Developing an Opacity Sensor :: Opacity Sensors Technology Essays

Developing an Opacity Sensor There is a huge range of different sensors in this world, designed to detect changes in temperature, size, distance and many other important factors. Their ability to do this makes them valuable for use in industry, in particular, production processes. I have chosen to develop a sensor which measures the concentration of solution, suitable for the factory production of pre-diluted fruit squashes. The sensor can control the machinery via a servo-mechanism, indicating whether more squash needs to be added. The context in which my sensor is to be used is an important consideration as it determines a suitable sensitivity and resolution. Factors to consider when designing a sensor: Sensitivity The ratio of change of output to change of input. A very sensitive sensor will give a big change of output for a small change of input. A sensor which detects very small changes of input will need to be very sensitive so these changes can be observed. Resolution The smallest change the sensor can detect in the quantity it is measuring. If you had a quantity which was displayed as 0.0076, this sensor would have quite a high resolution (depending on what it was measuring). If the last digit were fluctuating this would be the maximum resolution of the sensor as it can only just resolve a change of that magnitude – 10,000th. A cooks oven may only need a resolution of 5 ºC whereas a baby monitoring system requires a resolution of 0.5 – 1 ºC. Response time The time a sensor takes to respond to a change in input. If changes occur more rapidly than this then they will usually be averaged out. The response time should be suitable to detect important changes as they occur. Systematic Most sensors are affected by changes in temperature, even those that aren’t designed to detect them. Some sensors may also be affected by other environmental changes depending on their methods of detection, for example a LDR might produce different readings on a sunny day if the whole system isn’t enclosed. Such influences will produce readings that display the correct trend but each reading is erroneous by the same amount. For this reason systematic errors are difficult to detect and an only by making another more accurate measurement. Today â€Å"smart systems† process information to compensate for disturbing influences. Noise, random error, fluctuations The input signal may fluctuate or the sensor itself may generate noise. Unsystematic variations are present in all experimental data and their size determines the reliability of the data and limits the precision with which a measurement can be made. Taking an average over repeated measurements can improve the final result