Intelligent Trends in Supercritical Equipment

Driven by the dual impetus of global industrial intelligent transformation and the "dual carbon" goals, supercritical technology-leveraging its core advantages of high efficiency and energy conservation-has deeply penetrated key sectors such as thermal power, petrochemicals, bio-extraction, and high-end manufacturing. With the integrated application of technologies such as the Industrial Internet, artificial intelligence, and edge computing, supercritical equipment is evolving from "basic automation" to "intelligent perception + autonomous decision-making + remote collaboration." Among these, the "precision + intelligence" of automated control and the "global + predictive" nature of remote monitoring are key upgrade directions, significantly improving equipment operating efficiency, reliability, and operational maintenance (O&M) capabilities.

Supercritical equipment (such as ultra-supercritical thermal power units, supercritical extraction units, and supercritical fluid forming equipment) faces challenges in adapting to complex scenarios due to high operating parameters (temperature, pressure, and flow) and highly coupled operating conditions. Currently, automated control is achieving the transition from "stable operation" to "efficient optimization" through three key dimensions: "empowerment by advanced algorithms, wide-load adaptability, and multi-system integration."

Advanced algorithms such as model predictive control (MPC), active disturbance rejection control (ADRC), and fuzzy control build mathematical models of all equipment operating conditions, enabling multivariable coordinated control and early disturbance compensation, significantly improving control accuracy.

(1)In the thermal power sector, a combined "MPC + ADRC" strategy is employed to address coal quality fluctuations and sudden load changes, controlling main steam temperature fluctuations to within ±2°C and reducing adjustment time by 30%–50%.

(2)In the bioextraction sector, fuzzy control is used to coordinate the pressure, temperature, and CO₂ flow rate of the extraction kettle, increasing the extraction rate of the target component by 10%–15% while simultaneously reducing energy consumption.

(3)In the petrochemical sector, model predictive control is used to optimize reaction parameters in hydrogenation units, reducing byproduct formation and extending catalyst life by over 20%.

To meet the needs of renewable energy grid integration and flexible production, the control system achieves energy savings and consumption reductions through dynamic rate adaptation, coordinated optimization of auxiliary equipment, and real-time energy consumption optimization:

(1)For thermal power units: Developing a multivariable adaptive strategy has increased the load adjustment rate to 2.5% Pe/min, reduced the minimum stable combustion load to below 20% Pe, and decreased unit coal consumption by 5–8 g/kWh.

(2)For extraction equipment: Real-time optimization of pressure and temperature combinations reduces energy consumption by 12%–18% compared to fixed parameter operation.

(3)For fluid forming equipment: Pre-diagnosis of raw material characteristics allows for pre-adjustment of extrusion parameters, reducing response time to seconds for changing operating conditions.

Leveraging the deep integration of sensors, actuators, and intelligent algorithms, this system breaks down the "information silos" of traditional control:

(1)Real-time perception: Key parameters are collected through infrared temperature sensors, vibration sensors, flame image analyzers, and other devices, with a sampling frequency exceeding 100 Hz.

(2)Parameter correction: Based on online identification of coal quality and medium composition, thermal power units automatically adjust air distribution and coal feed, achieving a blending ratio of low-quality coal exceeding 40%.

(3)Intelligent execution: Utilizing intelligent electric actuators, millisecond-level communication is achieved via Industrial Ethernet, with control command latency below 50 ms.

Leveraging the Internet of Things (IoT), big data, edge computing, and artificial intelligence technologies, remote monitoring breaks the "on-site on-call" model. By building a data hub, connecting O&M links, and establishing a predictive system, it achieves full lifecycle optimization, reducing O&M costs and shortening downtime.

Using an "edge terminal + industrial gateway + cloud platform" architecture, standardized processing and visual management of multi-source data are achieved:

(1)Data fusion: Integrates equipment operation, process, environmental, and status data, processed through a unified model (such as the OPC UA protocol), with access latency below 200 ms.

(2)Visual collaboration: Leveraging WebGIS and digital twin technology to build a 3D virtual image, thermal power companies achieve cross-plant resource scheduling through a platform spanning plant, region, and group levels.

(3)Case application: The Tiantuo Sifang IoT platform connects to thousands of monitoring points, increasing fault detection efficiency by 60%.

Addressing network limitations in remote scenarios (such as offshore wind power and mining extraction), "local response + remote collaboration" improves operational flexibility:

(1)Real-time processing at the edge: Deploy edge gateways to analyze vibration and temperature data, triggering local protection commands in emergencies with response latency under 100 ms.

(2)Mobile O&M: Data viewing and alarm reception are enabled through mobile apps and tablets, combined with "video + AR" guidance for maintenance, reducing response time by 40%–50%.

(3)Scenarios: In offshore and remote scenarios, edge computing reduces data transmission by 90%, reducing bandwidth reliance.

Based on historical data and machine learning, a "prediction–diagnosis–optimization" system is constructed:

(1)Fault prediction: Utilizing LSTM and random forest algorithms to analyze vibration and oil data, we predict faults such as bearing wear with an accuracy rate exceeding 85%. This allows us to provide early warning of rotor problems in steam turbine units 2–4 weeks in advance.

(2)Knowledge support: Accumulating fault cases and incorporating NLP technology enables intelligent retrieval based on "fault description–solution matching," improving maintenance efficiency by 30%.

(3)Full-lifecycle optimization: We assess the remaining life of equipment and develop personalized maintenance plans. We switched to "condition-triggered replacement" for the solvent pump in the extraction unit, reducing spare parts costs by 25%.

The accelerated advancement of intelligent supercritical equipment relies on the coordinated support of three key factors:

(1)Technological iteration: The Industrial Internet, AI, and edge computing are breaking bottlenecks; 5G is enabling high-speed communication; and digital twins are providing optimization tools.

(2)Policy guidance: "Made in China 2025" and the 14th Five-Year Plan are driving intelligent equipment upgrades, while energy and environmental protection policies require increased peak-shaving flexibility and emission reduction capabilities.

(3)Market demand: Traditional industries are seeking to reduce costs and increase efficiency, while emerging sectors are pursuing high-precision processes. Labor shortages are accelerating the implementation of remote O&M.

Technical barriers: The domestic production rate for high-end sensors, control chips, and industrial software is less than 30%, posing a bottleneck risk.

(1)Lack of standards: Data interfaces and communication protocols are not standardized, resulting in high costs for equipment interoperability.

(2)Talent gap: A shortage of interdisciplinary professionals with both process and digital technology expertise.

(3)Security risks: Remote monitoring faces the risks of cyberattacks and data leaks.

"AI + Control" integration: Generative AI automatically generates control logic, and large industrial models promote the coordinated optimization of multiple devices.

(1)Global digital twin: Construct a three-level model covering "equipment–workshop–factory" to enable full-process simulation and debugging.

(2)Accelerated localization: The localization rate of core software and hardware such as DCS and PLC will increase to over 50%.

(3)Green intelligent collaboration: AI optimizes combustion and carbon capture to achieve the goal of "high efficiency + low carbon."

The intelligentization of supercritical equipment is an inevitable trend in industrial upgrading. Through the precise adaptation of automated control and the predictive collaboration of remote monitoring, it breaks through the limitations of traditional operation and maintenance. In the future, with the continued promotion of technology, policies, and markets, supercritical equipment will become a core carrier of "intelligent manufacturing + green development." Enterprises must seize the trends of "platformization, agility, and intelligence" and accelerate technology upgrades and talent development to seize the initiative in this industry transformation.