Abstract The current study presents an intelligent method for calculating natural gas compressibility factor. The method requires three easily measurable properties including pressure, temperature, and speed of sound as inputs. As sound speed could be measured with ultrasonic flow meters, temperature, and pressure with appropriate sensors, the real-time natural gas compressibility factor could be calculated easily. The presented method eliminates the high cost of determining compressibility based on measuring natural gas composition. Artificial Neural Network is employed to develop the method. The artificial neural network is trained in a way to accept pressure, temperature, and speed of sound as inputs. To train an artificial neural network, the 30,000 random datasets of natural gas compositions were utilized. To check the validity and accuracy of the developed artificial neural network, four different natural gas compositions are selected and the compressibility factor are compared with the GERG-2008 equation of state (as standard and accurate method for calculating natural gas properties) results. The results reported the average absolute percent deviation is less than 0.7% for the compressibility factor calculation by utilizing the proposed method.

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一种智能计算天然气压缩系数的方法及其在超声波流量计中的应用

摘要 本研究提出了一种计算天然气压缩系数的智能方法。该方法需要三个易于测量的属性，包括压力、温度和声速作为输入。由于可以使用超声波流量计测量声速，使用适当的传感器测量温度和压力，因此可以轻松计算实时天然气压缩系数。所提出的方法消除了基于测量天然气成分确定可压缩性的高成本。采用人工神经网络来开发该方法。人工神经网络的训练方式是接受压力、温度和声速作为输入。为了训练人工神经网络，使用了 30,000 个天然气成分的随机数据集。为了检查开发的人工神经网络的有效性和准确性，选择了四种不同的天然气成分，并将压缩系数与 GERG-2008 状态方程（作为计算天然气特性的标准和准确方法）结果进行比较。结果表明，使用所提出的方法计算压缩系数的平均绝对百分比偏差小于 0.7%。