Three effect chemical industries evaporators design by artificial neural network
In this paper some three effect evaporators has reviewed for receiving an optimization design of evaporator systems. These systems commonly use at the chemical industries and food industries, specially, would be applicable for desalting units at the oil and gas refineries, for regenerate waste waters to purity waters or service waters. An artificial neural network, trained by feed forward model has used for this simulation. Input parameters used in this model contains of: feed flow rate input to the evaporator effects, temperature of feed, steam flow rate required for evaporation in each effect, temperature of the steam, temperature of the purity product and mol fraction. ANN technique would be replacing with the complicated mathematical methods and can save times of calculations for receiving higher and accreted optimization design. Finally, simulation results and their optimal limitation as: heat transfer of effects of evaporators(108801 up to 244087 Btu/hr);required steam(4988.7 up to 10369.2 Btu/hr);optimal areas(1320.4 up to 2849.63 ft^3);and economical factors required for design(2.53 up to 2.57) then finally estimate total utility ($/year).these out put parameters are very important for an optimal design of evaporator systems.