Optimization product value

The hydrogenolysis of glycerol was studied over a large range of pH in order to determine the optimal pH value at which the higher reaction rate and selectivity to 1,2-PDO are obtained. Selected results are summarized in Table 35.2. In the presence of Rh/C and under neutral conditions (pHi = 5.5), a low conversion of glycerol was achieved (< 4%) after 48 h. The main product was 1,2-PDO, but EG, 1-propanol, ethanol and 1,3-PDO were also detected. Increasing the initial pH from 5.5 to 12.0 had beneficial effects both on the conversion and selectivity. The selectivity to EG decreased from 13% under neutral condition to less than 1% while the selectivity toward the desired 1,2-PDO increased from 52% to 96%. 

For suspensions primarily stabilized by a polymeric material, it is important to carefully consider the optimal pH value of the product since certain polymer properties, especially the rheological behavior, can strongly depend on the pH of the system. For example, the viscosity of hydrophilic colloids, such as xanthan gums and colloidal microcrystalline cellulose, is known to be somewhat pH- dependent. Most disperse systems are stable over a pH range of 4-10 but may flocculate under extreme pH conditions. Therefore, each dispersion should be examined for pH stability over an adequate storage period. Any... 

Finally, future inventory planning is integrated in the overall optimization process as preprocessing phase. Alternatively, it can be run independently before the optimization to ensure usage of the most recent future product values for calculating capital costs in the optimization. 

Input product quantities like raw material consumption rates can be variable depending on utilization of the resource. Input product quantities are determined by linear recipe function with the recipe factors ap and bP pt on a tons per hour basis V r,s,/> e IPU. This is a key issue of the production and the entire supply model including procurement is to decide on the variable raw material consumption rates in production. Both production and procurement planning are highly interrelated, i.e. high production rates determine the amount of raw material that has to be supplied. In the overall context of value chain optimization, production rates have to comply with decisions reflected by the sales model e.g. on spot sales quantities and prices. 

Area definition of a model area allowing the planner to optimize the value chain as a whole or defined sub-models focusing on parts of the value chain like separated products or separated resources. 

After activation with MAO (molar ratios [Al] [Zr] = 1000) the polymerization of ethylene has been successfully carried out using the zirconocene functionalized dendrimer at 40 bar ethylene pressure and 70 °C. We obtained high activity and productivity values for the ethylene polymerization and polymers with very high molecular masses in the range of 2 x 10 g/mol. The polydispersity of the polymer is quite low (3.0) indicating the single site character of the catalytically active species. Optimization of this system and study of the mechanism are stiU under investigation. Nevertheless, these preliminary results reveal the suitability of polyphenylene dendrimers as supports for zirconocene catalysts. 

Operational risk factor 02 Optimal objective value Expected variation in profit V(z0)(E + 8) Expected total unmet demand/ production shortfall Expected total excess production/ production surplus Expected recourse penalty costs Es Expected variation in recourse penalty costs Vs p = E[z ] - Es c a P... 

In a retrofit batch design, we optimize the batch plant profitability defined as the total production value minus the cost of any new equipment. The objective is to obtain a modified batch plant structure, an operating strategy, the equipment sizes, and the batch processing parameters. Discrete decisions correspond to the selection of new units to add to each stage of the plant and their type of operation. Continuous decisions are represented by the volume of each new unit and the batch processing variables which are allowed to vary within certain bounds. 

As a starting point for structural cost optimization, Clariant first of all has an optimized production network design that leverages scale effects and regional advantages. Second, a review of truly value-creating functions, processes, and process steps is performed on a regular basis. 

The values of the optimization variables calculated by Costa et al. (5) were S0 = 130 kg/m3, tr = 1.3 h, R = 0.3, and r = 0.25, which leads to productivity of 21 kg/(m3-h), % yield of 0.82, and conversion of 0.96. Costa et al. (5) analyzed 16 surfaces to determine the values of the four optimization variables and they cited that it was difficult to determine the best combination of optimization variable values without taking advantage of previous knowledge of the process. 

The optimization variables values are SQ = 118.3 kg/m3, tr = lh,R = 0.2, and r = 0.2. The values for % yield and productivity calculated by the statistical models are 0.82 and 26.73 kg/(m3-h), respectively. Using the values calculated for S0, tr/ R, and r in the rigorous model, however, the % yield calculated was 0.69 and productivity was 26.39 kg/(m3-h). Note that although the statistical model for yield developed by Costa et al. (5) has a high correlation coefficient and passed the F-test with 99% confidence, at the calculated optimal conditions, it presents a great deviation from the rigorous model. 

Note that although the productivity and % yield values are similar using the three approaches, the optimization variables values calculated are different, which shows that there are many combinations of values of the optimization variables that lead to high productivity and % yield. This conclusion had already been drawn in the work of Costa et al. (5) by analysis of the response surfaces. One of the advantages of the RSM is that it is possible to picture the behavior of the optimization variables in the region of interest. 

The previous section assumed that product composition (or product flow) requirements are fixed. In this very common situation, the optimum design minimizes the costs of achieving these requirements. Often, product specs are not fixed, but depend on economics. Even when a product must obey a "less than" purity spec, better purity may fetch a better price. The better price may justify additional investment in equipment and/or a higher operating cost. Here, a design must optimize product purity value versus distillation cost. This optimization is also important in an operating column and is commonly performed by on-line computer control. It is outlined below, and discussed in detail elsewhere (1,2). 

One variable not considered in the analysis is fluctuations in feed composition. Such fluctuations may have an effect on product values and on the separation, and need to be considered in the optimization. Where significant fluctuations are expected, it may be worthwhile to work with component recoveries (-D lk/ zlk) rather than D/F. 

Finally, it should be noted that the optimization described above is of a shortcut nature. The author is yet to encounter a practical situation where the recovery and separation optimization is performed more rigorously. In the majority of new designs experienced by the author, the optimization did not even go that far. Procedures for more rigorous optimization are available (e.g., Ref. 3), but uncertainties in product values, project life, and costs often makes further fine-tuning difficult to justify. 

Therefore the reaction conditions must be carefully considered in order to obtain the optimal product yield. Peptide bond formation by partitioning of acyl-enzyme intermediates may also be controlled by the specificity of the enzyme. Structural factors in the nucleophile which increase the extent to which it is bound productively at the S subsite of the enzyme promote peptide bond formation. The type and concentration of the nucleophilic amino component influence the partitioning of the acyl-enzyme intermediate. Since only the free-base form of the amino component reacts with the acyl-enzyrae intermediate, and the pK of this type of reactant is about 8, the pH of the reaction mixture should preferably be higher than this value. 

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