07 Life-cycle assessment and green metrics

Count the whole chain, then count the atoms.

Life-cycle assessment follows a product from raw materials to disposal and converts emissions into impacts wherever characterization factors exist. Green-chemistry metrics measure how efficiently a route turns reactants into product. Both belong inside the design loop, because the route chosen for a molecule can matter more than the molecule.

Computed for this page Mass flow per mole of [Bmim][BF4] for 1-methylimidazole + 1-chlorobutane, then anion exchange with NaBF4: stoichiometric quantities, 100% yield, standard atomic weights. Ribbons are colored by where their atoms end up. Solvents, excess reagents, work-up and losses are not included, so real routes waste more.

You will see

  • The ISO 14040/14044 framework and the matrix form of LCA
  • Characterization: from emissions to impacts, reproduced on published data
  • Uncertainty in LCA
  • Atom economy, E factor and process mass intensity
  • First-pass cost scaling

Labs in this chapter

7.1 The framework

ISO 14040 and 14044 define four phases: goal and scope (including the functional unit and the system boundary), inventory analysis, impact assessment and interpretation. The functional unit makes comparisons fair. The founder’s 2013 study compared an energetic ionic salt with TNT per megajoule of energy content, not per kilogram, because the two deliver different energy per mass (Methods 07).

7.2 The matrix form of LCA

Heijungs and Suh (2002) wrote inventory analysis as linear algebra. The technology matrix A holds each process’s inputs and outputs; scaling factors s make the system deliver the final demand f; the intervention matrix B converts scaled processes into emissions and resource use g; characterization factors Q turn those into impacts h:

A s = f  ⇒  s = A−1f,    g = B s,    h = Q g
(7.1)

Written this way, sensitivity and uncertainty analysis become matrix calculus, and a design optimizer can evaluate the life-cycle consequence of changing one process step.

7.3 Characterization: from emission to impact

For freshwater ecotoxicity, USEtox multiplies a fate factor (FF, days of persistence in the compartment), an exposure factor (XF, the dissolved fraction) and an effect factor (EF, from the HC50 of Chapter 6): CF = FF × XF × EF, with EF = 0.5/HC50 (USEtox 1.x; the 2023 global recommendations by Owsianiak and co-workers revised the effect basis). The impact of a release is its mass times the CF, in comparative toxic units (CTUe).

Lab 7.1 Computed for this page Published data

Characterization factors, recomputed from the published inputs

HC50 from the 2016 study’s Table 1; FF and XF from its Table 3. EF and CF are recomputed here with Eq. 6.3 and compared with the published values.

Recomputed vs published USEtox factors for five ionic liquids
Differences are within 0.6% and come from rounding of the published inputs.

7.4 Uncertainty in LCA

Inventory data, characterization factors and use-phase assumptions are all uncertain. The 2016 study propagated part of this by Monte Carlo: metal characterization factors were varied by ±1 order of magnitude; each property input in its Table 2 and each HC50 was given a uniform distribution spanning one order of magnitude either way; the use-phase release fraction was varied from 0.4% to 2%; and 1,000 samples gave 95% intervals. The 2013 study used a sensitivity analysis instead, varying the theoretical-to-actual energy conversion factor by ±30%. A design system should carry such intervals into the ranking, not report them after it (Chapter 5).

7.5 Green-chemistry metrics

AE = MproductΣ Mreactants × 100%,    E = mwastemproduct,    PMI = Σ minputsmproduct
(7.2)

Atom economy (Trost, 1991) uses the balanced equation and molar masses. The E factor (Sheldon) counts everything but the desired product as waste, per kilogram of product. Process mass intensity counts total materials used per kilogram of product. When both count the same inputs, E = PMI − 1.

Atom economy is a theoretical ceiling. At 100% yield with stoichiometric reagents and no solvent, the minimum E factor is 1/AE − 1 (with AE as a fraction); real E factors include solvents, excess reagents, work-up and yield losses.

Lab 7.2 Computed for this page

Atom economy of a two-step route to a [Bmim] ionic liquid

Step 1: 1-methylimidazole + 1-halobutane → [Bmim] halide (an addition, 100% atom economy). Step 2: anion exchange with a salt of the target anion, releasing an inorganic halide. Molar masses are computed from the formulas with standard atomic weights.

Alkylating agent

Target anion (exchange salt)

Stoichiometry only: no solvents, excess reagents or yield losses. It shows how much of the route’s mass ends as an inorganic by-product, before any process inefficiency.

7.6 First-pass cost

Early-stage capital estimates often scale a known plant by capacity with the six-tenths rule (Williams, 1947), C2 = C1(S2/S1)0.6. It is an order-of-magnitude tool: the exponent differs by equipment type, and bottom-up techno-economic models replace it once a flowsheet exists. In the design problem of Chapter 5 it supplies a cost estimate early enough to rule candidates out.

References

  1. ISO 14040:2006 and ISO 14044:2006, Environmental management — Life cycle assessment.
  2. Heijungs, R.; Suh, S. The Computational Structure of Life Cycle Assessment. Kluwer Academic, 2002. doi:10.1007/978-94-015-9900-9
  3. Owsianiak, M.; Hauschild, M. Z.; Posthuma, L.; Saouter, E.; Vijver, M. G.; Backhaus, T.; Douziech, M.; Schlekat, T.; et al. Ecotoxicity characterization of chemicals: global recommendations and implementation in USEtox. Chemosphere 2023, 310, 136807. doi:10.1016/j.chemosphere.2022.136807
  4. Mehrkesh, A.; Karunanithi, A. T. Environ. Sci. Technol. 2016, 50, 6814–6821 (Tables 1, 3; Monte Carlo method). doi:10.1021/acs.est.5b04721
  5. Mehrkesh, A.; Karunanithi, A. T. Energetic ionic materials: how green are they? A comparative life cycle assessment study. ACS Sustainable Chem. Eng. 2013, 1, 448–455. doi:10.1021/sc3001383
  6. Trost, B. M. The atom economy—a search for synthetic efficiency. Science 1991, 254, 1471–1477. doi:10.1126/science.1962206
  7. Sheldon, R. A. The E factor: fifteen years on. Green Chem. 2007, 9, 1273–1283. doi:10.1039/b713736m
  8. Jiménez-González, C.; Ponder, C. S.; Broxterman, Q. B.; Manley, J. B. Using the right green yardstick: why process mass intensity is used in the pharmaceutical industry to drive more sustainable processes. Org. Process Res. Dev. 2011, 15, 912–917. doi:10.1021/op200097d
  9. Williams, R., Jr. Six-tenths factor aids in approximating costs. Chem. Eng. 1947, 54, 124–125.

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