Engineering Computation
Deterministic Tools. Typed Interfaces.
MCP servers wrap peer-reviewed open-source engines and expose them as typed, auditable tools. Every input is validated. Every output is structured. Every call is logged.
How Engineering Computation Works in PuranOS
In most engineering firms, simulation models live in notebooks, Excel spreadsheets, or desktop applications disconnected from project state. An engineer runs a model, copies results into a report, and the model becomes stale the moment project parameters change.
PuranOS treats engineering computation differently. Each engine is API-first (not GUI-first), session-persistent across agent interactions, and produces results with explicit credibility metadata.
Agents don't interact with these tools through a chat interface. They are triggered by OpenProject task assignments, scheduled automation, or inbound emails — and they call engineering tools as part of structured skill execution.
Core Engineering Engines
QSDsan
University of Illinois · Biological and chemical treatment
Mass/energy balance, process simulation, LCA. IWA ADM1, ASM1, ASM2d models. Session-persistent simulations with model credibility metadata on every result.
WaterTAP
NAWI / DOE · Membrane and separation processes
Desalination, advanced treatment, costing. RO, NF, ED, crystallizer, evaporator. Cross-engine handoffs from QSDsan via typed converters with provenance tracking.
PHREEQC
USGS · Chemical equilibrium
Precipitation, dosing optimization, scaling potential analysis. Multi-database support (phreeqc.dat, minteq.dat, llnl.dat, pitzer.dat).
CoolProp + fluids
Open source · Fluid mechanics
120+ fluid properties, pipe sizing, pump TDH, control valve sizing (IEC 60534), blower power calculations.
Heat Transfer
Caleb Bell et al. · Thermal engineering
Heat exchangers, insulation design, heat trace. 390+ material conductivities (VDI/ASHRAE). Built on ht, chemicals, fluids, thermo.
Corrosion Engineering
USGS + Caleb Bell · Corrosion analysis
Sweet/sour corrosion, galvanic analysis, pitting risk assessment. Built on phreeqpython, fluids, ht.
Engineering (Diagrams & Tagging)
Process Intelligence Research · P&IDs and BFDs
DEXPI P&IDs, SFILES BFDs/PFDs, ISA 5.1 instrument tagging. Built on pyDEXPI, SFILES2, networkx.
Cross-Engine Handoffs
A QSDsan effluent composition becomes a WaterTAP influent specification through typed converters — with unit conversion, component mapping, and provenance from the source model.
Multiple component bases are supported (mASM2d for activated sludge, MCAS for WaterTAP solutes, mADM1 for anaerobic models) with typed converters between them. The handoff is not a copy-paste — it carries the full lineage of how the data was produced.
Converters preserve balance reports, track estimated fields explicitly, and maintain source breakdown so credibility does not silently degrade across engine boundaries. When a stream crosses from one engine to another, the downstream consumer sees exactly which fields are measured, which are estimated, and what assumptions the conversion applied.
Model Credibility Metadata
An agent-generated sizing is not automatically "engineering grade." Every simulation result carries explicit credibility metadata:
Model status
validated · calibrated · heuristic · preliminary · stub
Decision grade
design · budgetary · screening · order-of-magnitude
Validation basis
bench-tested · plant-data · literature · vendor · assumed
As models improve, credibility metadata becomes more important, not less. A more capable model produces more convincing but still physically wrong answers when applied outside its valid range. An LLM 100x more capable still cannot reliably compute steady-state effluent composition — it lacks numerical precision, validated kinetic parameters, and iterative solver architecture. The credibility metadata is what distinguishes a deterministic engine result from an LLM-generated number.
The credibility metadata is what allows a PE reviewer to know what level of trust to place in a result.
In Practice: RO System Sizing
A concrete example of how engines, typed handoffs, and credibility metadata work together:
Query plant-state schema
Agent queries the plant-state schema for current stream composition — 31-component mASM2d basis with flow, temperature, pressure, pH, and component concentrations.
Run activated sludge simulation
QSDsan runs the biological treatment simulation. Prior to the asset becoming operational, simulations are always uncalibrated — based on literature or bench-scale testing. Results carry credibility metadata: model_status: preliminary, validation_basis: literature.
Typed converter maps effluent to RO influent
Converter transforms QSDsan effluent (mASM2d basis) to WaterTAP influent (MCAS basis) with provenance tracking and estimated-field annotation.
Run RO simulation
WaterTAP runs the reverse osmosis simulation — permeate quality, energy demand, capital cost estimate. Results carry their own credibility metadata.
PE reviewer sees full provenance chain
Final results carry credibility from both engines. The reviewer sees which numbers are from calibrated models versus screening-level estimates, and which fields were measured versus estimated at each handoff boundary.
Every step operates on schema'd data. There is no retrieval ambiguity, no garbage context, and no question about what the data means.
These engines are open-source. The MCP server wrappers and architecture are documented on GitHub.