SungKu Heo

Associate professor

Kangnam University

South Korea

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Dr. SungKu Heo is an Associate Professor at Kangwon National University, South Korea. His research focuses on sustainable biorefinery processes, wastewater treatment performance, and the application of artificial intelligence in process systems engineering. Recent works include contributions to multi-agent reinforcement learning for wastewater treatment and predictive modeling for indoor air quality. Dr. Heo's innovative approaches aim to enhance system optimization and sustainability in various engineering domains.

Articles (10)

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2025

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2025

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

Collaborators (1)

ChangKyoo Yoo

Kyungpook National University

SOUTH KOREA

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SungKu Heo

Research Interests

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Articles (10)

Multi-objective pathway selection for sustainable biorefinery process with harvest scheduling under time-varying climate: Techno-economic analysis and life cycle assessment approach

Leveraging Generative AI and Large Language Model for Process Systems Engineering: A State-of-the-Art Review

Predictive monitoring of wastewater treatment performance: Seasonal microbial activity and data-informed water quality model

Multi-agent reinforcement learning-driven adaptive controller tuning system for autonomous control of wastewater treatment plants: An offline learning approach

Membrane-informed multi-mechanistic predictive maintenance for MBR plants: Early determination of membrane cleaning with biologically driven, physically deposited, and chemically induced fouling model

Real-scale demonstration of digital twins-based aeration control policy optimization in partial nitritation/Anammox process: Policy iterative dynamic programming approach

Explainable AI-driven high-fidelity IAQ prediction (HiFi-IAQ) model for subway stations: Spatiotemporal outdoor air quality interpolation using geographic data

AI-driven ventilation control policy proximal optimization coupled with dynamic-informed real-time model calibration for healthy and sustainable indoor PM2.5 management

XRL-FlexSBR: Multi-agent reinforcement learning-driven flexible SBR control with explainable performance guarantee under diverse influent conditions

Green hydrogen vs green ammonia: A hierarchical optimization-based integrated temporal approach for comparative techno-economic analysis of international supply chains

Collaborators (1)

ChangKyoo Yoo

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