A data-driven method for main steam temperature control in waste-to-energy plants: its theory and a 90-day continuous demonstration at a real-world plant
AI-Powered Waste-to-Energy Optimization
This paper presents a data-driven control method for waste-to-energy (WtE) plants to address the challenges of maintaining the main steam temperature, a crucial factor for energy efficiency. The proposed method utilizes a learned data model to track the optimal transition route to achieve the control setpoint. Through a 90-day demonstration operation at an active WtE plant, the method was validated in terms of interpretability/explainability, tracking performance, versatility in distinct operation modes, long-term operability and stability, and the working mechanism of the control logic. The results demonstrate that the proposed data-driven control method outperforms the existing PID control method, providing superior control performance and adaptability to various operational conditions. The capability of the method to acquire improved control laws from existing operational data showcases its potential for enhanced control performance. Further testing of the disabled components, which were theoretically presented but not utilized during the demonstration opera-tion, is expected to enhance the effective addressing of the non-Markovian nature of the process. The findings highlight the significance of data-driven control methods in achieving safe and efficient operations in WtE plants.
Our AI-driven solution delivers tangible benefits, enhancing efficiency and operational stability in Waste-to-Energy plants. The 90-day demonstration validates real-world impact:
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Demonstration Duration
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Efficiency Improvement
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Annual Electricity Increase
Deep Analysis & Enterprise Applications
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This category focuses on the overarching control strategy for WtE plants, particularly how the data-driven method integrates with existing systems to enhance stability and efficiency. It covers the interpretability of the learned models and their adaptability to diverse operational modes.
Days of Continuous Operation Validation
90 Days of Continuous Operation ValidationThe proposed data-driven control method underwent a rigorous 90-day continuous demonstration at an active Waste-to-Energy (WtE) plant. This extended validation period ensured comprehensive assessment of its robustness, reliability, and real-world applicability under diverse operating conditions.
This section delves into the theoretical underpinnings and practical performance of the data-driven control method. It examines the dynamic models, the learning agent's role in deriving optimal control laws, and the tangible improvements observed in steam temperature regulation.
Data-Driven Control Loop for Steam Temperature
Operational Data Collection
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Learning Agent (STP/ISD Matrix)
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Control Agent (Compute Ideal State)
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Determine Water Injection Rate
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Adjust Attemperator (Manipulative Value)
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Main Steam Temperature Control
The data-driven control method operates through a sophisticated loop. It begins with continuous data collection from the plant, which feeds into a learning agent that builds a State Transition Probability (STP) and Infinite Series Sum of Discounted State Transition Probability (ISD) matrix. This learned model is then utilized by a control agent to compute the ideal main steam temperature and its derivative, guiding the determination of the appropriate water injection rate. Finally, the attemperator is adjusted to achieve precise main steam temperature control.
Enhanced Control Logic for Non-Markovian Processes
The demonstration operation revealed that two specific corrective components within the control logic—Corr-Ex (based on deviation of flue gas temperature from moving average) and Corr-Dia (based on deviation of time-derivative of main steam temperature to its ideal value)—played a crucial role. These components were key in addressing the non-Markovian nature of the main steam temperature control system, characterized by complex, multi-stage combustion processes for MSW batches (unfolding, drying, ignition, combustion from outer to inner part). Their quick control actions enabled stable performance despite these challenges, demonstrating the method's robustness.
Crucial role of Corr-Ex and Corr-Dia in addressing non-Markovian process challenges, ensuring quick and effective control actions for stable WtE plant operation.
The WtE plant's combustion process is inherently non-Markovian due to the multi-stage nature of MSW incineration. The proposed control method effectively handles this complexity through specific corrective terms (Corr-Ex and Corr-Dia) that enable rapid and adaptive control actions. These terms, based on real-time deviations in flue gas temperature and the time-derivative of main steam temperature, were instrumental in maintaining stable control during the 90-day demonstration. This capability is vital for WtE plants, where heterogeneous fuel sources and variable combustion dynamics necessitate a control system that can adapt quickly to unpredictable changes.
Here, the economic and operational benefits of implementing this AI-driven solution are quantified. It also discusses the long-term operability, stability under varying conditions, and outlines future enhancements, including the testing of currently disabled control components.
| Evaluation Criterion | Existing PID Method | Proposed Data-Driven Method |
|---|---|---|
| Tracking Performance (Regular Operation) | Fluctuated with 15-20 min periods, average 398.7°C | Shorter fluctuations (10-15 min), average 400.0°C (target met) |
| Versatility (Soot-Blowing) | Slow return (approx. 1 hour) to setpoint with overshoot | Swift return (approx. 10 min) to setpoint without overshoot, average 396.8°C |
| Long-term Operability & Stability | Daily average temperatures lower and wider distribution (e.g., 395.0°C) | Daily average temperatures raised and tightly controlled (e.g., 396.8°C) |
| Acquisition of Control Laws | Relies on manual tuning and expert rules | Learned from existing operational data (even PID data) for improvement |
A comparative analysis between the proposed data-driven control method and the existing PID control method reveals significant advantages across several critical performance criteria. The data-driven approach demonstrates superior tracking performance, adaptability to distinct operational modes like soot-blowing, enhanced long-term stability, and the unique ability to learn improved control laws from historical operational data, including that generated by the PID method itself.
Main Steam Temperature Increase
+1.2 °C Main Steam Temperature IncreaseThe proposed data-driven control method successfully raised the main steam temperature by an average of 1.2°C during demonstration, compared to the existing PID method. This increase is a direct contributor to improved energy efficiency and power generation, as higher steam temperatures at the turbine inlet enhance the thermodynamic cycle of the WtE plant.
Efficiency Improvement
+0.15 Efficiency ImprovementBased on a Rankine-cycle-based analysis, the 1.2°C increase in main steam temperature translates to an estimated 0.15% improvement in electricity generation efficiency. This seemingly small percentage represents a significant annual increase in power output for continuous operation.
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Your AI Implementation Roadmap
A structured approach ensures seamless integration and maximum impact. Our proven methodology guides your enterprise through every step of the AI transformation journey.
Phase 1: Data Integration & Model Training
Establish secure data pipelines from existing plant SCADA/DCS systems. Collect historical operational data (approx. 100 days) for initial model training (STP and ISD matrices). Validate data quality and prepare for learning agent deployment. This phase avoids repetitive trial-and-error in live systems.
Duration: 4-6 Weeks
Phase 2: Offline Simulation & Validation
Deploy the learned data model in a simulation environment using historical data. Verify expected control behaviors (as visualized in Fig. 3) and fine-tune initial control gains (GainEx, GainPia, GainDia, GainIsa) without impacting live operations. This ensures interpretability and safety before real-world application.
Duration: 3-5 Weeks
Phase 3: Phased Live Deployment & Monitoring
Implement the data-driven control method in a controlled, phased manner (e.g., one incinerator first). Continuously monitor performance against PID, tracking key metrics like steam temperature stability and energy efficiency. Gather new operational data under the AI control for model refinement, ensuring long-term operability and stability.
Duration: 8-12 Weeks
Phase 4: Advanced Component Activation & Optimization
Gradually enable and test additional control components (GainPsi, GainTia, GainIsi) to further address non-Markovian process challenges and optimize control performance. Conduct A/B testing or comparative analysis with previously achieved performance to validate improvements. Expand deployment across entire plant.
Duration: 6-10 Weeks
Ready to Innovate?
The successful 90-day demonstration of this data-driven control method in a real-world Waste-to-Energy plant highlights its potential for revolutionizing WtE operations. By dynamically learning from operational data, it ensures superior main steam temperature control, leading to enhanced energy efficiency and stability across various modes. The interpretability of its models, combined with its ability to adapt and improve control laws from existing data, paves the way for safer, more efficient, and robust WtE plant management.
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