References

Academic References

Textbooks

1. Seborg, D.E., Edgar, T.F., Mellichamp, D.A., Doyle III, F.J. (2016). Process Dynamics and Control, 4th Edition. John Wiley & Sons.

   • Comprehensive coverage of process control fundamentals

   • Excellent treatment of PID control and tuning methods

   • Modern topics including model predictive control

2. Stephanopoulos, G. (1984). Chemical Process Control: An Introduction to Theory and Practice. Prentice Hall.

   • Classic reference for chemical process control

   • Strong theoretical foundation

   • Emphasis on chemical engineering applications

3. Bequette, B.W. (2003). Process Control: Modeling, Design, and Simulation. Prentice Hall.

   • Modern approach to process control

   • Excellent simulation examples

   • Good coverage of advanced control topics

4. Marlin, T.E. (2000). Process Control: Designing Processes and Control Systems for Dynamic Performance, 2nd Edition. McGraw-Hill.

   • Practical approach to control system design

   • Emphasis on industrial applications

   • Excellent coverage of loop tuning

5. Ogunnaike, B.A., Ray, W.H. (1994). Process Dynamics, Modeling, and Control. Oxford University Press.

   • Advanced treatment of process control

   • Strong mathematical foundation

   • Comprehensive coverage of multivariable control

Journal Articles

6. Åström, K.J., Hägglund, T. (2004). “Revisiting the Ziegler-Nichols step response method for PID control.” Journal of Process Control, 14(6), 635-650.

7. Rivera, D.E., Morari, M., Skogestad, S. (1986). “Internal model control: PID controller design.” Industrial & Engineering Chemistry Process Design and Development, 25(1), 252-265.

8. Skogestad, S. (2003). “Simple analytic rules for model reduction and PID controller tuning.” Journal of Process Control, 13(4), 291-309.

9. Hägglund, T., Åström, K.J. (2002). “Revisiting the Ziegler-Nichols tuning rules for PI control.” Asian Journal of Control, 4(4), 364-380.

10. Lee, Y., Park, S., Lee, M., Brosilow, C. (1998). “PID controller tuning for desired closed-loop responses for SI/SO systems.” AIChE Journal, 44(1), 106-115.

Online Resources

Educational Websites

11. Kantor, J.C. Chemical Process Control. https://jckantor.github.io/CBE30338/

    • Excellent online course materials

    • Jupyter notebook examples

    • Modern Python-based approach to process control

12. Control Guru - PID Control Resources http://www.controlguru.com/

    • Practical PID tuning guides

    • Industrial control system insights

    • Real-world application examples

13. MATLAB Control Systems Toolbox Documentation https://www.mathworks.com/help/control/

    • Comprehensive control system design tools

    • Extensive documentation and examples

    • Industry-standard software reference

Software and Tools

14. Python Control Systems Library https://python-control.readthedocs.io/

    • Open-source Python library for control systems

    • Compatible with this standard process control library

    • Active development community

15. OpenLoop - Open Source Control Design https://openloop.sourceforge.net/

    • Free control system design software

    • Educational and research applications

16. Scilab/Xcos - Open Source Alternative to MATLAB/Simulink https://www.scilab.org/

    • Free alternative for control system design

    • Good educational resource

Specialized Topics

Model Predictive Control

17. Rawlings, J.B., Mayne, D.Q., Diehl, M. (2017). Model Predictive Control: Theory, Computation, and Design, 2nd Edition. Nob Hill Publishing.

18. Camacho, E.F., Alba, C.B. (2013). Model Predictive Control. Springer Science & Business Media.

Batch Process Control

19. Bonvin, D., Srinivasan, B., Hunkeler, D. (2006). “Control and optimization of batch processes.” IEEE Control Systems Magazine, 26(6), 34-45.

20. Flores-Cerrillo, J., MacGregor, J.F. (2005). “Latent variable MPC for trajectory tracking in batch processes.” Journal of Process Control, 15(6), 651-663.

Process Optimization

21. Biegler, L.T. (2010). Nonlinear Programming: Concepts, Algorithms, and Applications to Chemical Processes. SIAM.

22. Edgar, T.F., Himmelblau, D.M., Lasdon, L.S. (2001). Optimization of Chemical Processes, 2nd Edition. McGraw-Hill.

Advanced Control Topics

23. Zhou, K., Doyle, J.C., Glover, K. (1996). Robust and Optimal Control. Prentice Hall.

24. Green, M., Limebeer, D.J. (2012). Linear Robust Control. Courier Corporation.

25. Åström, K.J., Murray, R.M. (2010). Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press.

Standards and Guidelines

Industrial Standards

26. ISA-5.1-2009. Instrumentation Symbols and Identification. International Society of Automation.

27. ISA-88.01-2010. Batch Control Part 1: Models and Terminology. International Society of Automation.

28. ISA-95.00.01-2010. Enterprise-Control System Integration Part 1: Models and Terminology. International Society of Automation.

Software Engineering

29. PEP 8 - Style Guide for Python Code https://www.python.org/dev/peps/pep-0008/

30. NumPy Documentation and Development Guidelines https://numpy.org/doc/stable/dev/

Historical References

Foundational Works

31. Ziegler, J.G., Nichols, N.B. (1942). “Optimum settings for automatic controllers.” Transactions of the ASME, 64, 759-768.

    • Original Ziegler-Nichols tuning method

    • Historical significance in control engineering

32. Bode, H.W. (1945). Network Analysis and Feedback Amplifier Design. Van Nostrand.

    • Foundation of frequency domain analysis

    • Origin of Bode plots

33. Nyquist, H. (1932). “Regeneration theory.” Bell System Technical Journal, 11(1), 126-147.

    • Nyquist stability criterion

    • Fundamental stability analysis method

34. Kalman, R.E. (1960). “A new approach to linear filtering and prediction problems.” Journal of Basic Engineering, 82(1), 35-45.

    • Kalman filter development

    • State-space methods foundation

Related Libraries and Packages

Python Scientific Computing

35. NumPy - Fundamental package for scientific computing https://numpy.org/

36. SciPy - Scientific computing library https://scipy.org/

37. Matplotlib - Plotting library https://matplotlib.org/

38. SymPy - Symbolic mathematics library https://sympy.org/

Optimization

39. PyOMO - Python Optimization Modeling Objects https://www.pyomo.org/

40. CVXPY - Convex optimization library https://www.cvxpy.org/

41. GEKKO - Dynamic optimization library https://gekko.readthedocs.io/

Chemical Engineering

42. Cantera - Chemical kinetics, thermodynamics, and transport https://cantera.org/

43. DWSIM - Chemical process simulator https://dwsim.inforside.com.br/

44. ChemPy - Chemical calculations in Python https://github.com/bjodah/chempy

Citation Information

Citing This Library

If you use this Standard Process Control Library in your research or educational work, please cite:

@software{chemical_process_control_library,
  title = {Standard Process Control Library},
  author = {Gressling, T. and Kantor, J.},
  year = {2025},
  url = {https://github.com/your-repo/sproclib},
  version = {1.0.0}
}

Citing Educational Materials

This library is inspired by educational materials on chemical process control:

@misc{kantor2023processcontrol,
  title = {Chemical Process Control},
  author = {Kantor, Jeffrey C.},
  year = {2023},
  url = {https://jckantor.github.io/CBE30338/}
}

Acknowledgments

SPROCLIB was developed by Thorsten Gressling (gressling@paramus.ai) based on excellent educational materials from:

• Professor Jeffrey C. Kantor - Chemical process control educational resources

• The Python scientific computing community - NumPy, SciPy, Matplotlib developers

• The chemical engineering control community - Researchers and educators who have advanced the field

The library structure and examples are inspired by modern best practices in:

• Software engineering and documentation

• Scientific computing and reproducible research

• Chemical engineering education and industrial practice

Additional Resources

Professional Organizations

• American Institute of Chemical Engineers (AIChE) - https://www.aiche.org/

• International Society of Automation (ISA) - https://www.isa.org/

• Institute of Electrical and Electronics Engineers (IEEE) Control Systems Society - https://www.ieeecss.org/

Conferences

• American Control Conference (ACC)

• IEEE Conference on Decision and Control (CDC)

• IFAC World Congress

• AIChE Annual Meeting - Process Development Division

Journals

• Journal of Process Control

• Industrial & Engineering Chemistry Research

• Control Engineering Practice

• Automatica

• IEEE Transactions on Control Systems Technology