Multi-objective resource integration for sustainable industrial clusters
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
With the current global climate change and resource depletion concerns, industrial clusters are challenged to focus on implementing sustainability designs and policies. These policies target the decrease of greenhouse gas (GHG) emissions, primarily CO2, and the reduction of the usage of water and non-renewable energy sources. A useful element in the area of process design is material and energy integration. Numerous tools for resource integration (material and energy) have been developed to achieve sustainability goals. The design of sustainable integration networks of clusters includes economic and environmental consideration, which often conflict. A trade-off normally exist between profit and reducing environmental impact due to the addition of emission capture and water treatment units, sequestration, or the usage of cleaner energy sources which are more expensive. The existence of this trade-off between objectives led to the development of multi-objective optimization (MOO) tools, which attempt to simultaneously optimize more than one objective function. A prominent MOO tool is the -constraint method, and a recent improvement of it is the augmented -constraint method. MOO tools are applied to resource integration tools for simultaneous targeting of economic as well as environmental performance. When applied to integration networks, MOO tools generate pareto-optimal set of solutions which illustrate the trade-off between the objective functions, where each solution correspond to a specific cluster design and an integration network. However, recent integration tools (as well as MOO application to those tools) allow the interaction of only specific species; hence limit the possibility of obtaining an optimal solution. A recent resource integration tool was developed that allows the integration of multiple material and energy resources in a cluster. This work introduces a holistic multi-objective resource integration tool, which applies the augmented -constraint multi-objective tool to the holistic resource integration approach. The tool generates pareto surfaces that captures the trade-offs and propose different integration networks. The tool is illustrated through two case studies, where the profit, emission impact, and water consumption of industrial clusters are optimized simultaneously.
