• Title of article

    Estimation of the water content of natural gas dried by solid calcium chloride dehydrator units

  • Author/Authors

    Ghiasi، نويسنده , , Mohammad Mahdi and Bahadori، نويسنده , , Alireza and Zendehboudi، نويسنده , , Sohrab، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2014
  • Pages
    10
  • From page
    33
  • To page
    42
  • Abstract
    Natural gas is an important source of energy. It is efficient, versatile and abundantly available. Calcium chloride (CaCl2) dehydrator is the most common non-regenerative adsorption system in natural gas industry. As the need for natural gas increases, calcium chloride dehydration can help to make some gas wells more profitable to operate gas from remote or offshore wellheads, gas of a low flow rate, or gas which is high in sulphur content may benefit from this dehydration. s article two mathematical-based models are developed to estimate approximate water content of natural gas dried by calcium chloride dehydrator units for both freshly recharged and just prior to recharging conditions as a function of temperature and pressure. Firstly, a simple empirical correlation is presented to estimate water content of natural gas dried by solid calcium chloride dehydrator, Secondly, a multilayer perceptron (MLP) neural network is developed for the same calculations. The results of both presented models are found to be in excellent agreement with reported data in the literature. The tools developed in this study can be of immense practical value for engineers to have a quick check on water content of natural gas dried by calcium chloride dehydrator units as a function of dehydrator temperature and pressure at various conditions without opting for any experimental trials. In particular, engineers would find the approaches to be user-friendly with transparent calculations involving no complex expressions.
  • Keywords
    Gas dehydration , Artificial neural network , Calcium chloride , empirical correlation , natural gas
  • Journal title
    Fuel
  • Serial Year
    2014
  • Journal title
    Fuel
  • Record number

    1471296