+32(0) 494 70 84 74 info@entras.be

We cooperate with the academic world and support research. We joined forces with the KULeuven Technology Campus De Nayer, which resulted in a master thesis on CHP modelling, published in 2016 & written by Scott Van Wetter. Prof. Filip Logist acted as promotor, and Frank Alaerts as external promotor.

Here is the abstract:

Combined heat and power installations (CHP) produce steam and electrical power simultaneously. CHP save primary energy and reduce emissions of pollutants. Today, the operation of this type of installations is not always optimised. In this Master thesis an overview will be given of various techniques to optimise the operations of energy production units at lowest achievable costs; this includes simultaneous optimisation of heat production, electricity production, energy purchases, emissions etc. within set boundary conditions. 

Production units (CHP, back-up boilers and steam turbine) are modelled using system identification techniques like artificial neural networks in MATLAB. The objective is to integrate mathematical modelling techniques and economic evaluations: the total cost function is optimised using non-linear programming tools. The optimisation is performed for 8 different setups. The result is a unit commitment schedule for the near future. The conclusions summarize the method and results, and offer insights in the feasibility of different modelling techniques. 

For more information, feel free to contact us.

As an inspiration, here is an extract of the literature reference list:

  1. Çengel, Y. & Boles, M. (2004). Thermodynamics, an engineering approach. McGraw-Hill Education
  2. Asgari, H., Chen X. & Sainudiin, R. (2013). Analysis of ANN-Based Modelling Approach for Industrial Systems. International Journal of Innovation, Management and Technology, 4, nr.1
  3. Fast, M., Assadi, M. & De, S. (2008). Development and multi-utility of an ANN model for an industrial gas turbine. Applied Energy, January 2009, Vol.86(1), pp.9-17 [Peer Reviewed Journal].
  4. Fast, M. & Palmé, T. (2009). Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant. Energy, Vol.35, pp.1114-1120 [Peer Reviewed Journal].
  5. De, S., Kaiadi M., Fast, M. & Assadi, Mi. (2006) Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy Volume 32, Issue 11, November 2007, Pages 2099–2109
  6. Arrigada, J., Genrup, M., Loberg, A. & Assadi, M. (2003) Fault diagnosis system for an industrial gas turbine by means of neural networks
  7. Al Shamisi M., H. Assi A., & A.N. Hejase H. (2011). Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City. International Journal of Green Energy, 2013, Vol.10(5), p.443-456 [Peer Reviewed Journal].
  8. Fast, M. (2005) Artificial Neural Networks for Gas Turbine Modeling and Sensor Validation. Masterthesis Lund University
  9. Nikpey, H., Assadi, M. & Breuhaus, P. (2013) Development of an optimized artificial neural network model for combined heat and power micro gas turbines. Applied Energy. Volume 108, August 2013, Pages 137–148
  10. May, R., Dandy, G. & Maier, H. (2011). Review of Input Variable Selection Methods for Artificial Neural Networks. Artificial Neural Networks – Methodological Advances and Biomedical Applications, Prof. Kenji Suzuki (Ed.), ISBN: 978-953-307-243-2, InTech, DOI: 10.5772/16004.
  11. Elaiw A., Shehata M. & Alghamdi M. (2013) A Model Predictive Control Approach to Combined Heat and Power Dynamic Economic Dispatch Problem
  12. Mitra, S., Sun, L. & E. Grossmann I. (2013). Optimal scheduling of industrial combined heat and power plants under time-sensitive electricity prices. Energy, 1 June 2013, Vol.54, pp.194-211 [Peer Reviewed Journal].
  13. Suk, Kim J. & F. Edgar, T. (2014). Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming. Energy, 2014, Vol.77, pp.675-690 [Peer Reviewed Journal].