Blending control system

Fig. 4 Raw material mixing and blending control system (Siemens)...

It is impossible, of course, for the process to respond instantaneously to a step in set point. Since the controlled variable will lag behind the set point, a positive error will develop before the new set point is reached. The feedback controller, being in automatic, will integrate the error, changing its output to a new but incorrect value it must then bring its output back to the previous state by generating a negative error, equal in area to the earlier positive error. The effect is the same as that shown in Fig. 6.14, produced by the blending control system. 

The blending control system in the previous section is quite simple, because there is only one controlled variable and one manipulated variable. For most practical applications, there are multiple controlled variables and multiple manipulated variables. As a representative example, we consider the distillation column in Fig. 1.7, which has five controlled variables and five manipulated variables. The controlled variables are product compositions, Xd and xg, column pressure, P, and the hquid levels in the reflux drum and column base, hj) and The five manipulated variables are product flow rates, D and B, reflux flow, R, and the heat duties for the condenser and reboiler, Qjy and Qb- The heat duties are adjusted via the control valves on the coolant and heating... 

A current-to-pressure (or voltage-to-pressure) transducer is required if the control loop contains both electronic instruments and a pneumatic control valve. The term final control element refers to the device that is used to adjust the manipulated variable. It is usually a control valve but could be some other type of device, such as a variable speed pump or an electrical heater. The operation of this blending control system has been described in Section 1.2. 

For example, the blending control system in Fig. 8.1 has five components in the feedback control loop the process, the sensor, the controller, the I/P transducer, and the control valve. 

The selection of controlled and manipulated variables is of crucial importance in designing a control system. In particular, a judicious choice may significantly reduce control loop interactions. For the blending process in Fig. 8-40(d ), a straightforward control strategy would be to control x by adjusting w, and w by adjusting Wg. But... 

There are many advanced strategies in classical control systems. Only a limited selection of examples is presented in this chapter. We start with cascade control, which is a simple introduction to a multiloop, but essentially SISO, system. We continue with feedforward and ratio control. The idea behind ratio control is simple, and it applies quite well to the furnace problem that we use as an illustration. Finally, we address a multiple-input multiple-output system using a simple blending problem as illustration, and use the problem to look into issues of interaction and decoupling. These techniques build on what we have learned in classical control theories. 

However, we can describe the basic structure of several feedforward control systems. Figure 8.7 shows a blending system with one stream which acts as a disturbance both its flow rate and its composition can change. In Fig. 8.7a the conventional feedback controller senses the controlled composition of the total blended stream and changes the flow rate of a manipulated flow. In Fig. %.lb the manipulated flow is simply ratjoed to the wild flow. This provides feedforward control for flow rate changes. Note that the disturbance must be measured to implement feedforward control. 

A blend controller block diagram is shown in Fig. 6, A system for preparing bread and pastry dough is shown in Fig. 7. Applications for continuous blending systems are frequently found in the petroleum, petrochemical, fond and beverage, building materials, pharmaceutical, automotive, and chemical industries, among others. 

The designer of a blending/filling system must have a full understanding of the process its limitations, the raw materials, the packaging materials and their levels of contamination and the means of controlling the contamination to enable wholesome product with adequate shelf life to be produced. 

The effects of bioethanol use on NOx emissions are not consistent between different vehicles and studies. Larsen et al. [37] provided an overview of available studies, and concluded that bioethanol use can lead to either increases or decreases in NOx emissions during tests, depending on the experimental conditions. This is consistent with the non-linear behaviour of the emission-control system in petrol vehicles, where the smallest deviations from stoichiometry greatly affect NOx emissions. Leaner mixtures lead to an increase in NOx emissions, and richer mixtures lead to a decrease in NOx emissions. This erratic behaviour suggests that any direct fuel effects are masked by the ability of the fuelling system to maintain stoichiometry when changing from petrol to bioethanol blends in the different vehicles tested. 

Traditional ZN catalysts are typically complex heterogeneous systems, consisting of multiple active sites each of which produces polymers and copolymers with different structure (e.g., tacticity, molecular weight, composition). The result is the production of polymer blends. Controlling blend composition through modification of the heterogeneous catalyst surface was challenging and dominated R D in this area for decades. 

Three examples of simple multivariable control systems are shown in Fig. 8-39. The in-line blending system blends pure components A and B to produce a product stream with flow rate w and mass fraction of A, x. Adjusting either inlet flow rate wA or wB affects both of the controlled variables w and x. For the pH neutralization process in Fig. 8-39b, liquid level h and exit stream pH are to be controlled by adjusting the acid and base flow rates wA and wB. Each of the manipulated variables affects both of the controlled variables. Thus, both the blending system and the pH neutralization process are said to exhibit... 

Fuel density is important for blending characteristics, but also relates to emission levels, fuel consumption, and emission control systems. Similarly, the viscosity of the fuel is important. Kinematic Viscosity is included in the Biodiesel Standard because it relates directly to the injection system performance. In the Biodiesel Standards, viscosity is often set at a specific temperature point. With most fatty acid methyl esters this is never a problem, but viscosity changes at low temperature can be much more problematic. Biodiesel tends to thicken faster than fossil diesel. Specific additives might be required to deal with this. 

Applications ranging from 0.5% to 1.5% based on fiber weight are recommended depending upon fiber type, blend, processing system, and the amount of fiber control to meet dust level standards. 

Quality control systems usually used for judging the quality of oils and fats or oil blends used in margarine production could evaluate color, color stabihty, flavor, flavor stabihty, free fatty acid, peroxide value, active oxygen method (AOM) stabihty, iodine value, shp melting point, fatty acid composition, refractive index, crystallization rate, and sohd fat/temperature relationship (solid fat index) (5, 91, 112, 113). 

Combustion of waste wood presents special problems from a regulatory standpoint, and research should continue on reducing pollutant emissions. There is some potential for blending with clean wood without substantially altering emission control systems in existing units. Better fuel characterization is needed for waste materials. 

To investigate the TWC behaviour of the prepared samples in an environment which resembled the exhaust A/F fluctuations in a closed-loop emission control system we used a similar apparatus to that developed previously by Schlatter et al. [11]. Two fast-acting solenoid valves allowed one to cycle between the two following feedstreams prepared in two independent gas blending systems ... 

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