A MULTI SCALE METHODOLOGY FOR ORC INTEGRATION OPTIMIZATION IN AN INDUSTRIAL PROCESS
Résumé
In industrial processes, a large amount of energy is usually lost as waste heat. This waste source reduces not only the energy efficiency of industrial processes but also contributes to greenhouse gases emissions and thermal pollution. In this context, The CERES-2 project (CERES denotes “Energy paths for energy recovery in industrial systems”), supported by the French National Research Agency, aims at developing a decision-making tool to identify the optimal solutions of industrial waste heat recovery. This platform leans on energy integration and multi-objectives optimization to identify and design the best waste heat recovery solutions, according to technical and economic criteria, for a given industrial process. The solutions gather direct heat recovery, heat pumping and electricity production technologies.
This paper presents how the developed multi scale methodology helps optimizing the integration and the architecture of an ORC in an industrial process.
On the process scale, CERES platform uses a MILP algorithm that uses Grand Composite Curve of the industrial process to specify the best integration location of the ORC in a systematic manner. The algorithm is based on exergy criteria and a simplified modeling of the ORC. This algorithm tests every possible couple of temperature level and chooses the best ones for the location of the heat recovery systems.
Once the ORC operating conditions defined, its detailed design and optimization is performed thanks to a model developed in Modelica language permitting to design the working fluid and the heat exchangers. The multi-objectives optimization of the cycle is performed by using self-adaptive version of Strength Pareto Evolutionary Algorithms 2 (SPEA2) implemented in CERES platform.
The methodology is successfully used in an industrial case where an ORC is integrated to the process.