Replace manual longitudinal temperature measurement and increase measurement frequency

Intelligent regulation of heating gas pressure, Reduce gas consumption by 1-4%

Allow fully automatic control on the very day of production, completely eliminating smoke and fire

Machine vision prevents unhooking, collision, red coke falling and increase recognition rate to ≥ 99.99%

Replace manual horizontal temperature measurement and improve heating uniformity in length and height directions. Protect oven body, improve post reaction strength of coke and effectively reduce standard temperature

Complete on-site data transmission and analysis by combining IoT, machine vision technologies and on-site control. Change the situation of labor intensiveness in coking, heavy pollution and poor control using technologies such as artificial intelligence and data mining; promote "efficient, clean, low-carbon, cyclic and green" development of coking industry by improving energy utilization, energy saving and emission reduction and unmanned operation.

Improve comprehensive efficiency of circulating fans, boilers and steam turbines; reduce self power consumption and increase net generation

Improve coal blending efficiency and optimize coal blending costs

   

Collect digital images from conveyor belt using digital image processing technology, carry out real-time calculation of particle size and analysis of distribution ofthe particles and identify material grain size and distribution to quickly provide accurate grain size information for blast furnace operators.

The system carries out furnace feed surface scanning and 3D modeling through mechanical scanning radar to provide reference for material distribution. Blast furnace operators adjust material distribution considering feed surface, influence gas flow distribution by changing distribution of furnace charge at furnace throat and feed surface shape so that positions and shapes of softening zones are changed, gas utilization is improved and stable and smooth operation and higher output and efficiency of blast furnace are achieved.

Tripper car positioning, charge type tracking, charge level detection and fully automatic unloading are applied to allow intelligent materials management.

Proportional extreme value adjustment method is used for intelligent analysis and evaluation of hot-blast furnace, and fuzzy thinking is applied to establish a mathematical model in sections to handle overshoot with high inertia and periodically correct fuel proportioning. Reduce excessive combustion air or gas so that hot-blast furnace is at optimal combustion state for the majority of the time.

Renovate cranes and conveyor belts and the functions and improve slag pool management system to allow unmanned operation and intelligent management of slag

Allow automatic safety interlock control during coal loading, stamping, injection and blowing, reladling and pressure relief, and high-precision ceramic regulating valves and algorithm models are used for coal powder injection stability control to allow stable furnace conditions and reducing coke ratio. Long-term stable coal injection rate error is less than ± 5%, resulting in increase in coal injection rate, coke usage reduction, production cost reduction and pollution reduction.

Integrate level-L2 functions such as water temperature differential heat load, hearth erosion, material distribution model, furnace type, hearth balance and hearth activity.

   

Provide automatic or manual production scheduling according to production plan received. Quickly determine the optimal production processes, paths and sch- edules for the processes in the steel making workshop.

Use advanced technologies such as wireless WiFi and RFID to complete overhead traveling crane positioning and tracking of ladle, molten iron and scrap steel ta- nk, scrap steel, steel ladle, molten steel and heat number.

Accuracy of decision by quality model is effectively improved by collecting a large amount of quality related data, predicting and controlling quality of products from steel making workshop and optimizing parameters of quality decision models.

Accurate control of molten steel compositions and temperature during smelting in converter, higher molten steel quality and lower production cost are achieved by building a converter smelting process model and utilizing big data analysis.

Independently developed converter tilting and zero-speed hovering, precise ladle car positioning, automatic rotating chute following, converter mouth slag lev- el and steel flow image identification and dynamic steel tapping models and other technologies are used to allow automatic steel tapping from converter, stable, safe and efficient steel tapping from converter, lower labor intensity and cost and higher efficiency.

Real-time on-site images collected by industrial cameras and recognition processing technologies are used for automatic identification of labels to allow corres- pon dence of production process parameters to quality status and develop producthealth records, allow accurate tracking of products and ensure integrity of p- roduct file information and material flow.

Provide factories complete intelligent transformation plan for energy instruments and instrument data collection plan to allow real-time data collection and ener- gy application process monitoring.

Technologies such as fault diagnosis, remote monitoring, video surveillance, automation, intelligent and unmanned operations are used to allow centralized cont- rol and unified management of operating rooms in steel making area and production information data sharing and interflow.

   

Using with high-speed cameras and optimized algorithms, allow quick recognition of single-frame images, with recognition rate within 25 ms. From image collection to detection of steel-heaping prediction alarm signal and transmission to PLC, it takes less than 180 ms.

Identify type-C hook number through visual recognition or RFID identification, and complete automatic tracking of wire coils in collection area through hook number identification. Apply edge detection, contour recognition and other technologies to determine presence of type-C hook tilting and reduce production accidents.

Provide multiple analysis tools to analyze rolling data, and provide warning against abnormalities during production to allow operators to know current production status and optimal equipment operation status.

Realistically present on-site scenarios, synchronize data of basic automation, quality control system, status monitoring system and energy control system, monitor production anomalies and provide support for auxiliary operations.

The electronic labeling system based on mobile devices such as mobile phones and PADs allows maintainers to provide equipment maintenance information feedback, eliminate misoperations caused by human error and protect safety of personnel and equipment during equipment maintenance. Quantify workload of maintenance personnel and develop reasonable maintenance pian.

Create risk assessment model for quality influencing factors, explore variable control ranges, find optimal process parameters, conduct quality traceability analysis and assist process management personnel in improving quality and optimizing production processes.