Development of optimization tools for the systematic exploration of industrial symbiosis opportunities in the process industry

Social advance based on unlimited resource consumption has derived in serious environmental problems. As a response, several initiatives to disassociate resources consumption and human progress have arisen over the past few years, being sustainability one of their crucial pillars. In this context, industrial ecology and industrial symbiosis offer more sustainable alternatives to the exploitation of natural resources by considering the co-operation of different companies. While modelling approaches to optimize energy and water sharing networks have been widely studied in the literature, little attention has been paid to resource sharing between different industries, as outlined by Boix et al. in (Boix et al. 2015). Hence, there is a need of systematic procedures allowing the identification and assessment of potential sharing alternatives. Thus, this permits focusing on the targets to be further investigated and eventually optimized. This work aims to develop increasing complexity models to identify symbiosis opportunities regarding material sharing in the chemical and process industry. The modelling approach explores the concept that waste from a process may be used as or transformed into added value raw materials for another, emphasizing the reuse and recycling of materials and thus diminishing the consumption of fresh resources. Given a set of inlet waste streams to be managed or disposed, a set of demanded raw material to feed different processes and a set of potential material transformations (chemical reactions) with related costs, the objective is to determine the targets or symbiosis opportunities within a network of chemical process industries. Decision variables address the amount of raw material demand covered by reused/recycled streams, the amount of waste entering the system to be treated and the amount to be disposed, among others. Models include mass and energy balances and an economic objective function and lead to different Linear Programming (LP) and Mixed Integer Linear Programming (MILP) problems. The models have been implemented in GAMS and tested on a case study recreating an industrial network based on ethylene, chorine and related chemicals. Results show the potential of this targeting approach allowing identifying and assessing promising industrial symbiosis opportunities, helping in the decision-making process. The work discusses the limitations and further developments required to address the systematic decision-making support to the efficient management of industrial symbiosis networks in the chemical process industries