Process intensification control: Advancing efficiency and sustainability, a review

Abstract

Process intensification has revolutionized chemical process design by integrating reaction and separation, enhancing efficiency, reducing energy consumption, and promoting sustainability. However, these advancements introduce significant control challenges due to increased process complexity, nonlinear interactions, and dynamic constraints. Over the past 25 years, conventional control strategies have been progressively replaced by predictive, adaptive, and data-driven methods, which are better suited for managing multivariable interactions and real-time optimization. The widespread adoption of predictive control frameworks has improved stability, reduced response times, and enhanced energy efficiency in reactive and extractive distillation, dividing-wall columns, and hybrid separation processes. Furthermore, integrating intelligent decision-making tools has enabled real-time adaptability, ensuring robust performance under fluctuating operating conditions. The emergence of hybrid control strategies, which combine predictive models with data-driven learning techniques, has further enhanced the ability to address nonlinearities and process uncertainties. This shift underscores a transition toward more intelligent and sustainable process operations, where control systems not only optimize efficiency but also minimize emissions and improve resource utilization. As process intensification continues to advance, future research should focus on scalable, autonomous, and computationally efficient control solutions to ensure operational reliability and economic feasibility in sustainable chemical manufacturing.