To optimize the conveying efficiency, priority should be given to controlling the viscosity of the medium. In an industrial circulation system with a 90% duty cycle, when the viscosity of 60℃ crude oil is reduced to 45cSt (with the addition of 0.3% pour point depressant), the resistance loss along the pipeline can be reduced by 38%, and the flow rate can be increased by 1.5m/s under the same power. The Shell Canada oil sands project’s actual measurement shows that by raising the temperature of heavy oil from 25℃ to 85℃ through the preheating device, the transmission efficiency has been increased by 41%, and the annual electricity cost for the oil transfer pump has been saved by 1.27 million US dollars.
Pipeline layout design is crucial for flow attenuation. For each 90° elbow added to the DN300 pipe, the local resistance coefficient reaches 0.25 (equivalent to increasing the straight pipe length by 40 times), and the use of 3D-printed taped-down elbow pipes (with a curvature radius of 1.5D) can control the pressure loss at 14% of the original system. After the application of this technology in the Norwegian undersea gas pipeline project, under the high-pressure working condition of 25MPa, the flow attenuation rate at the end of the 300-kilometer pipeline section decreased from 12.6% to 3.8%, and the power demand of the compressor decreased by 18%.
Equipment upgrades directly enhance operational stability. Variable frequency control technology enables the Fuel Pump to operate within the rated speed range of 80%-110%, reducing the fuel supply response time to 0.8 seconds (while traditional mechanical regulation requires 3 seconds). The Toyota hybrid system adopts a two-stage impeller electric fuel pump. When the peak flow rate is 45L/h, the pressure pulsation amplitude is as low as ±5kPa (±25kPa for standard pumps), eliminating 18% of the ineffective circulation flow under high-load conditions. The matching ceramic bearings further reduce the friction loss by 67% and extend the service life to 15,000 hours.
The intelligent monitoring system realizes preventive regulation. The Internet of Things sensors detect the pipeline pressure (with ±0.1% accuracy), temperature (±0.5℃) and viscosity parameters in real time, and predict the inflection point of flow rate decline through machine learning algorithms. After the deployment of a certain chemical enterprise, the system automatically optimized the start and stop sequence of the material transfer pump, reducing the average daily operating time of 40 pieces of equipment from 16.2 hours to 13.5 hours, and lowering the equipment maintenance cost by 31%. Historical data shows that such solutions usually recover the investment cost within 18 months (ROI 23% per year), and the failure rate is 64% lower than that of the manual monitoring mode.