Intelligent sonic online flow measurement. Pouriya Niknam Piramoon System Qeshm, Iran

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1 Intelligent sonic online flow measurement Pouriya Niknam Piramoon System Qeshm, Iran

2 Table of Content Introduction measurement principal modeling verification Challenges and conclusion

3 Introduction The Piramoon System Qeshm, was the first company of middle east to introduce the complete analyzer packages such as Online Analyzers, sampling systems, analyzer houses laboratories sampling systems. The PSQ its activities in 2007 in the Qeshm Free Zone area, and is on the preferred list of the National Oil Organization and its subsidiaries like NPC, NIOEC, SPGC, and NIGC. The current flowmeter is one of the ongoing projects of R&D section of Piramoon.

4 Introduction intelligent Sonic flowmeter Why sonic range? Easy to record Low cost Well-Known signal processing algorithms for feature extraction. Why intelligent? Utilize from artificial modeling approaches. Data classification algorithm Neural network modeling A clamp-on flowmeters enable one to measure flow velocity without even contacting (or disturbing) the fluid by simply pressing a transducer on the outer surface of the pipe.

5 Introduction Features Low price (economic solution) Non Invasive configuration with clamp-on receiver. Easy installation with no pipe cutting Portable compact & lightweight kit Simple operation Wide application Large pipes (cost) Flashing fluids Corrosive fluids Hazardous fluids Self-compensation in case of flow fluctuation Self-calibration in case of fluid properties alteration they can be used where there are no suspended solids or bubbles in the fluid

6 Principal Ultrasonic flowmeters operate using sound waves in the ultrasonic range ( beyond human hearing ability, typically at a frequency of 1 MHz). Ultrasonic (or acoustic) flowmeters operate by generating sound waves with a transducer and measuring the propagation of those waves through a flowing fluid. Types of ultrasonic flowmeters: transit time (equipped with two transducers.) Doppler-effect (or frequency shift) Doppler signal is reflected by turbulent swirls in the liquid rather than by solid particles or bubbles.

7 Principal The project is mainly modeling, assisted by theory of ultrasonic flowmeters Some New ultrasonic flowmeters have bridged gap towards use of cleaner liquid applications. Requiring no solids or entrained gas, this type of flowmeter can work on applications typical ultrasonic Doppler meter cannot measure. Principles of operation are similar, with exception that Doppler signal is reflected by turbulent swirls in the liquid rather than by solid particles or bubbles. Because of this, they can be used where there are no suspended solids or bubbles in the fluid. Receiving the natural sonic Nosie from the turbulent swirls and eddies (no reflection) Natural source transmitter receiver

8 Principal Overview of the development

9 Principal Simulation detail CFD simulation Acoustic simulation of the flow Simulation and modeling Signal processing Data classification Signal characteristic extraction Prepare for different condition Neural network modeling Final model for prediction for new cases

10 Principal CFD Simulation 1-CFD simulations Accurate simulation to find the Eddies and the consequence Sonic noise Effect of Boundary layers Side source of errors

11 Principal Signal Processing Extraction the characteristics Of Received signals

12 Principal The characteristics of both signals are extracted which form two matrices. Each of them includes signal properties e.g. amplitude, RMS, period, average of signal and so on. A new statistical measure of signal similarity is applied on two matrices and sonic parameters are derived.

13 Principal Data classification Wide range of data is used volume flow pipe size fluid composition Temperature pressure All data extracted from the simulation 10% of the data is verified by experimental data. In first generation of the flowmeter More than 1000 cases are used.

14 Principal Neural network modeling Data classification Wide range of volume flow, pipe size, Temperature and pressure either by simulation or experimental tests. Black box modeling by Neural Network Deep learning of the inlet database Automatic input (Sensor reading parameters) Fluid temperature Adapted sonic parameter No1 (P1) Adapted sonic parameter No2 (P2) Manual entry as flow constant X = f(, pipe Material, Pipe thickness, fluid major composition, Pressure)

15 Experimental verification In this project the flowmeter for liquids will be tested under various inlet flow configurations to determine their sensitivity to flow and side affecting parameters. The true velocity profiles will be determined with a Commercial instrument by Siemens (SITRANS FS230). pipe size in experimental tests: DN50 to DN160 the average accuracy of 20 different cases is found to be %

16 Comparison with Ultrasonic flowmeters item ultrasonic New Intelligent Flowmeter 1 Two transducers (transmitter-receiver pairs) Moderate cost Receiver only lower cost 2 symmetric profiles Both symmetric and asymetric 3 Doppler type flowmeters Require liquid to be measured contain minimum of at least 25 PPM of suspended solids or bubbles at least 30 microns or larger in size. 4 Applicable for a specific pipe size and fluid type set by factory Self-compensation Applicable on cleaner liquid and does not need bubbles/markers/suspend solid

17 Key Challenges 1. External noise Pipework vibrating, Leaks / drips, Noise, especially tonal, Changes from yesterday / last trip an average noise of an industrial Workshop is within db Signal Interference in presence of noise, e.g., air conditioner in compensated by noise cancelation algorithm. The accuracy and reliability is improved by using two point of signal recording. 2. Condition variation Signal amplitude variation with temperature which is compensated by online temperature measurement. 3. Flow hydrodynamic Dependency on flow regime but it has capable of flow measurement either in laminar or turbulent flow. accuracy for turbulent flow is ±2%. accuracy for laminar flow is ±5%. (Higher error is due to low signal to noise ratio)

18 Conclusion key features: Low price A single flowmeter is applicable for a wide cases all calibrate data is previously stored in the database and limited calibration is required Self-compensation for fluctuation of flow condition Intelligent external noise cancelation Intelligent flow computer is used based on Neural network

19 Thank you for your attention