tool applied for forecasting in waste management

Transcription

1 tool applied for forecasting in waste management Dr. Martin PAVLAS Version 1.0, Justine represents a sophisticated tool for simultaneous forecasting of waste amounts and waste parameters at different territorial units. From general perspective, the tool can be applied to any problem, where forecasts are performed based on spatially distributed data from previous years. This data are supposed to be incomplete, sometimes even uncertain. Both terms incomplete and uncertain, as understood in this text, will be explained later on. One of the key applications of Justine sofar has addressed household waste produced in the Czech Republic. So, this case is utilised to introduce the basic idea, principle and outcomes of this tool, Task introduction A hierarchical structure of the investigated area is reflected. Different levels of details are taken into account: country level (L0), regional level (L1), micro-regional level (L2), municipalities (L3) etc.) (see Fig. 1) Fig. 1 Different levels of detail considered by Justine tool The problem handled by Justine may be visualized by a 3D plot as depicted by Fig. 2. There are different types of waste, grouped according to their unique code numbers or similar properties (e.g. residual waste, separately collected waste consisting of plastics, paper, glass, see vertical axis and sections in Fig. 2). Considering this waste streams the aim is to forecast future changes in total production of key components, the distribution of components between residual waste and separately collected waste etc. (see time axis). This is done at all territorial units at the same time. As we move in geographical aggregation axis we include more and more municipalities. Waste produced by higher and higher number of inhabitants is analysed. 1

2 Fig. 2 The problem of forecasting illustrated in a 3D space Why is an advanced tool needed? Justine has been developed to overcome limitations of traditional approaches, which are dominantly based on statistical methods (regression analysis, trend analysis). Novel approach contributes to: analysing rational recovery targets by reflecting the current situation in individual (micro) regions and municipalities investigating historical data from different regions and formulating general models on the production and composition of waste exploiting examples of good practice from regions with high recovery rates combining analogy with rigorous regression models (historical data from one region can serve as one scenario for another region) Statistical analysis applied to waste quantities data - some general remarks In the following text short summary on pros and cons of statistical methods are shortly summarized: Regression analysis (RA) 2

3 aim is to explain variations in production among producers typical explaining parameters are as follows: gross domestic product, income, share of different types of housing, type of heating, tourism rate, container distance, etc. Strict requirements on data to be met before its application (normal distribution, outliers, etc.) Formulated models provide enormous variation in quantities (high residual values) These models describe an average Frequency diagrams are used to set benchmarks and future targets (75 percentile, etc.) Explanatory parameters may be forecasted, introduced into the models to forecast future waste production Trend analysis (TA) forecasts are derived by extrapolating historical data time as the sole explanatory parameter hardly applicable for long-term forecasting due to short time series extrapolation = business-as-usual scenario - no significant changes in the course is expected. In reality, the most significant progress in waste management is a response to new stipulating measures and drivers (landfill bans and taxes). To sum up, several models have been formulated and published recently. They involve various explaining parameters. However, they applicability to different region is problematic. On the other hand, any model is appreciated, even if its accuracy is not secured since it provides valuable information. In fact, our approach benefits from strong points of above mentioned methods. Inputs - data necessary for calculation For our particular case addressing household waste presented below, the following inputs are expected: Separated plastic yield (all points, all levels (L0 to L3), several years) Separated paper yield (all points, all levels (L0 to L3), several years) Separated glass yield (all points, all levels (L0 to L3), several years) Separated biowaste yield (all points, all levels (L0 to L3), several years) Residual waste amount (all points, all levels (L0 to L3), several years). An example of spatially-distributed data for the Czech Republic, year 2014 and level of detail L2 micro-regions is illustrated in Fig. 3. Similar maps are available for previous years as well. The production is expressed as specific per capita and year. 3

4 Fig. 3 Data on waste quantities represents a key input for the calculation Additional and very valuable input information is on: Residual waste composition Often, this type of data is available only from few points, since the complex waste composition analysis is demanding on labour and time. In addition, the result is only relevant to specific time period and particular location. Therefore this data is denoted as INCOPLETE and UNCERTAIN. There is strong difference in composition of waste produced in cities and villages (see Fig. 4). Fig. 4 Variation in composition of residual waste generated in different types of residential areas. Plant material and energy balances coupled with known collection area represent another source of information: 4

5 Waste-to-energy plants - for example, ash balance may determine ash content in incinerated waste, energy balance is influenced by lower heating value (plastic content, etc.) Sorting lines - balance of plastic fractions determine plastics bin content. Remarks Spatially distributed Incomplete Uncertain in our content it means data reported from different geographical locations and areas. Units are clearly organized within a tree-structure (state is divided into regions, regions consisting of micro-regions, municipalities). some information is missing; not all information from all locations are available, particular information is available only from few units some data may suffer on errors or they may not explain the reality precisely; due to extreme variability it is not able to make any projection Output: results from the calculation From general point of view the outcomes from the calculations are future predictions of key-parameters at all locations and integrated/aggregated values for higher organizational units More specifically, for our case of household waste it is: production of individual fractions and their distribution between observed streams composition of residual waste and recyclables lower heating value separation rate for different fractions separation efficiency for different fractions Additionally, performance of particular geographical unit is compared with other ones. Producers performing below, around, and above the average may be classified. In this respect, results may be used as benchmarks and future targets can be specified. For example, it is recommended that those below the average will reach the current average in the next decade Regarding the aforementioned limits in statistical analysis methods, we developed a computational system for simulating and forecasting in uncertain and spatially distributed data problems. Forecasting is performed for all locations and several streams simultaneously. The computational system processes a variety of spatially distributed forecasts on production and composition at every point obtained by the aforementioned regression or trend analysis. These models are bound together through additional 5

6 equations and constraints which refers to mass and energy balances and which must be kept to. Fig. 5 demonstrates one particular result for a selected micro-region and selected fraction plastics. Fig. 5 b) shows the constant projected production of plastic waste in MSW and ineffective plastics separation, as only 12% of its production was separated in 2013 (Fig. 5 c). This very low value is highlighted in Fig. 5 a), where a frequency diagram for all 206 micro-regions is provided. A future increase in yield is expected, resulting in lower amounts of plastics in residual waste (Fig. 5 b), and significantly increasing efficiency (Fig. 5 c). Similar outcomes were obtained for all territorial units Fig. 5 FIG: An example of result: The production and separation efficiency of plastics in selected micro-region, results of a complex analysis How JUSTINE works? The approach incorporates the steps depicted in Fig. 6. 6

7 Fig. 6 Steps in the approach STEP 1: REGRESSION ANALYSIS (RA) Data are gathered, verified and detailed regression analysis is performed. The goal is to develop models, which help explain variation in parameters within the investigated area. In case of household waste it was: model on PAP, PLA, GLASS yields as separated and its residual values as a function of housing structure model on composition of residual waste as a function of housing structure model of PAP, PLAS, GLASS and other components generation as a function of socio-economic factors Models are later on used to get complete information for all nodes. Where input data is missing, it is substituted by models. STEP 2: TREND ANALYSIS (TA) Selected parameters are extrapolated using trend analysis. Unfortunately, one has to cope with short time series. From mathematical point of view the accuracy is rarely secured if the series consists only of few points. On the other hand, these models still provide important information about the trend from engineering point of view. TA is performed for data at all hierarchical levels (i.e. LO, L1, L2), so models on future production in all micro-regions, all regions and for the whole country are formulated. 7

8 Forecasting at bottom level (small territorial units, see Fig. 7 b)) - suffers from large variability in the historical data, many outliers, uncertainty. Less precise results are expected due to uncertain input data. Forecasting at higher level (aggregated data, see Fig. 7 a)) - The variability is suppressed by areal aggregation, so TA for larger geographical areas provides better and more robust predictions. On the other hand, forecasting on top-level data introduces the secondary issue of how to distribute the production down into the regions and micro-regions. Current trends (even though the data is variable) in these bottom units should be addressed at the same time. a) b) Fig. 7 Various data quality based on the level of detail Comments/Findings: In contrary to the bottom level area, for higher hierarchical levels more models are formulated. First, we make forecasting at bottom level data and then we make a sum to get forecast at higher level. Second, we perform forecasting at upper level data directly (the is summed fist and forecasts follow). So, the result is depending on the order of operation - geographical sum and forecasting, see the two different extrapolations in Fig. 7 a) above. Anyway, there is always very basic balance equation: "amount in higher unit is equal to the sum of amounts in all units the higher one consists of" in action. One expects that it is not only met for historical data but also for future forecasts. This basic principle formulates very important element of JUSTINE tool and its denoted as AREAL CONSTRAINT (Fig. 8).. 8

9 Fig. 8 Visualisation of consistent forecast, where the order of operation sum ( ) and forecasting ( ) does not influence the result. Due to its principle, outcomes from trend analysis describe so called Baseline scenario or business-as-usual scenario, where no significant changes in the course are expected. In some cases extrapolation, due to its principle in combination with data, provides unrealistic models, which leads to overestimation or under estimation. As an example, model on future amounts of separately-collected bio-waste/yard waste for particular rural micro-region is presented in Fig. 9. There is no expectation, that the production will follow the exponential model on a long term basis. The sharp increase reported by latest data as a response on new legislation introduced in 2013 will be exhausted within a couple of years as soon as waste management system in majority of municipalities will be adjusted. Therefore, an additional corrective model specifying realistic future target respecting rural character of the area is needed. The second example of a doubtful model (Fig. 10) describes very moderate future development in separation efficiency of plastics for particular micro-region. Although lots of effort have been done in the region to achieve higher plastic yield, the current values (35 %) do not correspond to bestpractice achievements reported from countries with well-developed systems. 9

10 Fig. 9 Exponential trend of yard-waste generation for particular rural microregion Fig. 10 Stable separation efficiency in particular micro-region Both above mentioned cases are typical representatives of situations, where trend analysis leads to faulty conclusions. No significant changes in the system are expected. Here, rather than precise statistical analysis, we have to rely on expert models and utilize experience from pilot projects and from other countries and regions where those particular measures have been implemented and their effect can be justified. This is additional group of models entering the tool. STEP3 - BALANCE CONSTRAINTS Some of these additional constrains are obvious, since they describe the distribution of individual components of MSW (paper, plastic, biowaste, minerals etc.) into separated fractions (SEP), Their remaining amounts forming residual waste. There were approx. 14 extra equations used in case of municipal solid waste forecasting. All of them were applied for all locations and all levels. They are called COMPOSITION constraints (Fig. 11). 10

11 Fig. 11 Graphical representation of composition constraint Second group of constrains were mentioned previously and denoted as AREAL constraints. They secure mass conservation within the tree structure: The sum of forecasted values for all lower organizational units in the region must be equal to the result of the forecast performed on the aggregated data of the region (Fig. 12). Fig. 12 Graphical representation of areal constraint Additional constraints describe fraction mass balance over specific plants. For example: separated plastic is collected with specific area including several locations by garbage truck. The amount is subsequently sorted in the plant. Outputs from plant are weighted indicating production within the collection area. 11

12 STEP4 - CORE CALCULATION - BALANCING (BA) The computational system processes a variety of spatially distributed forecasts (models) on production and composition at every point obtained by the aforementioned regression or trend analysis. From a mathematical point of view, it follows the principle of regression analysis, where least square method is applied. Least square method, in its traditional applications, results in a description, where square distances between each of the input data and the description (model) are minimized. In our case input data from TA and RA is horizontally fragmented forming vertical groups of points. Each of groups is associated with one unknown parameter. Input point estimates may differ and even frequently provide contradictory information. Balancing performs corrections where distances between resulting values (an unknown parameter) and all available forecasts are minimized, taking into account each of the locations, all territorial units and all fractions (See red points in Fig. 13). The task cannot be decomposed due to the additional constraints and reasons mentioned above. Fig. 13 Principle of models adjustments and final forecasted values The calculation principle may be visualized as a spring network (Fig. 14). The idea is to relocate the balls (initials models) to get (1) minimum deviations from original positions and (2) to meet all composition and areal constraints. As stiffness of springs may varies, so does the importance of individual models (based on data quality evaluation). As a result of this elaborated system of expected inaccuracies and weights, the forecasted values are determined. The result is balanced taking into account all models and constraints. 12

13 Fig. 14 The principle illustrated as a spring network Justine follows the principle of regression analysis, where a least squares approach is applied. The sum of the squares of the distances between results and models is minimized on the future waste production available in each of the territorial units. The importance of additional constraints was highlighted. These constraints concern the mass conservation equation in the tree structure. Since data about the production of municipal solid waste and its components are often missing or inadequate, research opportunities accelerated by this tool were proposed Ing. Martin PAVLAS, Ph.D. Coordinator of Energy&Simulation research group pavlas@fme.vutbr.cz tel