We develop a pilot-scale semicontinuous aqueous mineral carbonation process that captures 40 tons of CO 2 per day by combining a 20 wt % aqueous Ca(OH) 2 solution with flue gas containing 15 vol % CO 2 . From the pilot-plant operation recipe (the so-called "base case"), we propose two new operation recipes to minimize the quantity of reactant used (Lt) and maximize the replenishment period (Pr): a sequence including both a continuous and discrete flow (the so-called "continuous case") and one involving additional reactant replenishment (the so-called "buffer case"). A multiobjective Bayesian optimization was adopted to optimize the operation recipe and minimize the number of simulations. Compared to the base case, the two proposed recipes were found to prolong the Pr by factors of ∼12 and ∼2.4 with increases in Lt of ∼4.6 and ∼2.6%, respectively. We anticipate that the two proposed recipes will provide operational flexibility by extending the boundaries.
With increasing demands on product quality and safety requirements, modern industrial processes are highly instrumented and the data collected are being utilized to monitor and diagnose processes. In many cases, the process records include labels that indicate process operating conditions or prior knowledge of the sample points, which can be used to improve diagnostic performance. For this reason, semisupervised methods that can utilize both labeled and unlabeled data are recently gaining interest. In this article, we propose a novel manifold learning-based semisupervised process monitoring method, named Clustered Manifold Approximation and Projection (CMAP). In CMAP, a data manifold is approximated ahead of projection, where the distance on the manifold is defined by the pairwise interaction between the data points induced by metric and nonmetric attributes. This allows simultaneous utilization of limited labeled data and abundant unlabeled data, as well as enables tracking and controlling their effect on the projection. By postulating a well-behaved manifold that preserves discriminant and temporal characteristics of the process, CMAP shows superior performance in the process monitoring and fault diagnosis tasks. The effectiveness of the proposed method is assessed on a dataset obtained from the Tennessee Eastman process and compared with five competing methods.
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