3 Method and case studies
4.1 Process descriptions
4.1.6 Decentralised treatment
4.1.7.2 Data and assumptions used for the transport model
Hamburg is administratively structured into seven wards each with several districts, which in turn are divided into different neighbourhoods. Detailed data on these administrative units (e.g. size, inhabitants, housing types, etc.) is available from the statistics agency (Statistisches Amt für Hamburg und Schleswig‐Holstein, 2007). For this analysis the year 2006 is used as reference year. A digital layer map is available showing the street system of Hamburg including the length of the individual streets. In total, Hamburg’s street system has a length of about 4640 km. A second digital layer including the 103 districts of Hamburg is used to allocate the streets to the respective districts in the GIS. The street data is not prepared as a network but as separated lines which cannot be combined to a network due to the complexity. Thus, it is not possible to use the network analysis within ArcGIS, but a combination of analyses in ArcGIS and MapPoint is used.
The source separated wastewater is collected from the households or from neighbourhood facilities, like storage vessels or cluster treatment plants. It is then transported to further treatment or storage (see Figure 4.6). These secondary stations are assumed to be depots for the collection lorries so that the lorry trips start and end at the respective stations. In Systems 2 NuRS and 4 CoDig the existing central WWTP is assumed to be the only station (see trip 2 in Figure 4.6). The location of the existing plant south of the river Elbe however, is restricting the access for lorries, since this would lead to enormous congestion problems concerning the access roads to the plant, if traffic increases due to transport of urine or blackwater. Therefore, it is assumed for Systems 3 NuRU, 5 BlaD and 6 CompU that four such stations exist across the city. This is in line with the four currently existing maintenance and storage facilities of Hamburg Wasser, which could possibly be converted into treatment facilities for blackwater or urine83. For the analysis, the different districts are allocated to one of the four stations (Bergedorf, Harburg, West or North). This is done using ArcGIS analyses based on least distance. This results in the allocation of districts and inhabitants as shown in Annex A7.
The transport needs are generally composed of trips going to the area where the respective flow (i.e, urine, blackwater, organic waste) is collected and trips within the area until the vehicle is fully loaded. These are called in the following sections “access trips” and “collection routes” respectively. The total transport distance is therefore the sum of the access trip from the depot to the neighbourhood (and return) and the
83 Also the Hamburg waste management department has five depots within the town borders.
collection route within the neighbourhood. Figure 4.7 illustrates the calculation algorithm using ArcGIS, MapPoint and Excel commands.
Figure 4.7: Calculation procedure for transport requirements from households to processing
stations Access trips
The distance between the households/neighbourhoods and the secondary storage and processing facilities (access trips) is analysed using the route planning possibilities of the software MapPoint. First, the geometric centre of every district is determined using a layer analysis in GIS. Subsequently, the distance and travel time spent between the centre of each district and the central WWTP, as well as the four stations, is calculated in MapPoint, based on the street system of Hamburg.
The required access trips and their total length in one collection cycle are calculated by following algorithm:
−1
Naj: number of access trips to district j [‐]
inhj: number of inhabitants in district j [p]
vol: volume or load to be collected (per person and day) [kg p‐1 d‐1] or [m3 p‐1 d‐1] d: collection intervall (i.e. every d days) [d]
M: maximum load of collection lorry [kg] or [m3]
∑
⋅= Naj DDist
L [km] (4‐15)
with
L: total length of access trips in one collection cycle [km]
Naj: number of access trips to district j [‐]
DDist: distance between district/neighbourhood and depot [km]
The maximum load M of a collection lorry is set to be 11 tonnes for solids collection and 16 m3 for liquids collection (based on Giese, Th., Hamburg Wasser, personal communication, 20 Nov 2006 and Hamburg Sanitation Department cited in Grünauer, 2007). A fortnightly collection interval d is assumed.
Collection routes
For the calculation of the collection routes it is assumed that every road needs to be fully serviced by the lorries and, thus, the full length of all roads is included in one collection cycle. On the one hand this might result in some overestimations, since source separated flows from houses in smaller roads might be drained in pipes towards the collection containers on main roads. This means that lorries would not need to drive along these small roads to empty the containers. On the other hand the collection containers might be located on the left and on the right side of large roads requiring a bidirectional collection trip, which is also not included in this assumption. Therefore, taking into account that every road will be serviced once represents a trade‐off between these considerations.
Results and discussion of the transport model
The distance travelled resulting from the transport model is shown in Annex A7. As expected, the results show that the transport of liquids such as urine or blackwater is considerably greater than other transports. Particularly blackwater, due to its relatively high volume, requires extensive transports. Comparing trips number 2 and 3 emphasises the need to consider the implementation of several distributed storage and treatment centres instead of one centralised facility. For the case of Hamburg, four semi‐
centralised facilities decrease the distance travelled per year by about 56% as compared
to a single facility. It should be noted that the distance travelled shown here is of course dependent on underlying assumptions such as a fortnightly collection cycle, the amount of flushwater used, separation effectiveness, etc.
Another point of interest is to check whether the accuracy increases significantly when using this detailed calculation procedure, instead of using average transport distances.
The MapPoint analysis shows that for the case of Hamburg the average distance from every neighbourhood to the nearest of the four treatment facilities is about 9.2 km, whereas the average distance to the centralised treatment plant is about 17.8 km. Using the result for the urine mass flow per year (565,000 m3 y‐1, see Annex C), a lorry capacity of 16 m3 and an average distance (one‐way) of 9.2 km, this would result in a total distance of 649,750 km y‐1 as compared to 693,879 km y‐1 as derived from the detailed analysis(see Annex A.7). The comparison of required distance travelled for blackwater transport to a centralised facility shows a similar picture (10,502,000 km y‐1 compared to 11,084,050 km y‐1). This means that for both cases the relative difference obtained by using the simplified calculation is about 5% to 6%. It is therefore suggested that for future analyses the differentiation between access trips and collection routes as well as the detailed GIS‐based model can be neglected and that average distances are sufficient.
Energy
Energy requirements are calculated based on the required travel distance multiplied by the fuel consumption and its respective energetic value. Regarding fuel consumption, it is taken into account that the lorries are empty while going forth and full on return. The data that is included in the energy model is summarised in Annex A.7.
Costs
Costs for transport could be calculated in a detailed manner using factors such as transport distances, average speed, time for loading and unloading, labour costs etc. To simplify the calculation an average total cost per km is used in the cost model. Arlt (2003) compared standard prices and own calculations regarding the logistic costs of waste and sewage sludge transport. From his results it can be concluded that average costs for lorry trips between 25 and 50 km are about 200±20 € trip‐1 and for trips between 75 and 100 km are about 300±20 € trip‐1. This can be converted to costs between 3 and 8 € km‐1, depending on the trip length. For the calculation an average of 4±0.5 € km‐1 is assumed; this includes capital costs, consumables, labour and maintenance.