Generalized mixed-integer nonlinear programming modeling of eco-industrial networks to reduce cost and emissions

Eco-industrial networks are a concept which has received study for almost 30 years as a strategy for improving economics of production facilities while reducing waste. This study focuses on developing a generalized modeling framework in terms of mixed-integer nonlinear programming of an eco-industrial network. The problem is formulated as a multi-objective optimization with the objective to reduce life-cycle emissions and maintaining favourable economics. This model lends itself as a developmental tool to assess environmental and economic feasibility of applying eco-park concepts to new construction projects. In addition, the model is formulated to determine feasible transportation distances between facilities, select the appropriate transportation technologies and is built with the intention of modification toward stochastic import/export pricing. The sensitivity of the network configuration to variances in the weighting factors placed on economics or environmental emissions is discussed and the results from multiple weighting scenarios are presented. These results show that the estimated reductions in cost and emissions are less significant than those from previously published work but are founded upon a more realistic network model. Based on our findings, the cost for the integrated set of facilities is shown to be reduced by 24% while the emission reduction is observed at 12.7% for the base scenario examined by the earlier models. These results show less promise than the earlier findings but represent a more realistic assessment of the proposed network. The tools developed by this research present a novel approach to facility planning and policy development for chemical industries. As such, the framework presented herein has the potential to impact facility construction/operation and public policy governing industrial producers; additionally, this research contributes an empirical approach for assessing the practicality of establishing eco-industrial networks and provides an approach for further analyses.