SUMMARY

This thesis considers the problem of how to design an industrial network to reduce cost, increase efficiency, and reduce environmental burdens. There are a number of classical ways to approach this. This thesis will develop a recent approach based on an analogy with biological food webs. Previous bio-inspired engineering focused on small scale product design, this approach works at a larger scale for network design, begun only very recently and in much need of development. In studies of ecological networks it is known that the critical quantities of interest include the internal cycling of energy, the level of connectedness, and the density of interactions. Metrics summarizing these quantities are all derived from the basic quantities species and links. A key component of this research will be to find the correct definitions of analogous quantities for industrial networks. To analyze industrial networks using these ecological metrics, a first step will be to develop a comprehensible and reliable data set of industrial networks. A data set of biological networks will be assembled as well to calculate sustainable benchmark values for said metrics. These benchmarks will become goals for the industrial designs. Much of the previous work in bio-inspired design has been philosophical rather than quantitative, an essential difficulty in the field. One goal of this research is to place our findings on a quantitative footing. We will do this by reconstructing found relationships from science and engineering 1st principles, such as thermodynamic 1st law efficiency. The successful completion of this work would result in industry-wide savings, efficiency increases, and overall reductions in the environmental burdens through a reduction in raw material consumption and waste disposal. This thesis will support the view that financial competitiveness and sustainability need not be mutually exclusive.