Junghwan Kim

Wet flue gas desulfurization, which is performed in many thermal power plants, has a high desulfurization efficiency and produces desulfurized gypsum as a by-product. Currently, high-grade limestone with a CaCO3 content of 94 wt% or more is used to produce desulfurized gypsum with a purity of 93 wt% or more. However, high-grade limestone resources are depleting, so new substitutes to this are required. The objective of this work is to optimize the wet flue gas desulfurization system using recycl

Distillation is an energy-consuming process in the chemical industry. Optimizing operating conditions can reduce the amount of energy consumed and improve the efficiency of chemical processes. Herein, we developed a machine learning-based prediction model for a distillation process and applied the developed model to process optimization. The energy consumed in the distillation process is mainly used to control the temperature of the distillation column. We developed a model that predicted temper

This work suggests an optimal strategy to sort and recycle plastic waste as a renewable energy resource with maximizing economic feasibility and mitigating environmental pollution. To derive the optimal sorting and recycling strategies of plastic waste, a novel optimization model is developed; it calculates the overall profit by subtracting the profit of recycling plastic from the total annualized cost. Then the model is used to identify the optimal strategy to sort and recycle plastic waste as

This study mainly focuses on determining the optimal configuration that efficiently utilizes liquefied natural gas (LNG) cold energy in hydrogen precooling for liquid hydrogen production. To achieve this goal, two different configurations are designed: (a) adding LNG cold energy to the existing hydrogen precooling cycle and (b) replacing the existing hydrogen precooling cycle with LNG cold energy. An equilibrium hydrogen model is developed to reflect the thermodynamic property of ortho-para conv

The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs b

Blue hydrogen is gaining attention as an intermediate step toward achieving eco-friendly green hydrogen production. However, the general blue hydrogen production requires an energy-intensive process for carbon capture and storage, resulting in low process efficiency. Additionally, the hydrogen production processes, steam methane reforming (SMR) and electrolysis, emits waste heat and byproduct oxygen, respectively. To solve these problems, this study proposes an oxy-fuel combustion-based blue hyd

Conventional methods for developing heterogeneous catalysts are inefficient in time and cost, often relying on trial-and-error. The integration of machine-learning (ML) in catalysis research using data can reduce computational costs and provide valuable insights. However, the lack of interpretability in black-box models hinders their acceptance among researchers. We propose an interpretable ML framework that enables a comprehensive understanding of the complex relationships between variables. Ou

In wet flue gas desulfurization system, the resource depletion of high-grade limestone, used as conventional SOx absorbent, is becoming serious for SOx capture and utilization. This paper proposes optimal selection and blending ratio of waste seashells as an alternative to high-grade limestone depletion using a deep neural network (DNN)-based surrogate model. Cost optimization proceeds as follows: data generation, data preprocessing, development of DNN-based surrogate model, and derivation of co

In this study, we suggested an optimal strategy to sort plastic waste to improve recycling efficiency considering economic feasibility. To derive the optimal sorting strategy, we developed a novel optimization model that considers the overall cost, which is sorting cost minus the revenue obtained by selling the recycling plastic from the sorting cost. Then we used the developed model to identify the optimal strategy to sort plastic waste in mixed-integer programming that minimizes the overall co