| 1. |
- Blackburn, Landen D., et al.
(författare)
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Development of novel dynamic machine learning-based optimization of a coal-fired power plant
- 2022
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Ingår i: Computers and Chemical Engineering. - : Elsevier BV. - 0098-1354. ; 163
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Tidskriftsartikel (refereegranskat)abstract
- The increasing fraction of intermittent renewable energy in the electrical grid is resulting in coal-fired boilers now routinely ramp up and down. The current state-of-the-art operation for such boilers is to apply steady-state, neural network-based optimization to make control decisions in real-time, and this work demonstrates the feasibility of extending this to dynamic, neural network-based optimization using a long short-term memory neural network. A simplified numerical simulation of a t-fired coal boiler and supporting equipment is used to represent a real plant subjected to both steady-state, neural network-based optimization and dynamic, neural network-based optimization. Using the same intervals and a particle swarm optimization algorithm, the dynamic optimization outperforms the steady-state optimization and realizes up to 4.58% improvement in thermal efficiency. Dynamic optimization with a long short-term memory neural network is shown to both be feasible and beneficial for operation of a coal-fired boiler under changing load.
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| 2. |
- Mohammadi, Kasra, et al.
(författare)
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A review on the application of machine learning for combustion in power generation applications
- 2023
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Ingår i: Reviews in Chemical Engineering. - : Walter de Gruyter GmbH. - 2191-0235 .- 0167-8299. ; 39:6, s. 1027-1059
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Forskningsöversikt (refereegranskat)abstract
- Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.
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| 3. |
- Blackburn, Landen D., et al.
(författare)
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Dynamic machine learning-based optimization algorithm to improve boiler efficiency
- 2022
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Ingår i: Journal of Process Control. - : Elsevier BV. - 0959-1524. ; 120, s. 129-149
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Forskningsöversikt (refereegranskat)abstract
- With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. The best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.
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| 4. |
- Tuttle, Jacob F., et al.
(författare)
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A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling
- 2021
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Ingår i: Applied Energy. - : Elsevier BV. - 1872-9118 .- 0306-2619. ; 292
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Tidskriftsartikel (refereegranskat)abstract
- Ten established, data-driven dynamic algorithms are surveyed and a practical guide for understanding these methods generated. Existing Python programming packages for implementing each algorithm are acknowledged, and the model equations necessary for prediction are presented. A case study on a coal-fired power plant's NO emission rates is performed, directly comparing each modeling method's performance on a mutual system. Each model is evaluated by its root mean squared error (RMSE) on out-of-sample future horizon predictions. Optimal hyperparameters are identified using either an exhaustive search or genetic algorithm. The top five model structures of each method are used to recursively predict future NO emission rates over a 60-step time horizon. The RMSE at each future timestep is determined, and the recursive output prediction trends compared against measurements in time. The GRU neural network is identified as the best candidate for representing the system, demonstrating accurate and stable predictions across the future horizon by all considered models, while satisfactory performance was observed in several of the ARX/NARX formulations. These efforts have contributed 1) a concise resource of multiple proven dynamic machine learning methods, 2) a practical guide explaining the use of these methods, effectively lowering the “barrier-to-entry” of deploying such models in control systems, 3) a comparison study evaluating each method's performance on a mutual system, 4) demonstration of accurate multi-timestep emissions modeling suitable for systems-level control, and 5) generalizable results demonstrating the suitability of each method for prediction over a multi-step future horizon to other complex dynamic systems.
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| 5. |
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| 6. |
- Yuan, Q., et al.
(författare)
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Automatic robot taping with force feedback
- 2017
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Ingår i: 2017 IEEE International Conference on Robotics and Automation (ICRA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509046331 ; , s. 1821-1826
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Konferensbidrag (refereegranskat)abstract
- In surface treatment processes like plasma spraying and spray painting of workpieces, protecting the uninvolved surface by applying masking tape is a common process. Due to the operation complexity for different geometries, such taping tasks depend on a lot of manual works, which is tedious and tiring. This paper introduces an automatic agile robotic system and the corresponding algorithm to do the surface taping. The automatic taping system consists of a 3D scanner for workpiece 3D model reconstruction, a taping end-effector which is mounted on a robot manipulator to handle the taping task, and a rotating platform that is used to hold the workpiece. The surface covering method and the taping path planning algorithms using the scanned model are introduced. With the implementation of the compliance mechanism, the force feedback and the tape cutting mechanism, the system is able to tape flat, cylindrical, freeform, and grooved surfaces. Experiments conducted on taping an engine inner liner shows that the surface can be covered with uniform taping overlap and very little wrinkle. The proposed system is a useful taping package for industrial applications such as workpiece repairing and surface protection, where surface treatments are involved.
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