Heuristics final product heuristic

Final Product Heuristic Produce final products as distillate products, not as bottoms products. 

Golhar and Sarker (1992) and Jamal and Sarker (1993) consider the basic model under policy (a) with the assumption that the conversion rate from raw material to final product is one to one. Two cases are considered (i) Imperfect matching - production uptime and cycle time are not exact integer multiples of finished product delivery cycle (ii) Perfect matching the above numbers are integers. An iterative heuristic is used to solve the problem. Sarker and Parija (1994) consider exactly the same problem except that the conversion rate from raw material to final product is not assumed to be one to one. An exact algorithm is proposed. 

Extensions of the basic model with multiple types of raw materials needed for producing a single product are studied by Sarker and Parija (1996) and Sarker and Khan (1999, 2001). Sarker and Parija give a closed-form solution for the problem under policy (b) and with the assumption that the production cycle time is an integer multiple of the pre-specified finished product delivery cycle. Sarker and Khan propose a heuristic for the problem under policies (a) and (c) and the assumption that delivery of final product to the customer is carried out in the end of the whole production lot. 

The most volatile product (myristic acid) is a small fraction of the feed, whereas the least volatile product (oleic—stearic acids) is most of the feed, and the palmitic—oleic acid split has a good relative volatility. The palmitic—oleic acid split therefore is selected by heuristic (4) for the third column. This would also be the separation suggested by heuristic (5). After splitting myristic and palmitic acid, the final distillation sequence is pictured in Figure 1. Detailed simulations of the separation flow sheet confirm that the capital cost of this design is about 7% less than the straightforward direct sequence. 

Such infrequent measurements make the control of the product quality difficult. At best, the operators have learned a set of heuristics that, if adhered to, usually produces a good product. However, unforeseen disturbances and undetected equipment degradations not accounted for by the heuristics still occur and affect the product. In addition, there are periods of operation when the final process step produces a degraded product in spite of near-perfect upstream operations. 

Heuristic based approaches are more relevant for structured products. The most well known heuristic based approach is the hierarchical decomposition method developed by Douglas (Douglas, 1988). In the first level of this method one only looks at the input-output structure of the process. In subsequent levels more detail is added, finally ending with the entire flowsheet. Design decisions are made by using heuristics and short-cut models. An alternative method is due to Siirola (1996) means end analysis. In this method the properties of the feedstock and the desired products are compared. Tasks are defined to eliminate the property differences between the feedstock and the desired product. 

Acetic acid is the distillate product, and should be obtainable in adequately pure form to meet specifications. The acrylic acid bottom product contains nearly all of the heavier con nents from the reactions. It needs to be distilled in order to meet specifications, as suggested by the heuristic to finally recover product streams as distillate products, and this is done in column C-3. 

The effiuant streams from the first separator are examined and, if necessary, subjected to further separation. The sequence [s developed, gnided by the cost factor and the heuristic to choose the cheapest of all candidate separations next. The flowsheet is complete when all final effluent streams contain only one of the products of the predetermined set. 

Split generation and their sequencing can be managed by means of heuristics. General heuristics are presented in Table 7.12, which are inspired by the use of distillation. Firstly must be removed corrosive, hazardous, and any troublesome materials. This is particularly true for gas phase separations. For example, components that freeze, as water and CO2, may foul the equipment in cryogenic distillation. Then, the next should be splits that separate directly a product. The same priority is valid for the removal of the most plentiful component, which reduces the cost of downstream operations. Finally, if possible, a 50/50 split is the best solution for further sequencing. 

Finally, before leaving this topic, the reader should note that process creation and development of a base-case process are the subjects of Part One of this book, entitled Product and Process Invention— Heuristics and Analysis (Chapters 1-5.)... 

Very frequently non-optimal setpoint trajectories are used for controlling reactor temperatures in batch reactors [25,39,179,180]. Reactor temperatures maybe allowed to increase from ambient temperatures up to a maximum temperature value, in order to use the heat released by reaction to heat the reaction medium and save energy (reduce energy costs). The temperature increase is almost always performed linearly, because of hardware limitations and simplicity of controller programming. After reaching the maximum allowed temperature value, reactor temperature is kept constant for a certain time interval, for production of polymer material at isothermal conditions. At the end of the batch, the reaction temperature is increased in order to reduce the residual monomer content of the final resin, usually with the help of a second catalyst. Heuristic optimum temperature trajectories were also formulated for batch polymerizations of acrylamide and quaternary ammonium cationic monomers, in order to use the available heat of reaction [181]. The batch time was split into two batch periods an isothermal reaction period and an adiabatic reaction period. 

The next step is to find a sequence which allows all of these products to be produced yet does not require a product to be produced in sequence next to another of its own products. To set up the sequence, one heuristic is to schedule the product with the longest average time between models first. Create a time line as shown in Table 9.6 where the time measure is based on the overall cycle time needed. Schedule C first with 7 minutes between production. This means that Model C is produced at minute 1 and minute 8. Then schedule Model B every 3 minutes. So, if B starts at 2, another is scheduled at 5 and another at 9 since there is already a C being produced at minute 8. But, this would leave an A being produced at minutes 3 and 4 then 6 and 7. To split production of the A models more, B could start in minute 3, then minute 6. So, the final schedule, a portion of which is shown in Table 9.6, repeats the sequence of C, A, B, A, A, B, A. It is clear that a C is produced every 7 minutes. Since the cycle repeats every 7 minutes, it will be done 60 times a day. So, 240 A are produced (60 4), 120 B are produced (60 2), and 60 C are produced (60 1). 

Finally, the case of easy separation between the heavy reactant and the heavy product is explored. The relative volatilities are ac = 16, ua = 8, as = 4, and ao = 1- Feed tray location optimization gives Nf a = 9 and Nps = 12. In this case, 14.7% energy can be saved (from 0.0365 to 0.0311 kmol/s Table 18.3). Once again heuristic H2 applies, and the percentage of energy saved is also similar to the high activation energy counterpart (Table 18.2). 

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