Victor Chang

Sensor for determining the water content of oil-in-water emulsion by specific admittance measurement

It is important to know the water content of oil-in-water emulsions for applications such as oil transportation. A new on-line sensor has been designed for this purpose. The real part of the specific admittance is used for the water-content determination. A simplified Van Beck mixing law and an appropriate frequency are applied to obtain the water content without polarization effects. Adaptive compensation is used in order to compensate temperature and salinity changes. On-line real-time parametric identification of conductivity versus temperature deviations observed in the water used in the emulsion is implemented. A microprocessor-based system with an open architecture and modular scheme allows the implementation of the electronic instrumentation and algorithms needed. Other features, such as auto-zero and auto-range, give the instrument a good performance and versatility for a wide range of salinities.

A sensor for on-line measurement of the viscosity of non-Newtonian fluids using a neural network approach

Non-Newtonian fluids are characterized by a nonlinear relationship between viscosity and shear rate. Typical examples are some emulsions. The graph of viscosity as a function of the shear rate is known as the rheogram or the rheological chart. In this work, a very simple mechanical device with minimal moving parts has been used as a sensor for measuring both the viscosity and the shear rate. A mathematical model to measure the viscosity in Newtonian fluids has been confirmed. However, a mathematical model of the sensor for the case of non-Newtonian fluids is difficult because several variables defining the transducing properties are not independent and the measurement is made during transient dynamic conditions. In solving this problem, a neural network approach has been used. The input consists of two voltages representing the sensor response and the temperature. The outputs are the viscosity and the shear rate. The measurement system is made out of a sensor head, an electronic circuit for powering the sensor and signal conditioning, and a neural network software, based in the back propagation learning algorithm. The neural net has been trained using experimental data from a laboratory viscometer and a simplified mathematical correlation relating the viscosity and input voltages. The sensor has been tested by measurement of the viscosity and the shear rate for emulsions of heavy crude oil (bitumen) and water. Good correlation between experimental data and the system output was observed after the neural net training. On-line tests of the sensor are being conducted.

Piezoelectric and thermoelectric response of cuprous oxide, Cu2O

Abstract In the present work, polycrystalline samples of cuprous oxide, Cu 2 O, are fabricated and their piezoelectric and thermoelectric properties evaluated both as a polycrystalline material and as a composite material. Samples are prepared from electrical or analytical grade copper, in powder or solid form. Oxidation is carried out at 1050 °C for 4 h. For the polycrystalline form, the Seebeck coefficient is about 1 m V/°C. For the composite form, the coefficient is between 0.2 and 0.4 mV/°C. A piezoelectric response is also found. For the polycrystalline material, the ratio between generated voltage and applied voltage is about 0.2 at the resonance frequency. For the composite samples, this ratio lies between 0.01 and 0.02. Resonance frequencies are in the range 150 kHz-20 MHz, depending on the specific sample. The mentioned properties are not greatly dependent on grain size or grain orientation, probably due to the cubic structure of the Cu 2 O elemental cell.