Ilias Sarantakos, Annabel Bowkett, Adib Allahham, Timur Sayfutdinov, Alan Murphy, Kayvan Pazouki, John Mangan, Guanlan Liu, Enrong Chang, Eleni Bougioukou, Haris Patsios
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This article presents findings of the Clean Tyne project. This project was part of the Clean Maritime Demonstration, funded by the United Kingdom’s Department for Transport and delivered in partnership with Innovate UK.
Announced in March 2020 and part of the prime minister’s Ten Point Plan to position the United Kingdom at the forefront of green shipbuilding and maritime technology, the Clean Maritime Demonstration Competition was a £20 million investment from government alongside a further £10 million from industry to reduce emissions from the maritime sector. The contribution of Newcastle University in the project was to provide quantifiable evidence around the benefits of digitalization, by means of a real-time supervisory and data acquisition platform, in the reduction of carbon emissions as well as operating and infrastructural costs at the Port of Tyne.
The main aim of this article is to report and discuss the key outputs originating from the modeling performed by Newcastle University around specific operational scenarios at the port. These are intended to highlight the value of intelligent coordination of key energy processes and reduced uncertainty of associated data, both enabled by digitalization. For this purpose, we have designed and modeled current and future operational scenarios in which emission reduction technologies (ERTs) and infrastructure are introduced alongside increased capability for coordination of energy assets and data availability. [In this article, ERTs refer to 1) shore power; 2) electrification of cargo handling equipment (CHE), 3) renewables, and 4) energy storage.] In our analysis, we consider a centralized decision-making process where energy costs and carbon emissions are minimized subject to available infrastructure and data.
Our results can be divided into three categories: impact of ERTs, impact of coordination, and impact of uncertainty on investment deferral. Under certain credible modeling and data assumptions and considering energy operational costs and emissions, our findings are as follows. ERTs can yield significant emissions reductions of up to 93% in year 2040 compared to a present scenario, even if imported power is not 100% zero carbon; energy costs related with key operations can be reduced up to 45% in year 2050 compared to a scenario where assets are not coordinated; and, finally, confidence in data can yield significant reductions in infrastructural investment costs for key energy assets, such as energy storage. We have noted that reduction of uncertainty through data availability (due to digitalization) led to a £3.35 million reduction of capital expenditure (CapEx) for a particular case considering energy storage installed at the Port of Tyne.
We now continue by showing how we modeled port operational scenarios. We then perform a quantitative analysis of cost and carbon emission savings that can be achieved by intelligent coordination of key processes as well as savings in the form of deferral of network reinforcement and investment in new assets and technologies due to reduced uncertainty around historical data. We subsequently present our results and key findings and conclude this article, including some suggestions for future work.
The aim of the modeling described in this section is to demonstrate the ways in which data availability can assist in decarbonization of key port energy processes. The objectives developed to achieve this aim are as follows:
The remainder of this section presents the modeling scope and rationale, how the processes and assets at the Port of Tyne were modeled and simulated to form the base case, a description of the future scenarios, and the asset scheduling optimization problem.
Port of Tyne’s two main sites are the International Passenger Terminal at North Shields and Tyne Dock at South Shields. For this modeling, it was decided to focus on Tyne Dock as this is where most of the commercial cargo handling activities occur; it has a number of berths for different ship types and cargoes as well as an associated diverse mix of CHE. It also has a reefer (refrigerated container) storage area and various office, commercial, and industrial buildings. Altogether this gives a good breadth of activities and assets on which to investigate the effects of coordination. Modeling the processes formed by these activities and assets also enables the detail and complexity of actual port operations to be captured and is expected to yield the most informative insights.
The Tyne Dock site is shown in Figure 1, with its boundary outlined in red. The areas and berths are as follows. Tyne Car Terminal (TCT), having three berths, occupies the western area of the site. Moving east along the quay, Tyne Bulk Terminal (TBT), the Container Terminal (CT), Riverside Quay (RSQ), and Riverside Quay East (RSQE) have one berth each. The processes modeled at each of these berths, including the assets engaged and their respective energy sources, are summarized in Table 1.
Figure 1. Tyne Dock site boundary and berths. TCT: Tyne Car Terminal; TBT: Tyne Bulk Terminal; CT: Container Terminal; RSQ: Riverside Quay; RSQE Riverside Quay East: (Source: Google Maps.)
Table 1. Processes in the scope of this modeling.
The Tyne Dock site is supplied by two medium-voltage (MV) rings, each of which has its own grid supply point: the Tyne Coal 11-kV ring supplies the western and central area of the site (covering TCT, TBT, and the CT berth and crane), while the Slake Terrace 11-kV ring supplies the central and eastern area (covering RSQ, RSQE, and the reefer sockets in the container yard). In addition to the processes shown in Table 1, the energy demands of the office, commercial, and industrial buildings supplied by these two rings were also considered in the modeling. Other smaller CHE operating in the background—for example, CHE in transit sheds or moving cargo around the site independently of ship calls—is not considered. The processes modeled at each terminal are now described in more detail.
The main operation here is motor vehicle export and transhipment. For these operations, cars are driven on and off the ship individually and parked in the terminal area before/after. They are delivered to and from the port by road on car transporters. In this modeling, only the energy demand of the ship’s auxiliary engine (AE) at berth is considered as there is no CHE operating at the car terminal that could be optimized. In our study, only two of the three berths at TCT are considered as an analysis of vessel call data showed that the third berth was not used.
A bulk carrier at berth at TBT is unloaded by the port’s two electric portal cranes, which lift biomass from the ship’s holds into three eco hoppers on the quay. When the biomass in the ship’s holds reaches a certain level, it can no longer be accessed by the cranes, so two shovel loaders are lifted into the holds to shovel the biomass into the center of the holds, making it accessible for the cranes again. The eco hoppers deposit the biomass into eight-wheel tipper trucks that line up underneath and transport the biomass to storage sheds.
The assets and energy demand considered in this modeling include the ship’s AE and the port’s two electric portal cranes, three diesel hoppers, and two diesel shovel loaders. An additional mobile diesel harbor crane, which is also available for ship unloading at TBT, is also included. The eight-wheel tipper trucks and storage facilities are not considered as they belong to a third party and their data were not available.
Figure 2 explains the process followed by the arrival of a containership at the CT. When the ship arrives, containers are unloaded by the port’s electric ship-to-shore (STS) crane. The STS crane lifts containers from the ship onto diesel-powered container tractors, which are waiting on the quay to transport the containers to the container yard. Within the container yard, diesel-powered reach stackers and empty handlers lift containers on and off the container tractors and stack and move containers around the yard as needed. The process is reversed for transporting containers from the yard and loading them onto a ship. The container yard also has the capacity for storing 42 reefer containers, supplied by grid electricity. The process modeled for the CT includes all of the assets and activities described previously. Road vehicles that transport containers to and from the port are not included.
Figure 2. The process following the arrival of a containership. STS crane container tractor reach stacker and empty handler. STS: ship to shore. (Source: Port of Tyne; used with permission.)
The process modeled at RSQ is plywood import. This includes the ship’s AE at berth, two of the port’s mobile diesel harbor cranes, which unload the plywood from the ship onto the quay, and the port’s forklift trucks (FLTs), which transport the plywood from the quay to storage.
RSQE is used for biomass import for a power station. When a ship arrives at berth, biomass is unloaded by two of the port’s mobile diesel harbor cranes into two hoppers on the quay. From these hoppers, it is transported by conveyor to storage and rail loading silos. In this modeling, only the ship’s AE at berth and the port’s two mobile diesel harbor cranes are considered as the rest of the CHE (hoppers, conveyors, and silos) is already electric and fully automated, with its own MV ring and separate grid supply point, all operated independently by the power station company.
To quantify the impact of ERTs and asset coordination in future scenarios, a base case model was constructed to capture the present energy demand, cost, and carbon emissions of in-scope activities and assets at the port. This base case model represents the port as is in 2022, with no ERTs or coordination of assets.
To build the base case model, it was necessary to acquire, analyze, and combine asset data from a wide range of sources. Figure 3 summarizes the main datasets considered.
Figure 3. Input data for base case modeling.
To estimate the carbon emissions and total cost of each energy source [marine gas oil (MGO), diesel, and grid electricity] used by the assets in each process and of the building energy demand on the Tyne Coal and Slake Terrace 11-kV rings, the base case model was developed and simulated in MATLAB for a 24-h period.
For the 24-h simulation period, it was required to know how many assets would be operating and what their individual power demands would be. The requirement for main CHE items (such as cranes and hoppers) to be operating originates from the arrival of a ship at berth; thus, the starting point for each process was to determine the length of time a ship would be at each berth within a 24-h period. This was done by analyzing vessel call data from the port’s vessel tracking service; to obtain a broad sample, we collected these data over a continuous nine-month period. From these data, and for each berth, we summed the total duration of all ship calls in the nine months and then took a daily average. In this way, we arrived at the number of hours a ship would be at each berth in the 24-h simulation period.
Interviews with operational managers from the port’s container and bulk and conventional cargo business areas established the type and typical number of CHE assets allocated to each ship type. These assets, shown in Table 1, are allocated to a ship for the whole time that it is at berth, and during this time their operation is assumed to be continuous (breaks in operation for events such as refueling or other interruptions have not been considered). To estimate the typical operating power demand of each asset, its rated engine or motor output power was multiplied by a load factor. The rated output power was obtained from manufacturer data sheets for most CHE models, but in cases where the exact model was not known or the data sheet was not available, data sheets for similar models were used. CHE load factors were taken from an emissions inventory conducted by Starcrest Consulting Group for the Port of Los Angeles, which has long been at the forefront of port emissions reduction and decarbonization.
The typical power demand of ships’ AEs at berth was also estimated by applying a load factor to the total installed AE power. The total installed AE power for each ship that called at each berth was identified from the Clarksons World Fleet Register using ships’ International Maritime Organization (IMO) numbers from the vessel call data, while AE load factors were selected through a comprehensive literature review. The estimated at-berth AE power demands for each ship type and size were compared with estimates made in the IMO fourth Greenhouse Gas Study and found to be similar. For the simulation, the average AE power demand of all in-scope ships at each berth was used.
The power demand of the ships and CHE involved in each process was multiplied by the process duration to give the energy demand of each asset. Data regarding costs and emissions factors for the different energy sources were then used to produce estimated energy cost and carbon emissions for each asset individually, and as a total for each energy source, for the 24-h simulation period.
For the building energy demand on the Tyne Coal and Slake Terrace 11-kV rings, single line diagrams (SLDs) and substation schematics were analyzed to determine from which substations the buildings were supplied. The peak power demand for each building was estimated, and load profiles were applied to produce the building energy demand for the 24-h simulation period.
Considering the port’s existing electrical network and site layout, points of connection for ERTs in future scenarios would be on the Tyne Coal and Slake Terrace 11-kV rings. Accordingly, these rings were also modeled in MATLAB and simulated for the 24-h period, considering the network topology, power flow, and voltage limits. Substation locations, asset nameplate data, and cable lengths were obtained from SLDs, while substation connected loads (buildings, electric CHE, and reefer storage) were identified from substation schematics. Establishing the base case power flow enables the effect of the ERTs installed in future scenarios to be understood.
Future scenarios considered for the Port of Tyne are summarized in Table 2. Beyond 2022, ERTs are progressively introduced, so that by 2050 the port infrastructure is fully decarbonized. Each scenario provides a snapshot of the port on its decarbonization journey, with the amount of ERTs installed in each year increasing. This enables the impact of ERTs on carbon emissions and energy cost to be quantified. As more ERTs are introduced, more assets can be coordinated; thus, the degree of coordination also increases in each scenario, which allows the impact of coordination on carbon emissions and energy cost to be quantified as well. These future scenarios would of course require CapEx on new infrastructure and CHE; however, our modeling focuses on operational optimization and thus only considers operational (energy) costs.
Table 2. A summary of future scenarios.
By 2030, shore power has been installed at TCT1 and TCT2. At TBT, the diesel harbor crane and three diesel hoppers have been electrified; at the CT, diesel container tractors, reach stackers, and empty handlers have been replaced with battery-powered models; at RSQ, diesel FLTs have also been replaced with a battery-powered fleet; at RSQE, two diesel cranes have been electrified. All other CHE remains diesel powered. Twenty electric vehicle (EV) charging points have also been installed across the site. Except for a single 752-kW rooftop photovoltaic (PV) installation at Warehouse 21, there are no renewables or energy storage on site. In this scenario, operation of electrified cranes, charging of battery-powered CHE, and the refrigeration demand of reefer storage are coordinated for cost and carbon intensity.
By 2040, shore power has been installed at all remaining berths (TBT, CT, RSQ, and RSQE), and all remaining CHE in scope (two cranes at RSQ and two shovel loaders at TBT) has been electrified or replaced with battery-powered versions. Forty more EV charging points have also been installed, and the port is now considered “all electric.” To help offset the resultant increase in grid energy demand, 1,460 kW of rooftop solar PV installations and two 500-kW wind turbines have been installed as well as energy storage in the form of two 1.25-MW/2.5-MWh batteries (one connected to each MV ring). In this scenario, coordination is extended to the operation of all electrified (mains-powered) CHE, charging of battery-powered CHE, EV charging in parking bays, and energy storage system (ESS) charging and discharging.
By 2050, a third 500-kW wind turbine and three 400-kW PV car park canopies have been installed, along with additional energy storage in the form of 2-MW/4-MWh hydrogen. The port is now considered to be operating as a microgrid/local energy system with full coordination of all connected assets and the capability to export energy to the grid.
Each future scenario was modeled and simulated in the same way as the base case for a 24-h period. However, each scenario is simulated in two stages; initially, only the infrastructural changes in each scenario are simulated so that the impact of ERTs on carbon emissions and energy cost can be determined. Then, each scenario is repeated but with the asset coordination incorporated into each simulation. This enables a comparison of carbon emissions and energy costs with and without asset coordination. The following section provides a high-level overview of how the optimization problem for asset coordination was formulated.
The aim of our optimization problem is to coordinate flexible demand (and storage) to minimize the cost of all fuels (MGO, diesel, and electricity) as well as the carbon emission cost. This is an optimal scheduling problem, in which the main decision variables are flexible load power consumptions, which are adjusted (in terms of magnitude and time, i.e., how much and when) to minimize cost of energy and carbon emission cost. A high-level overview of our model is shown in Figure 4.
Figure 4. A high-level overview of the developed mathematical model.
The objective function is to minimize the total cost, which comprises the energy cost (electricity, MGO, diesel) and the carbon emission cost. The electricity price and grid carbon intensity vary throughout the day, as shown in Figure 5. Therefore, the lower the values of electricity price and carbon intensity are at a specific time step, the more grid electricity usage is encouraged.
Figure 5. Electricity price and grid carbon intensity for a day in February 2022.
We formulate the constraints as mathematical equations. Key constraints represent 1) the network, 2) mains-connected (e.g., cranes) and battery-powered (e.g., FLTs) CHE, 3) reefers, 4) energy storage, and 5) EVs. The constraints express the following information:
Having provided a high-level overview of our mathematical model, this section presents the results, which are divided into three subsections: 1) impact of coordination (optimization), 2) impact of ERTs (shore power, CHE electrification, on-site renewables, and energy storage), and 3) impact of uncertainty on investment deferral.
We have simulated three scenarios, which correspond to a representative day in 2030, 2040, and 2050, respectively. The associated results are presented in the following sections. In each of these scenarios, results are compared with and without coordination/optimization, and some key results are illustrated.
Table 3 provides an overview of the simulation results for the 2030 scenario. The impact of coordination is manifested by the savings in terms of carbon emissions and total cost (total cost is cost of energy plus cost of carbon emissions). The carbon emission savings are equal to 48 kg CO2e/day, which accounts for 1% of the “2030 no optimization” case, while the total cost savings are 2%. Note that only reefers and cranes are optimized in this scenario, and approximately 50% of the assets are considered to have been electrified.
Table 3. Results overview of 2030 scenario.
The “no optimization” case considers two cranes operating all of the time during which the ship is at berth at TBT. The “optimized” case considers three cranes available, which not only gives the port (considered as a microgrid operator) the opportunity to increase the rate of unloading the ship, but also allows it to stop operation at times when the electricity price and/or carbon intensity is high (see Figure 5).
Of the total 48 kg savings, 9 kg comes from coordination of cranes at TBT, 16 kg comes from coordinating reefers, and the remaining 23 kg comes from coordinating cranes at RSQE.
The results overview for 2040 is shown in Table 4. The impact of coordination is greater here because 1) more asset types are coordinated (cranes, reefers, EVs, and battery-powered CHE), and 2) all assets have been electrified. Carbon emission savings are now 134 kg CO2e/day, which account for 12% of the “2040 no optimization” case. In terms of total cost, the corresponding saving is 16%.
Table 4. Results overview of 2040 scenario.
Sixty (7-kW/24-kWh) EVs are charged between 8 a.m. and 5 p.m., with the assumption that their initial state of charge is 50%, and the final is 90%. The “no optimization” case assumes uniform charging across the whole time period during which EVs are parked at the port, while the “optimized” case optimizes charging according to variable electricity and carbon emission prices (shown in Figure 5). The resulting carbon emission saving is 2 kg CO2e/day.
A results overview for the 2050 scenario is shown in Table 5. The total cost saving is now £916/day, which accounts for 45% of the “2050 no optimization” case. In this scenario, for approximately half of the day, the network exports to the grid, which leads to an operating profit of £371/day. Exporting, due to renewables and storage, could represent an additional revenue for the port.
Table 5. Results overview of 2050 scenario.
This section briefly describes the impact of ERTs. Shore power and electrification (as well as one PV installation) from 2022 to 2030 results in a 62% reduction from base case emissions. Going all electric by 2040 further reduces emissions by 93% compared to 2022. This is also due to the installation of two 500-kW wind turbines, multiple PV installations (2.2 MW in total), and 2.5 MW/5 MWh of energy storage. Emissions are zero in 2050. The decarbonization of the U.K. electricity supply is the main driver behind this result. More discussion follows in the section “Key Findings and Discussion.”
To investigate the impact of uncertainty on investment deferral, we now take the previous “future scenario 2040” as a new base case. Within this scenario, we focus on the Tyne Coal 11-kV ring, which has a battery ESS (BESS) of 1.25 MW/2.5 MWh connected. The simulation of this scenario results in carbon emissions of 1.033 tCO2e/day from the Tyne Coal ring. Without knowledge of historical data, we assume 50% uncertainty of net demand (load minus renewable generation) at each bus of the Tyne Coal network. As an example, the average shore power demand at TCT1 is 1.04 MW; assuming 50% uncertainty means that the actual shore power demand profile could range from 0.52 to 1.56 MW. Possible profiles are produced using a Monte Carlo simulation, where at each time step we sample shore power demand from the following interval: [0.5·1.04, 1.5·1.04] = [0.52 MW, 1.56 MW]. In this base case, the probability of exceeding the emissions of 1.033 tCO2e/day is around 50%.
If we now double the size of the BESS to 2.5 MW/5 MWh and perform the same Monte Carlo simulation (with 50% uncertainty), the resulting probability of exceeding the base case emissions of 1.033 tCO2e/day decreases to 10%. A bigger BESS gives the capability to manage carbon emissions more effectively by taking advantage of the variability of the grid carbon intensity; the BESS can charge when the grid carbon intensity is low and discharge when it is high, so that the demand on Tyne Coal is met with lower emission electricity. However, the bigger BESS will come at a significantly higher capital cost.
If sufficient historical data for net demand were available, the uncertainty would be significantly reduced. For example, if we assume that sufficient data would reduce the uncertainty of net demand to 10%, more accurate scheduling would be possible, and, therefore, a smaller BESS would be required to achieve the same emissions. By trying different sizes of BESS, we found that this is achieved with a 1.5-MW/3-MWh battery, which is only 20% bigger than the initial battery and significantly smaller than the 2.5-MW/5-MWh battery that would be required in the case of 50% uncertainty of net demand. This in turn would result in substantial CapEx savings.
This section presents the key findings and discusses the results reported previously.
Figure 6 illustrates the difference coordination can make in 2030, 2040, and 2050. Electrification of assets combined with data collected in a digital platform enables coordination of assets, leading to significant CO2 and total cost reductions. The impact of coordination increases as more assets are electrified and as more renewables and storage are added. In a fully electrified port, coordination can enable negative CO2 emissions and power export from on-site renewable energy sources to the grid, generating additional revenue. Table 6 summarizes the results of coordination impact.
Figure 6. The impact of coordination (operational optimization) on carbon emissions/total cost in 2030, 2040, and 2050. (a) 2030 CO2 emissions: 1% reduction compared to non-optimized case; (b) 2040 CO2 emissions: increases to 12%; (c) 2050 total cost: 45% difference in total cost between optimized and non-optimized cases.
Table 6. Summary of coordination impact results.
ERTs (shore power, electrification of CHE, and on-site renewables and energy storage) have a significant emission reduction impact, as shown in Figure 7. Table 7 presents the CO2 reduction in 2030, 2040, and 2050 compared to 2022.
Figure 7. The impact of ERTs (without coordination/optimization).
Table 7. Impact of ERTs on carbon emissions.
As part of our analysis, we have also considered a pessimistic scenario, where the U.K. electricity supply is not fully decarbonized in 2050. In that case, the carbon intensity level is equal to that of 2040, which is 40% of the 2022 level. In this case, there is a remaining 4% CO2 emissions in 2050 (compared to the 2022 level), which is further decreased to 2% with optimization. What was interesting in this case was that one of the port networks effectively produced negative emissions, which shows that ports (or more generally local energy systems) can help achieve net zero by exporting electricity to the grid when the grid carbon intensity is nonzero. Exporting when the carbon intensity is zero would not make any difference, but exporting when there is nonzero carbon intensity would reduce electricity supply emissions. Coordinating local energy systems at a national scale would then be able to offset any remaining emissions stemming from the electricity supply.
To evaluate the impact of uncertainty on investment deferral, we have performed a feasibility study to obtain two BESS solutions that result in the same value of emissions, while each corresponds to a different level of uncertainty (Figure 8). In the first case, without a digital platform, the lack of data results in a high uncertainty of net demand—assumed to be 50%. With this level of uncertainty, a 2.5-MW/5-MWh BESS is required to achieve emissions of 1.033 tCO2e/day. In the second case, data made available through a digital platform are estimated to reduce uncertainty of net demand to 10%. With this reduced uncertainty, the size of BESS required to achieve the same emissions of 1.033 tCO2e/day is now only 1.5 MW/3 MWh. This analysis shows that a 40% reduction in data uncertainty results in a 1-MW/2-MWh reduction of required BESS capacity; based on capital costs of £1.16 million/MW and £1.095 million/MWh, this results in a CapEx saving of £3.35 million. The main conclusion, therefore, is that a 40% reduction of uncertainty through data availability leads to a £3.35 million reduction of CapEx for a particular case considering energy storage installed at the port.
Figure 8. Uncertainty and investment deferral overview. (a) Without digital platform/digitalization. (b) With digital platform/digitalization.
Through this work, we have produced a range of results targeted at providing quantifiable evidence on the potential contributions digitalization (in the form of a digital platform) can have in the reduction of carbon emissions as well as operating and infrastructural costs. Our results have been divided into three main categories: impact of ERTs, impact of coordination, and impact of uncertainty on investment deferral. Under certain credible modeling and data assumptions and considering mainly energy operational costs and emissions, our key findings are as follows.
In our modeling, the optimal coordination of CHE, such as cranes for energy cost and carbon emissions reduction, was achieved by making additional assets available to service ships at berth that would otherwise not be in use; for example, by making a third crane available at TBT to unload a bulk carrier. As a result, there was no increase in the time taken to unload a ship and thus no financial penalties incurred by the port for breaching the vessel turnaround time agreed in its contract(s) with the relevant parties.
Future work could consider such penalties into the formulation of the optimization problem to explore the potential benefits of extending the stay of a vessel at berth on cost, carbon emissions, and network constraints. Optimization can also be beneficial as a tool to better inform negotiations between the port and customers who are willing to accept a longer ship turnaround time if it results in lower carbon emissions. A shorter handling time would require a more intensive asset utilization, which would be preferred by the customer but might put pressure on the port’s CHE availability and also increase emissions and cost and potentially violate electricity network constraints. This tool could assess each option and provide the optimal tradeoff among duration, CHE utilization, emissions, and cost, while also ensuring all network constraints are satisfied.
This work was supported by the U.K. government’s Department of Transport and Innovate UK, as part of the Clean Tyne project. The authors would like to thank Panagiotis Sarantakos, Paraskevas Stratigakis, Meltem Peker, Dimitrios Kloudas, James Wright, Jak Johnson, Ian Lightfoot, and Ian Blake for sharing their knowledge on ship and port operations.
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Ilias Sarantakos (ilias.sarantakos@newcastle.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Annabel Bowkett (annabelbowkett@gmail.com) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Adib Allahham (adib.allahham@ncl.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Timur Sayfutdinov (timur.saifutdinov@xjtlu.edu.cn) is with Xi’an Jiaotong-Liverpool University, Suzhou 215123, China.
Alan Murphy (a.j.murphy@ncl.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Kayvan Pazouki (kayvan.pazouki@ncl.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
John Mangan (john.mangan@ncl.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Guanlan Liu (g.liu7@ncl.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Enrong Chang (e.chang@soton.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Eleni Bougioukou (eleni.bougioukou@portoftyne.co.uk) is with Port of Tyne, NE34 9PT South Shields, U.K.
Haris Patsios (haris.patsios@newcastle.ac.uk) is with Newcastle University, NE1 7RU Newcastle Upon Tyne, U.K.
Digital Object Identifier 10.1109/MELE.2022.3233114
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