COMPLEX APPROACH TOWARDS THE ASSESSMENT OF WASTE-TO-ENERGY PLANTS FUTURE POTENTIAL

Transcription

1 COMPLEX APPROACH TOWARDS THE ASSESSMENT OF WASTE-TO-ENERGY PLANTS FUTURE POTENTIAL M. PAVLAS, O. PUTNA, J. KROPÁČ, P. STEHLÍK Institute of Process Engineering, Faculty of Mechanical Engineering, Brno University of Technology, Brno Czech Republic IRRC Waste-to-Energy, 6th September 2016, Vienna

2 2 Motivation Recovery shares of municipal solid waste (MSW) in EU27 in 2014 (material and/or energy) Mapa recyklace ČR CZECH REPUBLIC 506 kg 35% material recovery 12% incineration 48% landfilling 3+1 WTE plants in operation

3 3 Motivation Advanced computational tools to suport decisionmaking in waste management

4 Motivation 4

5 5 Content Complex logistic-based tool supporting decion-making in waste management (NERUDA) Forecasting of future amounts using advanced regression model-based tool Justine Selected results obtained by NERUDA Conclusion

6 6 Basic principle 206 nodes for CZE (county-level cities, L2) Incineration Landfilling Co-firing Facility (WTE, MBT, landfill, etc.) Producer (municipality) Gate fee? Geographical data and statistics behind Optimization in a transportation problem WTE min j dv j x j + j a ij x j p i i, Transportation cost Processing cost Simplified version WTE facilities only

7 7 Visualization of result One particular scenario (one simulation run) Short-distance transport Intermodal transport Refuse Derived Fuel transport Railway transport Stochastic approach = Up-to-date approach Novel types of outcomes

8 8 Recent applications/references Municipal solid waste (MSW) Country level analysis Ministry of Industry and Trade of the Czech Republic (2013) Ministry of Environment of the Czech Republic (2015) Regional level analysis Analysis within Waste Management Plan creation processes (2015, 2x) Microregions Development of strategies for residual waste treatment (2015, 2x) Investors and future operators Pre- feasibility studies for large WtE plant (2012, 2013) NERUDA = open tool ready for real applications worldwide

9 9 NERUDA effective modular structure MBT = Mechanical - Biological Treatment GIS = Geographical Information System LCA = Life Cycle Assessment

10 10 Content Complex logistic-based tool supporting decion-making in waste management (NERUDA) Forecasting of future amounts using advanced regression model-based tool Selected results obtained by NERUDA Conclusion

11 11 Aim Investigated commodity and area Hierarchical structure definition Data input Simultaneous forecasting of waste amounts at different territorial units Data verification Regression analysis Trend analysis Balancing Municipalities (L3) Results

12 12 Aim Investigated commodity and area Hierarchical structure definition Data input Simultaneous forecasting of waste amounts at different territorial units Data verification Regression analysis Trend analysis Balancing Micro-regions (L2) Results

13 13 Aim Investigated commodity and area Hierarchical structure definition Data input Simultaneous forecasting of waste amounts at different territorial units Data verification Regression analysis Trend analysis Balancing Country (L0) Results

14 14 Inputs yield and production data Investigated commodity and area Hierarchical structure definition Data input Data verification Regression analysis Trend analysis Balancing Results Spatially distributed data Sometimes uncertain and incomplete

15 15 Input - composition data Investigated commodity and area Hierarchical structure definition Data input Data verification Regression analysis Trend analysis Balancing Results Incomplete and uncertain data

16 16 Trend analysis Investigated commodity and area Hierarchical structure definition Data input Data verification Regression analysis Trend analysis Balancing Results TOP LEVEL DATA (L0) Data quality BOTTOM LEVEL DATA (L2)

17 17 Balancing Investigated commodity and area Hierarchical structure definition Data input Data verification Regression analysis Trend analysis Balancing Results Visualisation of calculation principle as a spring network. The idea is to relocate the balls to get a minimum deviations from original positions defined by trend analysis.

18 18 Constraints Investigated commodity and area Hierarchical structure definition Data input Geographical constraints Applied according to the hierarchical structure defined before Data verification Regression analysis Trend analysis Balancing Composition constraints applied on all levels Results

19 19 Result of the calculation Investigated commodity and area Hierarchical structure definition Data input Data verification Regression analysis Trend analysis For each level (region, subregions and their parts) the following was forecasted: future production of fractions (PAP, PLA, GL, BIO) as separately collected in residual waste composition of residual waste separation efficiency of PAP, PLA, BIO, etc. The result is balanced, i.e. current trends and additional constrains are respected. Balancing Results

20 20 Result an example Investigated commodity and area Hierarchical structure definition Plastics: amounts, balance and separation efficiency, micro-region (L2) Data input Data verification Regression analysis Trend analysis Balancing Results

21 21 Content Complex logistic-based tool supporting decion-making in waste management (NERUDA) Forecasting of future amounts using advanced regression model-based tool Selected results obtained by NERUDA Conclusion

22 22 Visualization of result (one scenario) Short-distance transport Intermodal transport Refuse Derived Fuel transport Railway transport

23 Treated amount [kt/y] 23 WtE vs MBT regional level Outcome of complex analysis. Project for the Czech Ministry of Environment, Proposal of an optimum network for residual waste treatment Allocation of processing capacities. MBT WtE Parameter: Landfill tax

24 24 Balance analysis particular region Outcome from computations within the framework of regional Waste management plan, Residual waste, CZE, Comparision of two possible scenarios

25 25 Cost analysis for wider area Future processing cost incl. transport in a wider area (CZE), residual waste

26 26 Cost analysis for specific micro-region Possible alternatives for residual waste treatment, particular micro-region (L2)

27 Survival function VÝSLEDKY - PŘÍKLADY Economic sustainability of two WtE projects intended in two different localities YES NO ŠOMPLÁK et al. Logistic model-based tool for policy-making towards sustainable waste management. Clean Technologies and Environmental Policy

28 Risk analysis Influence of accumulated risks on project economy

29 Waste availability factor (WAF) Intended plant capacity C ref = 150 kt/r Based on future competition modelling available amounts (m W ) were evaluated for 2 scenarios: SC1: higher gate-fee WAF = m W / C ref = 0,8 SC2: lower gate-fee WAF = m W / C ref = 1,3 Ferdan et al. A waste-to-energy project: A complex approach towards the assessment of investment risks, Applied Thermal Engineering, 89 (2015)

30 30 Content Complex logistic-based tool supporting decion-making in waste management (NERUDA) Forecasting of future amounts using advanced regression model-based tool Justine Selected results obtained by NERUDA Conclusion

31 31 Conclusions Unique calculation tools Justine and NERUDA have been introduced NERUDA represents a logistic-based tool for early investment planning in waste management. NERUDA s practical relevance has been proven by several recent applications. Initially, it was mainly devoted for processing residual household waste. It can be applied for other types of streams and technologies. JUSTINE represents a tool for forecasting in incomplete spatially distributed data problems. Its application for determination of future household waste amounts in the Czech Republic was presented.

32 Project supported by Technology Agency of the Cezch Republic The authors gratefully acknowledge financial support provided within the research project No. CZ.1.07/2.3.00/ Excellent young researchers at Brno University of Technology, further with financial support from the MEYS under the National Sustainability Programme I (Project LO1202) and financial support provided by Technology Agency of the Czech Republic within the research project No. TE "Waste-to-Energy (WtE) Competence Centre Thank you for your attention! Dr. Martin Pavlas (pavlas@fme.vutbr.cz)

33 Diese Präsentation erfolgte im Rahmen der Veranstaltung: IRRC IRRC WASTE-TO-ENERGY 5. und 6. September 2016