LCA – Life-Cycle-Assessment - modeling with an industrial waste example

1. Not Analysis LCA – Life-Cycle-Assessment - modeling with an industrial waste example T. H. Christensen, A. Damgaard, L. Kai-Sørensen Brogaard, T. Astrup & A. Boldrin Department of Environmental Engineering Technical University of Denmark 5th International Conference on Industrial and Hazardous waste Crete 2016

2. Introduction:LCA is strong tool for consistent system analysis • Waste is heterogeneous and separation is often a way to recover valuables • All waste technologies consume materials and energy and have emissions and are as such a load to the environment –the valuables have a value according to what they substitute • A waste technology is usually not ideal – there are rejects • Waste management is a full solution to the problem • LCA is made to handle complex systems Waste

3. Introduction:LCA has become a part of waste management • The EU waste directive refers to Life-Cycle-Thinking (LCT) as a supplement to the traditional Waste Hierarchy • LCA is the most advanced quantitative approach to LCT • A few countries have established national tools: USA (WARM+), UK (WRATE) and partly Denmark (EASETECH) • EU Joint-Research-Center (Ispra) provides: • Guideline for use of LCA in waste management • Provides databases (ELCD/ILCD) European Commission (2011). European Commission - Joint Research Centre - Institute for Environment and Sustainability: Supporting Environmentally Sound Decisions for Waste Management - A technical guide to Life Cycle Thinking (LCT) and Life Cycle Assessment (LCA) for waste experts and LCA practitioners. EUR 24916 EN - 2011. Luxembourg. Publications Office of the European Union, 2011

4. For each process/ technology we need info about: • Mass balances/substance balances • Energy budgets • Emission accounts What is LCA? An LCA consists of four steps Defines the system so that it addresses the question and allows for comparison Describes the technical systems, provides data and calculates the overall load from the system Often 2-3 iterations are needed Converts technical results into potential impacts and normalize the results Interprets the results. Answers the question

5. World survey of waste LCAs (late 2012) • Reviewed all publicly available literature of a certain quality –about 225 publications • Results published in WASTE MANAGEMENT: • Review of LCA studies of solid waste management systems – Part I: Lessons learned and perspectives. Laurent, Alexis; Bakas, Ioannis; Clavreul, Julie; Bernstad, Anna ; Niero, Monia; Gentil, Emmanuel ; Hauschild, Michael Zwicky; Christensen, Thomas Højlund.Waste Management, Vol. 34, No. 3, 2014, p. 573-588 • Review of LCA studies of solid waste management systems – Part II: Methodological guidance for a better practice. Laurent, Alexis; Clavreul, Julie; Bernstad, Anna ; Bakas, Ioannis; Niero, Monia; Gentil, Emmanuel ; Christensen, Thomas Højlund; Hauschild, Michael Zwicky. Waste Management, Vol. 34, No. 3, 2014, p. 589-606.

6. World distribution Review of LCA studies of solid waste management systems – Part I: Lessons learned and perspectives. Laurent, A; Bakas, I; Clavreul, J; Bernstad, A; Niero, M; Gentil, E; Hauschild, M Z; Christensen, T. Waste Management, Vol. 34, 2014, p. 573-588

7. A recent development:

8. Types of hazardous and special waste? LCA on hazardous, industrial and special waste; <10% of studies Waste distribution • Printed circuit boards • Ammunition • WEEE • Shredder waste • Steel production waste • Tires • Pharmaceuticals • Air-Pollution-Control residues • Hazardous waste • Paint waste • Lubricant waste • Pavement waste LCA on hazardous and special waste can be done But there are challenges Review of LCA studies of solid waste management systems – Part I: Lessons learned and perspectives. Laurent, A; Bakas, I; Clavreul, J; Bernstad, A; Niero, M; Gentil, E; Hauschild, M Z; Christensen, T. Waste Management, Vol. 34, 2014, p. 573-588

9. Issues in hazardous and special waste LCA There are several issues specifically important when LCAs are made on hazardous, industrial and special waste; here three key issues are mentioned • The 100-year time horizon • Technologies are often on different levels of development and therefore hard to compare • Technology data for rare elements • Toxic impacts versus global warming impacts • Data are few

10. Issues in hazardous and special waste LCA There are several issues specifically important when LCAs are made on hazardous, industrial and special waste; here three key issues are mentioned • The 100-year time horizon • Technologies are often on different levels of development and therefore hard to compare • Technology data for rare elements • Toxic impacts versus global warming impacts • Data are few

11. 1st challengeThe 100 year time horizon: Usually emissions are considered for 100 years • Compromise between covering the full emission period and having data -e.g gas from a landfill is fully covered • Leaching is usually a longer period and for specific waste fractions data from leaching test are often used: L/S = 2 corresponds roughly to 100 years • LCA uses masses and excludes emissions beyond 100 years 100 years of infiltration of 200mm/year through 10 m of waste with a density of 1 ton/m3 equals an L/S ratio of 2

12. 1st challengeThe 100 year time horizon: APC-residue leached in columns for extended time >1 mg/l Pb for more than 5000 years LCA covers 100 years or L/S 2

13. Issues in hazardous and special waste LCA There are several issues specifically important when LCAs are made on hazardous, industrial and special waste; here three key issues are mentioned • The 100-year time horizon • Technologies are often on different levels of development and therefore hard to compare • Technology data for rare elements • Toxic impacts versus global warming impacts • Data are few

14. Why the importance of uncertaintyanalysis • Uncertainty in all results: LCA modeling is a system approximation of the real system and the problem we address -> Epistemic uncertainty -> Variability of the inputs • What can we do? • Reduce it by better data • Assess (quantify if possible) • An important tool to show robustness of our results and increase reliability in LCA

15. Method for uncertaintyanalysis Ref: Clavreul, J., Guyonnet, D., & Christensen, T. H. (2012). Quantifying uncertainty in LCA-modelling of waste management systems. Waste management, 32(12), 2482–95.

16. What do people do? Ref: Laurent, A., Bakas, I., Clavreul, J., Bernstad, A., Niero, M., Gentil, E., Hauschild, M.Z., Christensen, T.H.: Review of LCA applications to solid waste management systems – Part I: Keylearnings and perspectives. Submitted to Waste Management. 16

17. Example with shredderwaste • Recovery of recyclables • <4 mm always to landfill • >4 mm alternative treatments

18. Scenario 1: Landfilling

19. Scenario 2: Incineration

20. Scenario 3: Pyrolysis

21. Scenario 4: Cement kiln

22. Inventory data • Datasets • Waste composition • Waste sorting • Incineration • Pyrolysis • Cement-kiln • Landfill • Data of highly varying quality

23. Regular LCA results

24. Define parameters for Monte Carlo simulation 250 parameters

25. LCI Sorting

26. LCI Incineration • Vestforbrænding (Jakobsen et al., 2013) • Air emissions (TC) • Distribution to solids • Leaching from residues

27. LCI Pyrolysis • Average pyrolysis process  10 publications • Average composition: • SR input • Char • Oil • Syngas • MFA + SFA: • Reconciled, uncertainty • Mass, En, Al, C, Ca, Cd, Cl, Co, Cr, Cu, Fe, Hg, Mn, N, Ni, Pb, S, V, Zn

28. Monte Carlo simulation for GWP – Scenario 3 results

29. Results – with uncertaintyestimates

30. UnbalancedtechnologiesLearnings by usinguncertainty • Unbalancedtechnologiescanbecompared –pyrolysisneeds more data • Identifywhererecoveriesare most important -non-ferrous and ferrous metals • Identifywhere emissions shouldbecontrolledbetter - air emission of Cu from cement kiln

31. Conclusion • LCA modelling is now an established part of waste management • Only few studies address hazardous, industrial and special waste • LCA on hazardous, industrial and special waste is feasible but challenges are often met in terms of: • The 100-year time horizon • Technologies are often on different levels of development and therefore hard to compare • Technology data for rare elements • Toxic impacts versus global warming impacts • Data are few • Introduction of uncertainty in the modeling – e.g. Monte Carlo simulations – may help in addressing unbalanced and uncertain data Efcharistó Thank you