In the metal smelting industry, smelting furnace as the core production
equipment, its life is directly related to the production cost and capacity
stability. Traditional maintenance relies on regular maintenance and
manual experience judgment, it is difficult to accurately predict the
furnace lining erosion, thermal stress fatigue and other hidden dangers,
resulting in frequent accidental furnace shutdowns, the average age of
the furnace is only 2-3 years. The application of industrial big data
analysis technology, through real-time monitoring and intelligent
diagnosis, has successfully extended the life of the smelting furnace
to more than 5 years, promoting smelting equipment management
into a new era of predictive maintenance.
I. Data-driven: building a full life cycle
health model of smelting furnace
The industrial big data system realizes millimeter-level sensing of
the smelting furnace status through the deep integration of three
types of core data sources:
1. Equipment operation data
Thermodynamic parameters: real-time collection of 100,000 sets
of data per second on furnace temperature (laser temperature
measurement accuracy ±5℃), cooling water flow (electromagnetic
flow meter error 0.5%), pressure fluctuations, etc;
Mechanical status: vibration sensors to monitor the deformation
of the furnace body, acoustic emission system to capture the
refractory material micro-cracks, data sampling rate of 100kHz;
Process parameters: charging rate, oxygen concentration,
metal composition, etc. 300 + dimensions of the production
process records.
2. Material performance data
Laboratory test data on thermal conductivity, slag erosion
resistance, etc. of furnace lining refractories;
The melting point of each batch of raw materials, impurity
content and its chemical corrosion strength analysis of the
furnace lining.
3. Environmental data
Modeling of the effect of ambient temperature and humidity on
heat dissipation from the furnace;
Analysis of the correlation between external factors such as power
fluctuations and air pressure changes and equipment aging.
Based on the above data, the system builds a digital twin of the
smelting furnace, and simulates the erosion rate of the furnace lining
under different working conditions through machine learning algorithms,
with a prediction accuracy of 92%, so that the maintenance strategy is
shifted from “regular replacement” to “on-demand intervention”.
Second, four scenarios: how to accurately
extend the age of the furnace with big data
Scenario 1: Thermal stress crack warning
By analyzing the temperature gradient data of the furnace wall
(8 thermocouples are deployed per square meter), a three-dimensional
thermal field model is established to calculate the difference in the
expansion coefficient of refractory materials in real time;
When the temperature difference between adjacent areas exceeds
150°C, it automatically triggers an early warning and suggests
adjusting the heating curve to avoid penetrating cracks caused
by thermal shock and reduce unplanned furnace shutdown by 70%.
Scenario 2: slag line erosion dynamic monitoring
The use of high-definition furnace camera + infrared imaging,
combined with image recognition algorithms to quantify the
depth of slag line erosion, with a resolution of 0.1mm;
When the local erosion rate exceeds the daily average of 0.5mm,
the system recommends optimizing the slag-making process or
adjusting the raw material ratio, so that the slag line life can be
extended from 4 months to 7 months.
Scenario 3: Cooling system efficiency optimization
Collect data on the temperature difference between the inlet and
outlet water of the water-cooled plate, flow rate and scale thickness,
and construct a heat transfer efficiency decay model;
Dynamically adjust the cooling water circulation speed to keep the
furnace shell temperature stable at 80±5°C, reduce thermal fatigue
damage, and extend the cooling system maintenance cycle by two times.
Scenario 4: Operation behavior compliance analysis
Compare operating parameters (e.g. charging interval, oxygen
lance height) during the historical quality furnace service period
and establish a best practice database;
Real-time monitoring of operating deviations and early warning,
reducing furnace lining damage caused by human operating errors by 45%.
The application case of a copper smelter shows that the big data
system will enhance the average age of the melting furnace from
26 months to 41 months, saving over 8 million yuan in overhaul
costs for a single furnace.
Third, technology advancement: from fault
warning to self-healing control of the leap
With the evolution of edge computing and AI algorithms, big data
analytics is driving the smelting furnace operation and maintenance
to leap to a higher level:
1. Adaptive control
Automatically adjusts the melting temperature and smelting cycle based
on the residual thickness prediction of the furnace lining, increasing the
lining utilization to 98%;
In aluminum smelting, dynamic control of electromagnetic stirring intensity
reduces furnace bottom lumps and reduces furnace cleaning frequency by 60%.
2. Material life prediction
Integrate the refractory material supplier data with real-time working
conditions to build the remaining life map of different materials
(magnesium-chromium bricks, corundum castables, etc.);
Support the performance simulation of multi-supplier furnace lining
mixing scheme, and optimize the procurement cost by 15%.
3. Synergistic optimization of energy efficiency
Analyze the linkage data of the melting furnace with the waste heat
boiler and dust removal equipment to achieve global optimization
of energy flow;
In one case, the flue gas waste heat recovery rate was increased to 92%,
and the comprehensive energy consumption of tons of metal
decreased by 8%.
Fourth, economic benefits: reconfiguring
the smelting cost structure
The life extension brought about by industrial big data analysis is
generating a significant chain reaction:
Direct cost savings: Reducing the number of furnace shutdowns for
overhaul, a smelter with an annual production capacity of 100,000
tons can save 12 million yuan in annual maintenance costs;
Capacity release: 80% reduction in unplanned downtime, equivalent
to an annual increase in production of 12,000 tons of metal;
Environmental compliance: through precise control of furnace
conditions, emissions of tons of metal are reduced by 12%, and the
risk of environmental fines is reduced by 90%;
Asset value-added: the residual value assessment of the smelting
furnace has been improved by 25%, and the cost of financing and
leasing has been reduced by 3 percentage points.
Industry data show that smelting enterprises deploying big data
analysis systems have increased their smelting segment gross margin
by an average of 4.7%, and shortened the return on investment
cycle to 8 months.
Fifth, the future trend: industrial meta-universe
in the smelting furnace immortality system
The deep integration of 5G+Industrial Internet is giving rise to a more
forward-looking operation and maintenance model:
Cloud expert library: global melting furnace data convergence to form
a knowledge map, new production equipment can instantly access the
optimal operating strategy;
Blockchain depository: the furnace lining replacement records, maintenance
logs and other data can not be tampered with, to enhance the trust of
second-hand equipment transactions;
AR remote operation and maintenance: technicians can view the 3D
model of the furnace through AR glasses, guiding accurate repair on site
and improving maintenance efficiency by 3 times;
Carbon footprint tracking: accurately calculating furnace age-related
carbon emissions per ton of metal, generating a dual-carbon compliance
certification report.
It is predicted that by 2030 the global metallurgical equipment predictive
maintenance market size will exceed $34 billion, of which the intelligent
operation and maintenance of smelting furnaces accounted for more than
40%, becoming one of the most valuable landing scenarios of Industry 4.0.
Conclusion
Industrial big data analytics technology is redefining the smelting furnace
“law of life”. From passive maintenance to active intervention, from
experience-driven to data-driven decision-making, this silent operation
and maintenance revolution not only prolongs the physical life of the
equipment, but also activates the deep potential of smelting production
through the digitization of all elements. Under the dual drive of “double
carbon” target and intelligent manufacturing, the “digital immortality” of
smelting furnace will become an important cornerstone for the high-quality
sustainable development of metallurgical industry.