Optimization strategies control selectivity

The variables that control the extent of a chromatographic separation are conveniently divided into kinetic and thermodynamic factors. The thermodynamic variables control relative retention and are embodied in the selectivity factor in the resolution equation. For any optimization strategy the selectivity factor should be maximized (see section 1.6). Since this depends on an understandino of the appropriate retention mechanism further discussion. .Jll be deferred to the appropriate sections of Chapters 2 and 4. 

Natural microbial fouling control strategies are environmentally sensible because they have been optimized by natural selection. A sensible innovation strategy then, is to observe natural control, try to understand it, attempt an imitation, and explain the copy1. The new chlorine alternative and its industrial water treatment applications were accordingly developed, as follows. 

The proposed optimization strategy will replace the traditional method of controlling the release of Oz. Today, the rate of 02 released is controlled to maintain the d/p between the electrolyte chambers in order to limit the force that the separation diaphragm has to withstand. When the pressure differential is detected and controlled by conventional d/p cells, the measurement cannot be sensitive or accurate therefore, the diaphragm has to be strong, and the electrolyzer (or fuel cell) must be bulky and heavy. In this optimized design (if a liquid electrolyte design is selected), differential level control (ALC-12) will be used, which can control minute differentials. 

A drawback of the LQR control strategy is the arbitrariness of the selection of matrices Q and R. Taking into consideration the high randomness of earthquake forces and the high cost of active structural control, such an arbitrariness is obviously a matter of concern, this suggest applying an optimization approach to select values. A method for performing this step is summarized in the next section. 

Another strategy controls feature selection by optimizing the final result, for instance by maximizing the performance... 

Many HVAC system engineering problems focus on the operation and the control of the system. In many cases, the optimization of the system s control and operation is the objective of the simulation. Therefore, the appropriate modeling of the controllers and the selected control strategies are of crucial importance in the simulation. Once the system is correctly set up, the use of simulation tools is very helpful when dealing with such problems. Dynamic system operation is often approximated by series of quasi-steady-state operating conditions, provided that the time step of the simulation is large compared to the dynamic response time of the HVAC equipment. However, for dynamic systems and plant simulation and, most important, for the realistic simulation... 

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