Minimum efficient scale

Commodity prices are typically volatile both intra-year and over several years, reflecting shifting demand and supply curves. For industrial commodities there is a traditional cycle of interaction between GDP-driven demand fluctuations and lags in supply which leads to pronounced boom-bust pricing variations. This is especially true in industries with a high minimum efficient scale (MES) of capacity such as oil refining and petrochemicals, where new plant may add materially to industry supply, leading to a big fall in prices. 

Where products are demanded in volume, process technologies have been developed to minimize unit costs. The concept of the minimum economic scale of plant (MES sometimes referred to as minimum efficient size) is the point beyond which increases in scale do not significantly reduce unit costs. 

Economics of scale, conventionally associated with manufacturing operations, is probably the most important of these costs drivers and exists wherever as the scale of production increases unit costs fall. A plant capacity has then an economic sense if a minimum efficiency plant capacity is reached. 

Short development time Allocate enough time for development may result in a less than, more time-efficient PEIA techniques complete knowledge of the hazards administrative controls to decide when to go to full scale production Establish minimum requirements transfer package for process knowledge Require development chemist to be present during initial product runs API RP 750 CCPS G-1 CCPS G-10 CCPS G-25... 

The purification of value-added pharmaceuticals in the past required multiple chromatographic steps for batch purification processes. The design and optimization of these processes were often cumbersome and the operations were fundamentally complex. Individual batch processes requires optimization between chromatographic efficiency and enantioselectivity, which results in major economic ramifications. An additional problem was the extremely short time for development of the purification process. Commercial constraints demand that the time interval between non-optimized laboratory bench purification and the first process-scale production for clinical trials are kept to a minimum. Therefore, rapid process design and optimization methods based on computer aided simulation of an SMB process will assist at this stage. 

Direct search methods use only function evaluations. They search for the minimum of an objective function without calculating derivatives analytically or numerically. Direct methods are based upon heuristic rules which make no a priori assumptions about the objective function. They tend to have much poorer convergence rates than gradient methods when applied to smooth functions. Several authors claim that direct search methods are not as efficient and robust as the indirect or gradient search methods (Bard, 1974 Edgar and Himmelblau, 1988 Scales, 1986). However, in many instances direct search methods have proved to be robust and reliable particularly for systems that exhibit local minima or have complex nonlinear constraints (Wang and Luus, 1978). 

Goldberg, A. (1997) An efficient implementation of a scaling minimum-cost algorithm. J Algorithms, 22, 1-29. 

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