We will showcase "Smart MPC," which realizes control optimization in manufacturing sites. ■ Event Overview Date: December 3 (Wed) - December 5 (Fri), 2025, 10:00 AM - 5:00 PM Venue: Tokyo Big Sight, South Hall 14 Exhibition: Smart Factory Japan 2025 ■ Features of Smart MPC Achieves AI control without specialized knowledge through model predictive control and machine learning - Energy-saving effect: 5~15% reduction - Implementation period: as short as 3 days (data collection and operation start) - Temperature accuracy: ±0.2℃ (PID ratio ±0.5℃ → ±0.2℃) - Automatic adjustment: Adapts to aging deterioration through online learning ■ Solving these challenges ✓ Time-consuming PID parameter adjustments ✓ Difficulty in complex multivariable control ✓ Unstable control quality due to a lack of skilled personnel ✓ Desire to balance energy savings and quality A team of 36 engineers, including 20 with PhDs, will solve manufacturing site challenges using advanced mathematical optimization techniques. We will present live demonstrations and case studies at the venue. Please visit our booth.

We would like to introduce "Smart MPC," which realizes control optimization in manufacturing sites. ■ Event Overview Date: December 17 (Wed) - 18 (Fri), 2025, 10:00-17:00 Venue: My Dome Osaka M16-12 Exhibition: Startup Japan 2025 in Osaka ■ Features of Smart MPC Achieves AI control without specialized knowledge through Model Predictive Control × Machine Learning - Energy-saving effect: 5-15% reduction - Implementation period: as short as 3 days (data collection operation start) - Temperature accuracy: ±0.2℃ (PID ratio ±0.5℃ → ±0.2℃) - Automatic adjustment: Adapts to aging deterioration through online learning ■ Solving these challenges ✓ Time-consuming PID parameter adjustments ✓ Difficulty in complex multivariable control ✓ Inconsistent control quality due to lack of skilled personnel ✓ Desire to balance energy savings and quality A team of 36 engineers, including 20 with PhDs, will solve manufacturing site challenges using advanced mathematical optimization techniques. We will introduce case studies and tailored implementation methods at the venue. Please visit our booth.

Industrial furnaces are a major source of CO₂ emissions, accounting for about 15% of domestic emissions. Energy conservation and decarbonization have become unavoidable challenges for the manufacturing industry in terms of both cost reduction and environmental management. **Issues** Controlling industrial furnaces is complex. It requires precise adjustments of multiple heat sources during the heating, holding, and cooling processes, where even slight delays or deviations can lead to overheating and temperature inconsistencies. Traditional PID control struggles to flexibly respond to these complex thermodynamic changes and disturbances, resulting in accumulated structural energy losses due to "overreaction" and "delayed response." **Solution** In this seminar, we will introduce "Smart MPC," a model predictive control that combines machine learning and optimization technology. By utilizing past operational data, it achieves high-precision predictive control without the need for specialized tuning. The embedded controller "E-Smart MPC" allows for easy implementation with direct mounting to control panels and GUI operation. **Implementation Results** CO₂ and fuel reduction (up to 25%), improved control accuracy (up to 80%), and reduced setup time (up to 60%). **Target Audience** Production engineers, equipment managers, and site managers who want to promote energy conservation and decarbonization in thermal control equipment such as industrial furnaces and dryers.