Implementation Path and Key Technologies of Intelligent Operation and Maintenance for Municipal Solid Waste Incinerators
Intelligent operation and maintenance (O&M) of municipal solid waste incinerators integrates technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and digital twins to establish a closed-loop management system covering equipment monitoring, fault diagnosis, process optimization, and remote control. Based on practical applications in more than 100 waste-to-energy plants nationwide, intelligent O&M has increased the automatic commissioning rate of equipment to over 95%, improved steam flow stability by 23%, reduced manual workload by 87%, and increased annual power generation by 360 million kWh. This paper systematically elaborates on the technical architecture, core functions, and implementation pathways of intelligent O&M, and reveals how data-driven decision-making, algorithm-based control optimization, and lifecycle management promote the transformation of the waste incineration industry toward higher efficiency, lower carbon emissions, and improved operational safety.
Introduction
Municipal solid waste incinerators are core facilities in urban solid waste treatment, and their operational efficiency and stability are directly related to both environmental and economic benefits. Traditional operation and maintenance models rely heavily on manual experience, which often results in delayed fault response, coarse energy consumption control, and significant fluctuations in emissions. For example, in one waste incineration plant, delayed manual adjustments caused furnace temperature fluctuations of ±50 °C, leading to a 15% decrease in steam production. Intelligent operation and maintenance addresses these issues by establishing a closed-loop system of “perception–analysis–decision–execution,” enabling real-time monitoring of equipment status, dynamic optimization of process parameters, and early warning of potential faults. This approach has become a key pathway for the transformation and upgrading of the waste incineration industry.
Technical Architecture of Intelligent Operation and Maintenance
1. IoT Perception Layer
By deploying more than 1,000 sensors, over 2,000 parameters—including furnace temperature, flue gas composition, and equipment vibration—are collected in real time to build a comprehensive data network. For example, the Nanhai Waste-to-Energy Plant operated by Hanlan Environment has installed high-precision sensors at key locations such as the grate and air ducts, enabling millisecond-level responses for feed rate, air distribution, and furnace temperature control. Data transmission adopts a dual-mode communication architecture combining 5G and LoRa, ensuring a transmission reliability of up to 99.9%.
2. Big Data Analytics Layer
A distributed computing platform based on Hadoop and Spark is used to process an average of 10 TB of operational data per day. Historical operating conditions are stored using time-series databases (such as InfluxDB), while machine learning algorithms (such as LSTM neural networks) are employed to predict material demand and peak energy consumption. For instance, Alibaba Cloud’s Industrial Brain analyzed three years of operational data to establish a nonlinear mapping model between waste calorific value and steam production, reducing steam flow prediction errors to ±2%.
3. Artificial Intelligence Decision Layer
By integrating fuzzy control, genetic algorithms, and deep reinforcement learning, the system enables adaptive optimization of combustion parameters. For example, in an 8-ton low-temperature waste incinerator, the system dynamically adjusts grate speed and airflow according to the moisture content of incoming waste, improving combustion efficiency by 18%. In response to emergency conditions (such as feed interruption), the system can generate emergency control strategies within 30 seconds, preventing potential equipment damage.
4. Digital Twin Execution Layer
A virtual replica of the incinerator is constructed to mirror the real-time state of physical equipment. A three-dimensional visualization interface developed using the Unity3D engine supports remote inspection and fault simulation for operation and maintenance personnel. For example, the Baiguoyuan Waste-to-Energy Plant in Chongqing operated by Sanfeng Environment has applied digital twin technology to shorten equipment maintenance cycles by 40% and reduce spare-parts inventory by 30%.
Core Functions of Intelligent Operation and Maintenance
1. Intelligent Monitoring and Early Warning
Flame State Recognition: Computer vision technology is employed to evaluate combustion adequacy by analyzing flame color and texture. When the flame temperature drops below 850 °C or the CO concentration exceeds 200 ppm, the system automatically triggers an alarm.
Equipment Health Assessment: Based on vibration spectrum analysis, the system detects bearing faults in equipment such as fans and pumps. For example, the Fuzhou Waste-to-Energy Plant in Jiangxi operated by China Energy Conservation and Environmental Protection Group identified gearbox wear 30 days in advance by monitoring harmonic components in motor currents, thereby preventing unplanned shutdowns.
Emission Exceedance Prediction: An LSTM–Attention hybrid model is established to predict NOx and SO₂ emission concentrations for the next two hours. When the predicted values exceed preset thresholds, the system automatically adjusts the ammonia injection rate of the SNCR denitrification system.
2. Intelligent Optimization and Control
Closed-Loop Control of Steam Generation: Using the Model Predictive Control (MPC) algorithm, the system dynamically regulates grate speed and combustion air flow. This reduces steam flow fluctuations from ±5% to ±1.5%.
Optimization of Waste Bed Thickness: Radar level gauges are used to monitor the thickness of the waste layer in real time. Combined with grate current feedback, this enables precise matching between feeding rate and combustion speed. In one plant, this technology increased waste combustion completeness from 92% to 98%.
Intelligent Furnace Temperature Regulation: For low-temperature waste with moisture content greater than 50%, staged combustion technology is adopted. AI algorithms optimize the ratio of primary air to secondary air, stabilizing the furnace temperature within the range of 650–750 °C.
3. Intelligent Diagnosis and Maintenance
Root Cause Analysis of Faults: A Bayesian network-based knowledge graph containing more than 200 fault nodes is constructed. For example, when abnormal furnace pressure is detected, the system can trace the issue to six potential causes, such as induced draft fan failure or blockage in the bag filter system.
Predictive Maintenance: LSTM neural networks are used to predict the remaining useful life of equipment and formulate dynamic maintenance plans. For instance, ultrasonic thickness measurements of superheater tubes combined with corrosion rate models can extend maintenance intervals from one year to three years.
AR-based Remote Collaboration: Operation and maintenance personnel can wear HoloLens 2 devices to access equipment 3D models and operational guidance. For example, when dealing with a grate jamming fault, remote experts can annotate key components through augmented reality, guiding on-site staff to quickly locate and resolve the issue.
Implementation Path of Intelligent Operation and Maintenance
1. Data Governance and Standard Development
Data Standardization: A unified standard, Specification for Data Acquisition and Transmission of Waste Incinerators, is established to standardize interface protocols among more than 200 equipment suppliers, ensuring interoperability and consistent data exchange.
Data Quality Management: A data cleansing rule library is developed to automatically remove abnormal values caused by sensor drift or communication interruptions. For example, the Kalman filtering algorithm can be applied to correct step noise in temperature measurement points.
Knowledge Graph Construction: By integrating equipment manuals, operation and maintenance records, and expert experience, a fault diagnosis knowledge base containing over 100,000 rules is established to support intelligent decision-making.
2. Iterative Optimization of Algorithm Models
Application of Transfer Learning: A pre-trained ResNet-50 model is used to rapidly adapt flame recognition tasks for different incinerators. For example, the flame classification accuracy at a certain plant was improved from 85% to 97%.
Federated Learning Practice: Global models are jointly trained by multiple waste incineration plants while ensuring data security. For instance, by sharing gradient parameters, the generalization capability of the NOx emission prediction model can be improved by 30%.
Exploration of Reinforcement Learning: A combustion optimization strategy based on the Proximal Policy Optimization (PPO) algorithm is developed. Through more than 100,000 iterative training runs in a virtual environment, the system identifies globally optimal control strategies.
3. Platform Development and Ecosystem Collaboration
Industrial Internet Platform: A PaaS-based industrial platform is constructed to provide SaaS applications such as equipment management, energy optimization, and safety and environmental monitoring. For example, the waste incineration metaverse platform developed by E20 Environment supports the simultaneous connection of more than 1,000 devices.
Supply Chain Collaboration: Spare-part suppliers, logistics companies, and service providers are integrated to form an intelligent O&M ecosystem. For instance, blockchain technology can be used to record the full lifecycle information of spare parts, enabling traceability and quality management.
Conclusion
Intelligent operation and maintenance of waste incinerators, achieved through the deep integration of the Internet of Things (IoT), big data, artificial intelligence, and digital twin technologies, enables precise management throughout the entire lifecycle of equipment. Typical case studies demonstrate that intelligent O&M can increase equipment utilization by 20%, reduce pollutant emissions by 15%, and lower operation and maintenance costs by 30%.
In the future, with the advancement of edge computing, 5G-Advanced (5G-A), and general artificial intelligence technologies, intelligent O&M systems are expected to evolve toward autonomous decision-making and zero-intervention operation, providing critical support for the development of “zero-waste cities.”
Source: https://mp.weixin.qq.com/s/Jw6pKuSHnYHwpbkifFS7mg