Advancing fuel efficiency and production technologies can significantly reduce “Well to Wheels” and “Octane on Demand” emissions. This has been accomplished by integrating intensified technologies in fuel refineries. However, maintaining low energy consumption and high octane number while considering the impact of operating conditions under uncertainty on the process feasibility remains a challenge. Therefore, this article proposes a novel absorption heat pump reactive distillation (AHPRD). Aiming to maximize the economic potential of the AHPRD, an optimization framework that considers operating conditions under uncertainty (i.e. temperature (T), pressure (P), reflux ratio (RR), and feed stage (FS)) is developed. The optimization framework synchronizes Monte Carlo simulation and particle swarm optimization (PSO) to maximize the net present value (NPV). The framework used a hybrid Python-DWSim interface platform. The AHPRD was further investigated and compared against existing processes, namely, a conventional process refinery (CP) and reactive distillation (RD), to improve energy saving, increase financial return, and cut CO2 emissions. Attractively, the AHPRD outperforms existing processes, both economically and environmentally, doubling profit values and avoiding 52.2% − 77.4% of CO2 emissions. This study believes that the future direction will be to build an artificial intelligence model for process optimization and economic prediction.
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