Intelligent Industrial Automation for Dependent Parameters
Measurement And Control
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In a process industry, there are many parameters to be monitored and controlled. In market, controller/monitoring systems are available in small sizes with ON/OFF, P, PI, and PID control action; able to work independently. Actually speaking, the problem starts with this independent decision making. In industry, for 99% cases the physical parameters present are dependent; for example temperature should be 40 oC at the pressure of 20 PSI, flow should be 20 liters per minute for the pressure of 25 PSI, and so on. But the available instruments in the market are independent decision makers i.e. any...
In a process industry, there are many parameters to be monitored and controlled. In market, controller/monitoring systems are available in small sizes with ON/OFF, P, PI, and PID control action; able to work independently. Actually speaking, the problem starts with this independent decision making. In industry, for 99% cases the physical parameters present are dependent; for example temperature should be 40 oC at the pressure of 20 PSI, flow should be 20 liters per minute for the pressure of 25 PSI, and so on. But the available instruments in the market are independent decision makers i.e. any physical parameter controller, will not bother for remaining related parameter(s). This may lead to problematic situation in the process industry. Present Theories and Practices The options to resolve the problem of dependent decision making are many. The first and very simple remedy is, for each process design and develops your own 3 CUSTOM BUILT INSTRUMENT(S). But the same may not be suitable for all the processes. Second method is Multidrop networking.
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