Sensor systems level measurements

Thermal Methods Level-measuring systems may be based on the difference in thermal characteristics oetween the fluids, such as temperature or thermal conductivity. A fixed-point level sensor based on the difference in thermal conductivity between two fluids consists of an electrically heated thermistor inserted into the vessel. The temperature of the thermistor and consequently its electrical resistance increase as the thermal conductivity of the fluid in which it is immersed decreases. Since the thermal conductivity of liquids is markedly higher than that of vapors, such a device can be used as a point level detector for liquid-vapor interface. 

The electronic control circuitry is situated in two levels below the MCB layer with the modules. It is based on a microcontroller system for the micro liquid handling and the chemical analysis data. Implemented in the electrical circuitry are driving circuits for the micro pumps, sensing circuits for the flow sensors, optical absorption measurement circuitry, power management and communications using an RS232 interface. 

Effective RTO design requires that a number of issues be addressed, including measurement selection and sensor system design, model fidelity and updating strategies, and so forth. Much of the available literature has tended to focus on the interdependence of parameter uncertainty and measurements availability and accuracy. A number of critical open questions remain, such as what level of accuracy is required in the process model or parameter estimates what is the benefit of improving the accuracy how can the economic benefit of RTO be assessed in a systematic manner. 

In addition to reaction chambers and delivery systems, a number of supervising and sensor systems are of utmost importance for control and safety reasons. Sensors in automated workstations include measurement of temperature (thermocouple, thermistor, semiconductor), pressure, liquid flow and gas or liquid level. To monitor the presence or absence of vessels or devices, systems like capacitance, inductivity, ultrasonic monitors, magnetic sensors or optical sensors (reflective, beam interruption, color) can be integrated in automated workstations. 

Sensor systems are composed of the sensor, the transmitter, and the associated signal processing. The sensor measures certain quantities (e.g., voltage, current, or resistance) associated with devices in contact with the process such that the measured quantities correlate strongly with the actual controlled variable value. There are two general classifications for sensors continuous measurements and discrete measurements. Continuous measurements are, as the term implies, generally continuously available, whereas discrete measurements update at discrete times. Pressure, temperature, level, and flow sensors typically yield continuous measurements, whereas certain composition analyzers (e.g., gas chromatographs) provide discrete measurements. 

All feedback control processes involve a final control element, a process, and a sensor. That is, to change the manipulated variable level, a final control element is required. A sensor is also required to measure resulting changes in the controlled variable. The input to the final control element/pro-cess/sensor system is the controller output, c, and its output is the sensor reading. When evaluating the dynamic behavior of a process, the relative dynamics of the final control element, the process, and the sensor should be considered. For example, there are processes for which one or two of the dynamic components (final control element, process, or sensor) respond substantially faster than the slowest element and therefore can be neglected when analyzing the dynamic behavior of the overall process. 

This chapter primarily deals with the issue of correlation between sensor signal and analyte concentration in chemical measurements, i.e. the creation of a model based on standards, and the estimation of unknown samples based on that model. Sensor calibration is dependent upon the type of sensor signal such as linear versus non-linear response and sensor format involving one or many sensors simultaneously. The advantages of moving from one sensor to several sensors and from several sensors to several sensors coupled with analyte concentration modulation can yield remarkable information about a sample [1]. Each level of sensor system complexity, when coupled with the proper analysis tools, creates an unique situation which yields information about each component in a mixture and potential interferences. 

Sensors and sensor systems capable of quantifying various aspects of the physical environment are perhaps the most widely used in biotechnology. The signals from these probes are frequently utilized in process analysis and control schemes (eg, temperature, shaft power, foam detection, and liquid volume/level). Many of the instruments used to make the measurements shown in Figure 22-1 are familiar to workers in several fields and thus they will be discussed only briefly here. Details on these sensors can be found elsewhere [eg, 37-40]. 

Microfluidic velocity sensors are based on very sensitive structures and materials. Their sensor systems are developed using complicated fabrication technology and sophisticated measuring apparatus and at high production cost [10]. These are issues that require immediate attention because their commercialization is problematic. Microfluidic systems will achieve their full potential if micropumping operates at an optimum level where flow rate must be measurable and controllable based on the needs of the system. For an application that delivers protein to a microbioreactor at a precise rate using this micropumping operation, then some feedback... 

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