Combining Life Cycle Assessment with
Model-Based Systems Engineering
A normalized approach to evaluate the environmental impacts of a system
Life Cycle Assessment (LCA) is a widely recognized methodology that is normalized by several standards (ISO 14040 and ISO 14044) that “studies the environmental aspects and potential impacts throughout a product's life cycle (i.e., cradle-to-grave) from raw materials acquisition through production, use and disposal. The general categories of environmental impacts needing consideration include resource use, human health, and ecological consequences.”
An LCA study is composed of four main phases :
- Defining the Goal and Scope of the study.
- Creating a Life Cycle Inventory of flows from and to nature (ecosphere) generated by the analyzed system.
- Carrying out a Life Cycle Impact Assessment to evaluate the potential environmental and human health impacts of these flows.
- Interpreting the results to summarize the impacts and propose recommendations.
Different software tools, like SimaPro and OpenLCA, exist on the market to compute environmental impacts with this approach. These tools work with LCA databases, like EcoInvent, that define life cycle inventory data for raw materials or products.
The challenges of LCA for complex systems
Complex systems are characterized by a great number (several hundred or thousands) of various components that are interconnected, generating emergent properties.
Carrying out an LCA for a complex system is a complex task itself, for several reasons :
- The information required to create the inventory is often contained in heterogeneous office-based documents.
- It would be too long, too costly, and counter productive to study an inventory that includes all the components.
- During the early phases of the design, as the system is not completely defined, the inventory must be updated each time the concerned components evolve.
In an MBSE approach, the model describes the system and its components, across the whole spectrum from a business needs perspective, to the physical implementation, including the logical decomposition of the system. This information is available in a coherent, integrated, and computational format.
Considering this model as a reference to automatically generate a Life Cycle Inventory enables an iterative and pragmatic approach, starting from a coarse-grain analysis, and then focusing on the components that have the worst impacts.