Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

Article English OPEN
A.K. Gupta ; P. Kumar ; R.K. Sahoo ; A.K. Sahu ; S.K. Sarangi (2017)
  • Publisher: Elsevier
  • Journal: Journal of Computational Design and Engineering, volume 4, issue 1, pages 60-68 (issn: 2288-4300)
  • Related identifiers: doi: 10.1016/j.jcde.2016.07.002
  • Subject: Performance | Methods | Plate fin heat exchanger | Engineering design | Flow rate | TA174
    arxiv: Computer Science::Neural and Evolutionary Computation

An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA) utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA) results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS) has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN) model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN.
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