Automated Life Cycle Assessment: Recent Directions

1. Recent Directions Toward Automated Life Cycle Assessment Myeon-GyuJeong, James R. Morrison and HyowonSuh ISysE, KAIST

2. Presentation Overview • 1. Introduction • 2. LCA via CBR • 3. Case study • 4. Concepts for LCA at arbitrary levels of detail • 5. Concluding remarks

3. 1. Introduction Problem definition Related work Motivation Research purpose and scope Comparison to related work

4. 1.1 Problem definition Generic Product Development Process Mission Approval Critical design review Concept review Production approval System spec. review Planning Concept development System-level design Detail design Testing and refinement Production ramp-up Many Iteration Cycles for Design Improvement • [Manufacturing] - yet unknown • Piece part production processes • Design tooling • Define quality assurance processes • Begin procurement of long-lead tooling • [Design] - known • Define part geometry • Choose materials • Assign tolerances • Complete industrial design control doc. Input Environmental Impact Evaluation Life Cycle Assessment (LCA) Preceding conditions for eco improvement Standardized by ISO 14040~3 series Initial Design Improved Design

5. 1.1 Problem definition Concept and General Procedure of Life Cycle Assessment Goal Definition: which products or service are assessed? How to use the result of LCA? Resources Materials Parts Incineration Landfill Ass’y Product Use Disposal Scope definition Life cycle stage Unit process Recycle & Reuse Recycle Emission to air Emission to water CO2 SOx NOx T-N T-P metals Inventory analysis Measure envir. burden Considerable time and money to collect relevant data Ozone layer depletion Global warming Acidification Water pollution Impact analysis Impact to nature and human Check the reliability of data Detection of important issue Interpretation Review and reporting Make report Critical review

6. 1.1 Problem definition Limitations of LCA • Collecting all relevant data and information throughout the entire life cycle at the detail design stageis impossible • No matter what data is available, it requires considerable time and money • In case of the product have short development cycle such as cellular phone, LCA put a burden on whole PDP [Inventory Analysis] Input 1, 2, …,n output 1, 2, …,n Resources Streamlined LCA Input 1, 2, …,n output 1, 2, …,n Materials • is techniques that purposely adopt some sort of [simplified approach] to life cycle assessment Input 1, 2, …,n output 1, 2, …,n Parts Input 1, 2, …,n output 1, 2, …,n Assembly Input 1, 2, …,n output 1, 2, …,n Product Input 1, 2, …,n output 1, 2, …,n Use Input 1, 2, …,n output 1, 2, …,n Disposal

7. 1.2 Related work Three basic levels of LCA (Wenzel 1998) LCA scoping Numerical LCA Matrix LCA Product Attributes [Input] [Output] Product Attribute + Existing Full LCA results Material(Acquisition) Material, Energy Air, Water, Waste Learning/Fitting Manufacture Material, Energy Air, Water, Waste BLACK BOX (Numerical &Statistical) Use Scoringbased on checklist Material, Energy Air, Water, Waste Disposal Material, Energy Compare results Air, Water, Waste Matrix Operation (Weighting, Sum.) Results Environmental Impact Results (Omit indifferent process or in/output) Suggested by Graedel & Allenby(1995), Pommer(2001) Christiansen, K.(1997), Hur, T.(2003) InesSousa(2000), Seo, K.K(2006) Limitations Only qualitative assessment and low accuracy of result No systematic procedure to select life-stage and part/module of product Hard to learning or fitting the black box and applicable to only specific product category & environmental stressor

8. 1.3 Motivation • In general, most enterprises develop new products by revising or reusing the similar previous product, • Ifwe collect the LCA result of previous product, then we can estimate the LCA of new product from previous cases LCA result for regulation, certification Complicated LCA process Design output LCA result Case based reasoning for LCA Case retrieval Adaptation Engine Brake module Power train Interior Electrics Power train Interior Electrics Seat Battery

9. Representation Indexing CBR for LCA Adaptation Retrieval &selection 1.4 Research purpose and scope Generic Product Development Process Many Iteration Cycles System spec. review Critical design review Production approval Mission Approval Concept review Planning Concept development System-level design Detail design Testing and refinement Production ramp-up Support Design for Environment Case representation - FBSE expressions - Relations Case indexing - Case clustering by k-medoids Case adaptation - Geometry attribute based linear modeling algorithm - Multi-regression analysis Case retrieval & selection - Similarity measurement and computation

10. 1.5 Comparison to related work

11. 2. LCA via CBR Overview of the method An FBSe representation Similarity measure Case indexing Case retrieval and selection Case adaptation

12. 12 2.1 Overview of the method Similarity measure New problem w/o LCA Function similarity Functiondecomposition Product/part specification FBS modeling Behavior similarity Functiondecomposition Product/part specification FBSE modeling Weighting Structure (Non-numeric) Structure (Numeric) LCA result Similarity sum Case formulation Clustering(k-medoid) Findsimilar cluster Retrieve closecase set to P Constructnew cluster set Estimated LCA result Case adaptation Select Adaptation attribute Preprocessing area Regression modeling Case building Find optimal solution set Apply solution set to N Old case w/ LCA Legend Save as new case Flow Consider

13. 13 2.2 An FBSE representation [New FBSE model] [Function-Behavior-State(structure) model]byUmeda • Function: The purpose of the design • (e.g. the purpose of a fan is to move the air) • Behavior: The principle used to achieve the function • (e.g. propeller fan is a kind of fan to move the air) • Structure: The physical characteristics of the component • (e.g. geometry size, material, color) • Environmental impact: The component effect in the eleven eco-indicator 99 categories • (e.g. climate change, ozone layer) [Example of FBSE expression] Two shafts have same geometry and material T1 F T2 Problem space A B To support load To transfer torque No input at “Use” stage Require lubricant at “Use” stage Solution space Different LCA result

14. 14 2.2 An FBSE representation Function Behavior Structure Environmental effect

15. 2.3 Similarity measure Attribute type Layer Modeling language Similarity Measure function Tracing the degree of kinship from hierarchical function structure Function Attribute Functional basis by Hirtz et al., at NIST Function (f) consist of pairs of words: function verb (Fv) and function object (Fo)Ex) ((move), (air)) Behavior Attribute Standard or general engineering terminology Cosine similarity Nonnumerical value type Behavior (b) consists of up to 32 phrases Ex) ((cross, flow, fan)) Point matching function Structure Attribute Product specifications or BOM terminology Interval matching function Structures consist of three parts S=S1×S2×S3 S1 is a set of two phrase vectors used for nonnumeric descriptionsEx) ((material), ((galvanized), (sheet), (steel))) S2 is a set of vectors, each consisting of a phrase and a real numberEx) ((mass), 220) S3 is a set of vectors, each consisting of a phrase and two real numbersEx) (((revolution), (speed)), 500.0, 2250.0) Numerical value type Environmental effect Eco-indicator 99 method The environmental effect eE:= L×P32×R11 L is the the set of life cycle stage

16. 2.3 Similarity measure 4. Structure similarity a) Two phrase vectors used for nonnumeric descriptions 1. Function verb similarity Where, the indicator function I(x, y) = 1, if x=y, and 0, otherwise. 2. Function object similarity b) Structural descriptions with real number values 3. Behavior similarity c) The set of vectors, each consisting of a phrase and two real numbers 5. Overall similarity measure Where, I2(x, y) = 1, if x < y, and 0, otherwise

17. 2.4 Case indexing Clustering cases with representative case () as the center C2 c5 c1 c5 c1 c3 c3 k-medoidsclustering* r2 C1 c4 c2 c4 c8 c7 c2 c6 c8 r1 c6 c7 where, is attribute set of case aand is attribute set of case b In out study, each indexing layer(FBS) have different data type, and some of them have nonnumeric value. In contrast to the k-means algorithm, k-medoids chooses single case as center. By introducing clustering k-medoids algorithm, we can grouping some similar cases with representative case as the center, and indexing the rest cases according to distance with

18. 2.5 Case retrieval and selection Case memory r2 C2 c4 c7 This area is c3 C1 N c5 c3 Case clustering Similarity metric:,and k-medoids clustering algorithm r1 c4 c2 r3 c5 (v2) C3 c6 c4 r c3 c6 c2 c1 (v1) c5 c7 c3 c6 Retrieve cluster CR := {cCi: i=argmaxjU(,N)} c2 (v3) c1 c7 N c6 c1 c7 r Selected case set CN := {ciCR: U(ci,N) ≤ r} Filtering by life cycle stage (l) and unit process (p) Final adaptation case set

19. 2.6 Case adaptation c7 c3 N Where, t(j) is the original case index c6 r zt(j) contains 11 real numbers for the ecological effects of that case for life cycle l and unit process p. Each row of E contains the 11 errors for the eco-impact categories for a particular case ct(j)CN(l,p) , the matrix of observed ecological effects for M cases in CN(l,p) , KR is the number of non-null phrases is our decision variables (or independent variables) be our matrix of regression errors (residues) By least square error minimization, optimal decision variable values will be: The estimated ecological effect row vector zNR1×11 for new product module N in the life cycle stage l for unit process pis:

20. 3. Case study Outline of case study Case memory organization by k-medoids clustering Case adaptation and results Case scenario 1 Case scenario 2

21. 3.1 Outline of case study Target item New problem P: Cross flow fan in vehicle air purifier Raw material acquisition Part manufacturing Ass’y Transportation Use Disposal Interested Area Goal definition Estimate eco impact values of cross flow fan of vehicle air purifier Intended user: design engineer Scope definition Interested area is from raw material acquisition to part assembly (Upstream process)

22. 3.2 Case memory organization by k-medoids clustering Environmental impactwas evaluated by SimaPro 7 (Commercial SW) Classification of Impeller(blade) profile [Case memory of fan] Centrifugal Cross flow Axial flow Mixed flow Backward vaned Propeller Tubeaxial Forward vaned 13 2 Backwardcurved Backwardinclined Strip Tablock(Fergas) 33 3 Single Double Single Double 4 15 Total 100 cases were collected 10 10 5 5 Distance of P of each cluster medoid Cluster 2 is the closest cluster to P

23. 3.3 Case adaptation and results – Case scenario 1

24. 3.3 Case adaptation and results – Case scenario 1 Avg 2.56%

25. 3.3 Case adaptation and results – Case scenario 2

26. 3.3 Case adaptation and results – Case scenario 2 Avg 6.88%

27. 4. Concepts for LCA at arbitrary levels of detail

28. 4. 4. Concepts for LCA at arbitrary levels of detail Product A FA The function of sub-product(FA1) is comprised of functions of component(FA3, FA4) For example, {(move), (air)} and {(rotate), (fan)} is subset of {(make), (wind)}. Only leaf nodes of hierarchy have behavior, structure and environmental effect description,because leaf nodes indicate single component has detailed behavioral and structural attributes. And FBSE attribute is subordinate to upper node. Therefore, we can describe BA1 with BA3 and BA4. However, if the function of new problem N is not fully decomposed, we can not search the environmental effect from lower level, because there are no B, S information. Under these situation, construction the generalized functional hierarchy for specific product family will gives some help to find anticipated sub functions and associated environmental effect. In other words, with these generalized functional hierarchy, we can anticipate and support the design process with only high level functional attribute. The figures in next pages show these concepts. Sub-product A1 FA1 Sub-product A2 FA2 Component A3 • FA3, BA3, SA3 Component A4 • FA4, BA4, SA4 Component A5 FA5, BA5, SA5 Component A6 FA6, BA6, SA6 Electric fan {(make), (wind)} Fan • {(move), (air)} Motor • {(rotate), (fan)}

29. 4. 4. Concepts for LCA at arbitrary levels of detail Product B FB Product A FA Old case B Old case A Sub-product B1 • FB1, BB1, SB1, EB1 Sub-product B2 FB2 Sub-product A1 FA1 Sub-product A2 FA2 Component B3 • FB3, BB3, SB3, EB3 Component B4 • FB4, BB4, SB4, EB4 Component A3 • FA3, BA3, SA3, EA3 Component A4 • FA4, BA4, SA4, EA4 Component A5 FA5, BA5, SA5, EA5 Component A6 FA6, BA6, SA6, EA6 B1 is instance of C6 A4 is instance of C4 Model C isthe generalized functional hierarchy for specific product family Model C FC Case memory Sub-model C1 FA1 Sub-model C2 FA2 A3 is instance of C3 B4 is instance of C8 Sub-model C3 • FA3, BA3, SA3, EA3 Sub-model C4 • FA4, BA4, SA4, EA4 Sub-model C5 FA5 Sub-model C6 FA6, BA6, SA6, EA6 cases cases cases cases cases cases cases cases cases cases cases cases cases cases cases Sub-model C7 FA5, BA5, SA5, EA5 Sub-model C8 FA6, BA6, SA6, EA6

30. 4. 4. Concepts for LCA at arbitrary levels of detail If the function of product N is not fully decomposed,we cannot estimate the E of N1, N2 and N5. Product N FN Sub-product N1 FN1 Sub-product N2 FN2 Component C3 • FN3, BN3, SN3, EN3 Component C4 • FN4, BN4, SN4, EN4 Sub-product N5 FN5 Component c6 FN6, BN6, SN6, EN6 Model C FC Component C7 FN5, BN5, SN5, EN5 Component C8 FN6, BN6, SN6, EN6 Sub-model C1 FA1 Sub-model C2 FA2 Sub-model C3 • FA3, BA3, SA3, EA3 Sub-model C4 • FA4, BA4, SA4, EA4 Sub-model C5 FA5 Sub-model C6 FA6, BA6, SA6, EA6 cases cases cases cases cases cases cases cases cases cases cases cases cases cases cases Sub-model C7 FA5, BA5, SA5, EA5 Sub-model C8 FA6, BA6, SA6, EA6 However, if the product N is subset of Model C, C3, C4, C6, C7 and C8 will be anticipated lower function. After confirm the sub functions and associated behavior, structure,finally we can estimate environmental effect with LCA via CBR process.

31. 5. Concluding remarks

32. Concluding remarks • 1. Introduction • 2. LCA via CBR • 3. Case study • 4. Concepts for LCA at arbitrary levels of detail

33. Appendix

34. 34 * Functional basis reconciled function set Separate Branch Control Magnitude Isolate, sever, disjoin Actuate (6,13,0) Enable, initiate, start, turn-on Detach, isolate, release, sort, split,disconnect, subtract (1,1,0) (1,0,0) Divide (1,1,1) Regulate Control, equalize, limit, maintain (6,0,0) Extract Refine, filter, purity, percolate,strain, clear (1,1,2) (6,14,0) Increase (6,14,11) Allow, open Remove Cut, drill, lathe, polish, sand (1,1,3) Decrease (6,14,12) Close, delay, interrupt Distribute (1,2,0) Diffuse, dispel, disperse, dissipate, diverge, scatter Adjust, modulate, clear, demodulate, invert, normalize,rectify, reset, scale, vary, modify Change Import Channel (2,3,0) Form entrance, allow, input, capture (6,15,0) Export (2,0,0) Increment (2,4,0) Dispose, eject, emit, empty, remove, destroy, eliminate Amplify, enhance, magnify, multiply (6,15,13) Transfer Decrement Carry, deliver (6,15,14) Attenuate, dampen, reduce (2,5,0) Transport Advance, lift, move (2,5,4) Compact, compress, crush, pierce,deform ,form Shape (6,15,15) Functional basis Transmit (2,5,5) Condition Conduct, convey (6,15,16) Prepare, adapt, treat Guide Direct, shift, steer, straighten, switch Stop (0,0,0) End, halt, pause, interrupt, restrain (2,6,0) Translate (2,6,6) Move, relocate (6,16,0) Prevent Disable, turn-off (6,16,17) Rotate (2,6,7) Spin, turn Inhibit (6,16,18) Shield, insulate, protect, resist Allow DOF Provision Store (2,6,8) Constrain, unfasten, unlock Accumulate Contain (7,17,0) (7,0,0) connect Couple (7,17,19) Capture, enclose Associate, connect Collect (3,7,0) (3,0,0) (7,17,20) Join (7,18,0) Absorb, consume, fill, reserve (3,7,9) Assemble, fasten Supply Provide, replenish, retrieve Link (3,7,10) Attach Mix Signal Sense (3,8,0) Add, blend, coalesce, combine, pack Feel, determine (8,19,0) (8,0,0) Detect Discern, perceive, recognize (8,19,21) Stabilize Support (4,9,0) Steady Measure (8,19,22) Identify, locate (4,0,0) Secure (4,10,0) Constrain, hold, place, fix Indicate Announce, show, denote, record, register Position (4,11,0) Align, locate, orient (8,20,0) Track (8,20,23) Mark, time Condense, create, decode, differentiate, digitize, encode, evaporate, generate, integrate, liquefy, process, solidify, transform Display (8,20,24) (8,21,0) Emit, expose, select Convert Convert (5,12,0) Process Compare, calculate, check (5,0,0) * Reference: NIST Technical Note 1447 “A Functional Basis for Engineering Design: Reconciling and Evolving Previous Efforts”

35. *k-medoids clustering algorithm The k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoid shift algorithm. In contrast to the k-means algorithm, k-medoids chooses data points as centers (medoidsor exemplars). 1. Arbitrary select k of the n data points as the medoids 2. Associate each data point to the closest medoid, and calculate total cost of each cluster 3. Swapping medoid and random case, and calculate total cost 4. Finalized cluster set In our research, similarity measurement can be defined as the sum of functional distance, behavioral distance and structural distance. However each indexing layer have different data type, and some of them have nonnumeric value. Therefore, k-medoids clustering algorithm is appropriate to us than k-means.