Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks

The paper introduces the artificial intelligence (AI) approach as a general method for the design and optimization study of heat exchangers. Genetic Algorithms (GA) and Artificial Neural Networks (ANN) are applied in the paper. An AGENN model, combining Genetic Algorithms with Artificial Neural Networks, was developed and validated against the desired data on a large falling film evaporator. A broad range of operating conditions and geometric configurations are considered in the study. Four kinds of tubes are deliberated, including plain and enhanced tubes. Different tube pass arrangements, i.e., top-to-bottom, bottom-to-top, and side-by-side, are discussed. Finally, the effects of liquid refrigerant mass flow rate, as well as the number of flooded tubes on the performance of the evaporator, are analyzed. The total heat transfer rate of the evaporator, predicted by the model, is in good agreement with the desired data; the maximum error is lower than ±3%. The highest heat transfer rate of the evaporator is 1140.01 kW and corresponds to Turbo EHP tubes, and bottom-to-top tubes pass arrangements, which guarantee the best thermal energy conversion. The presented approach can be referred to as a complementary technique in heat exchanger design procedures, besides the common rating and sizing tasks. It is an effective and alternative method for the existing approaches, considering the complexity of analytical and numerical techniques as well as the high costs of experiments.

https://www.mdpi.com/1996-1073/12/23/4441/pdf

A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods

A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach Part 2. SO2 emissions from large- and pilot-scale CFB boilers in O2/N2, O2/CO2 and O2/RFG combustion atmospheres

The 4th Generation of CeSFaMB in numerical simulations for CuO-based oxygen carrier in CLC system

Purpose




The purpose of this paper is to first present the key features of the fuzzy logic (FL) approach as a cost-effective technique in simulations of complex systems and then demonstrate the formulation and application of the method.




Design/methodology/approach




The FL approach is used as an alternative method of data handling, considering the complexity of analytical and numerical procedures and high costs of empirical experiments. The distance from gas distributor, the temperature and the voidage of the bed, flue gas velocity and the load of the boiler are the input parameters, whereas the overall heat transfer coefficient for the membrane walls constitutes the output. Five overlapping sigmoid and constant linguistic terms are used to describe the input and the output data, respectively. The Takagi–Sugeno inference engine and the weighted average defuzzification methods are applied to determine the fuzzy and crisp output value, respectively.




Findings




The performed FL model allows predicting the bed-to-wall heat transfer coefficient in a large-scale 670 t/h circulating fluidized bed (CFB) boiler. The local heat transfer coefficients evaluated using the developed model are in very good agreement with the data obtained in complementary investigations.




Originality/value




The performed model constitutes an easy-to-use and functional tool. The new approach can be helpful for further research on the bed-to-wall heat transfer coefficient in the CFB units.

Fuzzy logic and bed-to-wall heat transfer in a large-scale CFBC

Investigation of erosion in CFB boilers

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Investigation of erosion in CFB boilers