Modeling and multi-objective optimization of a complex CHP process

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

Applied Energy, 2016, vol. 161, issue C, 309-319

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.

Keywords: Artificial Neural Networks; Adaptive Neuro-Fuzzy Inference System; CHP; Process modeling; Multi-objective optimization (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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DOI: 10.1016/j.apenergy.2015.10.003

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