Optimization goals

Airhandlers today are frequently controlled the same way as they were 20 or 30 years ago. For this reason, optimization can often cut their energy consumption in half—a savings that can seldom be achieved in any other unit operation. The optimization goals include the following ... 

The goal of optimization is safety at maximum profit, but this can only be done if the market value of each product is known. This is not the case when the products of a column are not final products but feed flows to other unit processes. When the product prices are unknown, it is still possible to perform optimization, but the optimization goal changes. The criterion in that case becomes the generation of the required products at minimum operating costs. This can be called an optimum with respect to the column involved, but only a "suboptimum" with respect to the plant of which the column is a part. 

We can summarize this optimization goal in a way that is consistent with the criteria described in chapter 4 (section 4.3.3) as follows ... 

We see that all optimization goals depend strongly on reducing . Experimentally, values down to 1 fitn and less have been realized. Such thin solute layers yield high-speed separations, often requiring only a few minutes for completion. However with small l, particle-wall interactions increase and in some cases lead to departures from theory [25]. 

Figures 5.1-5.3 show the temperature policies in the three runs. It can be seen from the results in Table 5.1 that by using the right combination of initiators combined with a rising temperature profile, one can reduce the batch time significantly while at the same time increasing the molecular weight (comparison of policies 3 and 1). The final residual initiator amount is also greatly reduced. Their experimental results confirm that the optimization goal was achieved by employing the proper control policy.

Preparative HPLC optimization goals which ultimately lead to a product with a given minimum purity may include the maximum amount of the purified material per weight unit of stationary phase per time unit (g/kg/day), the maximum amount of the purified material per mobile phase unit per time unit (g/L/day), the maximum production rate (g/day), the lowest cost ( /kg), the maximum recovery (%), and the maximum production rate with maximum recovery. Regardless of the differences in application, it is important to be aware of the following parameters that may affect the purity and recovery of the product as well as the time and cost required for the separation ... 

The only evaluation method that unifies the contributions of these optimization goals is the total separation cost. The position of the optimum after a cost optimization depends heavily on the magnitude of each contribution relative to each other. If the price of adsorbent or fixed costs (maintenance and capital cost) is a dominant factor, then the cost optimum will coincide with the maximum productivity. For separation problems with low solubility of the components and/or very high eluent price, the cost optimum is approximately equal to the minimum of eluent consumption (i.e. very high number of stages). In other words, only the optimization of total separation cost leads to the real (economically) optimum of the separation problem (Fig. 7.15). 

Figure 12 Optimization goals for a chromatographic purification and their interrelationship.

At each work-place, the manual assembly subtasks can be studied by operation method studies. Normally, there is a distinction between motion study and time study. In these studies, the layout of the work-place is analyzed and the placement of the components to assemble is described. The motion study concentrates on recording and analyzing the motion elements types and magnitude of motions. The optimization goal is minimizing movement. The time study is used to determine the normal (average) time consumption for optimized movements (Riggs 1987). 

The optimization goals were reached to a large extent. Only 248 bytes of buffer memory outside of the array are needed, all PEs are active at 100% of all cycles, and the architecture has matched throughput. Communication inside the array is also simple, and no control or I/O is needed for PEs not located at the borders. This fully optimized result has been feasible by adopting the tuned methodology proposed in section 2 and with the help of the synthesis techniques discussed in the previous sections. 

Optimization of devices and processes is very common in todays efficiency-oriented world with everyone striving to do things faster but with no loss of quality. It is, therefore, important to define the best approach in which optimization can be achieved and how the related quality can also be measured. There are numerous parameters in a technique such as HPLC of either independent of each other or that have a related nature to other parameters. Optimization goals can vary and mostly depend on specific needs of analytical laboratories. The most important optimization criteria in HPLC will be elucidated and discussed in this introductory section. 

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