New to process optimization? Start with the Optimization Overview to understand when to use which optimizer.

This guide explains how to use NeqSim’s simulation evaluators to integrate process simulation with external optimization frameworks like Python’s SciPy, NLopt, or other optimization libraries.

Related Documentation

Document	Description
Optimization Overview	When to use which optimizer
Production Optimization Guide	ProductionOptimizer examples
Practical Examples	Code samples
Capacity Constraint Framework	Installed equipment limits and bottleneck detection

Overview

NeqSim provides two black-box evaluator classes for external optimizers and one convenience helper for the common full-facility producer-ramp workflow:

Class	Model boundary	Decision variable addressing	Best use
ProcessSimulationEvaluator	One ProcessSystem	Unit name plus property name	Compact flowsheets, equipment tuning, single-process optimization
ProcessModelSimulationEvaluator	Full ProcessModel with named areas	Area-qualified ProcessAutomation addresses such as wells::feed.flowRate	Large facilities, multi-producer throughput studies, fixed-equipment bottleneck workflows
ProcessModelThroughputOptimizer	Full ProcessModel with named areas	Producer mappings plus installed capacity CSV tables	Increase producers until the full facility reaches its first fixed-equipment bottleneck

The evaluator classes provide a black-box interface that:

• Accepts a vector of decision variables
• Runs the process simulation
• Returns objective values, constraint margins, and feasibility status
• Supports gradient estimation via finite differences
• Exports problem definitions in JSON format

Use ProcessSimulationEvaluator when all manipulated variables and outputs belong to one process system. Use ProcessModelSimulationEvaluator when the optimization must run the complete plant model and evaluate constraints across several process areas. Use ProcessModelThroughputOptimizer when the practical question is a scalar producer ramp: find the maximum feasible throughput and report the active bottleneck case table.

Full ProcessModel Evaluations

ProcessModelSimulationEvaluator is intended for optimization studies where the process has already been split into named ProcessSystem areas and composed into a ProcessModel. This matches large offshore or gas-plant models where wells, separation, compression, export, utility, and recycle areas are solved together.

The practical workflow is:

1. Build and validate the full ProcessModel at the base case.
2. Attach installed CapacityConstraint objects to equipment with fixed design sizes.
3. Register producer/feed rates, pressure setpoints, or scenario multipliers as decision variables.
4. Add model-level objective functions, for example export gas flow or total compressor power.
5. Call addEquipmentCapacityConstraints() to include enabled equipment limits as hard constraints.
6. Pass getBounds(), getInitialValues(), evaluate(...), evaluatePenalizedObjective(...), or estimateGradient(...) to the external optimizer.
7. Inspect EvaluationResult.getConstraintMargins() and EvaluationResult.getActiveBottleneck() for engineering interpretation.

For full-model studies, area-qualified addresses keep optimizer scripts independent of Java object wiring. Examples include wells::feed.flowRate, separation::separator.gasOutStream.flowRate, and compression::export compressor.outletPressure.

Capacity constraints remain explicit engineering data. Strategy-generated defaults can identify candidate bottlenecks, while installed equipment limits should be attached directly to equipment when the study assumes equipment sizes are fixed.

Full-Facility Throughput-to-Bottleneck Helper

ProcessModelThroughputOptimizer wraps the evaluator for Chapter-15-style facility studies. It maps producers, optionally loads installed equipment limits from a CSV table, performs a robust scalar throughput search, and returns a ProcessModelThroughputResult containing the best feasible case, first infeasible case, and all evaluated case rows.

ProcessModelThroughputOptimizer optimizer = new ProcessModelThroughputOptimizer(model);
optimizer.addProducer("feed", "wells::feed.flowRate", 1.0, 2.0, "kg/hr");
optimizer.setObjective("exportGas", new ToDoubleFunction<ProcessModel>() {
    @Override
    public double applyAsDouble(ProcessModel processModel) {
        return processModel.getVariableValue("separation::separator.gasOutStream.flowRate", "kg/hr");
    }
}, "kg/hr");
optimizer.loadInstalledCapacities("installed_capacity.csv");

ProcessModelThroughputResult result = optimizer.findMaximumThroughput(1.0, 2.0, 0.01);
ThroughputCaseRow best = result.getBestFeasibleCase();
ThroughputCaseRow firstLimit = result.getFirstInfeasibleCase();
result.exportToCSV("throughput_trace.csv");

The installed-capacity CSV format is intentionally small and auditable:

area,equipment,constraint,currentValueAddress,designValue,maxValue,unit,severity,enabled
separation,separator,installedGasCapacity,wells::feed.flowRate,15000,16500,kg/hr,HARD,true

By default, the helper uses explicit installed capacity limits attached directly to equipment. Enable strategy-generated constraints with setIncludeStrategyCapacityConstraints(true) when you want generic screening limits to participate in addition to installed design data.

Each throughput row records minimumConstraint so engineering-unit margins remain unambiguous. For a maximum-directed limit, capacityMargin = limit - current; for a minimum-directed limit, capacityMargin = current - limit. A non-negative margin is therefore feasible in both cases, and the reported designValue is the applicable finite limit even when the equipment constraint was constructed with setMinValue(...) only. Each row also carries dataSource, copied from the underlying CapacityConstraint, so Python and external optimizers can distinguish an installed data-sheet limit from mechanical-design output, an operating envelope, or an untagged not_set limit. Preserve this provenance in ranked recommendations and result archives.

Evidence metadata is also snapshotted from the active CapacityConstraint. Java rows expose hasConfidence(), getConfidence(), hasValidityRange(), getValidityMinimum(), getValidityMaximum(), and isCurrentValueWithinValidityRange(). JSON represents unset numeric and applicability values as null; CSV includes the presence flags and leaves unset value cells blank. External and AI optimizers should preserve these diagnostics, require engineering review for missing or out-of-range evidence, and must not treat confidence as a probability of safety. The fields do not change feasibility or ranking automatically.

utilizationMargin remains 1 - utilization for both directions.

Key Concepts

Decision Variables (Parameters)

Parameters are the values the optimizer will adjust. Each parameter has:

• Equipment name: The name of the unit operation
• Property name: The property to adjust (e.g., “flowRate”, “pressure”)
• Bounds: Lower and upper limits
• Unit: Engineering units (for clarity)

Objectives

Functions to minimize or maximize. By default, objectives are minimized. For maximization, the evaluator automatically negates the value.

Constraints

Process restrictions that must be satisfied:

• Lower bound: g(x) ≥ bound
• Upper bound: g(x) ≤ bound
• Range: lower ≤ g(x) ≤ upper
• Equality: g(x) = target ± tolerance

For both ProcessSimulationEvaluator and ProcessModelSimulationEvaluator, one completed evaluate(...) call samples each objective and constraint callback exactly once after the simulation. Raw and sign-adjusted objectives, plus constraint values, margins, feasibility, and penalties, are derived from those same scalar samples. This matters when result extraction includes costly reporting or serialization and prevents inconsistent fields when a callback reads mutable diagnostics. Callbacks should nevertheless remain side-effect free.

Converting Constraints to the Internal Optimizer

Use the plural conversion when a ConstraintDefinition is passed to ProductionOptimizer:

List<ProductionOptimizer.OptimizationConstraint> internalConstraints =
    externalConstraint.toOptimizationConstraints();

A lower or upper bound produces one immutable-list element. A range produces name_lower and name_upper; an equality target produces the equivalent target - tolerance lower side and target + tolerance upper side. Each generated side keeps the source evaluator, hard/soft severity, and penalty weight.

The singular toOptimizationConstraint() method remains available for compatibility. It is lossless only for one-sided constraints; for range and equality types it retains the historical upper-side-only behavior. Do not use that method for operating envelopes, product-quality bands, equipment turndown ranges, or market-nomination tolerances. Treat lower- and upper-side sensitivities separately because only one side can normally be active at a given operating point.

Java Setup

import neqsim.process.util.optimizer.ProcessSimulationEvaluator;
import neqsim.process.equipment.stream.StreamInterface;

// Create evaluator with process system
ProcessSimulationEvaluator evaluator = new ProcessSimulationEvaluator(processSystem);

// Add decision variables
evaluator.addParameter("feed", "flowRate", 1000.0, 100000.0, "kg/hr");
evaluator.addParameter("valve", "pressure", 10.0, 50.0, "bara");

// Add objective (minimize compressor power)
evaluator.addObjective("power",
    process -> process.getUnit("compressor").getEnergy("kW"));

// Add constraints
evaluator.addConstraintLowerBound("minPressure",
    process -> ((StreamInterface) process.getUnit("outlet")).getPressure("bara"),
    30.0);

evaluator.addConstraintUpperBound("maxTemperature",
    process -> ((StreamInterface) process.getUnit("outlet")).getTemperature("C"),
    80.0);

Python integration through neqsim-python

Installation

pip install neqsim scipy numpy

Basic setup

The public neqsim package starts and configures the Java gateway. Do not start a second JVM or assume that a local NeqSim JAR file exists.

import numpy as np
from scipy.optimize import differential_evolution, minimize
from neqsim import jneqsim

ProcessSimulationEvaluator = (
    jneqsim.process.util.optimizer.ProcessSimulationEvaluator
)
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
Stream = jneqsim.process.equipment.stream.Stream
ThrottlingValve = jneqsim.process.equipment.valve.ThrottlingValve
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos

Creating the Process

# Create a simple gas processing system
fluid = SystemSrkEos(273.15 + 25.0, 50.0)
fluid.addComponent("methane", 0.9)
fluid.addComponent("ethane", 0.1)
fluid.setMixingRule("classic")
fluid.setTotalFlowRate(10000.0, "kg/hr")

feed = Stream("feed", fluid)
feed.run()

valve = ThrottlingValve("valve", feed)
valve.setOutletPressure(30.0)
valve.run()

# Build process system
process = ProcessSystem()
process.add(feed)
process.add(valve)

Setting Up the Evaluator

# Create evaluator
evaluator = ProcessSimulationEvaluator(process)

# Add parameters (decision variables)
evaluator.addParameter("feed", "flowRate", 1000.0, 50000.0, "kg/hr")

# Add objective
evaluator.addObjective("outletPressure",
    lambda p: p.getUnit("valve").getOutletStream().getPressure("bara"))

# Add constraints
evaluator.addConstraintLowerBound("minFlow",
    lambda p: p.getUnit("feed").getFlowRate("kg/hr"),
    5000.0)

Using SciPy Optimizers

Gradient-Based Optimization (L-BFGS-B)

def objective(x):
    """Wrapper for SciPy"""
    result = evaluator.evaluate(x)
    return result.getObjective()

def objective_with_gradient(x):
    """Objective with gradient for L-BFGS-B"""
    obj = evaluator.evaluateObjective(x)
    grad = np.array(evaluator.estimateGradient(x))
    return obj, grad

# Get bounds from evaluator
bounds = [(b[0], b[1]) for b in evaluator.getBounds()]
x0 = np.array(evaluator.getInitialValues())

# Run L-BFGS-B optimization
result = minimize(
    objective_with_gradient,
    x0,
    method='L-BFGS-B',
    jac=True,
    bounds=bounds,
    options={'maxiter': 100, 'disp': True}
)

print(f"Optimal x: {result.x}")
print(f"Optimal objective: {result.fun}")

Constrained Optimization (SLSQP)

def objective(x):
    return evaluator.evaluateObjective(x)

def constraints_func(x):
    """Returns constraint margins (positive = satisfied)"""
    return np.array(evaluator.getConstraintMargins(x))

# Define constraints for SLSQP
constraints = [{
    'type': 'ineq',
    'fun': lambda x: constraints_func(x)  # All margins must be ≥ 0
}]

result = minimize(
    objective,
    x0,
    method='SLSQP',
    bounds=bounds,
    constraints=constraints,
    options={'maxiter': 100, 'disp': True}
)

Global Optimization (Differential Evolution)

def penalized_objective(x):
    """For global optimizers without explicit constraints"""
    result = evaluator.evaluate(x)
    return result.getPenalizedObjective()

result = differential_evolution(
    penalized_objective,
    bounds,
    maxiter=100,
    seed=42,
    disp=True
)

Multi-Objective Optimization

from scipy.optimize import minimize

# Setup with multiple objectives
evaluator.addObjective("power", lambda p: p.getUnit("compressor").getEnergy("kW"))
evaluator.addObjective("throughput",
    lambda p: p.getUnit("product").getFlowRate("kg/hr"),
    ProcessSimulationEvaluator.ObjectiveDefinition.Direction.MAXIMIZE)

def weighted_objective(x, weights):
    result = evaluator.evaluate(x)
    return result.getWeightedObjective(weights)

# Pareto front approximation via weighted sum
pareto_points = []
for w1 in np.linspace(0.1, 0.9, 5):
    weights = np.array([w1, 1.0 - w1])
    result = minimize(
        lambda x: weighted_objective(x, weights),
        x0,
        method='L-BFGS-B',
        bounds=bounds
    )
    pareto_points.append({
        'weights': weights,
        'x': result.x,
        'objectives': evaluator.evaluate(result.x).getObjectivesRaw()
    })

Using with NLopt (Python)

import nlopt
import numpy as np

def nlopt_objective(x, grad):
    """NLopt objective function"""
    if grad.size > 0:
        gradient = evaluator.estimateGradient(x)
        for i, g in enumerate(gradient):
            grad[i] = g
    return evaluator.evaluateObjective(x)

def nlopt_constraint(x, grad, idx):
    """NLopt constraint function"""
    if grad.size > 0:
        jacobian = evaluator.estimateConstraintJacobian(x)
        for i, j in enumerate(jacobian[idx]):
            grad[i] = -j  # NLopt uses g(x) ≤ 0, we return -margin
    margins = evaluator.getConstraintMargins(x)
    return -margins[idx]  # Convert to ≤ 0 form

# Create optimizer
n = evaluator.getParameterCount()
opt = nlopt.opt(nlopt.LD_SLSQP, n)

# Set bounds
opt.set_lower_bounds(evaluator.getLowerBounds())
opt.set_upper_bounds(evaluator.getUpperBounds())

# Set objective
opt.set_min_objective(nlopt_objective)

# Add constraints
for i in range(evaluator.getConstraintCount()):
    opt.add_inequality_constraint(
        lambda x, g, idx=i: nlopt_constraint(x, g, idx),
        1e-6
    )

# Optimize
opt.set_maxeval(200)
x_opt = opt.optimize(evaluator.getInitialValues())

Using with Pyomo

An ordinary Pyomo Objective(rule=...) or Constraint(rule=...) must build a symbolic expression. Reading .value from Pyomo variables inside those rules and immediately calling NeqSim evaluates only the construction-time values; it does not create a live connection that Pyomo can differentiate or re-evaluate while solving.

NeqSim does not currently provide a maintained Pyomo ExternalFunction or PyNumero callback adapter. Use the SciPy or NLopt black-box patterns above, or implement and validate a dedicated Pyomo external-function bridge with explicit value, gradient, lifecycle, and failure handling. Do not present a construction-time numeric callback as a Pyomo optimization model.

Advanced Features

Custom Parameter Setters

For complex parameter mappings:

# Java lambda for custom setter
evaluator.addParameterWithSetter(
    "customParam",
    lambda process, value: process.getUnit("valve").setOutletPressure(value * 1.1),
    10.0, 50.0, "bara"
)

Evaluation accounting

ProcessSimulationEvaluator counts evaluation attempts:

print(f"Total evaluations: {evaluator.getEvaluationCount()}")
evaluator.resetEvaluationCount()

These methods measure work; they do not enable result caching. Add caching in the external optimizer only when the complete parameter vector, model identity, operating case, and mutable process state are part of a safe cache key.

Gradient Configuration

# Configure finite difference step
evaluator.setFiniteDifferenceStep(1e-6)

# Use relative step size
evaluator.setUseRelativeStep(True)  # step = h * max(|x_i|, 1)

# Optional second-order stencil for smooth interior operating points
FiniteDifferenceMethod = jneqsim.process.util.optimizer.ProcessModelSimulationEvaluator.FiniteDifferenceMethod
evaluator.setFiniteDifferenceMethod(FiniteDifferenceMethod.CENTRAL)

ProcessModelSimulationEvaluator keeps FORWARD as the default because it requires only one perturbed simulation per parameter. Both objective gradients and constraint Jacobians use the actual perturbation remaining inside each parameter’s bounds; they no longer divide by a requested step that was partly removed by bound clamping. CENTRAL uses symmetric in-bounds points when both directions are available and falls back to a one-sided difference at an active bound. A fixed parameter has zero derivative because it has no feasible perturbation direction. Treat either finite-difference result as a local sensitivity, not as an optimizer-independent shadow price, and check step-size stability before using it to rank debottlenecking value.

For a reusable quality record, run the coarse step and one halved step through the combined objective/constraint API:

quality_result = evaluator.estimateSensitivitiesWithQuality(x)
gradient = quality_result.getObjectiveGradient()
jacobian = quality_result.getConstraintJacobian()

objective = quality_result.getObjectiveSnapshot()
print(
    objective.getName(),
    objective.getDirection(),
    objective.getUnit(),
    objective.getBaseRawValue(),
    objective.getGradient(),
)

for parameter in quality_result.getParameterSnapshots():
    print(
        parameter.getIndex(),
        parameter.getName(),
        parameter.getAddress(),
        parameter.getUnit(),
        parameter.getBaseValue(),
    )

for constraint in quality_result.getConstraintSnapshots():
    print(
        constraint.getIndex(),
        constraint.getName(),
        constraint.getType(),
        constraint.getUnit(),
        constraint.getBaseMargin(),
        constraint.getMarginGradient(),
        constraint.getAreaName(),
        constraint.getEquipmentName(),
    )

for parameter_quality in quality_result.getParameterQuality():
    print(
        parameter_quality.getParameterName(),
        parameter_quality.getStencil(),
        parameter_quality.getCoarseStep(),
        parameter_quality.getFineStep(),
        parameter_quality.getMaximumRelativeDisagreement(),
        parameter_quality.isAllEvaluationsConverged(),
        parameter_quality.isAllEvaluationsFeasible(),
    )

estimateSensitivitiesWithQuality(...) returns the fine-step derivatives and preserves an immutable record for every perturbation: signed applied step, actual parameter value, process convergence, hard-constraint feasibility, and evaluation error. Its relative disagreement is abs(D_h - D_h/2) / max(abs(D_h), abs(D_h/2)), or zero when both derivatives are zero. Use isNumericallyStable(tolerance) with a tolerance justified for the engineering decision. The method needs four perturbed simulations per interior central parameter and two per one-sided parameter, while a fixed parameter needs none; objective and constraint derivatives reuse those same simulations. Existing estimateGradient(...) and estimateConstraintJacobian(...) remain the lower-cost APIs.

The same result snapshots the derivative identities at the base point. Parameter snapshots retain registration index, name, automation address, unit, bounds, and the bounded value actually evaluated. The objective snapshot retains direction, unit, weight, raw and minimizer-convention base values, and the gradient. Each constraint snapshot retains type, unit, hard/soft flag, penalty, bounds or tolerance, capacity area/equipment origin, sampled base value, margin, and its Jacobian row. These records are immutable and serializable, so later evaluator mutations or process runs cannot silently relabel archived sensitivities. Raw margins and derivatives keep their declared units; do not compare or rank unlike constraints without explicit engineering scaling.

Convergence and numerical agreement are necessary but not sufficient. Inspect perturbation feasibility separately, test nearby operating points, and reject or qualify sensitivities that cross equipment/control regimes. An infeasible perturbation is retained as evidence rather than silently invalidating a constraint-margin derivative.

Use an explicit qualification policy before passing local derivatives into a bottleneck or operating-action workflow:

Policy = (
    jneqsim.process.util.optimizer.ProcessModelSimulationEvaluator
    .SensitivityQualificationPolicy
)

# Requires a feasible base and feasible perturbations. One-sided bounded stencils are allowed.
strict_policy = Policy.strict(0.05)
assessments = quality_result.assessConstraintSensitivities(strict_policy)

for assessment in assessments:
    print(
        assessment.getConstraint().getName(),
        assessment.getParameter().getName(),
        assessment.getRawObjectiveDerivative(),
        assessment.getRawObjectiveDerivativeUnit(),
        assessment.getMarginDerivative(),
        assessment.getMarginDerivativeUnit(),
        assessment.isAccepted(),
        list(assessment.getEvidenceFlags()),
        list(assessment.getRejectionReasons()),
        list(assessment.getDiagnostics()),
    )

accepted = quality_result.getAcceptedConstraintSensitivities(strict_policy)

Qualification performs no additional process evaluations. Every assessment binds one constraint row and parameter column to the immutable snapshots, reports both raw and minimizer-convention objective derivatives, and retains the exact stencil plus objective and constraint coarse/fine disagreements. Convergence failures, evaluation errors, non-finite derivatives, unstable refinement, and fixed parameters always reject a pair. The policy explicitly controls whether base or perturbation infeasibility and one-sided stencils reject it. Even when allowed, these conditions remain visible in getEvidenceFlags() and the diagnostics.

Policy.numericalOnly(tolerance) is useful for diagnosing a violated or boundary-crossing case, but acceptance under that policy is numerical evidence only. It is not engineering approval. Results remain in declared units and are intentionally not ranked across constraints. Explicit scaling, regime validation, active-set logic, and optimizer-specific KKT evidence are separate requirements.

Use explicit engineering references before comparing dimensionless margins or exposing a candidate-active set:

import jpype

Analyzer = jneqsim.process.util.optimizer.ConstraintActivityAnalyzer
ArrayList = jpype.JClass("java.util.ArrayList")

reference_by_name = {
    # Values are positive and use each constraint's declared unit.
    "export compressor power": (12_000.0, "installed motor rating"),
    "gas export nomination": (1_000_000.0, "daily nomination basis"),
}

scales = ArrayList()
for constraint in quality_result.getConstraintSnapshots():
    reference, provenance = reference_by_name[str(constraint.getName())]
    scales.add(
        Analyzer.ConstraintScale.fromSnapshot(
            constraint,
            reference,
            provenance,
        )
    )

activity_policy = Analyzer.ActivityPolicy.hardConstraints(
    0.05,  # candidate active when 0 <= normalized margin <= 5% of its reference
    strict_policy,
)
activity = Analyzer.assess(
    quality_result,
    scales,
    activity_policy,
)

for item in activity:
    print(
        item.getConstraint().getName(),
        item.getScale().getReferenceValue(),
        item.getScale().getUnit(),
        item.getScale().getProvenance(),
        item.getNormalizedMargin(),
        item.getStatus(),
        list(item.getDiagnostics()),
    )
    for derivative in item.getSensitivities():
        print(
            derivative.getSensitivityAssessment().getParameter().getName(),
            derivative.getNormalizedMarginDerivative(),
            derivative.getNormalizedMarginDerivativeUnit(),
            derivative.isUsable(),
            list(derivative.getRejectionReasons()),
        )

candidate_active = Analyzer.getCandidateActiveConstraints(activity)
violated = Analyzer.getViolatedConstraints(activity)

Scales may be supplied in any order, but exactly one identity-matched scale is required for every constraint and results remain in registration order. A stale scale fails if type, bounds, hardness, penalty or capacity origin changed. Soft constraints are excluded unless the policy explicitly includes them. A normalized derivative remains auditable even when rejected; require isUsable() before consuming it. Keep violated constraints separate from feasible CANDIDATE_ACTIVE constraints, and never interpret the candidate set as optimizer KKT evidence or rank economic value without an optimizer-specific solution and objective scaling.

Declare reversible continuous and discrete operating actions

Use ProcessModelOperatingAction when an optimizer candidate must retain stable engineering identity, provenance, exact value semantics, and an explicit restoration token instead of being an anonymous numeric setter:

Action = jneqsim.process.util.optimizer.ProcessModelOperatingAction
JDoubleArray = jpype.JArray(jpype.JDouble)

feed_target = Action.continuous(
    "field-feed",
    "Field feed target",
    "wells::feed.flowRate",
    5000.0,
    20000.0,
    "kg/hr",
    "approved operating envelope revision A",
)
pressure_mode = Action.discrete(
    "compressor-lineup",
    "Compressor line-up",
    "compression::lineup-selector.value",
    JDoubleArray([1.0, 2.0, 3.0]),
    "count",
    "installed train line-up table revision B",
)

capability = feed_target.inspectCapability(process_model)
if not capability.isAvailable():
    raise RuntimeError(list(capability.getDiagnostics()))

baseline = feed_target.capture(process_model)
application = feed_target.apply(process_model, 12000.0)
if not application.isApplied():
    raise RuntimeError(application.getDiagnostic())

# Running and validating the candidate remains explicit.
process_model.run()
# Inspect convergence, constraints, conservation, and product specifications here.

restoration = feed_target.restore(process_model, baseline)
if not restoration.isApplied():
    raise RuntimeError(restoration.getDiagnostic())

Continuous candidates must lie inside the inclusive declared bounds. Discrete candidates must equal one enumerated value exactly; the API never interpolates a line-up. A readable brownfield baseline may lie outside the candidate domain and is still capturable for restoration. apply(...) writes through the existing area-qualified ProcessAutomation address and verifies the read-back without running the process.

Register an action with the established model evaluator when an external optimizer should own the candidate loop:

binding = feed_target.registerWith(evaluator)
print(binding.getParameterIndex(), binding.getInitialValue())

lineup_binding = compressor_lineup.registerWith(evaluator)
allowed_lineups = list(lineup_binding.getAllowedValues())

Registration requires the current model value to belong to the declared candidate domain; an off-domain brownfield baseline may be captured and restored but is not silently promoted to an optimizer starting point. For a discrete binding, the evaluator vector exposes only the numerical envelope; enumerate getAllowedValues(). Any intermediate proposal fails the evaluation explicitly and leaves the previous verified value unchanged. Evaluator callback setters are transient, consistent with objective and constraint callbacks, so re-register actions after deserializing an evaluator. Captured actions, bindings, capability/application results, and state tokens are immutable and Java-serializable for JPype workflows.

Capability inspection proves only that the exact address is readable in the declared unit. Application proves only write/read-back consistency. Neither runs NeqSim, changes topology, establishes process feasibility, selects a line-up, or constitutes operating or safety approval.

Evaluate and restore one hydraulic operating candidate

Use ProcessModelOperatingActionEvaluator when an optimizer needs a complete candidate result against exact enabled reservoir, well, gathering, or pipeline capacity constraints and the shared model must be returned to its baseline state before the callback returns. Configure objectives and other process constraints on a zero-parameter ProcessModelSimulationEvaluator; the transactional wrapper owns the action write and automatically registers enabled equipment capacities.

SimulationEvaluator = (
    jneqsim.process.util.optimizer.ProcessModelSimulationEvaluator
)
ActionEvaluator = (
    jneqsim.process.util.optimizer.ProcessModelOperatingActionEvaluator
)
HydraulicRole = (
    ActionEvaluator.HydraulicLimitRole
)

simulation = SimulationEvaluator(process_model)
simulation.setIncludeStrategyCapacityConstraints(False)

well_rate = Action.continuous(
    "producer-rate",
    "Producer gas rate",
    "Subsurface::producer.flowRate",
    0.5,
    1.5,
    "MSm3/day",
    "approved well operating envelope revision A",
).withReadBackTolerance(
    1.0e-5,
    0.0,
    "producer flow-control tag resolution in MSm3/day",
)
hydraulic = ActionEvaluator(simulation, well_rate)
hydraulic.requireHydraulicConstraint(
    HydraulicRole.WELL_INFLOW_OUTFLOW,
    "Subsurface",
    "well",
    "well drawdown",
    "installed maximum drawdown basis",
)

candidate = hydraulic.evaluate(1.2)
if not candidate.isBaselineRestored():
    raise RuntimeError(list(candidate.getDiagnostics()))
if not candidate.isBaselineSimulationConverged():
    raise RuntimeError("Restored baseline did not reconverge")

print(candidate.getOutcome())
for constraint in candidate.getHydraulicConstraints():
    print(
        constraint.getBinding().getQualifiedConstraintName(),
        constraint.getCurrentValue(),
        constraint.getUnit(),
        constraint.getUtilization(),
        constraint.getMargin(),
        constraint.getDataSource(),
        constraint.getEvidenceApplicability(),
    )

Exact names are intentional: a missing or disabled bound constraint fails closed instead of silently accepting a different limit. Finite utilization at or below one is insufficient when an explicit validity range exists and the candidate lies outside it. Missing validity metadata is reported as NOT_ASSESSED; confidence remains evidence-quality metadata, not a safety probability. Where automation conversion or control-tag resolution is coarser than the default floating-point comparison, configure withReadBackTolerance(absolute, relative, provenance). The action unit applies to the absolute tolerance. Application diagnostics retain requested, read-back, residual, allowed-tolerance, and tolerance-provenance evidence; this qualifies only write/read-back consistency, not process feasibility. The immutable result is Java-serializable and exposes defensive arrays and immutable lists for JPype/Python consumers.

The wrapper evaluates one steady-state action at a time and is synchronized because it mutates the supplied model. It does not coordinate routing/topology transactions, solve multiple simultaneous actions, replace WellFlow or pipeline correlations, or infer mechanical, safety, environmental, product-quality, or market approval. Register all other applicable constraints on simulation before constructing the wrapper, and use separate model instances for parallel candidate calls.

Evaluate a coupled well-allocation candidate atomically

Use ProcessModelOperatingActionSetEvaluator for a candidate vector that must be applied as one transaction, such as two well-rate targets competing for one gathering limit. All actions are captured before the first write. Duplicate IDs or automation addresses fail at construction. If a later value is rejected, the partial candidate is not simulated and every captured action is restored in reverse declaration order.

ActionSetEvaluator = (
    jneqsim.process.util.optimizer.ProcessModelOperatingActionSetEvaluator
)

allocation = ActionSetEvaluator(
    "field-allocation",
    "Field production allocation",
    "approved well envelopes and gathering basis revision A",
    simulation,
    [well_a_action, well_b_action],
)
allocation.requireHydraulicConstraint(
    HydraulicRole.WELL_INFLOW_OUTFLOW,
    "Subsurface",
    "well A",
    "well drawdown",
    "well A installed maximum drawdown",
)
allocation.requireHydraulicConstraint(
    HydraulicRole.WELL_INFLOW_OUTFLOW,
    "Subsurface",
    "well B",
    "well drawdown",
    "well B installed maximum drawdown",
)
allocation.requireHydraulicConstraint(
    HydraulicRole.GATHERING_HYDRAULICS,
    "Gathering",
    "inlet separator",
    "installed gathering rate",
    "shared installed gathering capacity",
)

candidate = allocation.evaluate([well_a_rate, well_b_rate])
for action_result in candidate.getActionEvidence():
    print(
        action_result.getAction().getId(),
        action_result.getBaselineValue(),
        action_result.getRequestedValue(),
        action_result.getReadBackValue(),
        action_result.getReadBackResidual(),
        action_result.getReadBackTolerance(),
        action_result.getReadBackToleranceProvenance(),
        action_result.isRestored(),
    )

if not candidate.isBaselineRestored():
    raise RuntimeError(list(candidate.getDiagnostics()))
if not candidate.isBaselineSimulationConverged():
    raise RuntimeError("Restored coupled baseline did not reconverge")

The candidate array follows action declaration order and each value uses its action’s declared unit. Wrong-length vectors, invalid continuous or discrete values, missing addresses, failed read-back, missing/non-finite/out-of-range exact constraints, hard violations, non-convergence, or incomplete restoration fail closed with distinct outcomes. Results retain defensive arrays and fresh immutable Java lists for JPype consumers and are Java-serializable.

The evaluator does not optimize the vector, interpolate discrete line-ups, change routing or hydraulic correlations, or establish operating approval. Validate conservation, constraint residuals, product specifications and nearby operating points with the underlying NeqSim model.

Search a fixed-total continuous allocation

ProcessModelAllocationOptimizer composes the atomic evaluator when all allocation actions are continuous, use one exact unit, and must preserve a declared shared total. Its deterministic transfer search retains every candidate result instead of reducing simulator evidence to one score.

AllocationOptimizer = (
    jneqsim.process.util.optimizer.ProcessModelAllocationOptimizer
)

optimizer = AllocationOptimizer(
    "field-rate-allocation",
    "Field rate allocation",
    "approved well envelopes and host capacity basis revision A",
    allocation,
    total_rate,
    "kg/hr",
)
optimizer.setInitialAllocation([initial_well_a_rate, initial_well_b_rate])
optimizer.setObjectiveIndex(0)
optimizer.setInitialStepFraction(0.10)
optimizer.setRelativeStepTolerance(1.0e-3)
optimizer.setObjectiveImprovementTolerance(
    1.0e-6,
    "validated export-rate calculation resolution",
)
optimizer.setMaximumEvaluations(100)

search = optimizer.optimize()
if not search.isModelRecovered():
    raise RuntimeError(list(search.getDiagnostics()))

best = search.getBestFeasibleCandidate()
if best is not None:
    best_rates = list(best.getCandidateValues())
    bottlenecks = list(search.getRankedHydraulicConstraintsAtBestFeasible())
    for constraint in bottlenecks:
        print(
            constraint.getBinding().getQualifiedConstraintName(),
            constraint.getUtilization(),
            constraint.getMargin(),
            constraint.getDataSource(),
        )

    for capacity in search.getInstalledCapacityEvidenceAtBestFeasible():
        print(
            capacity.getQualifiedConstraintName(),
            capacity.getNormalizedUtilization(),
            capacity.getNormalizedUnit(),
            capacity.getCurrentValue(),
            capacity.getApplicableLimit(),
            capacity.getRequiredRelief(),
            capacity.getPhysicalUnit(),
            capacity.getConstraintOrigin(),
            capacity.getEvidenceStatus(),
        )

sampled_gap = search.getSampledObjectiveOpportunityGap()

The result is Java-serializable and exposes defensive arrays and fresh immutable lists through JPype. It freezes objective direction, unit and weight together with allocation identity, provenance, bounds, seed, budget, tolerances, terminal outcome, complete candidate results, ranked hydraulic evidence, and every discovered installed-capacity row. Normalized utilization/margin use unit "1"; physical current, applicable limit, margin, and required relief use getPhysicalUnit(). getBestSampledObjectiveCandidate() may be infeasible; its gap to the best feasible point is only an opportunity among evaluated points.

A converged transfer step is local numerical evidence, not a global optimum or shadow price. A budget-exhausted result retains the best feasible point but remains unconverged. Any incomplete baseline recovery stops the search and sets isModelRecovered() false. This API does not support discrete line-ups, routing changes, mixed-unit allocation, dynamic qualification, economics or operating approval.

Use independent models for parallel candidates.

Analyze sampled bottleneck relief without another simulation

After a fixed-total search, use the trace-only analyzer to retain exact hard-constraint identity, required in-unit relief, and action movement from the best feasible allocation. The call performs no model evaluation or mutation.

BottleneckAnalyzer = (
    jneqsim.process.util.optimizer.ProcessModelAllocationBottleneckAnalyzer
)

analyzer = BottleneckAnalyzer(
    "field-allocation-relief",
    "Field allocation bottleneck relief",
    "approved allocation study revision A",
)
analysis = analyzer.analyze(search)

for opportunity in analysis.getOpportunities():
    print(
        opportunity.getCandidateSequenceIndex(),
        list(opportunity.getCandidateValues()),
        list(opportunity.getActionDeltasFromBestFeasible()),
        opportunity.getObjectiveGain(),
        opportunity.getObjective().getUnit(),
        opportunity.getEvidenceClass(),
    )
    for relief in opportunity.getConstraintRelief():
        constraint = relief.getConstraint()
        print(
            constraint.getName(),
            constraint.getAreaName(),
            constraint.getEquipmentName(),
            relief.getRequiredMarginRelief(),
            relief.getUnit(),
        )

CandidateSetEvaluationResult.getObjectiveEvidence() and getConstraintEvidence() return fresh immutable lists in evaluator registration order. Each row freezes the definition metadata together with the sampled raw/minimizer objective or constraint value/signed margin. This prevents a serialized trace from being joined back to mutable evaluator definitions.

An opportunity requires a converged candidate, verified complete restoration and baseline reconvergence, a finite direction-aware improvement above the search tolerance, and at least one finite violated hard constraint. Installed-capacity relief is read from the same immutable current/applicable-limit snapshot that supplied normalized candidate feasibility, including installed constraints not selected as required hydraulic bindings; a general constraint uses max(0, -margin) in its declared unit. Soft violations are not reported, raw relief is never compared across unlike units, and missing/non-finite/out-of-validity hydraulic evidence produces EVIDENCE_LIMITED.

The ranking is over the common selected-objective unit and finite sampled points only. It is non-causal evidence, not a capacity-sizing answer, global optimum, KKT multiplier, shadow price, production-loss estimate, economic value, or operating approval.

Paired debottleneck studies from Python

ProcessModelDebottleneckStudy evaluates one documented direct installed-capacity alternative with the same ScenarioSearch in both cases. The built-in CandidateListSearch is useful when Python has already generated a bounded candidate design and NeqSim must remain the physical feasibility authority.

JPype callers should pass an ordered Java List<double[]>. Result arrays are defensive copies, and result lists are unmodifiable Java lists. Convert them explicitly when native Python containers are needed:

import jpype
from jpype.types import JArray, JDouble

ArrayList = jpype.JClass("java.util.ArrayList")
Study = jneqsim.process.util.optimizer.ProcessModelDebottleneckStudy

candidates = ArrayList()
candidates.add(JArray(JDouble)([800.0]))
candidates.add(JArray(JDouble)([999.0]))
candidates.add(JArray(JDouble)([1199.0]))

search = Study.CandidateListSearch(
    "throughput-grid",
    "Ordered throughput grid",
    "screening candidate set rev A",
    candidates,
    0,
    0.0,
)

# Configure CapacityAlternative, ProcessModelDebottleneckStudy, and metric
# definitions with explicit units/provenance as shown in the optimization guide.
result = study.evaluate()

baseline_parameters = list(result.getBaseline().getSelectedParameters())
alternative_parameters = list(result.getAlternative().getSelectedParameters())
metric_deltas = {
    row.getBaseline().getId(): row.getDelta()
    for row in result.getMetricComparisons()
    if row.isCalculable()
}
diagnostics = list(result.getDiagnostics())

Here the 999 and 1199 kg/hr candidates retain 1 kg/hr of declared headroom below nominal 1000 and 1200 kg/hr installed limits. Do not rely on a unit conversion or floating-point process reconstruction to land exactly on a hard constraint boundary.

The study independently re-evaluates the selected point after the search callback returns. A custom external search therefore supplies a candidate vector, not trusted convergence or feasibility flags. Do not mutate the same evaluator or process model concurrently. The study restores and reconverges the pre-study parameter vector before returning.

The immutable StudyResult retains the alternative identity and provenance, original and applied capacity state, paired objectives and constraint residuals, installed-capacity and process-boundary evidence, physical/screening metrics, evaluation counts, recovery flags, and diagnostics. It retains no process model, equipment, live capacity constraint, or Python callback and can be Java-serialized for restartable records. Screening economics and emissions remain caller-supplied metrics with explicit basis and provenance; NeqSim does not certify them.

Rank compatible paired studies from Python

ProcessModelDebottleneckRanking consumes completed immutable study results. It ranks one exact metric definition and rejects changed units, bases, provenance, periods, confidence, searches, or baseline evidence instead of constructing a normalized score.

Ranking = jneqsim.process.util.optimizer.ProcessModelDebottleneckRanking

policy = Ranking.RankingPolicy(
    "production-delta",
    "Production delta ranking",
    "screening portfolio rev A",
    "production",
    "Feed production",
    Study.MetricKind.PRODUCTION,
    "kg/hr",
    "wet feed mass rate",
    "NeqSim stream result",
    "single steady state",
    Ranking.RankingDirection.MAXIMIZE,
    1.0e-8,
    1.0e-8,
    0.5,
    0.9,
)

ranking = Ranking(
    "separator-portfolio",
    "Separator alternatives portfolio",
    "brownfield screening alternatives rev A",
    policy,
)

study_results = ArrayList()
study_results.add(result_1100)
study_results.add(result_1150)
study_results.add(result_1200)
portfolio = ranking.rank(study_results)

best = portfolio.getBestCandidate()
best_alternative_id = best.getAlternativeDefinition().getId()
best_delta = best.getDelta()
best_unit = portfolio.getPolicy().getUnit()

ranked_rows = [
    {
        "rank": row.getRank(),
        "alternative_id": row.getAlternativeDefinition().getId(),
        "delta": row.getDelta(),
    }
    for row in portfolio.getRankedCandidates()
]
rejected_rows = [
    {
        "alternative_id": row.getAlternativeDefinition().getId(),
        "status": str(row.getStatus()),
        "diagnostics": list(row.getDiagnostics()),
    }
    for row in portfolio.getRejectedCandidates()
]

The same deterministic baseline must be reproduced by every rankable study. A different candidate grid that selects another installed-case point is rejected even when its alternative delta is finite. The returned Java lists are unmodifiable and every CandidateEvidence retains its complete serializable StudyResult. Run separate policies for production, power, emissions, and screening economics; never sum their raw deltas or compare unlike units.

Export Problem Definition

# Get problem definition as Python dict
import json

problem_json = evaluator.toJson()
problem = json.loads(problem_json)

print("Parameters:", problem['parameters'])
print("Objectives:", problem['objectives'])
print("Constraints:", problem['constraints'])

Process cloning and parallel evaluation

Only ProcessSimulationEvaluator exposes setCloneForEvaluation(true). It clones the ProcessSystem used for an evaluation so the registered base process is not mutated by that call:

evaluator.setCloneForEvaluation(True)

This switch does not make one evaluator instance safe for concurrent calls: counters, last-result state, and registered definitions remain mutable. Use one evaluator and one JVM-owned process model per worker, and validate deterministic equivalence before parallel production studies.

Complete Example: Gas Processing Optimization

import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import minimize
from neqsim import jneqsim

ProcessSimulationEvaluator = (
    jneqsim.process.util.optimizer.ProcessSimulationEvaluator
)
ProcessSystem = jneqsim.process.processmodel.ProcessSystem
Stream = jneqsim.process.equipment.stream.Stream
Compressor = jneqsim.process.equipment.compressor.Compressor
Cooler = jneqsim.process.equipment.cooler.Cooler
SystemSrkEos = jneqsim.thermo.system.SystemSrkEos

# Create process
fluid = SystemSrkEos(273.15 + 30.0, 20.0)
fluid.addComponent("methane", 0.85)
fluid.addComponent("ethane", 0.10)
fluid.addComponent("propane", 0.05)
fluid.setMixingRule("classic")
fluid.setTotalFlowRate(50000.0, "kg/hr")

feed = Stream("feed", fluid)
compressor = Compressor("compressor", feed)
compressor.setOutletPressure(80.0)
cooler = Cooler("cooler", compressor.getOutletStream())
cooler.setOutletTemperature(273.15 + 40.0)

process = ProcessSystem()
process.add(feed)
process.add(compressor)
process.add(cooler)
process.run()

# Setup optimization
evaluator = ProcessSimulationEvaluator(process)

# Decision variables
evaluator.addParameter("feed", "flowRate", 10000.0, 100000.0, "kg/hr")
evaluator.addParameter("compressor", "outletPressure", 50.0, 120.0, "bara")

# Minimize compressor power
evaluator.addObjective("power",
    lambda p: p.getUnit("compressor").getEnergy("kW"))

# Constraints
evaluator.addConstraintLowerBound("minOutletPressure",
    lambda p: p.getUnit("cooler").getOutletStream().getPressure("bara"),
    60.0)

evaluator.addConstraintUpperBound("maxOutletTemp",
    lambda p: p.getUnit("cooler").getOutletStream().getTemperature("C"),
    50.0)

# Optimize with SLSQP
def objective(x):
    return evaluator.evaluateObjective(x)

def constraint_margins(x):
    return evaluator.getConstraintMargins(x)

bounds = [(b[0], b[1]) for b in evaluator.getBounds()]
x0 = evaluator.getInitialValues()

result = minimize(
    objective,
    x0,
    method='SLSQP',
    bounds=bounds,
    constraints={'type': 'ineq', 'fun': constraint_margins},
    options={'maxiter': 100, 'disp': True}
)

# Display results
print("\n=== Optimization Results ===")
print(f"Optimal flow rate: {result.x[0]:.1f} kg/hr")
print(f"Optimal outlet pressure: {result.x[1]:.1f} bara")
print(f"Minimum power: {result.fun:.1f} kW")
print(f"Constraint margins: {constraint_margins(result.x)}")
print(f"Total evaluations: {evaluator.getEvaluationCount()}")

jpype.shutdownJVM()

Troubleshooting

Common Issues

1. Simulation doesn’t converge: Check that parameter bounds are physically reasonable
2. Gradient estimation fails: Try larger finite difference step
3. Slow evaluations: Enable caching or reduce process complexity
4. Thread safety errors: Enable setCloneForEvaluation(True)

Performance Tips

1. Start with fewer parameters and add more iteratively
2. Use warm starts from previous solutions
3. For global optimization, use differential evolution first, then polish with L-BFGS-B
4. Profile with evaluator.getEvaluationCount() to identify bottlenecks

API Reference

ProcessSimulationEvaluator

Method	Description
evaluate(double[] x)	Full evaluation returning EvaluationResult
evaluateObjective(double[] x)	Quick objective-only evaluation
evaluatePenalizedObjective(double[] x)	Objective + constraint penalties
isFeasible(double[] x)	Check constraint satisfaction
getConstraintMargins(double[] x)	Get constraint slack values
estimateGradient(double[] x)	Finite-difference gradient
estimateConstraintJacobian(double[] x)	Constraint Jacobian matrix
getBounds()	Get parameter bounds array
getLowerBounds()	Get lower bounds vector
getUpperBounds()	Get upper bounds vector
getInitialValues()	Get initial parameter values
toJson()	Export problem definition

ProcessModelSimulationEvaluator

Method	Description
estimateSensitivitiesWithQuality(double[] x)	Primary-objective fine-step gradient and constraint-margin Jacobian, plus immutable parameter/objective/constraint identity, base values, capacity origin, perturbation convergence, feasibility, and step-halving evidence
estimateSensitivitiesWithQuality(double[] x, int objectiveIndex)	The same evidence for the selected registered objective
SensitivityQualityResult.assessConstraintSensitivities(policy)	Immutable evidence and acceptance/rejection diagnostics for every constraint/parameter pair; performs no process evaluations
SensitivityQualityResult.getAcceptedConstraintSensitivities(policy)	Accepted local pairs only; inspect the full assessment list to retain rejected evidence
ConstraintActivityAnalyzer.ConstraintScale.fromSnapshot(...)	Positive identity-bound constraint reference with declared unit and provenance
ConstraintActivityAnalyzer.assess(result, scales, policy)	Dimensionless margins, normalized local margin sensitivities and conservative activity diagnostics without process evaluations
getCandidateActiveConstraints(...) / getViolatedConstraints(...)	Registration-ordered feasible-near-boundary and violated subsets; neither is optimizer KKT evidence
EvaluationResult.getInstalledEquipmentCapacityEvidence()	Fresh immutable, utilization-ranked installed-capacity rows with stable identity/provenance, dimensionless feasibility, and separate physical residuals
snapshotInstalledEquipmentCapacityEvidence(model)	Read a live model once per enabled capacity supplier outside an evaluator run; no process simulation is performed

The sensitivity-quality methods belong to ProcessModelSimulationEvaluator; they are not methods on ProcessSimulationEvaluator.

ProcessModelAllocationOptimizer

Method	Description
optimize()	Run fixed-total continuous pair-transfer search through the atomic action-set evaluator
AllocationSearchResult.getCandidates()	Complete immutable candidate and restoration trace
getBestFeasibleCandidate()	Best feasible finite-objective candidate under the frozen direction
getBestSampledObjectiveCandidate()	Best sampled objective candidate, whether feasible or not
getSampledObjectiveOpportunityGap()	Non-negative sampled diagnostic gap; not global production loss or shadow value
getRankedHydraulicConstraintsAtBestFeasible()	Stable descending-utilization evidence at the feasible incumbent
getRankedHydraulicConstraintsAtBestSampledObjective()	Stable descending-utilization evidence at the best sampled objective
getInstalledCapacityEvidenceAtBestFeasible()	Complete immutable installed-capacity evidence at the feasible incumbent
getInstalledCapacityEvidenceAtBestSampledObjective()	Complete immutable installed-capacity evidence at the best sampled objective

ProcessModelDebottleneckStudy

Method	Description
evaluate()	Run the common search for baseline and alternative, independently verify each selected point, sample metrics, and recover state
CandidateListSearch(...)	Deterministic direction-aware search over an ordered bounded candidate list; equal objective values retain the first candidate
CapacityAlternative(...)	Stable direct-constraint identity plus proposed applicable limit, unit, direction, source, confidence, and validity range
addMetric(MetricDefinition)	Register one production, power, energy, emissions, screening-economic, or other metric with unit, basis, provenance, period, confidence, and required status
StudyResult.getBaseline() / getAlternative()	Immutable scenario evidence with selected parameters, objectives, margins, installed/boundary evidence, metrics, and diagnostics
StudyResult.getMetricComparisons()	Identically defined metric rows with alternative - baseline deltas
StudyResult.isCapacityRestored() / isProcessStateRestored()	Explicit transaction-recovery evidence

ProcessModelDebottleneckRanking

Method	Description
RankingPolicy(...)	Exact single-metric identity, unit, basis, provenance, period and direction; in-unit tie tolerance; dimensionless repeated-baseline relative tolerance; optional confidence floors
rank(List<StudyResult>)	Fail-closed qualification, exact-baseline comparison, deterministic ordering, and competition ranks
RankingResult.getRankedCandidates()	Compatible candidates in deterministic rank order with their complete paired-study evidence
RankingResult.getRejectedCandidates()	Incompatible studies with status and diagnostics; no synthetic normalized score
RankingResult.getCandidatesInInputOrder()	Complete audit trail in caller submission order
RankingResult.getBestCandidate()	Highest-ranked compatible candidate, or null when none qualifies

EvaluationResult

Method	Description
getObjective()	Primary objective value
getObjectives()	All objective values (transformed)
getObjectivesRaw()	Raw objective values
getPenalizedObjective()	Objective + penalty
getWeightedObjective(weights)	Weighted sum of objectives
getConstraintMargins()	Constraint slack values
isFeasible()	All constraints satisfied?
isSimulationConverged()	Process simulation converged?
getEvaluationNumber()	Sequential evaluation number
getAdditionalOutputs()	Custom output values

See Also

• OPTIMIZER_PLUGIN_ARCHITECTURE.md - Plugin architecture for equipment-specific optimization
• flow-rate-optimization.md - FlowRateOptimizer for lift curve generation
• pressure_boundary_optimization.md - Simplified pressure boundary optimizer
• PRODUCTION_OPTIMIZATION_GUIDE.md - Complete production optimization examples