The growing global population and the increasing scarcity of arable land highlight the urgent need for reliable and efficient food production systems. With their controlled environments, vertical farms (VFs) offer a promising solution for sustainable food security. Nevertheless, their high energy demands call for innovative approaches to optimize energy consumption while maintaining optimal growing conditions. This paper introduces a novel control-oriented model for VFs, capturing the interactions between crop growth conditions and energy consumption. To address the high energy demand of VFs, the model is integrated into a dynamic energy market characterized by time-varying energy prices and a demand response scheme, which includes a discrete reward to encourage flexible energy consumption. Then, centralized and decentralized receding horizon control approaches are proposed to minimize the energy cost of the VF while ensuring optimal crop growth. Experimental evaluations on real systems of varying scales demonstrate the effectiveness of the proposed approaches in reducing costs and ensuring sustainable agricultural practices. Note to Practitioners–This work addresses a growing challenge in operating vertical farms: reducing energy costs while maintaining optimal conditions for crop growth. We introduce a control system that helps vertical farms schedule energy-intensive activities to take advantage of dynamic electricity prices or incentives from grid operators. In particular, we focus on a binary reward structure reflecting real-world demand response programs, where financial incentives are granted only if strict consumption targets are fully met. The approach relies on forecasting and optimization techniques already compatible with standard industrial automation systems. Two control systems are proposed: a centralized controller that manages the entire facility from a single decision point and a decentralized version that allows each unit (e.g., a room or a growing tray) to make decisions independently. The decentralized version offers better scalability and can more easily adapt to farm layout or crop type changes. This framework could also be applied to greenhouses, food storage systems, or other indoor environments with high energy demand.

This paper presents an approach to optimizing the energy cost of heating a building during the cold season using geothermal energy. A building model is developed using the IDA-ICE software and calibrated with real building data for accuracy. The optimization problem is addressed with a deep reinforcement learning (DRL) agent, which minimizes overall energy costs by interacting with the building while considering real-time electricity price and weather forecasts. The proposed method adapts to dynamic conditions, ensuring efficient thermal management and occupant comfort. The results demonstrate the effectiveness of the DRL-based controller in reducing energy costs compared to the currently used rule-based control strategy while maintaining optimal comfort levels. This work contributes to the advancement of cost-efficient heating strategies for sustainable building energy management.

Industry 4.0 demands intelligent and customized production, driving the upgrade of traditional systems. However, many industrial devices lack source code or documentation, making control program migration highly challenging. This issue is critical for PLC replacement, system modernization, and integration of heterogeneous devices. Recently, large language models (LLMs) have demonstrated strong capabilities in code generation and logical reasoning. When combined with the IEC 61499 standard—known for its modular and event-driven design—LLMs offer new possibilities for automatic control software generation. This paper proposes a method that uses LLMs to transform industrial control logic into IEC 61499-compliant programs. It extracts finite state machines representing software logic, converts them into formal requirements, and generates the IEC 61499 function block with the assistance of LLMs. The method enables efficient, data-driven migration of control software.

Machine learning (ML) provides powerful tools for industrial process modeling by identifying patterns and optimizing performance. However, conventional ML models often struggle with accuracy and reliability because they rely solely on data and fail to incorporate process-specific knowledge. To address this limitation, we propose a physics-informed ML approach that integrates engineering insights into an ML model. Specifically, we develop a variational autoencoder-based generative regression model with a probabilistic regression layer, enabling uncertainty-aware predictions. Additionally, we derive process knowledge through finite element analysis and incorporate it into the model via a custom physics-informed loss function, ensuring consistency with real-world process dynamics. The effectiveness of this approach is demonstrated in the steel strip rolling process, a critical manufacturing operation where precise flatness control directly impacts product quality. By integrating data-driven learning with physics-based constraints, the proposed method enables more accurate flatness predictions, reducing defects and improving process stability. This research provides a practical and scalable solution for industrial applications, offering manufacturers a more reliable tool for optimizing production processes and ensuring product quality.