one solution. The system can be configured flexible, for instance, such that the distance-minimizing solution is chosen as long as delays don't exceed a certain threshold. Optionally, if certain KPIs reach undesirable thresholds, a human system operator may be identified directly. In any case, the stop sequences and vehicle routes are transmitted to the vehicles via the push service. These instructions can then be implemented by the vehicles. Note that simple updates of the vehicles' locations do not trigger a new execution of the VRP model.

However, there is the Traffic process (in yellow) that may do so. The vehicle location information arrives frequently through the Fleet API and is stored. Given the current trajectory (for instance, during the past five minutes) of the vehicle and the current state of the Traffic model, the Model Integration & Orchestration can decide whether the discrepancy (or prediction quality) of the Traffic model is too low. In that case, the model is newly configured (by the MMS through its Scenario Manager component that stores the simulation parameters and threshold settings) and estimated, making use of the latest traffic information. This traffic information is also monitored by the VRP process, so if it changes, the VRP is solved again to provide better routes to the active vehicles. The interaction of the two processes exemplifies one of the core insights from our literature review and use case analysis: An urban digital twin is likely to operate on multiple time scales and shows a complex interplay between different model types.

Finally, there is the Demand process which is triggered once a day. It makes use of the stored shipment information from the past day, but also other historical records. After finishing the (potentially long-running) demand prediction model, a synthetic transport demand is obtained. It represents the potential incoming shipments for multiple days in the future. For each of the instances (for instance, for the next 30 days) the VRP model is solved to simulate the last-mile deliveries. The model is then able to detect whether a (near) breakdown of the system is expected for a particular day in the future. If this is the case, the human staff of the UCC is informed, through the Context Entity Manager API that can push updates to the digital twin data model, to take relevant measures such adding more vehicles to the fleet or renting individual vehicles for the particular days when difficulties are expected. This last part of our use case example shows how our digital twin architecture is able to cover varying model types on the spectrum between reactive and predictive applications.

Discussion

In this work, we extended the state of the art with a generic framework for introducing digital twin applications for city logistics. We demonstrate in a generic business process how cities can leverage this framework to create value case scenarios by using their specific models. For this, a generic model library that contains a set of models, when combined, allow the user to model different value cases, according to their needs. This fills the lack of frameworks for model orchestration, data transformation and merging that enables the bi-directional exchange of physical and virtual data that compromises the adoption of digital twin technology for cities.

Through the use of a monitoring task, model integration and orchestration, the proposed approach can handle longer feedback loops (due to predictive models) that depend on the interaction of multiple independent agents, and potentially computationally intensive modeling pipelines that predict many aspects of the urban system over a rather long-term horizon.

As discussed in Section 4.4, the ongoing implementation involves several technologies and techniques that might represent some barriers that regard the utilization of the conceptual framework. This can be the acceptance (effort and expected benefits) or how the stakeholders will see the integration and compatibility efforts with other sub-systems of a decision support tool. In the bigger picture, the referred solution building blocks in this architecture exist in the market and can provide the necessary functionality, parameters and security aspects for the implementation of the framework. This does not mean that other alternatives could not provide a better performance, but the elements described here provide complete and mature solutions for the objectives of the proposed digital twin.

With respect of the limitations of this work, some requirements such as the integration with specific systems, network optimization or cyber-security issues (due for instance to the growth of the attack surface [START_REF] Hearn | Cybersecurity considerations for digital twin implementations[END_REF] are not considered at this level of detail. However, with regard to security and access control (through identity management) and secure data (thanks to the usage of the HTTPS protocol) the current framework already considers some of those constraints.

Conclusion

In this fast-changing environment, urban planners, business stakeholders, and city administrators need to be equipped with appropriate tools to conduct reliable and comprehensive analyses of upcoming business model innovations, technological changes and the changes these innovations will produce. In urban logistics, digital twins are starting to gain traction for its extensive role in supporting experimentation in urban logistics planning and policy development. However, the lack of digital twin framework for urban logistics enabling simulation model orchestrations, data transformation and merging that connect the exchange of physical and virtual data can compromise the adoption of digital twin technology for cities, which increasingly need to address city logistics problems with advanced data-driven solutions. Moreover, urban freight planning has different decision time horizons and is affected by multiple agents, that interact and are independent of each other, adding to the overall complexity of the system to be twinned.

To address these gaps, in this paper the authors present a first comprehensive platform architecture for using this data to inform individual, data-driven, and defensible policy-making in this field. To further demonstrate steps from the conceptual level to practical implementation, technical tools and implementation insights have been suggested. Moreover, real-world use cases of such framework have been presented demonstrating the capability of our framework to use bi-directional data flows and cope with different planning horizons. Through our detailed analysis of the many components needed to implement an adaptable and comprehensive framework, we intend to bring focus on the different possibilities of design architecture of DTs. Since current solutions are either highly individualized or academic oriented, cities would benefit with a more standarized and formal framework that can be deployed in a more standarized and formal way.

Figure 2 :

 2 Figure 2: Development of the relevant research studies 2015-2021

Figure 3 :

 3 Figure 3: Targeted Business Process

Figure 5 :

 5 Figure 5: Structure of the Meta-model

  Table 2 displays the total results of the SLR.

		Papers
	Total studies	133
	Not relevant	106
	High Appropriateness	8
	(DT for Smart city logistics)
	Medium Appropriateness	8
	(IoT and Big Data for Smart City
	logistics and Decision Making)
	Low Appropriateness	11
	(IoT for logistic systems)

Table 2 :

 2 Total research results of the systematic literature review (SLR)

Table 3 :

 3 Distribution of document types

and Model Management System DIS: Data Ingestion System MMS: Model Management System

  This component uses

	Physical world
			Context Entity Manager	Model Library Manager
					get/update		get
			Data Ingestion Manager	Scenario Manager	get	Decision System
	City operational	get
	data		get/update				set
			Semantic Manager	get/update
			Protocol	get/	Device	Simulation Configuration	Model Integration & Orchestration	Simulation Environment
		get/update	Manager	update	Manager
	IoT / Sensors
		Storage system
					Model library, Data, and General storage
			Figure 4: Proposed Framework

get APIs Data

Table 4 :

 4 Excerpt of additional implementation tools

https://www.leadproject.eu/

https://matsim.org/

https://www.ptvgroup.com/en/solutions/products/ptv-visum/

An IoT agent is a component that allows a range of devices to feed their data to be handled from a context broker by using their own native protocols.

https://www.docker.com

https://kubernetes.io

https://cloudify.co

https://www.openstack.org

https://www.cloudfoundry.org

https://cloudstack.apache.org

https://www.linux-kvm.org

https://www.eclipse.org/ditto

https://fiware-orion.readthedocs.io/en/master/

https://www.ansible.com

https://slurm.schedmd.com/documentation.html

https://grafana.com

https://www.influxdata.com

https://www.mongodb.com

Acknowledgment

This paper presents work developed in the scope of the project LEAD. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 861598. The content of this paper does not reflect the official opinion of the European Union. Responsibility for the information and views expressed in this paper lies entirely with the authors.

TOGAF, 2009. The Open Group Architecture Framework TOGAF™ Version 9. Basharat Hussain. Verlinde, S., Macharis, C., Milan, L., Kin, B., 2014