Variable operating costs chapter

In order to proceed with the optimization, choose an objective function. For this example, considering the reflux ratio as the only decision or design variable, the only costs that will be affected by changes in reflux are the capital and operating costs associated with the column. In order to account correctly for both one-time costs and operating costs, an objective function that takes into account the time value of money (see Chapter 10 for the different criteria used for assessing profitability) should be used. Use the before-tax NPy as the objective function ... 

For the establishment of the realistic limit, one has to take account of the rates of processes in which mass, heat, momentum, and chemical energy are transferred. In this so-called finite-time, finite-size thermodynamics, it is usually possible to establish optimal conditions for operating the process, namely, with a minimum, but nonzero, entropy generation and loss of work. Such optima seem to be characterized by a universal principle equiparti-tioning of the process s driving forces in time and space. The optima may eventually be shifted by including economic and environmental parameters such as fixed and variable costs and emissions. For this aspect, we refer to Chapter 13. 

The procedure adopted is to establish a base case, which is representative of the average operation of interest and then to address the sensitivity of the base case against the key economic variables. The base case is developed around the production of 500,000 t/y ethylene using ethane at a cost of 7.19/GJ ( 373.3/t). This ethane price is discussed in an earlier chapter and corresponds to a natural gas price of S6.37/GJ (average US 2007 price) into a suitably large-scale gas plant. 

An additional complication in formulating the objective function is the quantification of uncertainty. Economic objective functions are generally very sensitive to the prices used for feeds, raw materials, and energy, and also to estimates of project capital cost. These costs and prices are forecasts or estimates and are usually subject to substantial error. Cost estimation and price forecasting are discussed in Sections 6.3 and 6.4. There may also be uncertainty in the decision variables, either from variation in the plant inputs, variations introduced by unsteady plant operation, or imprecision in the design data and the constraint equations. Optimization under uncertainty is a specialized subject in its own right and is beyond the scope of this book. See Chapter 5 of Diwekar (2003) for a good introduction to the subject. 

A growing amount of evidence points to the desirability of incorporating dynamic controllability considerations into all phases of the plant design. It may be better in the long run to build a process that has higher capital and energy costs if the plant provides more stable operation and achieves less variability in product quality. We present several examples in this chapter illustrating the inherent trade-off between steady-state economics and dynamic controllability. 

This chapter explores variability as a root cause adding to supply chain costs. Because supply chains are complex, involving multiple trading partners, their productivity is especially vulnerable to variability because there are so many points at which variability can occur. Many variations from planned operations will be difficult to recover from, schedules will be uncertain, and effectiveness in the form of service and cost will deteriorate. The opening quotation reflects the importance of process details in one of the largest companies, are threatened by competitors that have mastered the art of attention to detail. 

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