{"componentChunkName":"component---src-templates-research-details-js","path":"/research/novel-feasible-set-learning-and-process-flexibility-analysis-method-using-deep-neural-networks","result":{"data":{"researchItem":{"date":"06/2024","introduction":"Chemical processes are designed under uncertainty: feed quality, reaction rates, temperatures, and other conditions may all shift during operation. Traditional flexibility analysis often asks how large a box or ellipsoid can be drawn around a nominal condition, but this can be misleading when the true safe region is irregular, nonconvex, disconnected, or high-dimensional. This work instead tries to learn the entire feasible set. A physics-informed neural network first acts as a fast surrogate for the process equations, replacing repeated expensive solves of mass-balance, energy-balance, and other equality constraints. A second neural network then maps uncertain operating conditions into a feature space where feasible points are pulled near a hypersphere center and infeasible points are pushed away. With this learned map, Monte Carlo sampling can estimate the volumetric flexibility index: the fraction of the uncertainty space where the process can still be operated safely. The method is demonstrated on heat-exchanger and reactor-cooler systems, including disconnected and five-dimensional feasible regions.","summary":"Design neural networks to find and measure scattered safe operating zones.","title":"Novel feasible set learning and process flexibility analysis method using deep neural networks","url":"https://pubs.acs.org/doi/full/10.1021/acs.iecr.4c00838"},"file":{"childImageSharp":{"fluid":{"base64":"data:image/jpeg;base64,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","aspectRatio":1.5100671140939597,"src":"/static/cae5f4110933f69afccd283d2c57b0a7/6f6b0/feasible-set-learning.jpg","srcSet":"/static/cae5f4110933f69afccd283d2c57b0a7/07ab6/feasible-set-learning.jpg 225w,\n/static/cae5f4110933f69afccd283d2c57b0a7/32fd5/feasible-set-learning.jpg 450w,\n/static/cae5f4110933f69afccd283d2c57b0a7/6f6b0/feasible-set-learning.jpg 846w","sizes":"(max-width: 846px) 100vw, 846px"}}}},"pageContext":{"slug":"novel-feasible-set-learning-and-process-flexibility-analysis-method-using-deep-neural-networks","image":"research_images/feasible-set-learning.jpeg"}},"staticQueryHashes":["362580002","426988268"]}