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46~54 item / All 54 items

As part of energy-saving measures in factories, the utilization of unused thermal energy and the optimization of thermal use are gaining attention as further energy-saving strategies. Factories have numerous facilities that utilize steam, and improving thermal efficiency and promoting energy conservation can lead to reductions in energy costs and CO2 emissions in food factories. To achieve this, it is necessary to quantitatively grasp the thermal balance, such as steam usage and waste heat amounts. However, measurement and calculation are not straightforward, and there are challenges such as the lack of personnel who can analyze thermal energy, including thermal efficiency. The thermal balance analysis system addresses these challenges. It enables the automatic visualization of the thermal balance of saturated steam utilization equipment, the determination of abnormal data, and factor analysis to improve thermal efficiency, thereby facilitating energy-saving measures in factories and reducing CO2 emissions.
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Heat is recovered from hot wastewater (60-80°C) discharged from factories and supplied as saturated steam (100-120°C). It supports remote monitoring and operation. By combining it with an energy management system, it enables energy conservation and optimization of energy use throughout the factory. [Main Features] - Heat is recovered from hot wastewater (60-80°C) discharged from factories and supplied as saturated steam (100-120°C). - By reusing waste heat, the load for high-temperature reheating in boilers can be reduced. - Depending on the amount of steam used, multiple units can be operated in connection (up to 10 units). - It is not subject to the High-Pressure Gas Safety Act, requires no special construction, and is easy to install and implement. [Energy Cost Reduction and CO2 Emission Reduction Effects] - By reusing waste heat, the load for high-temperature reheating in boilers can be reduced. - When generating the same amount of steam using a steam boiler fueled by city gas and a steam generation heat pump, the steam generation heat pump can produce steam with about 50% of the energy consumption of the steam boiler. - This allows for a reduction in boiler fuel costs, enabling energy conservation and CO2 emission reduction in factories.
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In food factories, steam energy is utilized in large quantities. While efforts to promote energy-saving measures and visualize energy consumption are progressing for electricity, energy-saving measures utilizing steam and heat have not advanced as much. One reason for the slow progress in energy-saving measures is that, compared to electricity, measuring steam is difficult, making it hard to grasp the energy-saving effects. The steam used in factories incurs losses due to heat dissipation from pipes and steam leaks after being produced in boilers. Ultrasonic flow meters for steam can be easily installed without construction, allowing for the visualization of steam flow in factories and equipment where installation has previously been difficult, without the need for equipment shutdown during flow meter installation. Furthermore, by establishing an energy management system, it enables energy savings and optimization of energy use throughout the entire food factory.
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In chemical plants and factories, efforts to save energy and visualize electric energy usage have already progressed, making it difficult to achieve further reductions. On the other hand, when it comes to steam energy, visualization and measurement are more challenging compared to electricity, leading to delays in energy-saving initiatives. The thermal energy from steam is utilized in various aspects of the factory, and optimizing the use of thermal energy is crucial for reducing energy loss and implementing further energy-saving measures. By introducing clamp-on ultrasonic flow meters for steam, measuring the thermal energy of steam becomes easier, enabling visualization and energy management for energy-saving initiatives. Moreover, by establishing an energy management system, it becomes possible to optimize energy use across the entire chemical plant or factory, facilitating further energy-saving measures and efforts towards carbon neutrality.
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In industrial infrastructure such as factories and plants, there are challenges such as the aging of plant equipment, the aging and shortage of safety personnel, and the transmission of skills and technology. As a means to address these challenges, IoT and AI technologies are gaining attention for ensuring the safe and stable operation of plants and safety operations. The Ministry of Economy, Trade and Industry is promoting regulatory reforms to encourage the demonstration of new technologies and their utilization, aiming to enhance industrial safety and productivity. At Fuji Electric, we utilize IoT and AI technologies to support the optimization of equipment maintenance, from planning maintenance strategies to monitoring equipment and proposing maintenance management measures, through our "Comprehensive Smart Safety Service." By enabling remote monitoring of factory and plant equipment, supporting maintenance management, early detection of abnormal signs, and reviewing management plans through data analysis, we facilitate the stable operation of chemical plant equipment and reduction of maintenance costs.
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The multivariate analysis tool (quality simulation) is an analytics and AI technology (analysis and optimization technology) with a wealth of achievements in manufacturing quality improvement and abnormal detection of equipment at the customer's site. It detects anomalies arising from multiple factors in the manufacturing process, determines the relationship between product quality and factors, and enables quality simulation, thereby achieving product quality improvement and yield enhancement. One of the features of this analysis tool is the ability to verify the effects of implementation early on. If existing data is available, a simple diagnosis can be performed in as little as one day through offline analysis. Even if there is no existing data, data collection can be easily facilitated by retrofitting an edge controller to the existing system, allowing for the creation of diagnostic models. We will propose a deep dive into the analysis content, the construction of an online analysis system, system operation, maintenance, and expansion while confirming the effects of implementation together with the customer.
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Are there any challenges regarding the productivity and maintenance of production and packaging machinery, as well as the utilization of manufacturing data in food manufacturing sites? The on-site diagnostic device SignAIEdge utilizes statistical analysis technology to solve these issues. It enables the detection of "something is different," similar to the intuition and experience of skilled workers (anomaly detection), and allows for improvements in "what is different" through analytics and AI (statistical analysis) (cause analysis). As a result, it achieves failure prediction and anomaly detection, addressing various challenges in food manufacturing sites.
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The rotating machinery fault prediction monitoring system can regularly measure the vibrations of rotating machines operating as production lines or critical equipment. For example, it automatically records the "low-frequency mechanical vibrations," "high-frequency bearing vibrations," and "temperature" of machinery such as rotating machines, fans, pumps, power generation equipment, and presses, and manages the trends. This enables the early detection of abnormal signs, contributing to the planning of appropriate preventive maintenance and the reduction of production loss costs.
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The FWOSP-Glass, a remote work support system, is a solution that utilizes smart glasses to grasp the on-site situation in real-time and provide instructions and support for tasks.
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