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A process capable of large-scale formic acid (FA) production via CO2hydrogenation is presented. This study provides the key strategies for use in developing a viable process for continuous operation. Based on the proposed strategies, a pilot-scale process with a capacity of 10 kg/day was constructed. The continuous operability of the process is demonstrated via pilot plant operation for >100 h, achieving a CO2conversion rate of 82% and producing FA with a high purity of >92 wt %. Techno-economic analysis and life cycle assessment results of the validated simulation model indicate that the proposed process significantly reduces the level of global warming impact by 42% while cutting the production cost by 37%, compared with the conventional process for producing FA. The contents of this study provide a comprehensive manual for developing a viable CO2utilization solution, showing economic profitability as well as environmental impact reduction.

This paper presents a dynamic optimization study on a cryogenic distillation unit in hydrogen isotope separation system. A packed distillation column with an equilibrator is considered. The packed column was modeled as a theoretical equilibrium-based stage column. The equilibrator was placed to enhance separation performance. An optimal control problem was formulated to minimize the tritium holdup under two product quality constraints, in a situation where feed flowrate is periodically fluctuated. An optimal control policy was obtained for two manipulating variables, namely distillate flowrate and reboiler heat duty. The optimal control policy was analyzed by examining the operating variable profiles of the column.

We develop a pilot-scale semicontinuous aqueous mineral carbonation process that captures 40 tons of CO2per day by combining a 20 wt % aqueous Ca(OH)2solution with flue gas containing 15 vol % CO2. 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.