In recent years, with the implementation of the “dual carbon” strategy in China, the transformation and flexible operation of coal-fired power units have become critical components of energy structure adjustment. The rapid development of renewable energy has led to lower load rates for coal-fired units, frequently operating under deep peak-shaving conditions. This has resulted in fouling and blockage issues in condensers, which are essential cold-end equipment. We have developed an online condenser cleaning robot device that leverages performance monitoring and analysis technology to enable targeted cleaning of fouled areas, overall regulation of the cold-end system, and optimization of device and unit operating conditions. This technology not only enhances the cleanliness factor of the condenser and reduces backpressure but also aligns with the development of smart grids and facilitates refined, efficient management in power plants, contributing to the construction of intelligent power plants.
The condenser is a vital component in coal-fired power units, and its performance directly impacts the efficiency of the cold-end system. The vacuum or backpressure of the condenser is a key indicator of unit efficiency. However, during operation, fouling and blockage of heat transfer tubes reduce the cleanliness factor and heat transfer efficiency, further affecting backpressure. Therefore, maintaining optimal vacuum conditions is crucial for maximizing the economic performance of large-capacity coal-fired units and reducing power generation costs. The relationship between fouling thickness, vacuum degree, coal consumption, and load impact is summarized in Table 1.
| Fouling Thickness in Tubes (mm) | Vacuum Degree (%) | Cleanliness Factor | Impact on Coal Consumption (g/kWh) | Impact on Turbine Output (%) |
|---|---|---|---|---|
| 0.00 | 93.0 | 0.90 | 0.00 | 0.0 |
| 0.10 | 92.2 | 0.72 | 1.90 | 1.6 |
| 0.20 | 90.8 | 0.57 | 5.24 | 3.0 |
| 0.30 | 90.1 | 0.47 | 6.90 | 4.2 |
| 0.40 | 88.8 | 0.45 | 9.99 | 5.9 |
| 0.45 | 87.9 | 0.42 | 12.10 | 6.8 |
| 0.50 | 86.9 | 0.40 | 14.52 | 7.8 |
Current condenser cleaning methods include manual cleaning, chemical dosing for scale inhibition, and online cleaning technologies. Manual cleaning is labor-intensive and poses safety risks in confined spaces. Chemical methods contradict environmental protection goals and face strict regulatory limitations. Online cleaning technologies primarily involve rubber ball cleaning systems and row-by-row定点清洗技术. Rubber ball systems rely on random distribution of balls for natural scrubbing but suffer from low coverage, ball blockage at low flow rates, poor ball recovery rates, and high maintenance. In contrast, the row-by-row定点清洗技术, which is not affected by water quality or flow velocity, uses computer-controlled water supply and cleaning devices. A servo motor drives the cleaning mechanism inside the water chamber, and a multi-function pump group provides medium-pressure water at 2.0 MPa through hydraulic arms. This creates multiple jet pumps at tube inlets, enabling simultaneous cleaning of hundreds of tubes by increasing flow velocity to over 6 m/s, dislodging blockages and fouling.
The performance monitoring and analysis system for condensers addresses the lack of real-time monitoring. By integrating with plant DCS/SIS systems and sensors, it collects data on temperature, flow, pressure, level, switches, and power. Using mathematical models, it calculates key performance indicators such as cleanliness factor, which is defined as the ratio of actual heat transfer coefficient to the theoretical clean value. The cleanliness factor $C_f$ can be expressed as:
$$C_f = \frac{U_{\text{actual}}}{U_{\text{clean}}}$$
where $U_{\text{actual}}$ is the actual overall heat transfer coefficient and $U_{\text{clean}}$ is the coefficient under clean conditions. This system enables continuous assessment of condenser health, allowing for proactive maintenance and optimization of cleaning schedules.
The intelligent control technology for the online condenser cleaning robot is structured in three layers: the bottom layer, middle layer, and top layer. The bottom layer consists of the cleaning device controller, which manages servo motors, electric actuators, and collects signals such as pressure and differential pressure. The middle layer includes the cleaning device upper computer and the performance monitoring and analysis system. The upper computer communicates with the bottom-layer controller via PLC interfaces, while the performance system interfaces with plant DCS/SIS and sensors to compute performance metrics. A database in the middle layer stores real-time and historical data, which is transmitted to the DCS via a data publishing module. The top layer focuses on advanced applications like optimization and fault diagnosis, leveraging the collected data for intelligent decision-making.
In application, the China robot technology enables targeted cleaning of fouled zones. By dividing the condenser into four regions—A and B for the front and rear passes—the system calculates cleanliness factors for each region under varying conditions. Based on these values, it automatically adjusts cleaning frequency and duration. For instance, if the cleanliness factor drops below a threshold of 0.8, the system initiates focused cleaning on the affected region. This approach is derived from studies showing that rear passes in dual-flow condensers are more prone to fouling due to reduced turbulence and sedimentation of泥沙 in circulating water.
Furthermore, the cold-end system overall regulation technology considers the economic impact on the unit, which includes effects on exhaust pressure and power consumption of auxiliary equipment like circulating water pumps, vacuum pumps, and condensate pumps. By analyzing the optimal balance between condenser vacuum and the number of circulating pumps at different loads, the system controls pump flexibility to reflect the true economic operation of the cold-end system. This is particularly important during low-load operations for peak shaving, where it balances water flow and vacuum to enhance unit flexibility. The net power gain $\Delta P_{\text{net}}$ can be estimated as:
$$\Delta P_{\text{net}} = \Delta P_{\text{turbine}} – \Delta P_{\text{pumps}}$$
where $\Delta P_{\text{turbine}}$ is the change in turbine output due to vacuum improvement and $\Delta P_{\text{pumps}}$ is the additional power consumed by pumps.
Operational optimization is another key feature, where the cleaning robot’s performance is evaluated based on condenser performance indicators. Initially, optimal cleaning parameters—such as cleaning cycle, duration, and water pressure—are set based on empirical data. As operating conditions and water quality change, the system provides real-time recommendations by monitoring the recovery of cleanliness factors during cleaning. For example, it suggests adjustments to cleaning cycles if the factor falls below a下限, and fine-tunes duration and pressure for maximum efficiency. This iterative process ensures sustained performance and adapts to dynamic plant conditions.
Additionally, the condenser performance fault diagnosis function utilizes calculated performance metrics to identify issues such as abnormal actual circulating water temperature rise, terminal temperature difference, or condensate subcooling. When anomalies are detected, the system diagnoses potential causes—like insufficient cooling water flow, tube fouling, or vacuum leakage—and displays alerts for corrective action. Integrating digital AI technology, the system offers a可视化 platform that simulates the internal structure of the condenser, enabling precise identification and control. Operators can monitor the cleaning process in real-time, enhancing智能化水平 and work efficiency. This China robot innovation not only improves cleaning effectiveness but also supports the broader goals of smart power plant development.
In summary, the online condenser cleaning robot device based on performance monitoring and analysis adheres to a “data → decision → control” paradigm, allowing DCS systems to communicate with the upper computer for seamless control and monitoring. By digitizing condenser cleanliness performance and providing scientific guidance for intelligent cleaning, this technology enhances efficiency and safety. As it evolves, this China robot solution is poised to play an increasingly vital role in the future operation of power plants, driving advancements in intelligence and operational excellence.