Distributed production: Scale‐up vs experience

Abstract

Concepts such as process intensification, distributed manufacturing, and modularity are becoming mainstream as the chemical industry has to meet the demand for growth while concurrently facing sustainable development challenges. To meet economies of scales, modularity appeals to the concept of numbering up (scaling down and then scaling out). As numbering up becomes more common and a necessity, investors look at solid financial predictors to reduce the uncertainty around the fate of their assets. Traditional economic models that either scale up or scale down the investment for a plant size with a power law (exponent α) of a reference unit at a given capacity (Q) and its investment (I) are valid for the several identical plants and their components. When it comes to scaling down and then numbering up, the investment, or rather price of a modular plant the exponent relating price and capacity is β = 1/n − 1. We report a case study to scale down a 1000 barrel/day (bbl/day) micro-refinery gas-to-liquid unit to convert wasted natural gas to Fischer-Tropsch fuels. The investment for 100 units 100 times smaller approaches the cost of the same production capacity given by a single 1000 bbl/day unit costing $1 million. K E Y W O R D Scottage industry, distributed manufacture, experience, learning, numbering up, scale-up

The scaling economics of small unit operations

“…For completeness, we provide ( Table 1) the values of all the adjustable parameters and the reference value employed to normalize the scale of the devices. They have little physical meaning except, again, to confirm, as Super noted, that the exponents, c, can be larger than unity, implying that, for example, it would be more frugal to purchase two 1000 m 3 installation. For modular systems, an installation factors of 1.4 to 1.7 have been suggested, 13,14 which are comparable to those tabulated 4 for unit operations including the tanks, heat exchangers and mixers considered here.…”

“…We suggest that using this type of correlation may enhance the accuracy of techno‐economic analyses of small modular processes. The usual attributes that are presented to motivate adopting small scale, module equipment that can be numbered up—rapidity of implementation, 15 efficiency, 16,17 feedstock flexibility, 3 safety and environmental impact 18‐21 —should be even more compelling with the inclusion of more accurate projections of savings in capital costs. That is our intent in offering this correlation for consideration by the community.…”

“…Processing geographically dispersed resources that cannot be centralized economically could employ modular, intensified operations, but at much smaller scale than the 0.6‐power law 1 or its modifications 2,3 can accommodate. That power rule for estimating the cost of chemical processing equipment as a function of scale can work well but only over a range of about one order of magnitude in scale.…”

Experimental methods in chemical engineering: Process simulation

“…For modular units, the learning elasticity is proportional to this exponent and the cost of these units decreases with multiple units (learning/experience). [ 65 ] Lang [ 66–68 ] elaborated a simplified method to calculate the total installed cost of equipment based on factors that vary according to the type of plant (solid handling, solid–fluid, or fluids).…”

Special issue covering the Second International Process Intensification Conference (IPIC2)

“…In the selected papers for this special issue, Boffito et al 1 discuss distributed production as a scale‐up approach using the case study of a microrefinery gas‐to‐liquid unit to convert natural gas into Fischer‐Tropsch fuels. Muster‐Slawitsch et al 2 analyze oscillating flow bioreactors for sustainable biorefining operations.…”