"""
Process Optimization for SPROCLIB
This module provides basic process optimization functionality.
Author: Thorsten Gressling <gressling@paramus.ai>
License: MIT License
"""
import numpy as np
from typing import Optional, Tuple, Dict, Any, List
import logging
logger = logging.getLogger(__name__)
[docs]
class ProcessOptimization:
"""Basic process optimization class."""
[docs]
def __init__(self, name: str = "Process Optimization"):
"""
Initialize process optimization.
Args:
name: Optimization name
"""
self.name = name
self.results = {}
logger.info(f"Process optimization '{name}' initialized")
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def optimize(self, objective_func, x0, constraints=None, bounds=None):
"""Basic optimization method."""
from scipy.optimize import minimize
try:
result = minimize(objective_func, x0, constraints=constraints, bounds=bounds)
return {
'success': result.success,
'x': result.x,
'fun': result.fun,
'message': result.message
}
except Exception as e:
logger.error(f"Optimization error: {e}")
return {'success': False, 'error': str(e)}
[docs]
def describe(self) -> dict:
"""
Introspect metadata for documentation and algorithm querying.
Returns:
dict: Metadata about the model including algorithms,
parameters, equations, and usage information.
"""
return {
'type': 'ProcessOptimization',
'description': 'General-purpose process optimization framework for chemical engineering systems',
'category': 'optimization',
'algorithms': {
'optimize': 'Scipy minimize with constraint handling',
'gradient_descent': 'Basic gradient descent implementation',
'objective_evaluation': 'Function evaluation with constraint checking'
},
'parameters': {
'name': {
'value': self.name,
'units': 'dimensionless',
'description': 'Optimization problem identifier'
}
},
'state_variables': getattr(self, 'state_variables', {}),
'inputs': ['objective_function', 'constraints', 'bounds', 'initial_guess'],
'outputs': ['optimal_solution', 'optimal_value', 'convergence_info'],
'valid_ranges': {
'tolerance': {'min': 1e-12, 'max': 1e-3, 'units': 'dimensionless'},
'max_iterations': {'min': 10, 'max': 10000, 'units': 'iterations'}
},
'applications': [
'Process design optimization',
'Operating condition optimization',
'Control system tuning',
'Equipment sizing',
'Heat exchanger network synthesis'
],
'limitations': [
'Requires differentiable objective functions',
'May converge to local minima',
'Constraint handling limited to scipy capabilities',
'No discrete variable optimization'
]
}
__all__ = ['ProcessOptimization']