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Modeling and multi-objective optimization of a complex CHP process

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  • Seijo, Sandra
  • del Campo, Inés
  • Echanobe, Javier
  • García-Sedano, Javier

Abstract

In this paper, the optimization of a real Combined Heat and Power (CHP) plant and a slurry drying process is proposed. Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are used to generate predictive models of the process. A dataset collected over a one-year period, with variables for the whole plant, is used to generate the predictive models. First, data mining techniques are used to obtain a representative dataset for the process as well as the input and target parameters for each model. Subsequently, models are used to optimize the plant performance in order to maximize the effective electrical efficiency of the process. For this purpose, 12 input parameters are selected as decision variables, i.e., variables which can change their values to optimize the plant. Plant performance optimization is a multi-objective problem with three goals: to maximize electrical production, minimize fuel consumption and maximize the amount of heat used in the slurry process. The optimization algorithm calculates the values of the decision variables for each time-step using Gradient Descent Methods (GDM). The simulation results show that optimization using a multi-objective function increases the CHP plant’s effective electrical efficiency by around 3% on average.

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  • Seijo, Sandra & del Campo, Inés & Echanobe, Javier & García-Sedano, Javier, 2016. "Modeling and multi-objective optimization of a complex CHP process," Applied Energy, Elsevier, vol. 161(C), pages 309-319.
    • Handle: RePEc:eee:appene:v:161:y:2016:i:c:p:309-319
    DOI: 10.1016/j.apenergy.2015.10.003
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    1. Ikeda, Shintaro & Ooka, Ryozo, 2015. "Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system," Applied Energy, Elsevier, vol. 151(C), pages 192-205.
    2. Sayyaadi, Hoseyn & Babaie, Meisam & Farmani, Mohammad Reza, 2011. "Implementing of the multi-objective particle swarm optimizer and fuzzy decision-maker in exergetic, exergoeconomic and environmental optimization of a benchmark cogeneration system," Energy, Elsevier, vol. 36(8), pages 4777-4789.
    3. Facci, Andrea Luigi & Andreassi, Luca & Ubertini, Stefano, 2014. "Optimization of CHCP (combined heat power and cooling) systems operation strategy using dynamic programming," Energy, Elsevier, vol. 66(C), pages 387-400.
    4. Zare, V. & Mahmoudi, S.M.S. & Yari, M. & Amidpour, M., 2012. "Thermoeconomic analysis and optimization of an ammonia–water power/cooling cogeneration cycle," Energy, Elsevier, vol. 47(1), pages 271-283.
    5. 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, Elsevier, vol. 108(C), pages 137-148.
    6. De, S. & Kaiadi, M. & Fast, M. & Assadi, M., 2007. "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, Elsevier, vol. 32(11), pages 2099-2109.
    7. Michel Feidt & Monica Costea, 2012. "Energy and Exergy Analysis and Optimization of Combined Heat and Power Systems. Comparison of Various Systems," Energies, MDPI, vol. 5(9), pages 1-22, September.
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