neqsim-agentic-process-optimization

Agentic Process-Model Optimization

This skill is the recipe for an agent that optimizes a large, already-built multi-area plant (e.g. an offshore separation + recompression + export train) by turning the newest NeqSim automation and introspection APIs into a robust optimization loop. It assumes the flowsheet is a ProcessModel assembled from several named ProcessSystem areas (see neqsim-platform-modeling).

Use neqsim-optimization-and-doe for the algorithm (SQP, PSO, BatchStudy, ProcessSimulationEvaluator → SciPy/Pyomo). Use this skill for the plumbing: how to read the decision space, evaluate one trial robustly, gate feasibility, and score the objective from real equipment results.

---

1. The four pillars (all verified in NeqSim ≥ 3.13.0)

Need	API	Returns
Decision space (bounded knobs)	ProcessAutomation.getAdjustableParameters() / getAdjustableParametersJson()	List<AdjustableParameter> with name/address/unit/lowerBound/upperBound/source
Robust convergence of a coupled plant	ProcessModel.runUntilConverged(int maxIterations, double tolerance)	boolean converged; pair with getConvergenceReportJson()
Per-trial feasibility / failure gating	ProcessModel.getRunStatus() / getRunStatusJson(), ProcessSystem.getRunStatus()	RunStatus (completed/success/failedUnitName/failedUnitError)
Objective + constraints from equipment	Compressor.getOperatingPoint(), Standard_ASTM_D6377.RvpResult	power, surge/stonewall margins; certified RVP

Why these matter for an agent: they replace the fragile "call .run() twice and hope" pattern with explicit did-it-converge and did-any-unit-fail signals, and they expose objective/constraint numbers (compression power, surge distance, RVP spec) as structured JSON the agent can parse without walking Java object trees.

---

2. Discover the decision space

from neqsim import jneqsim   # or devtools `ns` (see §8)
import json

auto = plant.getAutomation()                      # plant = ProcessModel
# NOTE: jpype returns java.lang.String, not Python str — json.loads needs str(...).
params = json.loads(str(auto.getAdjustableParametersJson()))
for p in params["parameters"]:
    print(p["name"], p["address"], p["unit"], p["lowerBound"], p["upperBound"], p["source"])