Proportional-integral-differential

PA PCP PCR PFA PGB PHA PID PLC PMACWA PMD POTW ppm PRH PRR psi psig PTFE PVDF PWS picric acid pentachlorophenol propellant collection reactor perfluoroalkoxy product gas burner preliminary hazards analysis proportional integral differential controller programmable logic control Program Manager for Assembled Chemical Weapons Assessment projectile mortar demilitarization (machine) publicly owned treatment works parts per million projectile rotary hydrolyzer propellant removal room pounds per square inch pounds per square inch gauge polytetrafluoroethylene (Teflon) polyvinylidene fluoride projectile washout system... 

The experiments were carried out in a 2-L Parr reactor fitted with an internal cooling coil to cool the reaction mixture if necessary and to prevent runaway reactions. The temperature was controlled by a proportional-integral-differential (PID) controller. All experiments started at ambient temperature, which was raised to the desired reaction temperature, 93°C, in a nearly linear ramp over about 15 minutes for the propellants or 25 and 35 minutes for Composition B-4 and tetrytol, respectively. 

The operation of the furnace is controlled by a temperature programmer which ensures a linear heating profile over a range of heating rates. A variety of electronic control circuits based on the PID (proportional integral differential) principle are used to provide linear heating profiles. The parameters of the PID circuit are normally set by the manufacturer for optimum performance under... 

PIA Plastics Institute of America PID proportional-integral-differential control... 

In contact mode, the value of this output is simply compared with a set point value. The difference, or error signal, is then conditioned using a proportional-integral-differential (PID) controller and sent to the Z-control. 

The control law This is the information flow structure through which the manipulated variables are handled based on the measurements. The complexity of the control law is determined by the diversity of the control objective. As a result, the controller can be simple (on—off, proportional, proportional-integrated differential), more complicated adaptive model-based, empirical (expert systems), fuzzy or neural network-based. Detailed references on the various control systems applied on anaerobic digesters can be found in Boe (2006) and Find et al. (2003). 

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