Abstract
Abstract This paper presents a novel data-driven modeling strategy for highly accurate prediction and optimization of complex chemical processes. The material balance, equilibrium, and heat balance equations addressed in a chemical process are nonlinear, thus making it very difficult to optimize. To overcome the above difficulty, a high dimensional model representation (HDMR) method was developed to represent a complex process (Pan et al., 2016), and a novel linear programming (LP) model was then proposed to find the HDMR parameters. Finally, the complexity of a chemical process model can be reduced significantly as its mechanism formulations were replaced with a simple nonlinear HDMR model. The resulting simple nonlinear optimization problem can be solved efficiently by using the iterative linear programming (LP) method proposed in the earlier work (Pan et al., 2013). To validate the proposed approach, a propane dehydrogenation (PDH) process was studied.
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