Abstract
This research presents a mathematical formulation for optimising integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The framework is constructed using mixed-integer linear programming (MILP) which exhibits rapid conversion and a global optimum with well-defined solution methods. The framework builds upon previous efforts in process integration and considers materials and energy with thermodynamic constraints imposed by formulating the heat cascade within the MILP. The model and method which form the fundamentals of process integration problems are presented, considering exchange restrictions and problem formulation across multiple time-scales to provide flexibility in solving complex design, planning and operational problems. The work provides the fundamental problem formulation, which has not been previously presented in a comprehensive way, to provide the basis for future work, where many process integration elements can be appended to the formulation. A case study is included to demonstrate the capabilities and results for a simple, fictional, example though the framework and method are broadly applicable across scale, time and plant complexity.
Highlights
Increasing demand for energy and raw materials necessitate commensurate improvements in material and energy efficiency in major process industries to slow the depletion of natural resources
Such gaps as methods for simultaneous optimization of resources and energy were identified in the published literature, exhibiting a lack of thorough optimization frameworks and their formulations to consider complex industrial symbiosis problems and find novel solutions for process integration within and between industrial sites
The framework developed for this approach uses a mixed-integer linear programming formulation, balancing imprecision from linearization with benefits of global optima and rapid resolution time when compared to mixed-integer non-linear programming formulations
Summary
Increasing demand for energy and raw materials necessitate commensurate improvements in material and energy efficiency in major process industries to slow the depletion of natural resources. Often proposed out of necessity from industries to respond to scarcity or market conditions, have been developed to analyse process efficiency and propose solutions Such methods are becoming increasingly important and complex due to additional objectives such as emission reductions and job creation/retention as well as additional economic, political, or environmental constraints. Process integration methods can be extended from the unit process or plant level to include exchange possibilities between plants to discover new opportunities for process efficiency improvements which address modern requirements for meeting environmental and social constraints while maintaining economic competitiveness. Such opportunities have traditionally been considered on an individual basis without decisionsupport from optimization tools or advanced mathematical programming methods.
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