# SPROCLIB - Standard Process Control Library
A comprehensive Python library for chemical process control, providing essential classes and functions for PID control, process modeling, simulation, optimization, and advanced control techniques.
**SPROCLIB provides a semantic API for chemical plant design that uses familiar patterns from machine learning frameworks like TensorFlow and Keras.**
## Installation
```bash
pip install sproclib
```
## Features
- **Semantic Plant Design**: Intuitive API similar to ML frameworks for building complex chemical processes
- **Process Units**: CSTR, pumps, heat exchangers, distillation columns, reactors, and tanks
- **Economic Optimization**: Built-in optimization algorithms for cost minimization and profit maximization
- **PID Controllers**: Classical and advanced PID control implementations with auto-tuning
- **Analysis Tools**: Transfer functions, simulation, and system identification
- **Advanced Control**: Model predictive control, state-space controllers, and IMC
- **Transport Models**: Continuous and batch transport for liquids and solids
## Quick Start
Create and optimize a chemical plant in just a few lines:
```python
from sproclib.unit.plant import ChemicalPlant
from sproclib.unit.pump import CentrifugalPump
from sproclib.unit.reactor import CSTR
# Define plant
plant = ChemicalPlant(name="Process Plant")
# Add units
plant.add(CentrifugalPump(H0=50.0, eta=0.75), name="feed_pump")
plant.add(CSTR(V=150.0, k0=7.2e10), name="reactor")
# Connect units
plant.connect("feed_pump", "reactor", "feed_stream")
# Configure optimization
plant.compile(
optimizer="economic",
loss="total_cost",
metrics=["profit", "conversion"]
)
# Optimize operations
plant.optimize(target_production=1000.0)
```
## Advanced Example
```python
# Traditional PID control example
import sproclib as spc
# Create a PID controller
controller = spc.PIDController(kp=1.0, ki=0.1, kd=0.05)
# Create a tank model
tank = spc.Tank(volume=100, area=10)
# Simulate step response
response = spc.step_response(tank, time_span=100)
```
## Requirements
- Python 3.8+
- NumPy >= 1.20.0
- SciPy >= 1.7.0
- Matplotlib >= 3.3.0
## License
MIT License
## Author
Thorsten Gressling <gressling@paramus.ai>
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"description": "# SPROCLIB - Standard Process Control Library\r\n\r\nA comprehensive Python library for chemical process control, providing essential classes and functions for PID control, process modeling, simulation, optimization, and advanced control techniques.\r\n\r\n**SPROCLIB provides a semantic API for chemical plant design that uses familiar patterns from machine learning frameworks like TensorFlow and Keras.**\r\n\r\n## Installation\r\n\r\n```bash\r\npip install sproclib\r\n```\r\n\r\n## Features\r\n\r\n- **Semantic Plant Design**: Intuitive API similar to ML frameworks for building complex chemical processes\r\n- **Process Units**: CSTR, pumps, heat exchangers, distillation columns, reactors, and tanks\r\n- **Economic Optimization**: Built-in optimization algorithms for cost minimization and profit maximization\r\n- **PID Controllers**: Classical and advanced PID control implementations with auto-tuning\r\n- **Analysis Tools**: Transfer functions, simulation, and system identification\r\n- **Advanced Control**: Model predictive control, state-space controllers, and IMC\r\n- **Transport Models**: Continuous and batch transport for liquids and solids\r\n\r\n## Quick Start\r\n\r\nCreate and optimize a chemical plant in just a few lines:\r\n\r\n```python\r\nfrom sproclib.unit.plant import ChemicalPlant\r\nfrom sproclib.unit.pump import CentrifugalPump\r\nfrom sproclib.unit.reactor import CSTR\r\n\r\n# Define plant\r\nplant = ChemicalPlant(name=\"Process Plant\")\r\n\r\n# Add units\r\nplant.add(CentrifugalPump(H0=50.0, eta=0.75), name=\"feed_pump\")\r\nplant.add(CSTR(V=150.0, k0=7.2e10), name=\"reactor\")\r\n\r\n# Connect units\r\nplant.connect(\"feed_pump\", \"reactor\", \"feed_stream\")\r\n\r\n# Configure optimization\r\nplant.compile(\r\n optimizer=\"economic\",\r\n loss=\"total_cost\",\r\n metrics=[\"profit\", \"conversion\"]\r\n)\r\n\r\n# Optimize operations\r\nplant.optimize(target_production=1000.0)\r\n```\r\n\r\n## Advanced Example\r\n\r\n```python\r\n# Traditional PID control example\r\nimport sproclib as spc\r\n\r\n# Create a PID controller\r\ncontroller = spc.PIDController(kp=1.0, ki=0.1, kd=0.05)\r\n\r\n# Create a tank model\r\ntank = spc.Tank(volume=100, area=10)\r\n\r\n# Simulate step response\r\nresponse = spc.step_response(tank, time_span=100)\r\n```\r\n\r\n## Requirements\r\n\r\n- Python 3.8+\r\n- NumPy >= 1.20.0\r\n- SciPy >= 1.7.0\r\n- Matplotlib >= 3.3.0\r\n\r\n## License\r\n\r\nMIT License\r\n\r\n## Author\r\n\r\nThorsten Gressling <gressling@paramus.ai>\r\n",
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