Application of soft computing techniques to multiphase flow measurement: A review
Introduction
Multiphase flow is defined as a simultaneous flow of materials with two or more different phases (i.e. gas, liquid or solid) or unseparated components (e.g. water and oil) [1]. Multiphase flow (including two-phase flow which is a common example of multiphase flow) is widely seen in many industrial processes. Oil/gas/water mixtures are perhaps the most common gas-liquid and liquid-liquid two-phase or three-phase flows during the processes of production, transportation and custody transfer in the oil and gas industry. Meanwhile, air entrainment is unavoidable when marine fuel is transferred from a bunker barge to a receiving ship, particularly during the start and stop processes of the bunkering. For the fiscal purpose, accurate mass flow metering of marine fuel is essential in bunkering centres. Pneumatically conveyed pulverized fuel (coal, biomass or mixture of both) in power plants forms gas-solid two-phase or three-phase flow. Individual flowrates and fractions of biomass and pulverized coal provide the information of co-firing ratio, which is useful to improve the combustion efficiency and reduce emissions of NOx and COx. In some circumstances hydraulic transport of solids, such as sand, iron concentrates and phosphate matrix, in the type of slurry flow is employed in the mining, chemical, pharmaceutical and food industries. In such industrial processes accurate measurement of multiphase flow is highly desirable to realize flow quantification, operation monitoring, process optimization, and product quality control. It must be noted that multiphase flow is not restricted in industrial processes and covers many other application areas such as regional particle deposition and airflow in human tracheobronchial airways [2], [3] in the medical area. However, this review focuses primarily on the measurement of multiphase flow in the process and related industries.
Individual flowrates (volumetric flowrate or mass flowrate) and phase fractions are most important parameters to characterize a multiphase flow. Over the past three decades substantial progress has been made to develop new techniques that may offer solutions to the industrial measurement challenges. Thorn et al. [4], [5] and Falcone et al. [6] have reviewed the developments of three-phase flowmeters, particularly for the petroleum industry. Possible techniques for the measurement of gas-solid flow in pneumatic conveying pipelines and circulating fluidized beds have been discussed in detail by Yan [7], Zheng and Liu [8], and Sun and Yan [9]. Albion et al. [10] have reviewed the intrusive and non-intrusive measurement techniques for monitoring slurry transportation in horizontal pipelines. Among these techniques, on-line multiphase flowmeters are the devices to measure the mixed flow without any separators and sampling lines. They can be classified into direct and indirect measurement groups according to measurement strategies deployed. The direct measurement of a phase flowrate is often realized using a Venturi flowmeter, Coriolis flowmeter and cross-correlation techniques etc., whilst a phase fraction is usually determined from radiation absorption, electrical impedance and microwave techniques etc. An indirect measurement method determines the individual phases through the analysis of the time variant signals acquired from a set of sensors. In general, the relationship between the sensor outputs and the flowrate or fraction of each phase cannot be deduced theoretically. In this case, empirical models are commonly developed from experimental data using statistical methods. With the recent development of artificial intelligence and machine learning, soft computing techniques provide alternative approaches to traditional statistical methods and extend the capabilities of empirical models.
This review focuses on the indirect methods incorporating soft computing techniques to measure the individual phase flowrates and fractions of multiphase flow. Section 2 outlines the principal constituents of soft computing techniques and provides a brief description of some techniques which are already applied in multiphase flow measurement, i.e. artificial neural network (ANN), support vector machine (SVM), genetic algorithm (GA), genetic programming (GP) and adaptive neuro-fuzzy system (ANFIS). Section 3 presents the example applications of soft computing techniques in two-phase or three-phase flow measurement. Section 4 summarizes the findings of the review and discusses the trends and future developments of soft computing techniques in the field of multiphase flow measurement. Section 5 concludes this review and likely future development.
Section snippets
Soft computing techniques
Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solution cost [11]. It is sometimes referred as computational intelligence, covering a range of computational techniques in computer science, artificial intelligence and machine learning. Sometimes, the term ‘soft computing’ is used interchangeably with soft sensors or virtual sensors. Soft sensor is a common name for a piece of software
Applications of soft computing techniques
The applications of soft computing techniques to multiphase flow measurement are mainly concentrated on the estimation of phase flowrates and phase fractions and the identification of flow regime. The estimation of phase flowrates and phase fractions is equivalent to solve a problem of function approximation while the identification of flow regime is a classification problem. As the review focuses on the measurement of phase flowrates and phase fractions, the research purely on flow regime
Sensor fusion
Traditional sensors incorporating soft computing techniques provide an effective solution to the measurement of phase flowrates and phase fractions. Table 2 summarizes the sensors used, soft computing methods, experimental conditions and measurement errors of the indirect measurement systems discussed in this review. It should be noted that ‘GVF variation’ in Table 2 represents the absolute error of the measurement from the reference value while the rest of the results is the averaged relative
Conclusions
This review has attempted to present the applications of soft computing techniques to multiphase flow measurement and define the state-of-the-art in the development of multiphase flowmeters incorporating such techniques. This review covers the research which have been conducted within the past 15 years and focuses on the measurement of phase flowrates and phase fractions using conventional sensors incorporating soft computing techniques.
Multiphase flow measurement is a complex and difficult
Acknowledgements
The authors would like to acknowledge the National Natural Science Foundation of China (No. 61573140), the Fundamental Research Funds for the Central Universities (No. 2016MS23) and China Postdoctoral Science Foundation (No. 2015M581045) for providing financial support for this research.
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