Modeling process and material alternatives in life cycle assessments
Background, Aims and Scope
Although LCA is frequently used in product comparison, many practitioners are interested in identifying and assessing improvements within a life cycle. Thus, the goals of this work are to provide guidelines for scenario formulation for process and material alternatives within a life cycle inventory and to evaluate the usefulness of decision tree and matrix computational structures in the assessment of material and process alternatives. We assume that if the analysis goal is to guide the selection among alternatives towards reduced life cycle environmental impacts, then the analysis should estimate the inventory results in a manner that: (1) reveals the optimal set of processes with respect to minimization of each impact of interest, and (2) minimizes and organizes computational and data collection needs.
A sample industrial system is used to reveal the complexities of scenario formulation for process and material alternatives in an LCI. The system includes 4 processes, each executable in 2 different ways, as well as 1 process able to use 2 different materials interchangeably. We formulate and evaluate scenarios for this system using three different methods and find advantages and disadvantages with each. First, the single branch decision tree method stays true to the typical construction of decision trees such that each branch of the tree represents a single scenario. Next, the process flow decision tree method strays from the typical construction of decision trees by following the process flow of the product system, such that multiple branches are needed to represent a single scenario. In the final method, disaggregating the demand vector, each scenario is represented by separate vectors which are combined into a matrix to allow the simultaneous solution of the inventory problem for all scenarios.
For both decision tree and matrix methods, scenario formulation, data collection, and scenario analysis are facilitated in two ways. First, process alternatives that cannot actually be chosen should be modeled as sub-inventories (or as a complete LCI within an LCI). Second, material alternatives (e.g., a choice between structural materials) must be maintained within the analysis to avoid the creation of artificial multi-functional processes. Further, in the same manner that decision trees can be used to estimate ‘expected value’ (the sum of the probability of each scenario multiplied by its ‘value’), we find that expected inventory and impact results can be defined for both decision tree and matrix methods.
For scenario formulation, naming scenarios in a way that differentiate them from other scenarios is complex and important in the continuing development of LCI data for use in databases or LCA software. In the formulation and assessment of scenarios, decision tree methods offer some level of visual appeal and the potential for using commercially available software/ traditional decision tree solution constructs for estimating expected values (for relatively small or highly aggregated product systems). However, solving decision tree systems requires the use of sequential process scaling which is difficult to formalize with mathematical notation. In contrast, preparation of a demand matrix does not require use of the sequential method to solve the inventory problem but requires careful scenario tracking efforts.
Here, we recognize that improvements can be made within a product system. This recognition supports the greater use of LCA in supply chain formation and product research, development, and design. We further conclude that although both decision tree and matrix methods are formulated herein to reveal optimal life cycle scenarios, the use of demand matrices is preferred in the preparation of a formal mathematical construct. Further, for both methods, data collection and assessment are facilitated by the use of sub-inventories (or as a complete LCI within an LCI) for process alternatives and the full consideration of material alternatives to avoid the creation of artificial multi-functional processes.
Recommendations and Perspectives
The methods described here are used in the assessment of forest management alternatives and are being further developed to form national commodity models considering technology alternatives, national production mixes and imports, and point-to-point transportation models.
KeywordsDecision trees expected value inventory analysis material choice process choice
- Flinn RA, Turban E (1970): Decision Tree Analysis for Industrial Research. Research Management 13(1) 27–34Google Scholar
- Famili A (1994): Use of decision-tree induction for process optimization and knowledge refinement of an industrial process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM 8(1) 63–75Google Scholar
- Carvalho DR, Freitas AA (2007): A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163(1–3) 13–35Google Scholar
- Tsai C, Lee C, Chen C, Yang W (2007): A multivariate decision tree algorithm to mine imbalanced data. WSEAS Transactions on Information Science and Applications 4(1) 50–58Google Scholar
- Heijungs R, Suh S (2002): The Computational Structure of Life Cycle Assessment. Kluwer Academic Publishers: DordrechtGoogle Scholar
- Morgan MG, Henrion M (1990): Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge University Press, New YorkGoogle Scholar
- Bage GF, Samson R, Sinclair-Desgagné B (2002): A Technicoeconomic Approach for the Selection of a Site Remediation Strategy — Part A: Theory Environmental Management 30(6) 807–815Google Scholar
- Bage GF, Samson R, Sinclair-Desgagné B (2003): A Technicoeconomic Approach for the Selection of a Site Remediation Strategy — Part B: Model Application Environmental Management 31(1) 69–78Google Scholar
- Swiss Centre for Life Cycle Inventories (2004): ecoinvent V11 — The Swiss National Life Cycle Inventory Database http://wwwecoinventch/