The industrial sector accounts for a significant proportion of total energy consumption. Factory Energy Management Systems (FEMSs) can be a measure to reduce energy consumption in the industrial sector. Therefore, machine learning (ML)-based electricity and liquefied natural gas (LNG) consumption prediction models were developed using data from a food factory. By applying these models to FEMSs, energy consumption can be reduced in the industrial sector. In this study, the multilayer perceptron (MLP) algorithm was used for the artificial neural network (ANN), while linear, radial basis function networks and polynomial kernels were used for support vector regression (SVR). Variables were selected through correlation analysis with electricity and LNG consumption data. The coefficient of variation of root mean square error (CvRMSE) and coefficient of determination (R²) were examined to verify the prediction performance of the implemented models and validated using the criteria of the American Society of Heating, Refrigerating, and Air-Conditioning Engineers Guideline 14. The MLP model exhibited the highest prediction accuracy for electricity consumption (CvRMSE: 17.35% and R²: 0.84) and LNG consumption (CvRMSE: 12.52% and R²: 0.88). Our findings demonstrate it is possible to attain accurate predictions of electricity and LNG consumption in food factories using relatively simple data.

The kinetic analysis method using the non-isothermal technique was proposed to determine the kinetic parameters for the transesterification reaction of waste vegetable oil (WVO) in supercritical alcohols. To investigate the transesterification of WVO, experiments have been carried out with WVO and alcohols at three molar ratios of 1:6, 1:12 and 1:18 for both supercritical ethanol (SCE) and supercritical methanol (SCM) at temperatures between 210 °C and 350 °C in a 25 mL batch reactor. The products were analyzed by gas chromatography mass spectrometry (GC-MS). To verify the effectiveness of the proposed kinetic analysis method, the experimental values were compared with the values calculated using the kinetic parameters obtained from this work. It was found that the proposed kinetic analysis method gave reliable kinetic parameters for the transesterification of WVO in supercritical alcohols. It was also seen that SCM was a better solvent than SCE for the transesterification of WVO.

Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability.

This study evaluates the environmental sustainability of hydrogen production from high-calorific mixed waste gasification through a Gate-to-Gate (GtG) Life Cycle Assessment (LCA) based on operational data from a 2 TPD pilot plant. The Global Warming Potential (GWP) was calculated to be 9.80 kg CO₂-eq per kg of H₂ produced. A contribution analysis identified the primary environmental hotspots as external electricity consumption (37.0%), chelated iron production for syngas cleaning (19.5%), externally supplied oxygen 18.6%), and plant construction (12.3%). A comparative analysis, contextualized within South Korea’s energy structure, demonstrates this GWP is competitive with regionally contextualized Steam Methane Reforming (SMR) and lower than coal gasification. Furthermore, a scenario analysis based on national energy policies reveals a clear pathway for GWP reduction. Aligning with the 2030 renewable energy target (20% RE share) reduces the GWP to 9.14 kg CO₂-eq, while a full transition to 100% wind power lowers it to 6.27 kg CO₂-eq. These findings establish this Waste-to-Hydrogen (WtH) technology as a promising transitional solution that simultaneously valorizes problematic waste. This research provides a critical empirical benchmark for the technology’s commercialization and establishes an internationally transferable framework. It confirms that the technology’s ultimate environmental sustainability is intrinsically linked to the decarbonization of the local electricity grid.