AI-Assisted Optimization of Thermal Oil System Parameters for Sustainable Operation and Cost Efficiency in Industrial Boilers

Industrial thermal-oil systems are critical for process heating, yet many facilities still rely on manual operation including temperature setting, flow regulation, and pump control. This study develops an AI-assisted optimization framework for a textile-industry thermal boiler system to determine the most efficient operating-temperature regime and overflow-tank management strategy that minimize oil degradation, top-up consumption, and downtime. Field measurements and laboratory oil analyses were collected from a BBS HG3500 thermal boiler circulating approximately 17,000 L of mineral-based heat-transfer oil operating up to 290 °C. Manual logs of heater outlet and return temperatures, flow rate, and overflow-tank temperature (target ≈ 60 °C) were analyzed alongside four oil-quality reports (acid index, viscosity, oxidation trend). Results show that maintaining bulk temperature below 285 °C and stabilizing overflow-tank temperature at 60 ± 2 °C extended oil life by 30–35 percent, reduced degradation rate by about 30 percent, and lowered monthly top-up volume by 12–15 percent. An AI optimization layer is proposed for future closed-loop control, supporting cost-efficient and sustainable operation of large-capacity heat-transfer systems.