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.

In this study, the Taguchi experimental design method using an L16 orthogonal array was attempted in order to investigate the optimal operating conditions for the CO₂ methanation process in Ni-based catalysts. The relative influence of the main factors affecting CO₂ conversion and CH₄ yield was ranked as follows: reactor pressure > space velocity > reaction temperature. The optimal combination of operating conditions was a reactor temperature of 315 °C, a pressure of 19 bar, and a space velocity of 6000 h⁻¹. The effect of the H₂/CO₂ ratio on CO₂ conversion and CH₄ yield was further considered under these optimal operating conditions. Moreover, the catalyst was characterized in order to investigate the production of coke through Brunauer–Emmett–Teller analysis, thermogravimetric analysis, and scanning electron microscopy. The amount of coke produced after the reaction for approximately 24 h was ~2 wt.%. Therefore, the desired CH₄ yield and long-term operational stability were successfully obtained using the Taguchi design method and catalyst characterization.

Solid fuelization technology can increase the heating value of sewage sludge such that it can be utilised as a fossil fuel substitutes. Reducing landfilling of bottom and fly ash resulting from heavy metals contained in sewage sludge is challenging. Hence, combustion melting technology (CMT), which can discharge bottom ash in the form of slag, has been proposed herein as an alternative to the conventional incineration technology. However, further research is required to improve the flowability of slag. Applicability of CMT for the stable treatment of heavy metals in the ash generated during the energisation of sewage sludge solid fuel has been reviewed. The change in the degree of fluidity was identified via a laboratory-scale fluidity measurement experiment following changes in melting temperature, mixing ratio of sewage sludge and sawdust, and basicity. The pouring index (PI) of sewage sludge solid fuel (pellet) was maintained at a level of about 60% at a basicity index of 0.8. Based on the results, the slagging rates and volume reduction rates, exhaust gas analysis, and heavy metal elution characteristics under oxygen enrichment were derived from a 2 ton/day combustion melting pilot plant experiment; thereafter, the feasibility of combustion melting of sewage sludge solid fuel was determined.

Rising energy costs due to environmental degradation, climate change, global conflicts, and pandemics have prompted the need for efficient energy management. Edge devices are increasingly recognized for improving energy efficiency; however, their role as primary computing units remains underexplored. This study presents PreEdgeDB, a lightweight platform deployed on low-power edge devices to optimize energy usage in industrial complexes, which consume approximately 57.29% of South Korea’s total energy. The platform integrates real-time data preprocessing, time-series storage, and prediction capabilities, enabling independent operation at individual factories. A low-resource preprocessing module was developed to handle missing and anomalous data. For storage, RocksDB—a lightweight, high-performance key–value database—was optimized for edge environments. For prediction, Light Gradient Boosting Machine (LightGBM) was adopted due to its efficiency and high accuracy on limited-resource systems. The resulting model achieved a coefficient of variation of the root mean squared error (CV(RMSE)) of 14.36% and a prediction score of 0.8240. The total processing time from data collection to prediction was under 300 milliseconds. With memory usage below 150 MB and CPU utilization around 60%, PreEdgeDB enables fully autonomous energy prediction and analysis on edge devices, without relying on centralized servers.

A factory energy management system, based on information and communication technology, facilitates efficient energy management using the real-time monitoring, analyzing, and controlling of the energy consumption of a factory. However, traditional food processing plants use basic control systems that cannot analyze energy consumption for each phase of processing. This makes it difficult to identify usage patterns for individual operations. This study identifies steam energy consumption patterns across four stages of food processing. Additionally, it proposes a customized predictive model employing four machine learning algorithms—linear regression, decision tree, random forest, and k-nearest neighbor—as well as two deep learning algorithms: long short-term memory and multi-layer perceptron. The enhanced multi-layer perceptron model achieved a high performance, with a coefficient of determination (R²) of 0.9418, a coefficient of variation of root mean square error (CVRMSE) of 9.49%, and a relative accuracy of 93.28%. The results of this study demonstrate that straightforward data and models can accurately predict steam energy consumption for individual processes. These findings suggest that a customized predictive model, tailored to the energy consumption characteristics of each process, can offer precise energy operation guidance for food manufacturers, thereby improving energy efficiency and reducing consumption.