Simulation of thermodynamic systems using soft computing techniques

Abstract

SUMMARYAn exact thermodynamical analysis of systems is only possible under several assumptions; each of them brings an uncertainty in the solution. Without these assumptions, a thermodynamical analysis of a real application requires thousands of nonlinear equations, whose solution is either almost impossible or takes too much computational time and effort. To overcome this obstacle, the artificial neural network (ANN) and fuzzy logic are magic tools, in particular to analyse the systems for arbitrary input and output patterns. This paper deals with the ANN and fuzzy logic analysis of various thermodynamic systems. The first is an efficiency and emission modelling of a stationary natural gas engine using an ANN model, which is able to obtain the effect of various operational parameters such as charge-pressure and temperature, air fuel equivalence ratio, combustion-start and duration and combustion form on the engine efficiency and NO x emission. The second is a cogeneration power plant with three gas turbines, one steam turbine and a district heating system. The effects of ambient-pressure and temperature, relative humidity, wind-velocity and direction on the plant power are investigated using the ANN model, which is based on the measured data from the plant. The last example is a dryer machine. The dryer machine is modelled first as a thermodynamical system. Then a fuzzy logic model is developed to predict the drying time and the power demand depending on condensation pressure and -temperature and evaporation pressure. All models studied here give very accurate and fast estimations which are comparable with the experimental results.

Fuzzy Modeling of Performance of Counterflow Ranque-Hilsch Vortex Tubes with Different Geometric Constructions

“…This results in one fuzzy subset to be assigned to each output variable for each rule. The process of fuzzy inference involves all of the pieces that have been described so far, i.e., membership functions, fuzzy logic operators, and IF-THEN rules [6,23,26,27].…”

Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network

“…Relatively few studies have considered the Steam Turbine (ST) in a combined cycle power plant (CCPP). In (Kesgin & Heperkan, 2005), the total power output of a cogeneration power plant with three GT, three HRSGs and one ST were predicted. In (Pınar,2014), the output of a CCPP was analyzed using different machine learning tools.…”

Continuous Conditional Random Field Model for Predicting the Electrical Load of a Combined Cycle Power Plant

“…In order to forecast the power generation accurately with these approaches, however, many assumptions, such as the existence of some empirical relationships, are necessary since they account for unpre-dictability in their solution. Without these assumptions, any analysis of a real application calls for many nonlinear equations, whose solution is either almost impossible or requires too much computational time and effort, and sometimes the result is still unsatisfactory and unreliable (Kesgin and Heperkan, 2005).…”

“…In (Kesgin and Heperkan, 2005), an artificial neural network and fuzzy logic are utilized to analyze various thermodynamic systems, including a CCPP. In Fan et al (2016), the authors point out that electric load forecasting is very important for power utility and they present a support vector regression (SVR) model blended with differential empirical mode decomposition (DEMD) and auto regression to forecast electric load.…”