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Development of a roadmap for dynamic process intensification by using a dynamic, data-driven optimization approach
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DOI：10.1016/j.cep.2019.04.002.png)
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The concept of process intensification involves any improvement in the process or equipment that leads to reduced cost and emissions, increased efficiency, or improved productivity. This paper proposes a novel dynamic process intensification approach that results in similar benefits as the traditional process intensification. The proposed approach utilizes a dynamic optimization approach in improving the operation of a plant. The main contribution of this work includes the development of a dynamic particle swarm optimization algorithm and a data-driven model of the plant while the operational and economical metrics of the plant are compared to the same system when steady-state optimization is practiced. Additionally, a roadmap is developed that enables the practitioners to apply the proposed dynamic process intensification concept to any similar systems. A CSTR reactor is used as the case study to demonstrate the benefits of dynamic process intensification over a static approach. The results demonstrate that a dynamic, data-driven optimization algorithm can achieve similar profit as gradient-based optimization approaches. Additionally, a 59.9% increase in profitability of the plant is observed when a dynamic approach is used for optimization, as opposed to a steady-state approach. This is well aligned with the targets pursued from the traditional process intensification concepts.
Keyword:
Dynamic process intensification
Dynamic optimization
Steady-state optimization
Particle swarm optimization
Data-driven modeling
CSTR
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