The current configuration recalculated for current supplies and every year from 2028 to 2045 under both scenarios. Design, location, batch, demand and facility settings stay fixed; only the grid, heat and chemical-supply factors change, and every year uses the same draws. Select a year in the chart or a row in the table to show it above.
Table: every year and scenario
Spearman rank correlation (ρ) of each sampled input with the footprint, within the sampled ranges. A screening signal: it is not a share of variance and does not by itself show which check improves the decision.
Enter a measured value for one input of this design and the range of that measurement, then add it. Each measurement is added on top of the ones before (nothing is saved to the data), and every step is recorded: the 90% range, whether the best design in Screen designs changes, and which input to check next.
From the same start, with the same number of steps and the same measurement range, compare filling the input with the highest rank correlation first (guided) with filling the inputs in the order the design lists them (fixed). Each filled input is set to its current central value, so this compares how fast the range narrows, not what real measurements will show.
Placeholder factors applied to the same flows; trade-offs to watch when a carbon reduction is chosen.
Compare manufacturing options
Options use the same random draws for inputs that come from the same data record (same source and range, in any process design), the reflux ratio of the same solvent, the common grid and chemical-supply spreads and each region's grid residual, so differences reflect the designs rather than sampling noise. A pairwise verdict is given only for the same medicine, reporting unit, annual demand, facility sharing and surplus handling. Add options from the Assess tab.
Spearman rank correlation of each sampled input with the difference between the two options. Not a share of variance; a screening signal only.
Screen production configurations
Morphological screen: every combination of process design, location and capacity is run to meet the same annual demand. Batches, sites and the facility base load follow from the demand, so a larger batch or more slots only helps if the demand uses them. Laboratory-only routes and laboratory equipment are excluded; feasible combinations are ranked with uncertainty.
P(lowest) is the share of draws in which a configuration has the lowest footprint among feasible ones; when several are tied in a draw, the draw is split equally between them. Capacity varies batch size (×0.5, ×1, ×2) for batch products and batch slots per site for patient-specific products. Select a row to open it in the Assess tab.
Check a module against reserved meter readings
The shared solvent-recovery module computes the heat to warm the whole feed and evaporate the recovered solvent at the operating pressure, then the electricity for the heating bath, condenser chiller, vacuum pump and standby. Bath efficiency and standby power are fitted on the development group and used, unchanged, to predict the test group. The Assess tab uses this same fit for laboratory recovery equipment.
For each operation of the chosen design: whether a shared module applies directly, applies once its equipment parameters are determined again, or the design needs records or a module of its own, with the data the design supplies and the modelling work. Equipment type and run conditions decide the class for solvent recovery.
Development records fit the module, calibration records set the prediction error carried into footprints, and test records are only scored. A new version starts whenever a record changes (a data file loaded, a case opened, a reset); a case file keeps this history.
Records without run conditions are kept for the check but cannot be predicted: the equipment model needs the pressure and the run time of each run. Where the missing conditions were filled with dummy values, the record is fitted or predicted above and marked “dummy conditions”; those results only show that the chain runs and are not evidence of accuracy. The heat the solvent itself needs (warming every solvent fed from 20 °C to its boiling point and evaporating the recovered share) is shown only as a check against the meter reading. The rest of the reading is equipment overhead (bath standby, vacuum pump, chiller), which depends on run time and equipment power.
Built-in evidence and scenario database
Every input carries a range, a label (measured, simulated or proxy; calculated or assumed for module records) and a status. Demo records are shown as “demo · label” so they cannot be mistaken for real evidence. Labels guide the screening advice: a proxy with a strong rank correlation is a candidate for a supplier enquiry or a measurement.
Load a JSON data file with module-check records and/or process parameters. The file is read in this browser only and is never uploaded; it replaces the matching records of the active dataset for this session, and every result is recalculated. The template contains placeholders only: it is rejected until every value is filled in.
Placeholder values standing in for premise IMAGE SSP2 backgrounds and NESO pathways.
Every heat input (process, solvent recovery, facility) is useful heat; fuel = useful heat ÷ boiler efficiency , then this factor applies.
Heat capacity, enthalpy of vaporisation and boiling point are handbook values. Incineration CO₂ is from carbon content. Production factors are illustrative.
Methods
How the tool works, module by module. The LCA calculation references PAS 2090:2025 (see Reference standard below). Numbers on this page come from the active dataset and model, so they change when a local data file is loaded.
How a module is reused
Try it: plug modules into a process
Pick a target process, then drag a module card onto its slot (near the top or bottom edge of the window the page scrolls), or double-click a card to place it in its slot. Double-click a slot to take a plugged module out and put the target's own back. With the keyboard or on a phone, select a card (Enter, Space or tap) and then the slot. The result is recalculated with the same draws. Inputs the target lacks are borrowed from another process and listed: that makes the result a what-if, not an assessment.