Integrated Techno-Economic Optimization of Heat Integration and Recycle Strategies in Multistage Chemical Processes
DOI:
https://doi.org/10.61978/catalyx.v2i4.1267Keywords:
techno-economic optimization, heat integration, recycle strategy, multistage chemical process, equation-oriented modeling, total annualized cost, process systems engineeringAbstract
This study presents a techno-economic optimization framework designed to improve the performance of multistage chemical production systems by simultaneously optimizing heat integration and recycle strategies. Multistage processes, which commonly include reaction, separation, and recycle units, pose economic and operational challenges due to their energy intensity and interconnectivity. The objective of this research is to minimize the Total Annualized Cost (TAC) by integrating process simulation and economic evaluation using equation-oriented modeling.The methodology combines mass and energy balance modeling with capital and operating cost estimation. Decision variables include recycle ratio, purge fraction, reflux ratio, and the minimum temperature difference (ΔTmin) in heat exchangers. The optimization problem was implemented in the IDAES platform using IPOPT, applying standard financial assumptions for mature (nth-plant) systems. Results show that recycle-only optimization reduced TAC by approximately 10%, while joint optimization led to a 24.4% reduction, primarily through increased material efficiency and energy savings. However, improvements came with trade-offs, such as increased control complexity and higher capital investment. Sensitivity analysis identified reflux ratio and ΔTmin as dominant variables affecting economic outcomes. The optimized ΔTmin of 12°C and a recycle ratio of 0.65 were consistent with industrial design guidelines. These findings underscore the value of integrated optimization in achieving economically viable and operable process designs. The framework is robust and scalable, with potential for application in larger systems and real-time industrial contexts. Future work will focus on incorporating surrogate models and hybrid optimization to enhance computational performance.
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