Use of Soft Computing Techniques for Transducer Problems

Abstract

In many control system applications Linear Variable Differential Transformer (LVDT) plays an important role to measure the displacement. The performance of the control system depends on the performance of the sensing element. It is observed that the LVDT exhibits the same nonlinear input-output characteristics. Due to such nonlinearities direct digital readout is not possible. As a result we employ the LVDTs only in the linear region of their characteristics. In other words their usable range gets restricted due to the presence of nonlinearity. If the LVDT is used for full range of its nonlinear characteristics, accuracy of measurement is severely affected. So, to reduce this nonlinearities different ANN techniques is being used such as single neuron structure, MLP structure, RBFNN and ANFIS structure.
Another problem considered here is with flow measurement. Generally flow measurements uses conventional flow meters for feedback on the flow-control loop cause pressure drop in the flow and in turn lead to the usage of more energy for pumping the fluid. An alternative approach for determining the flow rate without flow meters is thought. The restriction characteristics of the flow-control valve are captured by a neural network (NN) model. The relationship between the flow rate and the physical properties of the flow as well as flow-control valve, that is, pressure drop, pressure, temperature, and flow-control valve coefficient (valve position) is found. With these accessible properties, the NN model yields the flow rate of fluid across the flow-control valve, which acts as a flow meter. The viability of the methodology proposed is illustrated by real flow measurements of water flow which is widely used in hydraulic systems.
Control of fluid flow is essential in process-control plants. The signal of flow measured using the flow meter is compared with the signal of the desired flow by the controller. The controller output accordingly adjusts the opening/closing actuator of the flow-control valve in order to maintain the actual flow close to the desired flow. Typically, flow meters of comparatively low cost such as turbine-type flow meters and venturi-type meters are used to measure the volumetric quantity of fluid flow in unit time in a flow process. However, the flow
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meter inevitably induces a pressure drop in the flow. In turn, this results in the use of more energy for pumping the fluid. To avoid this problem, non-contact flow meters, i.e. electromagnetic-type flow meters, have been developed and are widely used in process plants not only because there is no requirement for installation in the pipeline but also because introduction to the differential pressure across pipelines is not necessitated. Unfortunately, the cost of such non-contact measurement is comparatively much higher than that of its conventional counterparts.
Here, an alternative approach is proposed to obtain the fluid flow measurement for flow-control valves without the pressure drop and the consequent power loss that appear in conventional flow meters. Without the flow meter, it is a fact that the flow rate can be determined from the characteristics of the control valve for flow measurements. In this method, the restriction characteristics of the control valve embedded in a neural network (NN) model are used in determining the flow rate instead of actual measurement using a conventional flow meter.
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