Seongmin Heo

Process monitoring is considered to be one of the most important problems in process systems engineering, which can be benefited significantly from deep learning techniques. In this paper, deep neural networks are applied to the problem of fault detection and classification to illustrate their capability. First, the fault detection and classification problems are formulated as neural network based classification problems. Then, neural networks are trained to perform fault detection, and the effe

A systematic method is proposed for control‐relevant decomposition of complex process networks. Specifically, hierarchical clustering methods are adopted to identify constituent subnetworks such that the components of each subnetwork are strongly interacting while different subnetworks are loosely coupled. Optimal clustering is determined through the solution of integer optimization problems. The concept of relative degree is used to measure distance between subnetworks and compactness of subnet

In this article, a conceptual process design for the production of lactide is proposed, where an one-step gas phase synthesis route is adopted. Then, the proposed process is evaluated and compared with the conventional two-step (polycondensation and depolymerization) lactide synthesis process. Specifically, techno-economic analysis and life cycle analysis are performed to compare both processes in terms of lactide conversion cost and global warming potential, respectively, to examine the sustain

In this article, the statistical process monitoring problem of the Tennessee Eastman process is considered using deep learning techniques. This work is motivated by three limitations of the existing works for such problem. First, although deep learning has been used for process monitoring extensively, in the majority of the existing works, the neural networks were trained in a supervised manner assuming that the normal/fault labels were available. However, this is not always the case in real app

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily

We illustrate the application of a graph reduction method developed recently to analyze complex energy‐integrated process networks. The method uses information on the energy flow structure of the network and the orders of magnitude of the different energy flows to generate, automatically, information on the time scales where the process units evolve, canonical forms of the reduced models in each time scale, and controlled variables and potential manipulated inputs available in each time scale. R

Organic electrochemical transistors (OECTs) have enormous potential for use in biosignal amplifiers, analyte sensors, and neuromorphic electronics owing to their exceptionally large transconductance. However, it is challenging to simultaneously achieve high charge carrier mobility and volumetric capacitance, the two most important figures of merit in OECTs. Herein, a method of achieving high-performance OECT with donor–acceptor conjugated copolymers by introducing fluorine units is proposed. A s