Profitability-Based Optimization

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. 

When the market values of the products are known, the column can be fully optimized, but additional variables must still be considered. These include the type of the market that exists for the products. If the market is limited, the goal is to generate the products at optimum separation and minimum operating cost. This cost varies as the feed flows and their compositions vary. When the market is unlimited and sufficient feedstock is available, the optimization task is more difficult, because one must determine both the optimum separation and the value of the feed streams. In this case the goal of optimization is either maximum loading or maximum energy efficiency. 

The configuration of a back-propagation neural network and its use as an internal model controller (IMC). 

256 Post- Oil Energy Technology After the Age of Fossil Fuels 

Optimization implies maximum profit rate. An objective function is selected, and manipulated variables are chosen that will maximize or minimize that function. Unit optimization addresses several columns in series or parallel. It is concerned with the effective allocation of feedstocks and energy among the members of that system. Plantwide optimization involves coordinating the control of distillation units, furnaces, compressors, etc., to maximize profit from the entire operation. All lower-level control functions respond to set points received from higher-level optimizers. 

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Altogether, the model supports the company to optimize monthly profits based on volume decisions consistent as far as possible to company s profit and loss structures. Ideally, it is fully consistent with the company s profit and loss statement requiring integrating costs for support areas such as further overhead costs or capital costs on receivables. This would be a long-term vision, where further research should be directed to. 

Probably the most important feature of this plant is that it is not only fully automated, but also that this automation includes profit-based plantwide optimization. This is different from the operation of traditional power plants, which basically have only one operating mode and only adjust their rate of... 

Case 1 of Table 9.3 is the base case. It shows the optimisation results using the cost parameters presented in Table 9.2. The maximum profit and optimal batch time obtained by optimisation shows very good agreement to those shown in Figure 9.8. The maximum profit shown in Figure 9.8 is between 3.99-4.13 ( /hr) with an optimum batch time between 12-14 hr. Each of the optimisation problems (i.e. solution of P2 with Equation 9.6) presented in Table 9.3 requires approximately 3- 4 iterations and about 3- 4 cpu sec using a SPARC-1 Workstation (Mujtaba and Macchietto, 1997). 

If it is necessary to drastically reduce the number of terms to be handled in a Cl process, it seems to be profitable to optimize the orbitals expansion produce the maximum efficiency as concerns correlation. This optimization is the aim of the multi-configuration (MC)—SCF (in which a limited set of functions < k is constructed and the expansion coefficients of the determinants and those of orbitals are optimized) and of alternative methods based on the use of natural orbitals. 

The use of a Fischer-Tropsch (FT) process to produce long-chain hydrocarbons is well known in industry, and achieving the desired selectivity from the FT reaction is crucial for the process to make economic sense. It is, however, well known that a one-alpha model does not describe the product spectrum well. From either a chemicals or fuels perspective, hydrocarbon selectivity in the FT process needs to be thoroughly understood in order to manipulate process conditions and allow the optimization of the required product yield to maximize the plant profitability. There are many unanswered questions regarding the selectivity of the iron-based low-temperature Fischer-Tropsch (Fe-LTFT) synthesis. 

The objective of the simulation study therefore was a comparison of the profitability and the flexibility between an existing standard multipurpose plant and different conceivable pipeless plant scenarios. Based on the production data of the existing plant, an optimal pipeless plant setup was developed and representative production plans were simulated and evaluated. 

From the different planning methods available within SNP, SNP optimization is selected because it offers the best fit to the customer requirements outlined above. The main reasons for this decision are the multisourcing characteristics of the supply network as well as the fact that the objective functions used by the SNP optimizer, profit maximization or cost minimization, correspond to the planning philosophy favored by the customer. In addition to SNP optimization with its cost-based approach, SNP offers several heuristic-based planning methods which follow a rule-based logic. 

As many other industries, the fine chemical industry is characterized by strong pressures to decrease the time-to-market. New methods for the early screening of chemical reaction kinetics are needed (Heinzle and Hungerbiihler, 1997). Based on the data elaborated, the digital simulation of the chemical reactors is possible. The design of optimal feeding profiles to maximize predefined profit functions and the related assessment of critical reactor behavior is thus possible, as seen in the simulation examples RUN and SELCONT. 

Leemans described a sampling scheme based on these algorithms that considers sampling frequency, sampling time, dead time and accuracy of the method of analysis to obtain optimal information yield or maximal profit when controlling a factory. 

In a deterministic planning environment the most likely scenario, here scenario 2, would be considered the base case and the optimization model would be solved based on this scenario. The optimal decision would be to open facility 1 in period 1 and facility 3 in period 2 leading to a total profit of 2,590. To assess the robustness of this network to alternative demand scenarios the profit achievable with this configuration in case of the alternative demand scenarios can be assessed. In the example, for scenario 1 the overall profit would be 1,640 and for scenario 3, 2,765 respectively. Considered individually, the optimal decision for scenario 1 would be to open only facility 1 with a total profit of 1,880 and for scenario 3 to open both facilities 1 and 2 in period 1 with a total profit of 2,931. In order to explicitly incorporate the uncertainties caused by the different realization probabilities of the three demand scenarios, the optimization model can be extended into a two-stage decision with recourse ... 

The advanced process control strategies that are most applicable to the optimization of the distillation process are usually based on white-box modeling, where the theoretical dynamic models are derived on the basis of the mass, energy, and momentum balances of this well-understood process. Although the optimization techniques described here can improve productivity and profitability by 25%, this goal will only be achieved if the distillation process is treated as a single and integrated unit operation and the variables, such as flows, levels, pressures, etc., become only constraints, and the controlled and optimized variables are productivity and profitability. 

For single separation duty, Diwekar et al. (1989) considered the multiperiod optimisation problem and for each individual mixture selected the column size (number of plates) and the optimal amounts of each fraction by maximising a profit function, with a predefined conventional reflux policy. For multicomponent mixtures, both single and multiple product options were considered. The authors used a simple model with the assumptions of equimolal overflow, constant relative volatility and negligible column holdup, then applied an extended shortcut method commonly used for continuous distillation and based on the assumption that the batch distillation column can be considered as a continuous column with changing feed (see Type II model in Chapter 4). In other words, the bottom product of one time step forms the feed of the next time step. The pseudo-continuous distillation model thus obtained was then solved using a modified Fenske-Underwood-Gilliland method (see Type II model in Chapter 4) with no plate-to-plate calculations. The... 

For single separation duty, Mujtaba and Macchietto (1993) proposed a method, based on extensions of the techniques of Mujtaba (1989) and Mujtaba and Macchietto (1988, 1989, 1991, 1992), to determine the optimal multiperiod operation policies for binary and general multicomponent batch distillation of a given feed mixture, with several main-cuts and off-cuts. A two level dynamic optimisation formulation was presented so as to maximise a general profit function for the multiperiod operation, subject to general constraints. The solution of this problem determines the optimal amount of each main and off cut, the optimal duration of each distillation task and the optimal reflux ratio profiles during each production period. The outer level optimisation maximises the profit function by... 

The optimum number of plates, the optimum values of the decision variables for both outer and inner loop optimisation problems, and optimal amounts and composition of all products are shown in Table 7.2. Typical composition profiles in the product accumulator tank are shown in Figure 7.6. Bold faced mole fractions in Table 2 are the specifications (all satisfied) and underlined mole fractions are decision variables which were optimised. Although the optimum number of plates is almost close to that of the base case, the optimal total operation time is 14% lower than the base case. The profit with the optimal design and operation is 35% higher than that for the base case (calculated using the same cost model). This is obtained... 

Two binary mixtures are being processed in a batch distillation column with 15 plates and vapour boilup rate of 250 moles/hr following the operation sequence given in Figure 7.7. The amount of distillate, batch time and profit of the operation are shown in Table 7.6 (base case). The optimal reflux ratio profiles are shown in Figure 7.8. It is desired to simultaneously optimise the design (number of plates) and operation (reflux ratio and batch time) for this multiple separation duties. The column operates with the same boil up rate as the base case and the sales values of different products are given in Table 7.6. 

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