Modelling, Design and Control Optimization of a Residential Scale CHP System

  • Nikolaos A. Diangelakis
  • Efstratios N. Pistikopoulos
Chapter
First Online:

Abstract

We present an analytical dynamic mathematical model and a simultaneous design and control optimization of a residential scale combined heat and power system (CHP). The mathematical model features a detailed description of the internal combustion engine based on a mean value approach, and simplified sub-models for the throttle valve, the intake and exhaust manifolds, and the external circuit. We treat the CHP unit as the interconnection of two distinct subsystems; the power production subsystem and the heat recovery subsystem. The validated zero-dimensional (0D) dynamic mathematical model of the system is implemented in gPROMS©, and used for optimization studies. A mixed-integer dynamic optimization problem is introduced that simultaneously determines the size of the internal combustion engine and the optimal control scheme of the CHP subsystems.

Keywords

Internal Combustion Engine Internal Combustion Engine Cylinder Wall External Circuit Engine Block 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Notes

Acknowledgments

Financial support from EPSRC (EP/I014640), Texas A&M University and Texas A&M Energy institute is gratefully acknowledged.

References

  1. Afgan, N., & Schünder, E. (1974). Heat Exchangers: Design and theory sourcebook. Scripta Book Company.Google Scholar
  2. Aghdam, E., & Kabir, M. (2010). Validation of a blowby model using experimental results in motoring condition with the change of compression ratio and engine speed. Experimental Thermal and Fluid Science, 34(2), 197–209.Google Scholar
  3. Al-Hinti, I., Akash, B., Abu-Nada, E., & Al-Sarkhi, A. (2008). Performance analysis of air-standard diesel cycle using an alternative irreversible heat transfer approach. Energy Conversion and Management, 49(11), 3301–3304.CrossRefGoogle Scholar
  4. Angulo-Brown, F., Fernández-Betanzos, J., & Diaz-Pico, C. (1994). Compression ratio of an optimized air standard otto-cycle model. European Journal of Physics, 15(1), 38–42.CrossRefGoogle Scholar
  5. Arici, O., Johnson, J., & Kulkarni, A. (1999). The vehicle engine cooling system simulation part 1—Model development. SAE Technical Paper 1999-01-0240.Google Scholar
  6. Arsie, I., Pianese, C., & Rizzo, G. (1998). Models for the prediction of performance and emissions in a spark ignition engine—A sequentially structured approach. SAE Technical Paper, 980779.Google Scholar
  7. Bahri, P., Bandoni, J., & Romagnoli, J. (1997). Integrated flexibility and controllability analysis in design of chemical processes. AIChE Journal, 43(4), 997–1015.CrossRefGoogle Scholar
  8. Baldea, M., & Harjunkoski, I. (2014). Integrated production scheduling and process control: A systematic review. Computers & Chemical Engineering, 71, 377–390.CrossRefGoogle Scholar
  9. Bansal, V. (2000). Anaylsis, design and control optimization of process systems under uncertainty. Ph.D. thesis, Imperial College, London.Google Scholar
  10. Bansal, V., Perkins, J. D., & Pistikopoulos, E. N. (2000). Flexibility analysis and design of linear systems by parametric programming. AIChE Journal, 46(2), 335–354.CrossRefGoogle Scholar
  11. Bansal, V., Perkins, J. D., & Pistikopoulos, E. N. (2002). Flexibility analysis and design using a parametric programming framework. AIChE Journal, 48(12), 2851–2868.CrossRefGoogle Scholar
  12. Bhattacharyya, S. (2000). Optimizing an irreversible diesel cycle—Fine tuning of compression ratio and cut-off ratio. Energy Conversion and Management, 41(8), 847–854.CrossRefGoogle Scholar
  13. Brengel, D. D., & Seider, W. D. (1992). Coordinated design and control optimization of nonlinear processes. Computers & Chemical Engineering, 16(9), 861–886.CrossRefGoogle Scholar
  14. Chi, R., Hou, Z., Huang, B., & Jin, S. (2015). A unified data-driven design framework of optimality-based generalized iterative learning control. Computers and Chemical Engineering, 77, 10–23.CrossRefGoogle Scholar
  15. Cook, J., & Powell, B. (1988). Modeling of an internal combustion engine for control analysis. IEEE Control Systems Magazine, 8(4), 20–26.CrossRefGoogle Scholar
  16. Diangelakis, N. A., Avraamidou, S., & Pistikopoulos, E. N. (2016). Decentralized multiparametric model predictive control for domestic combined heat and power systems. Industrial & Engineering Chemistry Research, 55(12), 3313–3326.CrossRefGoogle Scholar
  17. Diangelakis, N. A., Manthanwar, A. M., & Pistikopoulos, E. N. (2014). A framework for design and control optimisation: Application on a chp system. In Computer aided chemical engineering. Proceedings of the 8th international conference on foundations of computer-aided process design (Vol. 34, pp. 765–770). Elsevier.Google Scholar
  18. Diangelakis, N. A., Panos, C., & Pistikopoulos, E. N. (2014). Design optimization of an internal combustion engine powered CHP system for residential scale application. Computational Management Science, 11(3), 237–266.CrossRefMATHGoogle Scholar
  19. Fazlollahi, S. B., Mandel, P., Becker, G., & Maréchal, F. (2012). Methods for multi-objective investment and operating optimization of complex energy systems. Energy, 45(1), 12–22.Google Scholar
  20. Figueroa, J., Bahri, P., Bandoni, J., & Romagnoli, J. (1996). Economic impact of disturbances and uncertain parameters in chemical processes—A dynamic back-off analysis. Computers and Chemical Engineering, 20(4), 453–461.CrossRefGoogle Scholar
  21. Flores-Tlacuahuac, A., & Biegler, L. T. (2007). Simultaneous mixed-integer dynamic optimization for integrated design and control. Computers & Chemical Engineering, 31(5–6), 588–600.CrossRefGoogle Scholar
  22. Fraga, E., Hagemann, J., Estrada-Villagrana, A., & Bogle, I. (2000). Incorporation of dynamic behaviour in an automated process synthesis system. Computers and Chemical Engineering, 24(2–7), 189–194.CrossRefGoogle Scholar
  23. Gani, R. (2004). Chemical product design: Challenges and opportunities. Computers and Chemical Engineering, 28(12), 2441–2457.CrossRefGoogle Scholar
  24. Gebreslassie, B., Yao, Y., & You, F. (2012). Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a comparison between CVaR and downside risk. AIChE Journal, 58(7), 2155–2179.CrossRefGoogle Scholar
  25. Gutierrez, G., Ricardez-Sandoval, L., Budman, H., & Prada, C. (2014). An MPC-based control structure selection approach for simultaneous process and control design. In Computers and chemical engineering.Google Scholar
  26. Guzzella, L., & Onder, C. H. (2010). Introduction to modeling and control of internal combustion engine systems (2nd ed.). Springer.Google Scholar
  27. Hamid, M., Sin, G., & Gani, R. (2010). Integration of process design and controller design for chemical processes using model-based methodology. Computers and Chemical Engineering, 34(5), 683–699.CrossRefGoogle Scholar
  28. Heywood, J. B. (1989). Internal combustion engine fundamentals. McGraw-Hill, Inc.Google Scholar
  29. Konstantinidis, D., Varbanov, P., & Klemeš, J. (2010). Multi-parametric control and optimisation of a small scale CHP. Chemical Engineering Transactions, 21, 151–156.Google Scholar
  30. Kookos, I., & Perkins, J. (2001). An algorithm for simultaneous process design and control. Industrial and Engineering Chemistry Research, 40(19), 4079–4088.CrossRefGoogle Scholar
  31. Kopanos, G. M., & Pistikopoulos, E. N. (2014). Reactive scheduling by a multiparametric programming rolling horizon framework: A case of a network of combined heat and power units. Industrial & Engineering Chemistry Research, 53(11), 4366–4386.CrossRefGoogle Scholar
  32. Lee, H., Koppel, L., & Lim, H. (1972). Integrated approach to design and control of a class of countercurrent processes. Industrial and Engineering Chemistry: Process Design and Development, 11(3), 376–382.Google Scholar
  33. Lewin, D., Seider, W., & Seader, J. (2002). Integrated process design instruction. Computers and Chemical Engineering, 26(2), 295–306.CrossRefGoogle Scholar
  34. Liu, P., Georgiadis, M. C., & Pistikopoulos, E. N. (2013). An energy systems engineering approach for the design and operation of microgrids in residential applications. Chemical Engineering Research and Design, 91(10), 2054–2069. The 60th Anniversary of the European Federation of Chemical Engineering (EFCE).Google Scholar
  35. Luyben, M. L., & Floudas, C. A. (1994). Analyzing the interaction of design and control-1. A multiobjective framework and application to binary distillation synthesis. Computers & Chemical Engineering, 18(10), 933–969.CrossRefGoogle Scholar
  36. Malcolm, A., Polan, J., Zhang, L., Ogunnaike, B., & Linninger, A. (2007). Integrating systems design and control using dynamic flexibility analysis. AIChE Journal, 53(8), 2048–2061.CrossRefGoogle Scholar
  37. Mehleri, E. B., Sarimveis, H., Markatos, N., & Papageorgiou, L. (2011). Optimal design and operation of distributed energy systems. Computer Aided Chemical Engineering, 29, 1713–1717.Google Scholar
  38. Mehleri, E. D., Papageorgiou, L. G., Markatos, N. C., & Sarimveis, H. (2012). A model predictive control framework for residential microgrids. Computer Aided Chemical Engineering, 30, 327–331.CrossRefGoogle Scholar
  39. Menon, R., Paolone, M., & Maréchal, F. (2013). Study of optimal design of polygeneration systems in optimal control strategies. Energy, 55, 134–141.CrossRefGoogle Scholar
  40. Mohideen, M. J., Perkins, J. D., & Pistikopoulos, E. N. (1996). Optimal synthesis and design of dynamic systems under uncertainty. Computers & Chemical Engineering, 20(Supplement 2(0)), S895–S900.Google Scholar
  41. Moran, M., & Shapiro, H. N. (1992). Fundamentals of engineering thermodynamics. New York: Wiley.Google Scholar
  42. Narraway, L., Perkins, J., & Barton, G. (1991). Interaction between process design and process control: Economic analysis of process dynamics. Journal of Process Control, 1(5), 243–250.CrossRefGoogle Scholar
  43. Olsen, D., Svrcek, W., & Young, B. (2005). Plantwide control study of a vinyl acetate monomer process design. Chemical Engineering Communications, 192(10–12), 1243–1257.CrossRefGoogle Scholar
  44. Onovwiona, H., & Ugursal, V. (2006). Residential cogeneration systems: Review of the current technology. Renewable and Sustainable Energy Reviews, 10(5), 389–431.CrossRefGoogle Scholar
  45. Onovwiona, H. I., Ugursal, V. I., & Fung, A. S. (2007). Modeling of internal combustion engine based cogeneration systems for residential applications. Applied Thermal Engineering, 27(5–6), 848–861.CrossRefGoogle Scholar
  46. Organisation for economic co-operation and development/International Energy Agency. (2008). Combined Heat and Power. Technical report, OECD/IEA Head of communication and information office, 9 rue de la Fédération, 75739 Paris Cedex 15, France.Google Scholar
  47. Payri, F., Olmeda, P., Martn, J., & Garca, A. (2011). A complete 0D thermodynamic predictive model for direct injection diesel engines. Applied Energy, 88(12), 4632–4641.CrossRefGoogle Scholar
  48. Pistikopoulos, E., & Diangelakis, N. (2015). Towards the integration of process design, control and scheduling: Are we getting closer? Computers and chemical engineering.Google Scholar
  49. Pistikopoulos, E., Diangelakis, N., Oberdieck, R., Papathanasiou, M., Nascu, I., & Sun, M. (2015). PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chemical Engineering Science, 136, 115–138.CrossRefGoogle Scholar
  50. Process Systems Enterprise. gPROMS, 1997–2016.Google Scholar
  51. Rakopoulos, C., Kosmadakis, G., Dimaratos, A., & Pariotis, E. (2011). Investigating the effect of crevice flow on internal combustion engines using a new simple crevice model implemented in a CFD code. Applied Energy, 88(1), 111–126.CrossRefGoogle Scholar
  52. Rausen, D., Stefanopoulou, A., Kang, J.-M., Eng, J., & Kuo, T.-W. (2005). A mean-value model for control of homogeneous charge compression ignition (HCCI) engines. Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, 127(3), 355–362.CrossRefGoogle Scholar
  53. Ricardez-Sandoval, L. (2012). Optimal design and control of dynamic systems under uncertainty: A probabilistic approach. Computers and Chemical Engineering, 43, 91–107.CrossRefGoogle Scholar
  54. Ricardez-Sandoval, L., Budman, H., & Douglas, P. (2009). Integration of design and control for chemical processes: A review of the literature and some recent results. Annual Reviews in Control, 33(2), 158–171.CrossRefGoogle Scholar
  55. Sakizlis, V., Perkins, J. D., & Pistikopoulos, E. N. (2003). Parametric controllers in simultaneous process and control design optimization. Industrial & Engineering Chemistry Research, 42(20), 4545–4563.CrossRefGoogle Scholar
  56. Sakizlis, V., Perkins, J. D., & Pistikopoulos, E. N. (2004). Recent advances in optimization-based simultaneous process and control design. Computers & Chemical Engineering, 28(10), 2069–2086.CrossRefGoogle Scholar
  57. Sánchez-Sánchez, K., & Ricardez-Sandoval, L. (2013). Simultaneous process synthesis and control design under uncertainty: A worst-case performance approach. AIChE Journal, 59(7), 2497–2514.CrossRefGoogle Scholar
  58. Savola, T., & Fogelholm, C.-J. (2007). MINLP optimisation model for increased power production in small-scale CHP plants. Applied Thermal Engineering, 27(1), 89–99.CrossRefGoogle Scholar
  59. Savola, T., & Keppo, I. (2005). Off-design simulation and mathematical modeling of small-scale CHP plants at part loads. Applied Thermal Engineering, 25(8–9), 1219–1232.CrossRefGoogle Scholar
  60. Seferlis, P., & Georgiadis, M. (2004). The integration of process design and control-summary and future directions. Computer Aided Chemical Engineering, 17(C), 1–9.Google Scholar
  61. Van Schijndel, J., & Pistikopoulos, E. (2000). Towards the integration of process design, process control and process operability—Current stares and furore trends. Foundations of Computer-Aided Process Design, 96, 99–112.Google Scholar
  62. Vega, P., Lamanna, R., Revollar, S., & Francisco, M. (2014a). Integrated design and control of chemical processes—Part II: An illustrative example. Computers and Chemical Engineering.Google Scholar
  63. Vega, P., Lamanna de Rocco, R., Revollar, S., & Francisco, M. (2014b). Integrated design and control of chemical processes—Part I: Revision and classification. Computers and Chemical Engineering.Google Scholar
  64. Videla, J. I., & Lie, B. (2006). Simulation of a small scale SI ICE based cogeneration system in modelica/dymola. In SIMS conference, Helsinki, Finland.Google Scholar
  65. Washington, I., & Swartz, C. (2014). Design under uncertainty using parallel multiperiod dynamic optimization. AIChE Journal, 60(9), 3151–3168.CrossRefGoogle Scholar
  66. Way, R. (1976). Methods for determination of composition and thermodynamic properties of combustion products for internal combustion engine calculations. Proceedings of the Institution of Mechanical Engineers (London), 190(60), 686–697.Google Scholar
  67. Williams, F. (1985). Combustion theory (2nd ed.). The Benjamin Cummings Publishing Company, Inc.Google Scholar
  68. Wu, D., & Wang, R. (2006). Combined cooling, heating and power: A review. Progress in Energy and Combustion Science, 32(5–6), 459–495.CrossRefGoogle Scholar
  69. Würth, L., Hannemann, R., & Marquardt, W. (2011). A two-layer architecture for economically optimal process control and operation. Journal of Process Control, 21(3), 311–321.CrossRefGoogle Scholar
  70. Yokell, S. (1990). A working guide to shell and tube heat exchangers. Mcgraw-Hill (Tx).Google Scholar
  71. Yuan, Z., Chen, B., Sin, G., & Gani, R. (2012). State-of-the-art and progress in the optimization-based simultaneous design and control for chemical processes. AIChE Journal, 58(6), 1640–1659.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Nikolaos A. Diangelakis
    • 1
    • 2
  • Efstratios N. Pistikopoulos
    • 3
  1. 1.Centre for Process Systems Engineering, Department of Chemical EngineeringImperial College LondonLondonUK
  2. 2.Artie McFerrin Department of Chemical EngineeringTexas A&M UniversityCollege StationUSA
  3. 3.Artie McFerrin Department of Chemical Engineering and Texas A&M Energy InstituteTexas A&M UniversityCollege StationUSA

Personalised recommendations